The document discusses 4 scenarios that illustrate different levels of integration between business analytics (BA) functions and company strategy. Scenario 1 shows no formal link, where BA is not used for strategic decision making. Scenario 2 shows BA supporting individual functions but no feedback to strategy. Scenario 3 has dialogue between strategy and BA for continuous improvement. Scenario 4 treats information as a strategic resource for competitive advantage. The scenarios can help organizations assess their BA maturity and potential for optimization.

1. Link Between Strategy and BA Deployment
Strategy and BA Scenarios
Prepared By
Dr.Venkata Subbaiah
Annamacharya Institute of Technology and Sciences
Rajampet

2. STRATEGY AND BUSINESS ANALYTICS: FOUR
SCENARIOS
• scenarios that illustrate different degrees of integration between the BA function
and the company's strategy.
• The purpose of these scenarios is to prompt the reader to consider where his or
her organization is in relation to these scenarios.
• The scenarios can also give some insights into whether the organization has
understood and achieved the full potential inherent in BA, and thus whether more
effort should go into optimizing and maturing the deployment of BA.

4. Scenarios
Scenario 1: No Formal Link between Strategy and Business
Analytics
Scenario 2: Business Analytics Supports Strategy at a Functional
Level
Scenario 3 is dialogue between the strategy and the BA functions
Scenario 4 is information as a strategic resource

5. Scenario 1: No Formal Link between Strategy and
Business Analytics
• Companies that are separated in their strategy, without data or
with limited data distributed over a large number of source
systems, are typically unable to make a link between
corporate strategy and BA.
• In these companies, data is not used for decision making at a
strategic level.
• Instead, data is used in connection with ad hoc retrieval to
answer concrete questions along the way and automate
processes, but without any link to business strategy.

6. Scenario 1: No Formal Link between Strategy
and Business Analytics
• Many companies have realized that they do not have the data, the staff,
or the technology to perform the task.
• From a strategic perspective, it is evident that a maturing process could
be initiated. Alternatively, the company just continues with a business
strategy that is not based on information.

7. Scenario 2 is that BA supports strategy at
afunctional level.
• If companies, in connection with the implementation of a
strategy, request that the BA function perform monitoring of
individual functions' achievement of targets, we have
coordination between strategy and BA.
• However, if there is no flow back from BA to the strategic
level, then the BA function is reactive in relation to the
strategy function.
• In this case, the role of BA is merely to produce reports
supporting the performance of individual departments.

8. Scenario 3 is dialogue between the strategy
and the BA functions
• If the organization makes sure that individual functions optimize its way
of working based on BA information,
• but that the strategy function, too, takes part in the learning loop, we'll
get a BA function that proactively supports the strategy function.
• A learning loop is facilitated when the BA function is reporting on
business targets and is providing analyses of as well as identifying
differences between targets and actuals, with the objective of improving
both future strategies and the individual departments' performance.

9. Scenario 4 is information as a strategic
resource
• The characteristic of the fourth scenario is that information is treated
as a strategic resource that can be used to determine strategy.
• Companies that fit this scenario will systematically, while analyzing
the opportunities and threats of the market, consider how
information, in combination with their strategies, can give them a
competitive advantage.

10. Conclusion
• The four phases represent a maturity, ability, and willingness to work with information on
different levels. We can't really say that one level is better than another.
• The appropriate level must be chosen based on a strategic perspective.
• In some industries, BA is not essential to business success, while in others it will be a
central competitive parameter.
• What we can say, however, is that as a general trend over the last ten years,
organizations have increasingly focused on digitalization of processes

11. Conclusion
• Many decisions that used to be made by humans are now automated.
• Analytics can help make these decisions smarter, though it is
constrained by the available data and the skills of the analyst.
• Sometimes, as in Scenario 1, it's a simple matter of realizing that the
company does not possess the data, the staff, or the technology to
perform the task.
• Seen from a strategic perspective, the option exists to take steps toward
the next phase or to choose an alternative business strategy that is less
dependent on information.

12. Data ecosystem
 A data ecosystem is a collection of infrastructure,
analytics, and applications used to capture and
analyze data.
 Data ecosystems provide companies with data that
they rely on to understand their customers and to
make better pricing, operations, and marketing
decisions.
 The term ecosystem is used rather than ‘environment’
because, like real ecosystems, data ecosystems are

13. Why create a data ecosystem?
• Data ecosystems are for capturing data to produce useful insights.
• As customers use products–especially digital ones–they leave data
trails.
• Companies can create a data ecosystem to capture and analyze data
trails so product teams can determine what their users like, don’t like,
and respond well to.
• Ecosystems were originally referred to as information technology
environments.

14. Common applications for analytics platforms:
•Increasing user engagement
•Using machine learning to identify hidden relationships in
the data
•Sending alerts to notify teams of changes
•Messaging users directly
•Analyzing individual users or cohorts
•Tracking conversions and marketing funnels
•Increasing user retention

15. How to create a data ecosystem
There are three elements to every data ecosystem:
1.Infrastructure
• If a data ecosystem is a house, the infrastructure is the foundation. It’s the
hardware and software services that capture, collect, and organize data.
• The infrastructure includes servers for storage, search languages like SQL, and
hosting platforms. Infrastructure can be used to capture and store
• Three types of data: structured, unstructured, and multi-structured.,
• structured data is clean, labeled, and organized, such as a website’s total number
of site visits exported into an Excel spreadsheet.

