SlideShare a Scribd company logo
1 of 23
Download to read offline
Crunch time 6
Forecasting in a digital world
Prediction is very difficult,
especially if it’s about the future.
—Niels Bohr
Nobel Laureate in Physics
2
3
3
Forecasting in a digital world 4
Algorithmic forecasting 6
The ripple effect 9
It’s happening now 11
Algorithmic forecasting in action 14
Getting from here to there 16
17
Looking ahead 19
01
Before you go
02
03
04
06
07
08
09
Roadblocks 13
05
4
01
02
03
04
05
06
07
08
09
Human fascination with the future is part of our evolutionary heritage. Those who can
foresee and navigate risks have always been more likely to survive than those who
can’t. That’s just as true in business, where the ability to see ahead continues to
separate performance leaders from everyone else. That’s what forecasting is all about,
yet it’s surprisingly difficult (and often expensive) to do.
Companies have different motivations for improving their predictions.
For some, the ability to deliver reliable guidance to analysts and
markets might drive the decision. For others, being able to trim
production waste by predicting consumer demand is what matters
most. Still others want to improve cost management and streamline
the forecasting process.
Traditionally, forecasting has been a mostly manual process with
people gathering, compiling, and manipulating data, often within
spreadsheets. With more and more data available, old-school
forecasting has become an unwieldy, time-consuming process that
makes discerning what’s important next to impossible. As a result,
humans often resort to their own intuition and judgement, which opens
the door to unconscious biases and conscious sandbagging.
Forecasting in a digital world
01
5
01
02
03
04
05
06
07
08
09
There’s another way. Organizations are shifting to
forecasting processes that involve people working
symbiotically with data-fueled, predictive algorithms. It’s
all made possible by new technologies—advanced analytics
platforms, in-memory computing, and artificial intelligence
(AI) tools, including machine learning.
Similar digital tools are common in our consumer lives. We
use a mapping app to predict when we’ll arrive at our
destination. A real-time weather app can tell us precisely
when a rain shower will start or stop. So, it’s reasonable to
expect predictive capabilities when we’re at work.
Today, these technologies in the hands of expert
forecasting talent give companies the ability to discover
things they’ve always wanted to know—as well as things
they didn’t know they didn’t know—with more confidence
and speed.
CFOs are in a prime position to challenge the way their
enterprise looks at and consumes data. In the area of
forecasting, they can champion an innovative, data-driven
approach that will help people project the future of their
business. By modeling the potential impact of important
decisions, they can help generate smarter insights and
stronger business outcomes.
For an introduction to the digital capabilities that make
data-powered forecasting possible, read Crunch time 1:
Finance in a digital world.
09
01
6
01
02
03
04
05
06
07
08
09
Algorithmic forecasting
Machines and people working together to see what’s next
You can hardly turn around these days without bumping into a vendor claiming to have software that can predict the
future. As is often the case, the hype outstrips the reality. Here’s how we see things.
The basics
Algorithmic forecasting uses
statistical models to describe
what’s likely to happen in the
future. It’s a process that relies
on warehouses of historical
company and market data,
statistical algorithms chosen by
experienced data scientists, and
modern computing capabilities
that make collecting, storing,
and analyzing data fast and
affordable.
Beyond the basics
Forecasting models offer more
value when they can account for
biases, handle events and
anomalies in the data, and
course-correct on their own.
That’s where machine learning
comes into play. Over time,
forecasting accuracy improves
as algorithms “learn” from
previous cycles.
Models are also more valuable
when they’re grounded in richer,
more granular data. In some
cases, that might involve using
natural language processing,
which can read millions of
documents—including articles,
social posts, correspondence,
and other text—and feed them
directly into the algorithms.
The magic
The real lift from algorithmic
forecasting comes when it’s
combined with human
intelligence. Machines help keep
humans honest, and humans
evaluate and translate the
machine’s conclusions into
decisions and actions. It’s this
symbiotic relationship that
makes algorithmic forecasting
effective—especially when
humans are organized to
support and share their findings
across the enterprise.
The bottom line
Algorithmic forecasting doesn’t
create anything out of thin air,
and it doesn’t deliver 100
percent precision. But it is an
effective way of getting more
value from your planning,
budgeting, and forecasting
efforts.
We’ve seen companies
substantially improve annual and
quarterly forecast accuracy—
with less variance and in a
fraction of the time traditional
methods require—while building
their predictive capabilities in
the process.
02
7
01
02
03
04
05
06
07
08
09
Algorithmic forecasting depends on these elements working together
09
Human
intelligence
AI
applications
Modern
computing
capabilities
Data sources Advanced
analytics
platforms
02
8
01
02
03
04
05
06
07
08
09
Don’t we have bigger fish to fry?
CFOs are continually weighing the relative ROI of potential investments that vie for
their attention. Here are a few reasons why many leaders are moving algorithmic
forecasting up their priority list:
Competitive advantage
Leaders who can more accurately predict
future performance based on the underlying
business drivers—both internal and
external—are better positioned to recognize
and respond to early warning signals.
Increasing disruption
As companies’ business and operating models
change in response to disruptive competition,
historical patterns and trends used in
traditional forecasting are becoming less and
less relevant.
Growing complexity
Global market and supply chain complexity,
along with increasing volatility, means that
organizations should harness the agility that
algorithmic forecasting can deliver to help
make sense of new realities as—or even
before—they emerge.
02
9
01
02
03
04
05
06
07
08
09
While some aspects of forecasting using algorithmic models are fairly straightforward,
others can be trickier. Changing processes. Building trust and transparency. Creating
partnerships between people and machines. These are tougher challenges.
How work changes
With algorithmic forecasting, Finance does more insight-
development work and less manual drudgery. Instead of spending
their time grinding through spreadsheets, humans get to bring their
expert judgment to the process. Leading finance organizations are
already using automation tools to help with manually intensive work
like transaction processing. Automating routine forecasting tasks is
another area ripe for improvement.
How the workforce changes
Your finance talent model should evolve to keep up with changes in
how work gets done—and that will likely require a different mix of
people than you have in place today. Algorithmic forecasting depends
on collaboration among Finance, data analytics, and business teams.
Once they hit stride, these teams can move across the range of
forecasting needs, embedding capabilities in the business and driving
integration. These teams are integral to establishing an algorithmic
solution that can work for the business, bring insights to life within the
organization, and support continued business ownership of the
outcomes.
The ripple effect
03
10
01
02
03
04
05
06
07
08
09
Our experience has shown us that some finance
professionals are simply better at forecasting than others.
They’ve learned to set aside their biases and look at the
bigger picture objectively—and they have a knack for
understanding algorithmic models and uncovering flaws
that others may miss.
You’ll need storytellers, too—folks who really understand
the business and can translate analytical insights into
compelling narratives that trigger appropriate actions.
How decision-making changes
Making choices becomes a more interactive process with
advanced forecasting techniques, resulting in smarter,
more informed decisions, even on the fly. In-memory
computing, predictive analytic software, and visualization
tools enable management to easily and quickly ask “what
if” questions and produce a range of scenarios to help
them understand potential impacts on the business.
How the workplace changes
Forecasting isn’t limited to Finance. Functions from
marketing to supply chain to human resources all have
needs for predicting the future to drive important
decisions. While CFOs may not lead function-specific
forecasting, they should help shape these forecasting
initiatives since Finance will inevitably use the outputs they
generate.
A shared forecasting infrastructure—even a physical
Center of Excellence (CoE)—can help improve collaboration
and coordination while providing efficiencies in data
storage, tool configuration, and knowledge sharing. And
once the organization develops the forecasting muscle to
solve one problem, the capability can quickly be extended
and applied in other areas.
“Foresight isn’t a mysterious gift bestowed at birth. It
is the product of particular ways of thinking, of
gathering information, of updating beliefs. These
habits of thought can be learned and cultivated by
any intelligent, thoughtful, determined person.”
—Philip E. Tetlock in Superforecasting: The Art and Science of Prediction
09
03
11
01
02
03
04
05
06
07
08
09
Many of the companies we work with have begun their digital finance journeys by investing in cloud, in-memory computing, and robotic
process automation. Others have broadened their ambitions to include advanced analytics, with an emphasis on forecasting. They want
to create forecasts that enable faster and more confident decision-making. This is where those digital investments can begin to pay off.
Traditional approaches to forecasting can take far too long, cost far too much, and generate too little insight about potential future
outcomes.
Common applications for algorithmic forecasting
Top-down planning
Target setting
Integrated financial statement
forecasting
Working capital forecasting
Indirect cash flow forecasting
Demand forecasting
Competitive actions and implications
Tax tradeoffs and revenue/profit
implications
Bottom-up forecasting
Product-level forecasting
Market- or country-level forecasting
Direct cash flow forecasting
Function-specific
forecasting
Customer retention
Inventory optimization
Employee retention and attrition
modeling
External reporting
Market guidance
Earnings estimates
It’s happening now
04
12
01
02
03
04
05
06
07
08
09
Case study
Is fast growth a financial problem?
Yes, if you can’t explain it.
01
02
03
04
05
06
07
08
09
12
The FP&A team for a global consumer
product manufacturer frequently
outperformed their guidance to market
analysts. Problem was, they couldn’t
explain the unanticipated growth, and
holding credibility with the executive
team, board, and industry analysts was a
key priority.
The team suspected sandbagging was the
source of their headache. Individual
business unit (BU) leaders made their
own bottom-up forecasts as part of the
target-setting planning process, which
was used for performance incentives.
Finance had no objective way to verify or
push back on the BU leaders’ numbers.
What happened next
Finance leadership asked Deloitte to help them
develop an objective, data-driven forecasting
approach. Within 12 weeks, Deloitte’s data
scientists designed a top-down predictive
model that incorporated the company’s
internal historical actuals and external drivers
for each global market, including housing
starts, local GDP metrics, commodity prices,
and many additional variables.
The model enabled the FP&A team to deliver a
second-source forecast based on external
macro drivers that aligned to market
expectations and provided insightful, accurate
projections across the P&L, balance sheet, and
cash flow statements. Planners also gained the
ability to quickly create growth, recession, and
other scenarios using desktop visualization
software.
The toolkit
The Finance team received the fully functional
predictive model built on an open source
platform, which enables their CoE to manage
and model forecasts on an ongoing basis.
Leadership gained an objective, transparent,
and visual conversation starter for discussions
with business units about new opportunities
and upcoming challenges for their markets.
Looking ahead
Leadership sees this as a game changer, and
not just for their top-down financial forecasts.
The business segments do, too. Following the
successful delivery of a prototype focused on
corporate FP&A, Finance has added data
scientist capabilities to its talent model.
Socializing the results with the business has
created significant demand to dive deeper and
extend the solution to the business segments
and regions. Additionally, the client has taken
steps to industrialize the model and to provide
business users greater visibility into the driver
assumptions and relationships to the
financials.
The company continues the journey to scale
the solution within the organization on their
own. The client’s FP&A leader reports, “This is
a good-news story. We made an investment
[to prototype algorithmic forecasting], proved
the concept, and got the business interest. We
created significant demand for a new Finance-
developed forecast capability that will help the
organization improve forecast accuracy and
efficiency.”
13
01
02
03
04
05
06
07
08
09
Roadblocks
When you start having
conversations with colleagues
about replacing traditional
forecasting processes with
algorithms, you’ll quickly
discover that people have
preconceptions about what it
is, how it works, and what it
means for the organization.
That’s what happens when
change is in the air.
Fear and loathing
People are often scared of the
unknown. Even though Finance is
a discipline grounded in numbers,
some will resist the idea that
algorithmic forecasting might
enhance their own methods. Help
them see that machines can
handle the tedious number-
crunching work while enabling
people to spend more time
uncovering valuable insights.
Black boxes
There’s a myth that algorithms
are black boxes where magic
things happen. While not true,
some complicated models may
seem that way. To help build
trust across the organization,
consider using easier-to-
understand algorithms with
transparent inputs in the
beginning. As comfort grows, so
will the willingness to accept
more complex approaches.
Small thinking
Some people won’t take time to
see the bigger picture. They’ll go
along with algorithmic forecasting
if they have to, but they’ll start
small and stay small, looking for
what’s wrong instead of what
might be workable— all while
