AI Trends.pdf

EBOOK
3 Enterprise Trends
Driving AI Into Everyday Use:
2022 and Beyond

Introduction
1
https://aijourn.com/report/ai-in-a-post-covid-19-world/
2
https://appen.com/whitepapers/the-state-of-ai-and-machine-learning-report/
Appen’s State of AI and Machine Learning 2021 report confirms this notion — they observed
a shift away from the AI “silver bullet” to a more fit-for-purpose and internal facing suite of
applications such as internal efficiency gains and cost reduction2
. AI is a powerful tool that can
optimize every single process, but it needs to be embedded into the organization’s culture and
operating model in order to make an impact.
Not just any form of impact will do. That impact must be twofold — organizations need to
harness advanced analytics and AI for both the short term (think quick, high-value wins) and
the long term (establishing a transformative, AI-embedded culture that is enterprise wide). In
order to usher in this two-pronged approach in a scalable and sustainable way, organizations
need to embrace Everyday AI, which is all about making the use of data almost pedestrian — AI
that is so ingrained and intertwined with the workings of the day-to-day that it’s just part of
the business (not being used or developed exclusively by one team, such as the analytics team
or central data science team).
74% of business leaders anticipate that AI will deliver more efficient business processes, help
create new business models (55%), and enable the creation of new products and services
(54%), according to a survey by The AI Journal1
. However, whether an organization is solving
for one of the business objectives outlined above or something else, one thing is for certain: AI
can’t be put on a pedestal as this flashy, turnkey solution that will alter a business’s trajectory
overnight. It’s not a magical fix that will change everything about a business.
Business leaders
anticipate that AI will…
• Deliver more efficient business processes ( 74%)
• Help create new business models ( 55%)
• Enable the creation of new products and services ( 54%)
1 3 Enterprise Trends Driving AI Into Everyday Use: 2022 and Beyond EBOOK - Dataiku

Doing so opens the door to applying data and AI to any process and business decision and
understanding which opportunities require more data and analytics to drive efficiency is key
to success (and business value generation!). In this ebook, we highlight three visionary, broad-
stroke trends for 2022, (each with their own subtrends) based on our observations, customer
interactions, and some of the biggest AI implementations worldwide:
Business users start to deliver more value with AI than data scientists (and, more
broadly, how the pool of those who are involved in data projects is evolving)
Automation, business intelligence, and AI converge into one practice for the enterprise
More than 50% of machine learning projects organizations would like to make it into
production have made it there
It is our hope that as the understanding of how to work with (and harness) data grows, the
science and strategies behind it will continuously become more accessible and enable more
organizations to capitalize on its massive potential.
EBOOK - Dataiku 2
3 Enterprise Trends Driving AI Into Everyday Use: 2022 and Beyond

#1: Business Users Start Delivering
More Value With AI Than Data Scientists
2021 was a particularly defining moment in time for the data science and AI space because
many organizations realized that they are not going to scale AI impact without enlisting non-
experts to the cause. In this section, we’re going to highlight a myriad of ways that the pool of
people building and benefiting from AI is expanding.
We’ve observed an influx of roles that are now involved in AI projects — project management
and leads, risk managers, subject matter experts (SMEs), annotators, hardcore data science
internal think tanks who do most things in code, and more. What’s the catalyst behind this
diversification of involvement? With the increase in adoption and scale, new players are
joining the teams developing, deploying, and managing AI. With success from initial AI projects
comes more involvement from business stakeholders who want visibility into projects and
potentially even review and signoff at key steps.

For the scope of this ebook, though, let’s be clear with regards to who exactly we’re focusing
on. There are "traditional" roles, like data scientists, who are incredibly valuable to any
organization. The data scientist role is not going away — in fact, it was the #2 best job in
America for 2021 per Glassdoor3
— but, at the same time, organizations can't possibly hire
thousands of them due to a myriad of reasons (namely they're expensive, can be hard to retain
if not working on exciting, high-value projects, etc.).
3
https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.html
Dataiku customer MandM Direct, an online retailer in the United Kingdom, is a prime
example of this time saved in practice — they moved all of their available data out of silos
and into Dataiku, a unified, analytics-ready environment.
Instead, data teams can make the ones they do have more efficient through the proper
tooling. With Dataiku, for example, these key players are enabled to be 75% more productive
by reducing the manual work involved in data extraction and transformation through reusable
data products and models.
HavingaplatformlikeDataikuallowsourdatascientiststofocusonbuildingcoolthings,
not spending hours and hours on maintenance and making sure things are running.
With workflows deployed in Dataiku, we save literally days of work every month.
— Ben Powis, Head of Data Science at MandM Direct
EBOOK - Dataiku 4