16. • Unstructured is data that hasn’t been organized for analysis, for example, text
from articles.
• Multi-structured data is data that’s being delivered from different sources in a
variety of formats–it could be a combination of both structured and
unstructured.
• If ecosystems hold a large volume of data, they’ll need additional tools to make
it easier for teams to access it. Teams may use technologies like Hadoop or Not
Only SQL (NoSQL) to segment their data and allow for faster queries

17. 2.Analytics
• Analytics serve as the front door through which teams access their
data ecosystem house.
• Analytics platforms search and summarize the data stored within the
infrastructure and tie pieces of the infrastructure together so all data
is available in one place.
• While infrastructure systems provide their own basic analytics, these
tools are rarely sufficient.
• A dedicated analytics platform will always be able to dig much
deeper into the data, offer a far more intuitive interface, and include
a suite of tools purpose-built to help teams make calculations more
quickly.

18. 3.Applications
• Applications are the walls and roof to the data ecosystem house–
they’re services and systems that act upon the data and make it usable.
• For example, a product team might decide to port its analytics data into
its marketing, sales, and operations platforms.
• This would allow the marketing team to score leads based on activity,
the sales team to get alerts when ideal prospects engage, and
operations teams to automatically charge customers based on product
usage.

19. Things to consider when creating a data ecosystem
1.Data governance
• In an age where IT no longer has clear, central data oversight, companies need
to establish clear data governance rules, usually by publishing an internal
guideline for how data can be captured, used, stored, safeguarded, and
disposed of.
• Legislation like the European Union’s GDPR is forcing many product teams to
be more transparent, but those that want to build trust with their users should
get ahead of the trend.
• Every organization should publish and adhere to its own data governance
guidelines.

20. 2.Democratize data science
• Most teams can benefit from customer information, but if there’s only one person who
can access the data, that person will become a bottleneck.
• Many companies invest in analytics platforms that offer intuitive interfaces and allow
anyone throughout the company to access data.
• DocuSign, for example, deployed Mixpanel and handed out licenses to over one
hundred users across the company.
• “We’re building a data ecosystem now, gradually adding more data that we want
people to have easier access to,” said DocuSign Senior Product Manager Drew
Ashlock

21. Components that we recognize in every successful
data and analytics capability.
1.Roadmap and operating model
• An operating model turns a vision and strategy into tangible
organizational outcomes and changes.
• It is a single view of the capabilities within an organisation and the
way in which they deliver services internally, and to their customers.

22. 2.Platform and data architecture
right platform gives organisations the ability to store, process and
analyze their data at scale.
Modern, open-source data platforms developed by the likes of
Facebook, Yahoo and Google have made data storage cheaper,
whilst making data processing far more powerful

23. 3.Data security
Organisations need to ensure their data is stored, transformed &
exploited in a way that doesn’t compromise security.
4.Data governance and standards
• Data governance is one of the least visible aspects of a data and
analytics solution, but very critical.
• It includes the management and policing of how data is collected,
stored, processed and used within an organisation

24. 5.Software and tooling
Whether it is a simple report or performing advanced machine
learning algorithms, an analyst is nothing without their tool.
Finding the right combination of tools is a challenge – there are a lot
of them!
That means considering everything from the techniques analysts
want to apply to how they fit in with your data security and data
architecture.

26. 6.Legacy migration
• Organisations may need to migrate and transform legacy business
services onto a new platform to deliver new insight at a lower cost.
• When a client takes the bold step to upgrade their data or analytics
capability they might think the job is done upon completion of the
implementation phase.
• However, to drive the value from their investment they also need to
migrate existing analytical capabilities and services to their new
technology.

27. 7.Data acquisition
• Data volumes are exploding; more data has been produced in the last
two years than in the entire history of the human race.
• Traditional business data sources, such as data from EPoS, CRM and
ERP systems are being enriched with a wider range of external data,
such as social media, mobile and devices connected to the Internet of
Things.

28. 1.Skills and roles
• It is becoming increasingly difficult for our clients to find the right
skills they need to put data and analytics at the heart of their
organisations.
• “What does a data scientist do?” “Where can we find a data
scientist?” “What skills do our people need?” These are the
questions they are asking us every day.
• The people are the most important part of any business, so hiring
the right people with the right capabilities, giving them a platform
to improve and develop and keeping pace with industry best
practice / new technology is critical for all of our clients.

29. 9.Business intelligence and reporting
It is vital for organisations to understand their performance, identify trends and
inform decision making at all levels of management.
Without a strong BI capability they aren’t able to detect significant events or
monitor changes, and therefore aren’t able to adapt quickly.
10.Insights and analysis
Many organisations are acquiring more and more data from various sources.
However, data is only valuable if they can extract value from it.
Insights and analysis allows our customers to rapidly get valuable insight from their
data using visualizations to spot trends in their data allowing them to make critical
business decisions based on fact giving them a competitive advantage.

30. 12.Real-time analytics
• Industry leaders are moving towards real-time, probability based
and predictive analytical approaches.
• Organisations can now deliver ‘real-time’ analytical capability to
have the best of both worlds; digital customer experiences that are
analytically assessed and secure.
• This is a change from reactive organisations to one that actively
drives proactive interaction with customer through real time, in
the moment, analytics.

31. 1.Advanced analytics
• The pinnacle of a data and analytics capability is the application of
advanced analytics to discover deep insights, make predictions and
generate recommendations.
• Predictive analytics, text mining, machine learning and AI are all
making great strides across all industries. With the right people, data
and technology, all organisations are able to take advantage of these
capabilities.
• The important thing about all of these components is that they can
be improved individually. Building up your data and analytics
capability is not about huge transformational programmes, but about
incremental step changes in each of these components.