refusing to give up the old ways
of thinking and acting.
Turf battles
Is algorithmic forecasting a CIO
thing that needs to be brought into
Finance, or is it a CFO thing that
needs IT’s help? And when it
comes to bottom-up forecasting,
who’s on first—the business or the
business partner? The reality is
that effective algorithms require
data, computing power, and
business insight—so collaboration
is important. Making accountability
and ownership decisions up front
can improve collaboration and
value generation.
Engagement matters
It’s important to involve end
users to help conceive, design,
build, and implement algorithms.
After all, they know the sandtraps
that need to be avoided, and
their acceptance is key to
effective implementation.
Data, data, everywhere
Some companies want to try
new approaches to forecasting,
but they’re worried their data
problems will stymie them.
Whether those problems are the
result of mergers and
acquisitions, a history of poor
data management, disconnected
systems, or all of the above, you’re
not alone. The first step toward
improving forecasting for many
companies is getting their data
house in order— sometimes even
one room at a time.
Silver bullets
Algorithmic forecasting often
gets introduced with a lot of
hype. Don’t do that. It’s just a
tool. That said, when this kind of
powerful tool is combined with
human intellect, the impact can
be transformative.
05
14
01
02
03
04
05
06
07
08
09
Algorithmic forecasting in action
It’s 7:00 a.m., and you’re thinking about the day ahead.
By noon, you have to settle on a forecast about how
your business will perform over the next quarter. And at
2:00 p.m., you need to tell that story to a dozen board
members on a conference call.
In the past, your forecasting team would be pulling all-
nighters for days before your meeting. They’d be
grinding through spreadsheets, calculating growth
percentages, chasing down anomalies, and drinking way
too much coffee. That was then.
Today, your forecasting function is a well-oiled machine,
with more than 80 percent of the work happening
automatically. Every piece of financial data you could want
is available on your tablet. All you have to do is ask—
literally. Display the impact on profits if the cost of steel
goes up 20 percent in the next month. You can drill down,
roll up, set aside exceptions, and run a dozen more
scenarios before your conference call. And you can do it all
without an army of analysts scrambling to help.
06
15
01
02
03
04
05
06
07
08
09
What changed?
Almost everything.
More and
better data
Your organization now uses
data of all types—financial,
operational, and external—
to train your forecasting
model. Once the model is
set up and evaluated,
machine learning kicks in.
The model gets better over
time through constant and
automated iterations. It is
always up-to-date,
evaluating and determining
which of the latest inputs
add the most insight and
value to your predictions.
More models,
more options
With better models analyzed
more quickly, you have the
opportunity to understand
the impact of unforeseen
trends in the market and
factor them into your
planning. How are
unemployment and
disposable income trends
going to affect the business?
How much of that cut in
trade spending should I
expect to fall through to the
bottom line? Scenario
modeling done in
collaboration with business
units gives them advanced
capabilities to work smarter,
not harder, along the
forecasting journey.
A clearer view
of performance
drivers
Scenario modeling also
gives business leaders
visibility into performance
drivers. For example, you
can easily see how to
manage existing markets
by building out price,
product mix, and volume
analytics.
Improved accuracy,
more confidence
Where are you going to land
this quarter? This year? Do
you need to update your
guidance to the Street? If
so, by how much?
Algorithmic forecasting
answers these questions in
real-time, without having to
aggregate bottom-up
analysis from every corner
of the globe. Your
communications with
investors are more informed
and more efficient.
No sandbagging,
no rose-colored
glasses
Algorithmic forecasting
enables you to measure—
and remove—these two big
human biases, giving you
objective predictions you
can trust.
06
16
01
02
03
04
05
06
07
08
09
Getting from here to there
07
Identify the
problem to solve
Think about
how to proceed
Identify the
help you need
Prototype
and scale
Predictive
modeling
Dashboards and
visualizations Socialization Ongoing
• Define scope
and ambition
• Determine the level of
business to work with
• Identify targets
(geographies,
products, customers,
channels, etc.)
• Set a realistic
time horizon
• Decide if you want to
build this competency
in-house—or if
outsourcing to a
managed analytics
service makes more
sense
• Determine if algorithmic
forecasting is a
capability youwant to
provide as a service to
the enterprise
• Make sure your
organization has the
talent and culture to
embrace this
• Assess available talent
and their abilities
• Determine if additional
professionals are
needed—financial
analysts, data
scientists, data
visualization architects,
or others—and how
you’ll get them
• Determine external
vendor support needs
• Identify which tools you
already have in place
• Ask IT what other tools
may be needed
• Identify key
revenue and cost
drivers
• Collect and
structure relevant
data for analysis
• Align on an initial
set of priority
drivers
• Collect and clean
required data
inputs
• Test drivers for
significance
• Develop statistical
models for P&L line
items based on
relevant drivers
• Consider using
models that are easier
for the business to
understand, to build
trust and adoption
• Include end users in
the process of
conceiving, designing,
building, validating,
and implementing
forecasting models
• Test and validate the
models
• Link P&L forecasts to
the balance sheet and
cash flow
• Develop dashboard
views with key
metrics
• Develop
accompanying
visualizations
• Enable scenario
analysis functionality
• Elicit feedback from
dashboard users
• Use A/B testing to
optimize the
effectiveness of
dashboard displays
• Socialize results with
key stakeholders all
along the way
• Prepare detailed
documentation for
maintaining and
managing the model
• Track results in
parallel—keep score
• Train the organization to
understand the value of
this collaborative
approach
• Align on cyclesrequired
for refreshing your
models
• Assess opportunities
for machine learning
and cognitive
enhancements
Most clients we work with don’t attempt a wholesale change to their forecasting approach from the beginning. Instead, they select a part of their
business or a specific revenue, product, or cost element to use as a pilot or proof of concept. They often run algorithmic forecasting parallel to their
human-centric forecast for a period to compare accuracy and effort. Every company will make its own unique journey from its current approach to
planning and forecasting to an improved approach. That said, there are some things you’ll want to consider on your path forward.
1 2 3 4
A
Driver
analysis and
data cleansing
B C D E
17
01
02
03
04
05
06
07
08
09
A commitment to algorithmic forecasting is both a cultural thing and a statistical thing.
Making it happen involves great people working with elegant technology. Neither is
sufficient on its own. Here are some of the lessons we’ve learned while helping
companies move forward.
People lessons
• CFOs should be the ringleaders and champions. Reading at least one book on
the topic can help ground you in the culture of forecasting.
• Train your team—and yourself—on the basics of probabilistic thinking and how
to spot and correct for common human biases that can derail effective
forecasting.
• Management and employee engagement becomes easier when models are
transparent. Choosing a less accurate model with more intuitive drivers may
result in better business adoption than a more sophisticated model that people
can’t understand.
• Build an approach where accountability for “the number” remains local—in the
hands of those closest to the business.
• Assess your workforce needs with the understanding that changing from
traditional to algorithmic forecasting may create spare capacity as routine tasks
are automated and skill gaps where new professionals are needed.
• Don’t underestimate the value of visualization. It makes forecasting real.
Before you go
Statistical lessons
• For mature and stable businesses, predictive models usually require five or more
years of monthly data to train properly, with the most effective models often using
10 years of data to identify trends, seasonality, and driver correlation.
• Don’t overlook the possibility that past performance may not be relevant to future
performance predictions. Often the data can be used, but may need to be
adjusted or filtered by human experts. Experiment with different types of
modeling approaches to see which leading and lagging indicators improve the
relevancy of your results, especially in a changing or disruptive industry
environment.
• Aggregation across business units, divisions, groups, or the whole company
generally improves predictability.
• Products with higher sales frequencies yield stronger predictability due to the
greater number of data points. Long-cycle products can require additional data
history for the models to mature.
• Annual forecasts tend to be more accurate than quarterly. Same is true that
quarterly predictions tend to be more accurate than monthly. That’s because
variances tend to cancel each other out—up to a point. Over longer time
horizons, predictions can become less accurate as unexpected events become
more likely.
08
18
01
02
03
04
05
06
07
08
09
Want to learn more?
Start here.
Articles
Guszcza, Jim and Maddirala, Nikhil.
“Minds and machines: The art of
forecasting in the age of artificial
intelligence,” Deloitte Review, July 25,
2016.
Guszcza, Jim. “The importance of
misbehaving: A conversation with
Richard Thaler.” Deloitte Review,
January 25, 2016.
Books
Tetlock, Philip E., and Gardner, Dan.
Superforecasting: The Art and Science
of Prediction. Crown, 2015.
Silver, Nate. The Signal and the Noise:
Why So Many Predictions Fail—but
Some Don’t. Penguin Press, 2012.
Kahneman, Daniel. Thinking Fast and
Slow. Farrar, Straus and Giroux, 2011.
Online lecture
Hyndman, Rob. “Exploring the
boundaries of predictability: What can
we forecast, and when should we give
up?” Presented at Yahoo Big Thinkers,
June 26, 2015.
08
19
01
02
03
04
05
06
07
08
09
“The best qualification
of a prophet is to have
a good memory.”
—Marquis of Halifax
English writer and statesman
08
20
01
02
03
04
05
06
07
08
09
Looking ahead
Forecasting is something many companies struggle with—and where business leaders
say they need help from Finance. They’re being asked to make predictions about what
will happen in the future, and no matter how good they are, biases and guesswork
come into play.
Algorithmic forecasting is a transparent way to help improve the
forecasting process, while relieving Finance professionals of
tedious, repetitive work. The result can be more accurate and
timely forecasts—and more informed decisions.
You and your people can spend your time uncovering insights
and taking action, and less time grinding through mind-numbing
spreadsheets. Everybody wins.
09
21
01
02
03
04
05
06
07
08
09
Acknowledgements
Authors
Eric Merrill
Managing Director,
Predictive Analytics and
PrecisionView™ Offering
Leader
Deloitte Consulting LLP
Tel: +1 404 631 2141
Email: emerrill@deloitte.com
Steven Ehrenhalt
Principal, US and Global
Finance Transformation Leader
Deloitte Consulting LLP
Tel: +1 212 618 4200
Email: hehrenhalt@deloitte.com
Adrian Tay
Managing Director, Finance
Analytics and Insights Leader
Deloitte Consulting LLP
Tel: +1 909 979 7212
Email: adtay@deloitte.com
Contributors
Paul Thomson
United States
JoAnna Scullin
United States
James Guszcza
United States
Max Troitsky
United States
Jeff Schloemer
United States
Brandon Cox
United States
Ayan Bhattacharya
United States
Dave Kuder
United States
Tim Gross
United States
Tim Gaus
United States
Laks Pernenkil
United States
Sarah Logman
United States
Niklas Bergentoft
Advisory
Srini Raghunathan
United Kingdom
Tim Leung
United Kingdom
Anna Chroni
United Kingdom
Alie van Davelaar
Netherlands
09
22
01
02
03
04
05
06
07
08
09
Contacts
09
Soumen Mukerji
Partner and Service Line Leader,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98117 06159
Email: soumenmukerji@deloitte.com
Dhiraj Bhandary
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 95600 52435
Email: dbhandary@deloitte.com
Pallab Roy
Director,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98206 18260
Email: pallabroy@deloitte.com
Nitin Kini
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98861 36859
Email: nitinkini@deloitte.com
Eapen Koshy
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 99955 17771
Email: keapen@deloitte.com
Pragati Chakraborty
Director,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98681 87429
Email: chakrabortyp@deloitte.com
Debabrat Mishra
Partner,
Human Capital
Deloitte Touche Tohmatsu India LLP
Tel: +91 98203 39411
Email: mdebabrat@deloitte.com
Prithwijit Chaki
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98734 27991
Email: pchaki@deloitte.com
Pratim Kabiraj
Director,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98318 03302
Email: pkabiraj@deloitte.com
Deepak Mowdhgalya
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98457 04325
Email: dmowdhgalya@deloitte.com
Ritesh Mangla
Partner,
Finance Transformation
Deloitte Touche Tohmatsu India LLP
Tel: +91 98990 14196
Email: riteshmangla@deloitte.com
23
To find out more, please visit www.deloitte.com/us/crunchtime.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a
UK private company limited by guarantee (“DTTL”), its network of
member firms, and their related entities. DTTL and each of its member
firms are legally separate and independent entities. DTTL (also referred
to as “Deloitte Global”) does not provide services to clients. Please see
www. deloitte.com/about for a detailed description of DTTL and its
member firms. Please see www.deloitte.com/us/about for a detailed
description of the legal structure of Deloitte LLP and its subsidiaries.
Certain services may not be available to attest clients under the rules
and regulations of public accounting.
This publication contains general information only and Deloitte is not,
by means of this publication, rendering accounting, business,
financial, investment, legal, tax, or other professional advice or
services. This publication is not a substitute for such professional
advice or services, nor should it be used as a basis for any decision or
action that may affect your business. Before making any decision or
taking any action that may affect your business, you should consult a
qualified professional advisor.
Deloitte shall not be responsible for any loss sustained by any person
who relies on this publication.
Copyright © 2018 Deloitte Development LLC. All rights reserved.