So, while data scientists (and other data teams) are undoubtedly a critical piece of organizational
data and AI transformation, we know (and have observed throughout 2021) that they are not
enough to make the difference exclusively by themselves. There are three main groups of
business stakeholders we are going to refer to:
Below, we’ll break out the trends we’re seeing among these groups, along with concrete examples
from Dataiku customers.
To begin, there are other data roles that have existed for awhile
and that have always worked with data, such as business
analysts. These players are becoming increasingly effective
because of factors like the move out of spreadsheets and
internal enablement to upskill them into citizen data scientists.
Next are the business users who, in the past, may have operated
with data, but with low data and analytics maturity (i.e.,
not analysts, but people such as marketers or supply chain
managers). They are increasingly empowered to directly build
or co-build analytics workflows with experts because they
have more access to data which, combined with upskilling and
proper tooling (e.g., Dataiku), leads to faster time to impact.
We’re seeing a growth in the very end users who aren’t
necessarily the ones building the solutions or directly working
with data, but who are benefitting from it by being surfaced AI
tools and applications built by data teams or one of the other
groups above.
Analysts
Business Users
AI Consumers

Citizen Data Science Becomes Real Data Science,
Data-Minded People Become More Efficient + Effective
Business people, including analysts and domain experts, now have the tools to create
production-ready data science and machine learning (ML) projects that can be used to solve real
business problems. As data analytics becomes more democratized, companies are starting to
consider how citizen data scientists can help them reduce costs and risks.
A critical part of this equation is to empower citizen data scientists in smart ways. This doesn’t
just mean allowing them to crank out models without proper training or understanding of the
process such that those models are totally disconnected from the business questions they’re
trying to answer. In order to achieve transparency, citizen data scientists need to be equipped
with the right tools.
EBOOK - Dataiku 6

Citizen data scientists rely on end-to-end platforms that contain a powerful automated machine
learning engine. With such tools (like Dataiku), citizen data scientists can optimize and deploy
models with minimal intervention. It is important to note, though, that not all data science and
ML platforms are created equally in terms of enabling seamless production and value. When
considering an end-to-end data science platform, teams need to consider specific features and
capabilities — including AutoML for the citizen data science profile. Some elements include:
Usability: The system should be easily usable by non-developers with minimal
technical skill. Look for a system that supports augmented analytics by providing
contextual help and explanation for different parts of the data process and a visual,
code-free user interface.
Stability: Users without intimate knowledge of data storage technologies need
to be able to execute augmented analytics using a system that can be reliably
leveraged from one step of the data pipeline to another.
Transparency: It’s difficult to trust something you don’t understand. Therefore, the
best tool will be one that gives an accurate description of algorithms used, explains
why they were chosen, and provides the right level of knowledge necessary for
citizen data scientists to trust outcomes and determine if they are right for the
project at hand.
Adaptability: As data projects are often worked on by multiple individuals or
various roles, the chosen tool should have adaptability options. For example,
outputs should be able to be translated into Python code for full learning.
Controls and Auditability: Teams need to be able to control project access,
visibility, and permissions management both from a regulation and compliance
perspective and also to foster collaboration and eliminate duplicate work.

Dataiku upskills business teams while empowering IT to manage production to free data
scientists to pursue significant initiatives. For example, Standard Chartered Bank has developed
a data marketplace that people across the organization can use when they need to get answers
from other datasets (i.e., an analyst trying to understand the cost of property can use the balance
sheet from the data marketplace and plug in lease data).
The model represents a unique take on a self-service data program where the center of
excellence owns the core structured intelligence of the company, but enables other teams to
build experiences on top of that data, relevant to their specific function or line of business. As a
result, people from various teams around the organization can use shared analytics, datasets,
and apps within the enterprise-level data marketplace to get answers to day-to-day business
problems, which, therefore, gets more people to use data on a regular basis.
Separately, the team at Standard Chartered was originally spending tons of time working in
unwieldy spreadsheets, which wasn't scalable or collaborative. Now, two people armed with
the Digital MI team’s applications in Dataiku are doing the work of about 70 people limited
to spreadsheets — which means increased analyst productivity by a factor of 30 by replacing
spreadsheet-based processes with governed self-service analytics.
Another Dataiku customer Orange, one of the largest operators of mobile and internet services
in Europe and Africa and a global leader in corporate telecommunication services, realized that
in order to spend more time on machine learning, they needed to empower analysts to work on
their own data analysis projects.
By enabling analysts and business people to work on data analysis themselves, data practices
became much more infused throughout the client services organization and not just siloed
to one team (and the data team could then focus on more advanced data science work with
potentially bigger impact). An additional benefit is data consciousness — enabling the business
to look at data in an applied context is usually the cornerstone of data management strategies.
Today, there are more than 100 analysts and other business users across Orange who are
empowered to work with data (and do so in Dataiku).
EBOOK - Dataiku 8

Roles Who Historically Didn’t Use Data Are Now Empowered to
In the beginning stages, these people (who aren’t analysts but might be starting to incorporate
data into their day-to-day work such as marketers, engineers, and technicians) might need
to work with the data science team or the analysts mentioned in group one under more of a
Center of Excellence model but, eventually, they are empowered to work with data and even
build models on their own.
Dataiku customer NXP, one of the largest semiconductor suppliers in the world, has seen great
success empowering these exact people with its Citizen Data Science Program. Available to
anyone at the company to elevate his or her competencies and skills around data science,
the four-month program drives collaboration, upskilling, and self-service analytics at NXP by
improving advanced analytics competency and data literacy among non-data professionals,
addressing the challenge of solving business problems which have increasing complexity not
served by legacy BI tools/methods, and positioning their business leaders to make better,
more informed decisions.
Another Dataiku customer, Showroomprivé, is working to develop its capacity to use data for
improvements both in the product and in customer service as well as on the operational and
business side. Specifically, the company has empowered marketers by providing a webapp
(known as Targetor, which is powered by Dataiku and used 2-3 times per day by the business)
that allows them to generate their own machine learning-powered targeting recommendations.
We have more than 9,000 people within our company that are interacting with our data
and analytics program in some form or another — collecting data for analysis, presenting
and visualizing data for decision makers, etc. It’s a large community, and we see it as our
responsibility in IT to create communities of practitioners with tools like Dataiku.
— Lance Lambert, Director of Enterprise Business Intelligence, NXP Semiconductors

In parallel with the increase of business users generating tangible business value from AI
projects, we have observed the rise of the analytics pipeline to enable business teams to take
control of their data processes. This does not replace data pipelines built by IT, but instead
augments that by allowing business teams to use new data resources, like a Snowflake data
lake, to build analytics using low- or no-code tools. At Dataiku, we make visual pipelines
possible in the same place that analysis and ML can take place, which — as a welcomed
benefit — increases the transparency and makes it easier to answer the question, “Where did
this data come from and how was it created?”
In essence, we’ve seen that there are in fact two types of data pipelines: those created by data
engineering to merge, clean, and move data into storage for use by the business. These require
significant levels of quality assurance on resulting outputs. The second category covers
pipelines which are created by analytics and business teams in service to their analytics
initiatives. This realization helps to clarify which types of tools are needed and which teams
should be using them.
EBOOK - Dataiku 10

So, Where Does Governance Fit Into This Trend of More AI Builders and Consumers?
It’s effective governance that gives new people the license, freedom, and safety to “click
that button and see what happens” or, said differently, explore the capabilities of data
science and AI to improve their day-to-day tasks and decision making.
This year, we observed the continued rise of AI Governance (notably as it relates to
regulation, MLOps and Responsible AI), as it’s no longer just something for regulated
industries. In fact, it’s a necessary element of any organization’s scaling AI strategy. AI
Governance is a framework (enabling processes, policies, and internal enforcement) that
ties together operational (MLOps) and values-based (Responsible AI) requirements to
enforce consistent and repeatable processes aiming to deliver efficient AI at scale. More
specifically, it helps manage operational risks and maintain legal compliance for AI and
advanced analytics projects.
As AI continues to develop and progress (leading to more impact and criticality), there is a
stronger need to manage operations related to it, in order to ensure quality, responsibility,
and overall governance (especially as regulations and guidelines start to emerge to
create more controls). Consciousness is growing and the appetite is not to be “late to
implement the regulation and then pay a high price” (i.e., what happened with GDPR) and
simultaneously preserve brand reputation and sustain AI momentum.
In actuality, the self-service analytics that we’re seeing more and more by business users
enable governance — teams will have thousands of projects deployed in production, so
they need a framework to adhere to in order to avoid chaos and find the balance between
control and agility.