More Related Content

What's hot

Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...
Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...
Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...Accenture Insurance
 
The Work Ahead in Utilities: Powering a Sustainable Future with Digital
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalThe Work Ahead in Utilities: Powering a Sustainable Future with Digital
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalCognizant
 
Learning from COVID-19 pandemic
Learning from COVID-19 pandemicLearning from COVID-19 pandemic
Learning from COVID-19 pandemicaakash malhotra
 
Stewarding Data : Why Financial Services Firms Need a Chief Data Officier
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierStewarding Data : Why Financial Services Firms Need a Chief Data Officier
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierCapgemini
 
Realising Digital’s Full Potential in the Value Chain
Realising Digital’s Full Potential in the Value ChainRealising Digital’s Full Potential in the Value Chain
Realising Digital’s Full Potential in the Value ChainCognizant
 
Digital Transformation Review Number 3
Digital Transformation Review Number 3Digital Transformation Review Number 3
Digital Transformation Review Number 3Capgemini
 
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...Capgemini
 
Accelerate Business Growth and Outcomes with AI
Accelerate Business Growth and Outcomes with AIAccelerate Business Growth and Outcomes with AI
Accelerate Business Growth and Outcomes with AICognizant
 
The Digital Enterprise Vol 5 - A Framework for Transformation
The Digital Enterprise Vol 5 - A Framework for TransformationThe Digital Enterprise Vol 5 - A Framework for Transformation
The Digital Enterprise Vol 5 - A Framework for TransformationStuart Lamb
 
Digital economy and its effect on cyber risk
Digital economy and its effect on cyber riskDigital economy and its effect on cyber risk
Digital economy and its effect on cyber riskaakash malhotra
 
AI in Media & Entertainment: Starting the Journey to Value
AI in Media & Entertainment: Starting the Journey to ValueAI in Media & Entertainment: Starting the Journey to Value
AI in Media & Entertainment: Starting the Journey to ValueCognizant
 
Operations Workforce Management: A Data-Informed, Digital-First Approach
Operations Workforce Management: A Data-Informed, Digital-First ApproachOperations Workforce Management: A Data-Informed, Digital-First Approach
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
 
Wealth Management Top 10 Trends for 2016
Wealth Management Top 10 Trends for 2016Wealth Management Top 10 Trends for 2016
Wealth Management Top 10 Trends for 2016Capgemini
 
The Future of IT Infrastructure
The Future of IT InfrastructureThe Future of IT Infrastructure
The Future of IT InfrastructureCognizant
 
Courting the 21st century customer
Courting the 21st century customerCourting the 21st century customer
Courting the 21st century customerStuart Lamb
 
Turning AI into Concrete Value: The Successful Implementers' Toolkit
Turning AI into Concrete Value: The Successful Implementers' ToolkitTurning AI into Concrete Value: The Successful Implementers' Toolkit
Turning AI into Concrete Value: The Successful Implementers' ToolkitCapgemini
 
Blockchain Smart Contracts - getting from hype to reality
Blockchain Smart Contracts - getting from hype to reality Blockchain Smart Contracts - getting from hype to reality
Blockchain Smart Contracts - getting from hype to reality Capgemini
 
Why Businesses Must Embrace Digital Transformation
Why Businesses Must Embrace Digital TransformationWhy Businesses Must Embrace Digital Transformation
Why Businesses Must Embrace Digital TransformationDiscerning Digital
 
When Digital Disruption Strikes: How Can Incumbents Respond?
When Digital Disruption Strikes: How Can Incumbents Respond?When Digital Disruption Strikes: How Can Incumbents Respond?
When Digital Disruption Strikes: How Can Incumbents Respond?Capgemini
 

What's hot (20)

Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...
Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...
Future-ready Insurance Systems – An Insurer’s Guide to Optimizing Technology ...
 