Related to this, we’ve observed the interplay between data science and IT operator
functions and the benefits of segregation of duties when it comes to scaling MLOps.
In MLOps, the data science team and IT operations have a shared goal of continuously
keeping model systems in peak form amidst a wildly changing environment and doing so
in a controlled and efficient way. Enabling a productive segregation of duties in an end-to-
end environment like Dataiku helps each of these two groups stay focused on its own core
strengths and business charter, without sacrificing collaboration and transparency at any
step along the way.
IT has realized that MLOps is their business and has started to allocate resources to manage
the ever-growing number of AI and ML projects to prevent shadow IT proliferation (and
because the business can’t scale unless this is handled by IT with proper operations,
processes, and governance). At Dataiku, teams involved in design and deployment can use
the same concepts (i.e., the flow, which is Dataiku’s visual representation of datasets and
recipes in a pipeline) so there is no translation across language barriers when a project is
productionized but, simultaneously, data people and IT can come to an effective solution
for the business about how to govern the transition and ongoing monitoring.
EBOOK - Dataiku 12

As a Byproduct ofAI,AI Consumers Benefit
AI consumers might not know they’re using AI, but it’s making their lives better by drastically
improving their day-to-day work and decision making. With Dataiku, for example, these
stakeholders engage with AI systems in the context of day-to-day work as a part of existing
workflows, tools, and technologies. They can use dashboards that automatically update with
the latest data for accurate KPI and value tracking and get predictive insights with custom
visualizations and applications to make better everyday decisions.
At Morgan Stanley Wealth Management, Chief Analytics and Data Officer Jeff McMillan is
focused on empowering people to make the best decisions. This means ensuring that even
the most junior person in the company has a way to get all the information he or she needs to
best serve clients they are engaging with. By augmenting decision making (rather than simply
automating it, like in the case of a predefined dashboard), there’s room for human insight and
intuition to play a role.
Additionally, Morgan Stanley’s Next Best Action tool identifies the next best action the
company’s financial advisors should consider. The tool looks at every client in an advisor’s
book of business and produces recommendations (from investment ideas to holiday wishes)
that are relevant to each client and scores each idea using ML algorithms. Ultimately, it
determines the propensity of the client to use it based on their historical engagement, as well
as monitors the adequacy of wealth advisors with customers’ interests.
The recommendation is customized by client name and includes any relevant portfolio
information such as their asset allocation and the number of shares that they hold in any
given fund or issuer. The advisor can then tell if the client opened the email, clicked on the
content, and any resulting transactions they make, for example. The tool has become a central
capability for how Morgan Stanley’s financial advisors operate — helping them augment their
decisions and drive business success.

#2: Automation, Business Intelligence,
and AI Converge Into One Practice for
the Organization
Last year, we predicted that machine learning will be woven into more BI and analytics roles
and tools (especially as BI moves farther and farther away from traditional look-back analysis
and toward even more sophisticated predictive and prescriptive analytics). As we move into
2022, we believe the confluence of analytics and business intelligence (BI) with data science
and machine learning will continue and, instead of just becoming more interconnected, they
will actually become one practice for the organization.
EBOOK - Dataiku 14