The Work Ahead in Utilities: Powering a Sustainable Future with Digital
The Work Ahead in Utilities: Powering a Sustainable Future with DigitalThe Work Ahead in Utilities: Powering a Sustainable Future with Digital
The Work Ahead in Utilities: Powering a Sustainable Future with Digital
 
Learning from COVID-19 pandemic
Learning from COVID-19 pandemicLearning from COVID-19 pandemic
Learning from COVID-19 pandemic
 
Stewarding Data : Why Financial Services Firms Need a Chief Data Officier
Stewarding Data : Why Financial Services Firms Need a Chief Data OfficierStewarding Data : Why Financial Services Firms Need a Chief Data Officier
Stewarding Data : Why Financial Services Firms Need a Chief Data Officier
 
Realising Digital’s Full Potential in the Value Chain
Realising Digital’s Full Potential in the Value ChainRealising Digital’s Full Potential in the Value Chain
Realising Digital’s Full Potential in the Value Chain
 
Digital Transformation Review Number 3
Digital Transformation Review Number 3Digital Transformation Review Number 3
Digital Transformation Review Number 3
 
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...
The Currency of Trust: Why Banks and Insurers Must Make Customer Data Safer a...
 
Accelerate Business Growth and Outcomes with AI
Accelerate Business Growth and Outcomes with AIAccelerate Business Growth and Outcomes with AI
Accelerate Business Growth and Outcomes with AI
 
Bpm why
Bpm   whyBpm   why
Bpm why
 
The Digital Enterprise Vol 5 - A Framework for Transformation
The Digital Enterprise Vol 5 - A Framework for TransformationThe Digital Enterprise Vol 5 - A Framework for Transformation
The Digital Enterprise Vol 5 - A Framework for Transformation
 
Digital economy and its effect on cyber risk
Digital economy and its effect on cyber riskDigital economy and its effect on cyber risk
Digital economy and its effect on cyber risk
 
AI in Media & Entertainment: Starting the Journey to Value
AI in Media & Entertainment: Starting the Journey to ValueAI in Media & Entertainment: Starting the Journey to Value
AI in Media & Entertainment: Starting the Journey to Value
 
Operations Workforce Management: A Data-Informed, Digital-First Approach
Operations Workforce Management: A Data-Informed, Digital-First ApproachOperations Workforce Management: A Data-Informed, Digital-First Approach
Operations Workforce Management: A Data-Informed, Digital-First Approach
 
Wealth Management Top 10 Trends for 2016
Wealth Management Top 10 Trends for 2016Wealth Management Top 10 Trends for 2016
Wealth Management Top 10 Trends for 2016
 
The Future of IT Infrastructure
The Future of IT InfrastructureThe Future of IT Infrastructure
The Future of IT Infrastructure
 
Courting the 21st century customer
Courting the 21st century customerCourting the 21st century customer
Courting the 21st century customer
 
Turning AI into Concrete Value: The Successful Implementers' Toolkit
Turning AI into Concrete Value: The Successful Implementers' ToolkitTurning AI into Concrete Value: The Successful Implementers' Toolkit
Turning AI into Concrete Value: The Successful Implementers' Toolkit
 
Blockchain Smart Contracts - getting from hype to reality
Blockchain Smart Contracts - getting from hype to reality Blockchain Smart Contracts - getting from hype to reality
Blockchain Smart Contracts - getting from hype to reality
 
Why Businesses Must Embrace Digital Transformation
Why Businesses Must Embrace Digital TransformationWhy Businesses Must Embrace Digital Transformation
Why Businesses Must Embrace Digital Transformation
 
When Digital Disruption Strikes: How Can Incumbents Respond?
When Digital Disruption Strikes: How Can Incumbents Respond?When Digital Disruption Strikes: How Can Incumbents Respond?
When Digital Disruption Strikes: How Can Incumbents Respond?
 

Similar to Digital forecasting transforms business predictions

The Role of FInance | Finance Factory
The Role of FInance | Finance FactoryThe Role of FInance | Finance Factory
The Role of FInance | Finance Factoryaakash malhotra
 
Big Data & Analytics Trends 2016 Vin Malhotra
Big Data & Analytics Trends 2016 Vin MalhotraBig Data & Analytics Trends 2016 Vin Malhotra
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
 
The value of big data
The value of big dataThe value of big data
The value of big dataSeymourSloan
 
Modern Finance and Best Use of Analytics - Oracle Accenture Case Study
Modern Finance and Best Use of Analytics - Oracle Accenture Case StudyModern Finance and Best Use of Analytics - Oracle Accenture Case Study
Modern Finance and Best Use of Analytics - Oracle Accenture Case StudyJames Hartshorn FIRP MIoD
 
Analytics Trends 20145 - Deloitte - us-da-analytics-analytics-trends-2015
Analytics Trends 20145 -  Deloitte - us-da-analytics-analytics-trends-2015Analytics Trends 20145 -  Deloitte - us-da-analytics-analytics-trends-2015
Analytics Trends 20145 - Deloitte - us-da-analytics-analytics-trends-2015Edgar Alejandro Villegas
 
Co pilot of the business
Co pilot of the businessCo pilot of the business
Co pilot of the businessikaro1970
 
Analytics Isn’t Enough To Create A Data–Driven Culture
Analytics Isn’t Enough To Create A Data–Driven CultureAnalytics Isn’t Enough To Create A Data–Driven Culture
Analytics Isn’t Enough To Create A Data–Driven CultureaNumak & Company
 
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...How Accounts Payable Automation and Agility Drive Long-Term Business Producti...
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...Dana Gardner
 
The Future of Analytics: Predict, Optimize, Succeed
The Future of Analytics: Predict, Optimize, SucceedThe Future of Analytics: Predict, Optimize, Succeed
The Future of Analytics: Predict, Optimize, SucceedUncodemy
 
Baofortheintelligententerprise
BaofortheintelligententerpriseBaofortheintelligententerprise
BaofortheintelligententerpriseFriedel Jonker
 
Making Processes Smarter with Intelligent Automation
Making Processes Smarter with Intelligent AutomationMaking Processes Smarter with Intelligent Automation
Making Processes Smarter with Intelligent AutomationAccenture Insurance
 
Data Analytics 2-21-20.docx
Data Analytics 2-21-20.docxData Analytics 2-21-20.docx
Data Analytics 2-21-20.docxAfzalHossain73
 
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...E42 (Light Information Systems Pvt Ltd)
 
How artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfHow artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfOnlinegoalandstrategy
 
Modernizing Insurance Data to Drive Intelligent Decisions
Modernizing Insurance Data to Drive Intelligent DecisionsModernizing Insurance Data to Drive Intelligent Decisions
Modernizing Insurance Data to Drive Intelligent DecisionsCognizant
 
Decision-Making: The New Frontier for Automation
Decision-Making: The New Frontier for AutomationDecision-Making: The New Frontier for Automation
Decision-Making: The New Frontier for AutomationCognizant
 
Maintworld NEXUS v2
Maintworld NEXUS v2Maintworld NEXUS v2
Maintworld NEXUS v2Rafael Tsai
 
Benefits of ai enabled project management
Benefits of ai enabled project managementBenefits of ai enabled project management
Benefits of ai enabled project managementOrangescrum
 

Similar to Digital forecasting transforms business predictions (20)

Finance in a digital world
Finance in a digital worldFinance in a digital world
Finance in a digital world
 
The Role of FInance | Finance Factory
The Role of FInance | Finance FactoryThe Role of FInance | Finance Factory
The Role of FInance | Finance Factory
 
Big Data & Analytics Trends 2016 Vin Malhotra
Big Data & Analytics Trends 2016 Vin MalhotraBig Data & Analytics Trends 2016 Vin Malhotra
Big Data & Analytics Trends 2016 Vin Malhotra
 
The value of big data
The value of big dataThe value of big data
The value of big data
 
Modern Finance and Best Use of Analytics - Oracle Accenture Case Study
Modern Finance and Best Use of Analytics - Oracle Accenture Case StudyModern Finance and Best Use of Analytics - Oracle Accenture Case Study
Modern Finance and Best Use of Analytics - Oracle Accenture Case Study
 
Analytics Trends 20145 - Deloitte - us-da-analytics-analytics-trends-2015
Analytics Trends 20145 -  Deloitte - us-da-analytics-analytics-trends-2015Analytics Trends 20145 -  Deloitte - us-da-analytics-analytics-trends-2015
Analytics Trends 20145 - Deloitte - us-da-analytics-analytics-trends-2015
 
Co pilot of the business
Co pilot of the businessCo pilot of the business
Co pilot of the business
 
Analytics Isn’t Enough To Create A Data–Driven Culture
Analytics Isn’t Enough To Create A Data–Driven CultureAnalytics Isn’t Enough To Create A Data–Driven Culture
Analytics Isn’t Enough To Create A Data–Driven Culture
 
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...How Accounts Payable Automation and Agility Drive Long-Term Business Producti...
How Accounts Payable Automation and Agility Drive Long-Term Business Producti...
 