So, why is this convergence a positive change for enterprises working on all three? In practice, it:
• Reduces silos and the separation between people/teams working on RPA/BI initiatives and
AI initiatives, which also helps improve cross-team project visibility and collaboration
• Promotes a culture of reuse across projects (or pieces of a project) and decreases the
possibility of duplicate work between the initiatives
• Enables organizations to control things like governance centrally as opposed to
fragmented strategies for each (because, after all, project access, permissions
management, documentation, and security are important across RPA, BI, and AI initiatives)
• Helps teams in the organization who have a request that falls under one of these buckets
realize what capabilities are in fact available (which may in fact now be elevated due to the
combined practice)
• Organically encourages upskilling and training of staff because, the more automation there
is, the more important it is to ensure teams are data literate and know how to step in and
interpret/use the end results
• Creates environments that remove last-mile silos that are typically evident with traditional
BI while simultaneously reinforcing auditability
According to Gartner®, “Turning the collision of markets caused by the rise of augmented
analytics, into a constructive convergence that propels their organization’s analytics program
forward is a challenging but rewarding journey. Leaders responsible for analytics, BI, and
DSML solutions should:
Incorporate augmented analytics capabilities into the tool portfolio by managing
and governing their use while providing comprehensive capabilities across the
continuum of descriptive, diagnostic, predictive, and prescriptive analytics.
Extend capabilities by incorporating both data and analytics tools into the analytics
stack. In addition, include not only tools, but also people and processes to foster
communication and collaboration and to build trust.
Expand analytical capabilities, roles, and processes by focusing on the business
priorities and issues to be addressed. Think ‘end to end’ in relation to the analytics
lifecycle and ‘top to bottom’ in relation to the data and analytics stack4
.”
4
Gartner - Worlds Collide as Augmented Analytics Draws Analytics, BI, and Data Science Together - Idoine, Carlie, 11 June 2021. GARTNER is a registered trademark and service mark of Gartner,
Inc. and/or its aftiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Aligning people, processes, and technology to support and drive this convergence is key,
especially as organizations aim to automate everything (processes and intelligence alike).
Spurred in part by the global health crisis, we even anticipate a boom in the robotic process
automation market, as organizations aim to unlock new levels of automation alongside AI5
.
By looking at analytics and BI, data science, and AI as a range of capabilities that help teams
make better decisions, organizations will be able to move toward an analytics approach that is
fueled by systemization, agility, and augmentation.
In The Total Economic ImpactTM of Dataiku by Forrester Consulting, the automation of
manual, repeated tasks enabled operational efficiencies for interviewees' organizations. A key
area of impact was in reporting — Dataiku reduces 90% of the manual, repeated tasks involved
in regular reporting.
5
https://searchenterpriseai.techtarget.com/feature/RPA-market-booms-as-enterprises-automate-with-bots
EBOOK - Dataiku 16

With Dataiku customer Bankers' Bank, the company's goal was to improve data quality and
speed to insights. Specifically, one analysis tracks how effective each cross-sell was (to help
the sales team vet if they are prioritizing the right products). Originally, the report was created
on a quarterly basis and took 16 hours each time to prepare, meaning 64 hours annually.
Upon implementing Dataiku, the time spent on validation went down drastically and the
report is delivered in a predictable, consistent, and accurate manner and will soon be
delivered monthly — representing a time reduction of 87% as it now only takes 30 minutes to
prepare. By automating processes for gathering and ingesting data, the team has been able to
improve data quality and reliability and save time on low-value, repetitive tasks.
Why Both Augmented Intelligence + a Human-in-the-Loop Methodology Need to
Underpin This Convergence
We believe that, now more than ever, it’s a critical piece of any well-rounded AI strategy.
Augmented intelligence is all about bringing together the power and strengths of AI
with those of humans by integrating AI systems into the day-to-day work of people to
help them make better decisions. While augmented intelligence is easy to understand
in theory, many organizations struggle to implement it in practice and at scale.
With the example of better triage emails or cases (whether from customer or for an
internal department like IT), business experts (like customer service team members)
can greatly benefit from augmented intelligence systems that combine that power of
machines and humans. Both Rabobank (in the IT department) and Etihad Airways (in
the customer service department) use machine learning-powered automated triage to
enhance the problem solving ability and speed of those representatives.
While augmented intelligence is similar to the concept of human-centric AI, it’s slightly
different because the latter is more focused on infusing human intelligence back into
ML models. In other words, while the goal of augmented intelligence is to use machines
to enhance humans, human-in-the-loop ML is sort of the reverse. The well-rounded
strategy we mentioned earlier will include both elements in a way that is pervasive
throughout the organization, making the use of data and AI pedestrian and so ingrained
in the the workings of the day to day that it’s just part of the business (and not siloed to
just one team).

#3: The Efficiency Tipping Point:
More Than 50% of Machine Learning
Projects Organizations Would Like to
Make It Into Production Have Made It There
Let's back up first — what does it mean to go "into production"? Data scientists spend a large
chunk of their time extracting data, cleaning and preparing it, building features, training
a model, assessing performance, and iterating it over time. But, eventually, it needs to be
operationalized, meaning deployed for use across the organization. The test environment and
production environment are inherently different because the latter is continuously running
— data is constantly coming in, being processed and computed into KPIs, and going through
models that are retrained frequently.
The challenge of deploying into production first arises when the model is deemed sufficient
and has to be deployed onto the existing production environment. It also arises for every
iteration, whether it's to account for new analytical opportunities or changes in data (i.e.,
from data drift). It's critical to have a process in place to handle the transition between
development/testing and production environments to ensure data projects will be successful.
EBOOK - Dataiku 18

While there are certainly a chunk of projects that should not make it into production because
they are killed off early (i.e., they weren’t translatable to business value, they weren’t well
received by business stakeholders, etc.), we are witnessing the efficiency tipping point among
many organizations — more than half of ML projects that they would like to push to production
have made it there. In 2019, VentureBeat released an article that stated only 13% of data
science projects (or just one out of every 10) actually make it into production6
, so this observed
rise in productionalized models is actually a great indication of organizations becoming more
agile in their approaches to driving business value from their AI projects.
6
https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
7
Gartner - What Is the True Return on AI Investment? Ethan Cohen, Afraz Jaftri - 4 February 2021
On average, 53% of AI projects make it from pilot to production. And those that do often
incur significant unexpected maintenance costs7
.
— Gartner
The ongoing rise of robust MLOps practices over the course of 2021 is an indicator of the data
science and ML industry continuing to grow in maturity, as it demonstrates more and more
models are being deployed in production every day. It also shows that teams are taking
ownership of making sure they have a clearly defined plan for standardizing and managing the
entire ML lifecycle.

One of the reasons we’re observing this increase of projects making it to production is more
organizations have realized that they can not wait to have all of their data ducks in a row before
attempting to scale analytics and AI. They believe they need to conquer traditional or BI analytics
first (including data catalogs, data lineage, master data management, etc. before planning for AI).
Notably, though, they believe they need to have data quality sorted out. Contrary to popular belief,
data quality is actually best improved by using it to create value (i.e., operationalizing a project that
is going to drive tangible business value). If teams want to build a lasting data culture, they need
people to confront data, use it, understand its flaws, and then be encouraged to experiment with it
and become data champions.
Further, deployment to production isn't just a technical exercise, it's an organizational one.
Companies are not only catching on to the value of production, but they're investing in tools that
help make the process frictionless. Dataiku does just that by:
Technical Trends From the Dataiku AI Labs Team
Everyyear,ourAILabsteampresentstheirannualfindingsforup-and-comingMLtrends,
basedontheworktheydoinMLresearch.Last year,theteam highlighted:
• The rise in trustworthy and human-in-the-loop ML (which they envision going even
further to have self supervision on tabular datasets)
• An increase in causal inference maturity (which will continue to go from prediction
setting to prescription, getting closer to decision making)
Instead of providing new trends for 2022, the Labs team confirms that these trends from last
year (and even prior to that) are stable and still very relevant, a good indicator that the ML
industry is gradually maturing as things are no longer drastically changing year over year.
Providing a platform that makes it easy for data science and IT teams to collaborate
on building user-friendly, real-time and batch scoring systems
Offering production-related features such as scheduling, monitoring, and scenarios,
so that teams can build production-ready workflows from the first step
Enabling the ability to track the status of your production scenarios
Providing the infrastructure for organizations to govern AI projects at scale, including
production lifecycle management (monitoring, retraining, and testing)
EBOOK - Dataiku 20