The Future of Analytics: Predict, Optimize, Succeed
The Future of Analytics: Predict, Optimize, SucceedThe Future of Analytics: Predict, Optimize, Succeed
The Future of Analytics: Predict, Optimize, Succeed
 
Baofortheintelligententerprise
BaofortheintelligententerpriseBaofortheintelligententerprise
Baofortheintelligententerprise
 
Finance 2025
Finance 2025Finance 2025
Finance 2025
 
Making Processes Smarter with Intelligent Automation
Making Processes Smarter with Intelligent AutomationMaking Processes Smarter with Intelligent Automation
Making Processes Smarter with Intelligent Automation
 
Data Analytics 2-21-20.docx
Data Analytics 2-21-20.docxData Analytics 2-21-20.docx
Data Analytics 2-21-20.docx
 
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...
Cognitive Automation Solutions are Enhancing Business Strategy—Here's How! (1...
 
How artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdfHow artificial intelligence will change the future of marketing_.pdf
How artificial intelligence will change the future of marketing_.pdf
 
Modernizing Insurance Data to Drive Intelligent Decisions
Modernizing Insurance Data to Drive Intelligent DecisionsModernizing Insurance Data to Drive Intelligent Decisions
Modernizing Insurance Data to Drive Intelligent Decisions
 
Decision-Making: The New Frontier for Automation
Decision-Making: The New Frontier for AutomationDecision-Making: The New Frontier for Automation
Decision-Making: The New Frontier for Automation
 
Maintworld NEXUS v2
Maintworld NEXUS v2Maintworld NEXUS v2
Maintworld NEXUS v2
 
Benefits of ai enabled project management
Benefits of ai enabled project managementBenefits of ai enabled project management
Benefits of ai enabled project management
 

More from aakash malhotra

In-deloitte-tech-trends-2023-noexp.pdf11
In-deloitte-tech-trends-2023-noexp.pdf11In-deloitte-tech-trends-2023-noexp.pdf11
In-deloitte-tech-trends-2023-noexp.pdf11aakash malhotra
 
DeIoitte_The-future-of-work-in-technology.
DeIoitte_The-future-of-work-in-technology.DeIoitte_The-future-of-work-in-technology.
DeIoitte_The-future-of-work-in-technology.aakash malhotra
 
Toward True Organizational Resilience | Deloitte’s Global Resilience Report
Toward True Organizational Resilience | Deloitte’s Global Resilience ReportToward True Organizational Resilience | Deloitte’s Global Resilience Report
Toward True Organizational Resilience | Deloitte’s Global Resilience Reportaakash malhotra
 
Risk Advisory’s new narrative Mitigate risks effectively
Risk Advisory’s new narrative Mitigate risks effectivelyRisk Advisory’s new narrative Mitigate risks effectively
Risk Advisory’s new narrative Mitigate risks effectivelyaakash malhotra
 
India Banking Fraud Survey Edition IV - Deloitte
India Banking Fraud Survey Edition IV - DeloitteIndia Banking Fraud Survey Edition IV - Deloitte
India Banking Fraud Survey Edition IV - Deloitteaakash malhotra
 
Sustainability perspectives Key insights
Sustainability perspectives Key insightsSustainability perspectives Key insights
Sustainability perspectives Key insightsaakash malhotra
 
Global_Resilience_report_October_2022_wc.pdf
Global_Resilience_report_October_2022_wc.pdfGlobal_Resilience_report_October_2022_wc.pdf
Global_Resilience_report_October_2022_wc.pdfaakash malhotra
 
in-ra-service-brochure-December-23-noexp.pdf
in-ra-service-brochure-December-23-noexp.pdfin-ra-service-brochure-December-23-noexp.pdf
in-ra-service-brochure-December-23-noexp.pdfaakash malhotra
 
Yearly Status of School Education in states and union territories of India - ...
Yearly Status of School Education in states and union territories of India - ...Yearly Status of School Education in states and union territories of India - ...
Yearly Status of School Education in states and union territories of India - ...aakash malhotra
 
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...Annual Status of Higher Education (ASHE), 2023 In states and union territorie...
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...aakash malhotra
 
in-deloitte-tech-trends-2023-noexp.pdf
in-deloitte-tech-trends-2023-noexp.pdfin-deloitte-tech-trends-2023-noexp.pdf
in-deloitte-tech-trends-2023-noexp.pdfaakash malhotra
 
2025 banking and capital markets outlook
2025 banking and capital markets outlook2025 banking and capital markets outlook
2025 banking and capital markets outlookaakash malhotra
 
gx-er-corporate-procurement-renewable-energy-report.pdf
gx-er-corporate-procurement-renewable-energy-report.pdfgx-er-corporate-procurement-renewable-energy-report.pdf
gx-er-corporate-procurement-renewable-energy-report.pdfaakash malhotra
 
in-tax-Deloitte-Pre-budget-study-2023.pptx
in-tax-Deloitte-Pre-budget-study-2023.pptxin-tax-Deloitte-Pre-budget-study-2023.pptx
in-tax-Deloitte-Pre-budget-study-2023.pptxaakash malhotra
 
in-union-budget-2023-detailed-analysis-noexp.pdf
in-union-budget-2023-detailed-analysis-noexp.pdfin-union-budget-2023-detailed-analysis-noexp.pdf
in-union-budget-2023-detailed-analysis-noexp.pdfaakash malhotra
 
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INR
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INRKeeping an eagle eye on two ‘I’s will be imperative–Inflation and INR
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INRaakash malhotra
 
5G – Enable, elevate, and emerge Digitalising us to infinity and beyond
5G – Enable, elevate, and emerge Digitalising us to infinity and beyond5G – Enable, elevate, and emerge Digitalising us to infinity and beyond
5G – Enable, elevate, and emerge Digitalising us to infinity and beyondaakash malhotra
 
The trade landscape, as we know it, is changing: Is India prepared?
The trade landscape, as we know it, is changing: Is India prepared?The trade landscape, as we know it, is changing: Is India prepared?
The trade landscape, as we know it, is changing: Is India prepared?aakash malhotra
 
Deloitte Report: Transforming India Unleash your potential
Deloitte Report: Transforming India Unleash your potentialDeloitte Report: Transforming India Unleash your potential
Deloitte Report: Transforming India Unleash your potentialaakash malhotra
 

More from aakash malhotra (20)

In-deloitte-tech-trends-2023-noexp.pdf11
In-deloitte-tech-trends-2023-noexp.pdf11In-deloitte-tech-trends-2023-noexp.pdf11
In-deloitte-tech-trends-2023-noexp.pdf11
 
DeIoitte_The-future-of-work-in-technology.
DeIoitte_The-future-of-work-in-technology.DeIoitte_The-future-of-work-in-technology.
DeIoitte_The-future-of-work-in-technology.
 
Toward True Organizational Resilience | Deloitte’s Global Resilience Report
Toward True Organizational Resilience | Deloitte’s Global Resilience ReportToward True Organizational Resilience | Deloitte’s Global Resilience Report
Toward True Organizational Resilience | Deloitte’s Global Resilience Report
 
Risk Advisory’s new narrative Mitigate risks effectively
Risk Advisory’s new narrative Mitigate risks effectivelyRisk Advisory’s new narrative Mitigate risks effectively
Risk Advisory’s new narrative Mitigate risks effectively
 
India Banking Fraud Survey Edition IV - Deloitte
India Banking Fraud Survey Edition IV - DeloitteIndia Banking Fraud Survey Edition IV - Deloitte
India Banking Fraud Survey Edition IV - Deloitte
 
Sustainability perspectives Key insights
Sustainability perspectives Key insightsSustainability perspectives Key insights
Sustainability perspectives Key insights
 
Global_Resilience_report_October_2022_wc.pdf
Global_Resilience_report_October_2022_wc.pdfGlobal_Resilience_report_October_2022_wc.pdf
Global_Resilience_report_October_2022_wc.pdf
 
in-ra-service-brochure-December-23-noexp.pdf
in-ra-service-brochure-December-23-noexp.pdfin-ra-service-brochure-December-23-noexp.pdf
in-ra-service-brochure-December-23-noexp.pdf
 
Yearly Status of School Education in states and union territories of India - ...
Yearly Status of School Education in states and union territories of India - ...Yearly Status of School Education in states and union territories of India - ...
Yearly Status of School Education in states and union territories of India - ...
 
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...Annual Status of Higher Education (ASHE), 2023 In states and union territorie...
Annual Status of Higher Education (ASHE), 2023 In states and union territorie...
 
in-deloitte-tech-trends-2023-noexp.pdf
in-deloitte-tech-trends-2023-noexp.pdfin-deloitte-tech-trends-2023-noexp.pdf
in-deloitte-tech-trends-2023-noexp.pdf
 
2025 banking and capital markets outlook
2025 banking and capital markets outlook2025 banking and capital markets outlook
2025 banking and capital markets outlook
 
gx-er-corporate-procurement-renewable-energy-report.pdf
gx-er-corporate-procurement-renewable-energy-report.pdfgx-er-corporate-procurement-renewable-energy-report.pdf
gx-er-corporate-procurement-renewable-energy-report.pdf
 
GST on Online Gaming
GST on Online GamingGST on Online Gaming
GST on Online Gaming
 
in-tax-Deloitte-Pre-budget-study-2023.pptx
in-tax-Deloitte-Pre-budget-study-2023.pptxin-tax-Deloitte-Pre-budget-study-2023.pptx
in-tax-Deloitte-Pre-budget-study-2023.pptx
 
in-union-budget-2023-detailed-analysis-noexp.pdf
in-union-budget-2023-detailed-analysis-noexp.pdfin-union-budget-2023-detailed-analysis-noexp.pdf
in-union-budget-2023-detailed-analysis-noexp.pdf
 
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INR
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INRKeeping an eagle eye on two ‘I’s will be imperative–Inflation and INR
Keeping an eagle eye on two ‘I’s will be imperative–Inflation and INR
 
5G – Enable, elevate, and emerge Digitalising us to infinity and beyond
5G – Enable, elevate, and emerge Digitalising us to infinity and beyond5G – Enable, elevate, and emerge Digitalising us to infinity and beyond
5G – Enable, elevate, and emerge Digitalising us to infinity and beyond
 
The trade landscape, as we know it, is changing: Is India prepared?
The trade landscape, as we know it, is changing: Is India prepared?The trade landscape, as we know it, is changing: Is India prepared?
The trade landscape, as we know it, is changing: Is India prepared?
 
Deloitte Report: Transforming India Unleash your potential
Deloitte Report: Transforming India Unleash your potentialDeloitte Report: Transforming India Unleash your potential
Deloitte Report: Transforming India Unleash your potential
 

Recently uploaded

Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
 
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024StefanoLambiase
 
Automate your Kamailio Test Calls - Kamailio World 2024
Automate your Kamailio Test Calls - Kamailio World 2024Automate your Kamailio Test Calls - Kamailio World 2024
Automate your Kamailio Test Calls - Kamailio World 2024Andreas Granig
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentationvaddepallysandeep122
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceBrainSell Technologies
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalLionel Briand
 
英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作qr0udbr0
 
Salesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZSalesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZABSYZ Inc
 
Xen Safety Embedded OSS Summit April 2024 v4.pdf
Xen Safety Embedded OSS Summit April 2024 v4.pdfXen Safety Embedded OSS Summit April 2024 v4.pdf
Xen Safety Embedded OSS Summit April 2024 v4.pdfStefano Stabellini
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Mater
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanyChristoph Pohl
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Cizo Technology Services
 
Cloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEECloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEEVICTOR MAESTRE RAMIREZ
 
UI5ers live - Custom Controls wrapping 3rd-party libs.pptx
UI5ers live - Custom Controls wrapping 3rd-party libs.pptxUI5ers live - Custom Controls wrapping 3rd-party libs.pptx
UI5ers live - Custom Controls wrapping 3rd-party libs.pptxAndreas Kunz
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationBradBedford3
 
Implementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureImplementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureDinusha Kumarasiri
 
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfIntroduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfFerryKemperman
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commercemanigoyal112
 

Recently uploaded (20)

Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
 
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024
Dealing with Cultural Dispersion — Stefano Lambiase — ICSE-SEIS 2024
 
Automate your Kamailio Test Calls - Kamailio World 2024
Automate your Kamailio Test Calls - Kamailio World 2024Automate your Kamailio Test Calls - Kamailio World 2024
Automate your Kamailio Test Calls - Kamailio World 2024
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentation
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. Salesforce
 
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort ServiceHot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
 
Precise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive GoalPrecise and Complete Requirements? An Elusive Goal
Precise and Complete Requirements? An Elusive Goal
 
英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作英国UN学位证,北安普顿大学毕业证书1:1制作
英国UN学位证,北安普顿大学毕业证书1:1制作
 
Salesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZSalesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZ
 
Xen Safety Embedded OSS Summit April 2024 v4.pdf
Xen Safety Embedded OSS Summit April 2024 v4.pdfXen Safety Embedded OSS Summit April 2024 v4.pdf
Xen Safety Embedded OSS Summit April 2024 v4.pdf
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
 
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
Global Identity Enrolment and Verification Pro Solution - Cizo Technology Ser...
 
Cloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEECloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEE
 
UI5ers live - Custom Controls wrapping 3rd-party libs.pptx
UI5ers live - Custom Controls wrapping 3rd-party libs.pptxUI5ers live - Custom Controls wrapping 3rd-party libs.pptx
UI5ers live - Custom Controls wrapping 3rd-party libs.pptx
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion Application
 
Implementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureImplementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with Azure
 
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdfIntroduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdf
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commerce
 