Conclusion
To reach full embedding of data and AI at scale within an organization takes time, starting with
empowerment to start, build, understand, and continuously expand. More and more companies
want to break the historical gap between doers and consumers, with the upskilling need becoming
much more critical. However, that doesn’t mean it’s actually happening in practice.
It seems we’re at an impasse — organizations realize that upskilling is fundamentally
necessary to AI staffing, but it is overwhelmingly and woefully overlooked at most companies.
Organizations need to craft formal active continuous learning on AI into employee education
programs to quickly access high-performing talent and shape the talent into the emerging
needs associated with scaling AI.
On a related but separate note, while upskilling within organizations definitely has room to
improve, we have observed an increase in newly trained data professionals in the job market
who have taken training courses, bootcamps, or entire academic programs so they are more
marketable and attractive to companies that are hiring (and looking to recruit and retain
certain profiles like data scientists and analysts who come in with a baseline foundational
knowledge and skills instead of needing to start from scratch). Further, we're seeing a growing
number of business schools integrate data science courses as requirements for graduation.

On a larger scale, upskilling can play a significant role in establishing and maintaining a sound
data culture. For example, data teams are driven by knowledge, like learning a new language
or a new technique. Training, classes, or workshops on those new techniques could make
sense, which means an investment in classes, seminars, or the like. Does your organization
have its own data science academy internally? If not, why not?
Further, investing in data team productivity (i.e., tools that make it easy to share knowledge
and collaborate on projects) is the best investment to retain a data science team. From the
perspective of data scientists, data science is happening now and they don’t have time to lose.
If they are not well equipped to continuously learn new things, they will leave.
Everyday AI is a business game-changer because it expands the pool of people who can
analyze and develop meaningful business actions from data (i.e., a competitive edge,
untapped levels of productivity, increased revenues, risk mitigation). Organizations that truly
want to deliver value from AI at scale need to take a systemized approach that:
Enables data and analytics initiatives to work within the realities of an existing team,
while giving practitioners the freedom to be creative to produce the best output (i.e., like
Rabobank did, Dataiku lets teams start with a simple insight question, grow toward a more
specific predictive question, and eventually develop a model all within the same tool,
rather than having to switch between different environments)
Empowers everyone (from the most advanced data scientists to business analysts) to
be autonomous and work with data in their day-to-day roles, while also benefiting the
company as a whole (i.e., using governance and oversight to find the balance between
control and agility, centralizing work to make it reusable and reduce costs, ensuring
teams are working toward a common goal)
Creates environments that enable these different professionals to combine their
expertise to deliver transformative, business-embedded analytics outputs (i.e., like
NXP did, sharpening employees’ data and analytics skill sets in order to fuel collective
success and value-generating data projects)
Delivers quick, high-impact AI wins to keep the team, department, or company
moving in the short term and enables a transformative AI culture for the long term
(i.e., finding the middle ground between stakeholder requests, maintenance tasks,
and quick wins and the more strategic activities that might be more experimental but
could yield massive returns)
EBOOK - Dataiku 22

©2021 dataiku | dataiku.com
Braund, Mr. Owen Harris
Moran, Mr.James
Heikkinen, Miss. Laina
Futrelle, Mrs. Jacques Heath
Allen, Mr.William Henry
McCarthy, Mr.Robert
Hewlett, Mrs (Mary D Kingcome)
22
38
26
35
35
29
male
male
female
female
male
male
Natural lang. Integer
Gender
Name Age
Sex
Remove rows containing Mr.
Keep only rows containing Mr.
Remove rows equal to Moran, Mr.James
Keep only rows equal to Moran, Mr.James
Clear cells equal to Moran, Mr.James
Filter on Moran, Mr.James
Filter on Mr.
Toggle row highlight
Show complete value
Split column on Mr.
Replace Mr. by ...
Dataiku is the world’s leading platform for Everyday AI, systemizing the use of data for
exceptional business results. Organizations that use Dataiku elevate their people (whether
technical and working in code or on the business side and low- or no-code) to extraordinary,
arming them with the ability to make better day-to-day decisions with data.
450+ 45,000+
CUSTOMERS ACTIVE USERS
Data Preparation
DataOps Applications
Governance & MLOps
Visualization
Elastic Architecture Built for the Cloud
Machine Learning
Everyday AI,
Extraordinary People

AI Trends.pdf

Recommended

Recommended

More Related Content

Similar to AI Trends.pdf

Similar to AI Trends.pdf (20)

Recently uploaded

Recently uploaded (20)

AI Trends.pdf