Digital forecasting transforms business predictions

  • 1. Crunch time 6 Forecasting in a digital world
  • 2. Prediction is very difficult, especially if it’s about the future. —Niels Bohr Nobel Laureate in Physics 2
  • 3. 3 3 Forecasting in a digital world 4 Algorithmic forecasting 6 The ripple effect 9 It’s happening now 11 Algorithmic forecasting in action 14 Getting from here to there 16 17 Looking ahead 19 01 Before you go 02 03 04 06 07 08 09 Roadblocks 13 05
  • 4. 4 01 02 03 04 05 06 07 08 09 Human fascination with the future is part of our evolutionary heritage. Those who can foresee and navigate risks have always been more likely to survive than those who can’t. That’s just as true in business, where the ability to see ahead continues to separate performance leaders from everyone else. That’s what forecasting is all about, yet it’s surprisingly difficult (and often expensive) to do. Companies have different motivations for improving their predictions. For some, the ability to deliver reliable guidance to analysts and markets might drive the decision. For others, being able to trim production waste by predicting consumer demand is what matters most. Still others want to improve cost management and streamline the forecasting process. Traditionally, forecasting has been a mostly manual process with people gathering, compiling, and manipulating data, often within spreadsheets. With more and more data available, old-school forecasting has become an unwieldy, time-consuming process that makes discerning what’s important next to impossible. As a result, humans often resort to their own intuition and judgement, which opens the door to unconscious biases and conscious sandbagging. Forecasting in a digital world 01
  • 5. 5 01 02 03 04 05 06 07 08 09 There’s another way. Organizations are shifting to forecasting processes that involve people working symbiotically with data-fueled, predictive algorithms. It’s all made possible by new technologies—advanced analytics platforms, in-memory computing, and artificial intelligence (AI) tools, including machine learning. Similar digital tools are common in our consumer lives. We use a mapping app to predict when we’ll arrive at our destination. A real-time weather app can tell us precisely when a rain shower will start or stop. So, it’s reasonable to expect predictive capabilities when we’re at work. Today, these technologies in the hands of expert forecasting talent give companies the ability to discover things they’ve always wanted to know—as well as things they didn’t know they didn’t know—with more confidence and speed. CFOs are in a prime position to challenge the way their enterprise looks at and consumes data. In the area of forecasting, they can champion an innovative, data-driven approach that will help people project the future of their business. By modeling the potential impact of important decisions, they can help generate smarter insights and stronger business outcomes. For an introduction to the digital capabilities that make data-powered forecasting possible, read Crunch time 1: Finance in a digital world. 09 01
  • 6. 6 01 02 03 04 05 06 07 08 09 Algorithmic forecasting Machines and people working together to see what’s next You can hardly turn around these days without bumping into a vendor claiming to have software that can predict the future. As is often the case, the hype outstrips the reality. Here’s how we see things. The basics Algorithmic forecasting uses statistical models to describe what’s likely to happen in the future. It’s a process that relies on warehouses of historical company and market data, statistical algorithms chosen by experienced data scientists, and modern computing capabilities that make collecting, storing, and analyzing data fast and affordable. Beyond the basics Forecasting models offer more value when they can account for biases, handle events and anomalies in the data, and course-correct on their own. That’s where machine learning comes into play. Over time, forecasting accuracy improves as algorithms “learn” from previous cycles. Models are also more valuable when they’re grounded in richer, more granular data. In some cases, that might involve using natural language processing, which can read millions of documents—including articles, social posts, correspondence, and other text—and feed them directly into the algorithms. The magic The real lift from algorithmic forecasting comes when it’s combined with human intelligence. Machines help keep humans honest, and humans evaluate and translate the machine’s conclusions into decisions and actions. It’s this symbiotic relationship that makes algorithmic forecasting effective—especially when humans are organized to support and share their findings across the enterprise. The bottom line Algorithmic forecasting doesn’t create anything out of thin air, and it doesn’t deliver 100 percent precision. But it is an effective way of getting more value from your planning, budgeting, and forecasting efforts. We’ve seen companies substantially improve annual and quarterly forecast accuracy— with less variance and in a fraction of the time traditional methods require—while building their predictive capabilities in the process. 02
  • 7. 7 01 02 03 04 05 06 07 08 09 Algorithmic forecasting depends on these elements working together 09 Human intelligence AI applications Modern computing capabilities Data sources Advanced analytics platforms 02
  • 8. 8 01 02 03 04 05 06 07 08 09 Don’t we have bigger fish to fry? CFOs are continually weighing the relative ROI of potential investments that vie for their attention. Here are a few reasons why many leaders are moving algorithmic forecasting up their priority list: Competitive advantage Leaders who can more accurately predict future performance based on the underlying business drivers—both internal and external—are better positioned to recognize and respond to early warning signals. Increasing disruption As companies’ business and operating models change in response to disruptive competition, historical patterns and trends used in traditional forecasting are becoming less and less relevant. Growing complexity Global market and supply chain complexity, along with increasing volatility, means that organizations should harness the agility that algorithmic forecasting can deliver to help make sense of new realities as—or even before—they emerge. 02
  • 9. 9 01 02 03 04 05 06 07 08 09 While some aspects of forecasting using algorithmic models are fairly straightforward, others can be trickier. Changing processes. Building trust and transparency. Creating partnerships between people and machines. These are tougher challenges. How work changes With algorithmic forecasting, Finance does more insight- development work and less manual drudgery. Instead of spending their time grinding through spreadsheets, humans get to bring their expert judgment to the process. Leading finance organizations are already using automation tools to help with manually intensive work like transaction processing. Automating routine forecasting tasks is another area ripe for improvement. How the workforce changes Your finance talent model should evolve to keep up with changes in how work gets done—and that will likely require a different mix of people than you have in place today. Algorithmic forecasting depends on collaboration among Finance, data analytics, and business teams. Once they hit stride, these teams can move across the range of forecasting needs, embedding capabilities in the business and driving integration. These teams are integral to establishing an algorithmic solution that can work for the business, bring insights to life within the organization, and support continued business ownership of the outcomes. The ripple effect 03
  • 10. 10 01 02 03 04 05 06 07 08 09 Our experience has shown us that some finance professionals are simply better at forecasting than others. They’ve learned to set aside their biases and look at the bigger picture objectively—and they have a knack for understanding algorithmic models and uncovering flaws that others may miss. You’ll need storytellers, too—folks who really understand the business and can translate analytical insights into compelling narratives that trigger appropriate actions. How decision-making changes Making choices becomes a more interactive process with advanced forecasting techniques, resulting in smarter, more informed decisions, even on the fly. In-memory computing, predictive analytic software, and visualization tools enable management to easily and quickly ask “what if” questions and produce a range of scenarios to help them understand potential impacts on the business. How the workplace changes Forecasting isn’t limited to Finance. Functions from marketing to supply chain to human resources all have needs for predicting the future to drive important decisions. While CFOs may not lead function-specific forecasting, they should help shape these forecasting initiatives since Finance will inevitably use the outputs they generate. A shared forecasting infrastructure—even a physical Center of Excellence (CoE)—can help improve collaboration and coordination while providing efficiencies in data storage, tool configuration, and knowledge sharing. And once the organization develops the forecasting muscle to solve one problem, the capability can quickly be extended and applied in other areas. “Foresight isn’t a mysterious gift bestowed at birth. It is the product of particular ways of thinking, of gathering information, of updating beliefs. These habits of thought can be learned and cultivated by any intelligent, thoughtful, determined person.” —Philip E. Tetlock in Superforecasting: The Art and Science of Prediction 09 03
  • 11. 11 01 02 03 04 05 06 07 08 09 Many of the companies we work with have begun their digital finance journeys by investing in cloud, in-memory computing, and robotic process automation. Others have broadened their ambitions to include advanced analytics, with an emphasis on forecasting. They want to create forecasts that enable faster and more confident decision-making. This is where those digital investments can begin to pay off. Traditional approaches to forecasting can take far too long, cost far too much, and generate too little insight about potential future outcomes. Common applications for algorithmic forecasting Top-down planning Target setting Integrated financial statement forecasting Working capital forecasting Indirect cash flow forecasting Demand forecasting Competitive actions and implications Tax tradeoffs and revenue/profit implications Bottom-up forecasting Product-level forecasting Market- or country-level forecasting Direct cash flow forecasting Function-specific forecasting Customer retention Inventory optimization Employee retention and attrition modeling External reporting Market guidance Earnings estimates It’s happening now 04
  • 12. 12 01 02 03 04 05 06 07 08 09 Case study Is fast growth a financial problem? Yes, if you can’t explain it. 01 02 03 04 05 06 07 08 09 12 The FP&A team for a global consumer product manufacturer frequently outperformed their guidance to market analysts. Problem was, they couldn’t explain the unanticipated growth, and holding credibility with the executive team, board, and industry analysts was a key priority. The team suspected sandbagging was the source of their headache. Individual business unit (BU) leaders made their own bottom-up forecasts as part of the target-setting planning process, which was used for performance incentives. Finance had no objective way to verify or push back on the BU leaders’ numbers. What happened next Finance leadership asked Deloitte to help them develop an objective, data-driven forecasting approach. Within 12 weeks, Deloitte’s data scientists designed a top-down predictive model that incorporated the company’s internal historical actuals and external drivers for each global market, including housing starts, local GDP metrics, commodity prices, and many additional variables. The model enabled the FP&A team to deliver a second-source forecast based on external macro drivers that aligned to market expectations and provided insightful, accurate projections across the P&L, balance sheet, and cash flow statements. Planners also gained the ability to quickly create growth, recession, and other scenarios using desktop visualization software. The toolkit The Finance team received the fully functional predictive model built on an open source platform, which enables their CoE to manage and model forecasts on an ongoing basis. Leadership gained an objective, transparent, and visual conversation starter for discussions with business units about new opportunities and upcoming challenges for their markets. Looking ahead Leadership sees this as a game changer, and not just for their top-down financial forecasts. The business segments do, too. Following the successful delivery of a prototype focused on corporate FP&A, Finance has added data scientist capabilities to its talent model. Socializing the results with the business has created significant demand to dive deeper and extend the solution to the business segments and regions. Additionally, the client has taken steps to industrialize the model and to provide business users greater visibility into the driver assumptions and relationships to the financials. The company continues the journey to scale the solution within the organization on their own. The client’s FP&A leader reports, “This is a good-news story. We made an investment [to prototype algorithmic forecasting], proved the concept, and got the business interest. We created significant demand for a new Finance- developed forecast capability that will help the organization improve forecast accuracy and efficiency.”
  • 13. 13 01 02 03 04 05 06 07 08 09 Roadblocks When you start having conversations with colleagues about replacing traditional forecasting processes with algorithms, you’ll quickly discover that people have preconceptions about what it is, how it works, and what it means for the organization. That’s what happens when change is in the air. Fear and loathing People are often scared of the unknown. Even though Finance is a discipline grounded in numbers, some will resist the idea that algorithmic forecasting might enhance their own methods. Help them see that machines can handle the tedious number- crunching work while enabling people to spend more time uncovering valuable insights. Black boxes There’s a myth that algorithms are black boxes where magic things happen. While not true, some complicated models may seem that way. To help build trust across the organization, consider using easier-to- understand algorithms with transparent inputs in the beginning. As comfort grows, so will the willingness to accept more complex approaches. Small thinking Some people won’t take time to see the bigger picture. They’ll go along with algorithmic forecasting if they have to, but they’ll start small and stay small, looking for what’s wrong instead of what might be workable— all while refusing to give up the old ways of thinking and acting. Turf battles Is algorithmic forecasting a CIO thing that needs to be brought into Finance, or is it a CFO thing that needs IT’s help? And when it comes to bottom-up forecasting, who’s on first—the business or the business partner? The reality is that effective algorithms require data, computing power, and business insight—so collaboration is important. Making accountability and ownership decisions up front can improve collaboration and value generation. Engagement matters It’s important to involve end users to help conceive, design, build, and implement algorithms. After all, they know the sandtraps that need to be avoided, and their acceptance is key to effective implementation. Data, data, everywhere Some companies want to try new approaches to forecasting, but they’re worried their data problems will stymie them. Whether those problems are the result of mergers and acquisitions, a history of poor data management, disconnected systems, or all of the above, you’re not alone. The first step toward improving forecasting for many companies is getting their data house in order— sometimes even one room at a time. Silver bullets Algorithmic forecasting often gets introduced with a lot of hype. Don’t do that. It’s just a tool. That said, when this kind of powerful tool is combined with human intellect, the impact can be transformative. 05
  • 14. 14 01 02 03 04 05 06 07 08 09 Algorithmic forecasting in action It’s 7:00 a.m., and you’re thinking about the day ahead. By noon, you have to settle on a forecast about how your business will perform over the next quarter. And at 2:00 p.m., you need to tell that story to a dozen board members on a conference call. In the past, your forecasting team would be pulling all- nighters for days before your meeting. They’d be grinding through spreadsheets, calculating growth percentages, chasing down anomalies, and drinking way too much coffee. That was then. Today, your forecasting function is a well-oiled machine, with more than 80 percent of the work happening automatically. Every piece of financial data you could want is available on your tablet. All you have to do is ask— literally. Display the impact on profits if the cost of steel goes up 20 percent in the next month. You can drill down, roll up, set aside exceptions, and run a dozen more scenarios before your conference call. And you can do it all without an army of analysts scrambling to help. 06
  • 15. 15 01 02 03 04 05 06 07 08 09 What changed? Almost everything. More and better data Your organization now uses data of all types—financial, operational, and external— to train your forecasting model. Once the model is set up and evaluated, machine learning kicks in. The model gets better over time through constant and automated iterations. It is always up-to-date, evaluating and determining which of the latest inputs add the most insight and value to your predictions. More models, more options With better models analyzed more quickly, you have the opportunity to understand the impact of unforeseen trends in the market and factor them into your planning. How are unemployment and disposable income trends going to affect the business? How much of that cut in trade spending should I expect to fall through to the bottom line? Scenario modeling done in collaboration with business units gives them advanced capabilities to work smarter, not harder, along the forecasting journey. A clearer view of performance drivers Scenario modeling also gives business leaders visibility into performance drivers. For example, you can easily see how to manage existing markets by building out price, product mix, and volume analytics. Improved accuracy, more confidence Where are you going to land this quarter? This year? Do you need to update your guidance to the Street? If so, by how much? Algorithmic forecasting answers these questions in real-time, without having to aggregate bottom-up analysis from every corner of the globe. Your communications with investors are more informed and more efficient. No sandbagging, no rose-colored glasses Algorithmic forecasting enables you to measure— and remove—these two big human biases, giving you objective predictions you can trust. 06
  • 16. 16 01 02 03 04 05 06 07 08 09 Getting from here to there 07 Identify the problem to solve Think about how to proceed Identify the help you need Prototype and scale Predictive modeling Dashboards and visualizations Socialization Ongoing • Define scope and ambition • Determine the level of business to work with • Identify targets (geographies, products, customers, channels, etc.) • Set a realistic time horizon • Decide if you want to build this competency in-house—or if outsourcing to a managed analytics service makes more sense • Determine if algorithmic forecasting is a capability youwant to provide as a service to the enterprise • Make sure your organization has the talent and culture to embrace this • Assess available talent and their abilities • Determine if additional professionals are needed—financial analysts, data scientists, data visualization architects, or others—and how you’ll get them • Determine external vendor support needs • Identify which tools you already have in place • Ask IT what other tools may be needed • Identify key revenue and cost drivers • Collect and structure relevant data for analysis • Align on an initial set of priority drivers • Collect and clean required data inputs • Test drivers for significance • Develop statistical models for P&L line items based on relevant drivers • Consider using models that are easier for the business to understand, to build trust and adoption • Include end users in the process of conceiving, designing, building, validating, and implementing forecasting models • Test and validate the models • Link P&L forecasts to the balance sheet and cash flow • Develop dashboard views with key metrics • Develop accompanying visualizations • Enable scenario analysis functionality • Elicit feedback from dashboard users • Use A/B testing to optimize the effectiveness of dashboard displays • Socialize results with key stakeholders all along the way • Prepare detailed documentation for maintaining and managing the model • Track results in parallel—keep score • Train the organization to understand the value of this collaborative approach • Align on cyclesrequired for refreshing your models • Assess opportunities for machine learning and cognitive enhancements Most clients we work with don’t attempt a wholesale change to their forecasting approach from the beginning. Instead, they select a part of their business or a specific revenue, product, or cost element to use as a pilot or proof of concept. They often run algorithmic forecasting parallel to their human-centric forecast for a period to compare accuracy and effort. Every company will make its own unique journey from its current approach to planning and forecasting to an improved approach. That said, there are some things you’ll want to consider on your path forward. 1 2 3 4 A Driver analysis and data cleansing B C D E
  • 17. 17 01 02 03 04 05 06 07 08 09 A commitment to algorithmic forecasting is both a cultural thing and a statistical thing. Making it happen involves great people working with elegant technology. Neither is sufficient on its own. Here are some of the lessons we’ve learned while helping companies move forward. People lessons • CFOs should be the ringleaders and champions. Reading at least one book on the topic can help ground you in the culture of forecasting. • Train your team—and yourself—on the basics of probabilistic thinking and how to spot and correct for common human biases that can derail effective forecasting. • Management and employee engagement becomes easier when models are transparent. Choosing a less accurate model with more intuitive drivers may result in better business adoption than a more sophisticated model that people can’t understand. • Build an approach where accountability for “the number” remains local—in the hands of those closest to the business. • Assess your workforce needs with the understanding that changing from traditional to algorithmic forecasting may create spare capacity as routine tasks are automated and skill gaps where new professionals are needed. • Don’t underestimate the value of visualization. It makes forecasting real. Before you go Statistical lessons • For mature and stable businesses, predictive models usually require five or more years of monthly data to train properly, with the most effective models often using 10 years of data to identify trends, seasonality, and driver correlation. • Don’t overlook the possibility that past performance may not be relevant to future performance predictions. Often the data can be used, but may need to be adjusted or filtered by human experts. Experiment with different types of modeling approaches to see which leading and lagging indicators improve the relevancy of your results, especially in a changing or disruptive industry environment. • Aggregation across business units, divisions, groups, or the whole company generally improves predictability. • Products with higher sales frequencies yield stronger predictability due to the greater number of data points. Long-cycle products can require additional data history for the models to mature. • Annual forecasts tend to be more accurate than quarterly. Same is true that quarterly predictions tend to be more accurate than monthly. That’s because variances tend to cancel each other out—up to a point. Over longer time horizons, predictions can become less accurate as unexpected events become more likely. 08
  • 18. 18 01 02 03 04 05 06 07 08 09 Want to learn more? Start here. Articles Guszcza, Jim and Maddirala, Nikhil. “Minds and machines: The art of forecasting in the age of artificial intelligence,” Deloitte Review, July 25, 2016. Guszcza, Jim. “The importance of misbehaving: A conversation with Richard Thaler.” Deloitte Review, January 25, 2016. Books Tetlock, Philip E., and Gardner, Dan. Superforecasting: The Art and Science of Prediction. Crown, 2015. Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t. Penguin Press, 2012. Kahneman, Daniel. Thinking Fast and Slow. Farrar, Straus and Giroux, 2011. Online lecture Hyndman, Rob. “Exploring the boundaries of predictability: What can we forecast, and when should we give up?” Presented at Yahoo Big Thinkers, June 26, 2015. 08
  • 19. 19 01 02 03 04 05 06 07 08 09 “The best qualification of a prophet is to have a good memory.” —Marquis of Halifax English writer and statesman 08
  • 20. 20 01 02 03 04 05 06 07 08 09 Looking ahead Forecasting is something many companies struggle with—and where business leaders say they need help from Finance. They’re being asked to make predictions about what will happen in the future, and no matter how good they are, biases and guesswork come into play. Algorithmic forecasting is a transparent way to help improve the forecasting process, while relieving Finance professionals of tedious, repetitive work. The result can be more accurate and timely forecasts—and more informed decisions. You and your people can spend your time uncovering insights and taking action, and less time grinding through mind-numbing spreadsheets. Everybody wins. 09
  • 21. 21 01 02 03 04 05 06 07 08 09 Acknowledgements Authors Eric Merrill Managing Director, Predictive Analytics and PrecisionView™ Offering Leader Deloitte Consulting LLP Tel: +1 404 631 2141 Email: emerrill@deloitte.com Steven Ehrenhalt Principal, US and Global Finance Transformation Leader Deloitte Consulting LLP Tel: +1 212 618 4200 Email: hehrenhalt@deloitte.com Adrian Tay Managing Director, Finance Analytics and Insights Leader Deloitte Consulting LLP Tel: +1 909 979 7212 Email: adtay@deloitte.com Contributors Paul Thomson United States JoAnna Scullin United States James Guszcza United States Max Troitsky United States Jeff Schloemer United States Brandon Cox United States Ayan Bhattacharya United States Dave Kuder United States Tim Gross United States Tim Gaus United States Laks Pernenkil United States Sarah Logman United States Niklas Bergentoft Advisory Srini Raghunathan United Kingdom Tim Leung United Kingdom Anna Chroni United Kingdom Alie van Davelaar Netherlands 09
  • 22. 22 01 02 03 04 05 06 07 08 09 Contacts 09 Soumen Mukerji Partner and Service Line Leader, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98117 06159 Email: soumenmukerji@deloitte.com Dhiraj Bhandary Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 95600 52435 Email: dbhandary@deloitte.com Pallab Roy Director, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98206 18260 Email: pallabroy@deloitte.com Nitin Kini Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98861 36859 Email: nitinkini@deloitte.com Eapen Koshy Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 99955 17771 Email: keapen@deloitte.com Pragati Chakraborty Director, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98681 87429 Email: chakrabortyp@deloitte.com Debabrat Mishra Partner, Human Capital Deloitte Touche Tohmatsu India LLP Tel: +91 98203 39411 Email: mdebabrat@deloitte.com Prithwijit Chaki Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98734 27991 Email: pchaki@deloitte.com Pratim Kabiraj Director, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98318 03302 Email: pkabiraj@deloitte.com Deepak Mowdhgalya Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98457 04325 Email: dmowdhgalya@deloitte.com Ritesh Mangla Partner, Finance Transformation Deloitte Touche Tohmatsu India LLP Tel: +91 98990 14196 Email: riteshmangla@deloitte.com
  • 23. 23 To find out more, please visit www.deloitte.com/us/crunchtime. About Deloitte Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. Please see www. deloitte.com/about for a detailed description of DTTL and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication. Copyright © 2018 Deloitte Development LLC. All rights reserved.