For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way. Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers. This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.

1. 1
HIGHLIGHTS FROM
THE IE BUSINESS
SCHOOL CLASS
Data
Strategy
March 2019
Gam Dias, First Retail

2. IE Digital
Transformation
Program:
Data Strategy
Module
Part 1: Achieving Common Understanding of Data Strategy
Part 2: Examples of Data Strategy Across Different Industries
Part 3: Co-Creation of a Data Strategy

3. How does your
organization use
data?
Reporting History
Trends & Anomalies
Forecasts
Predictions
As part of the product
As the product

4. Data changes
everything
“Our future competition will likely not be
another mining company, and they will
compete with us by making better use of
data”
Scott Singer
Head of Global Business Services

5. Business and
Data
OPERATIONS: RUNNING THE BUSINESS AT A
TRANSACTIONAL LEVEL
MANAGEMENT: MONITORING
PERFORMANCE, TACTICAL INTERVENTIONS
STRATEGY: PERFORMANCE
IMPROVEMENTS, OPTIMIZATIONS,
BUDGETING, COURSE CORRECTION
TRANSFORMATION: NEW LINES OF
BUSINESS, NEW MARKETS, NEW PRODUCTS
TRANSACTIONAL APPLICATIONS
AUTOMATE PROCESSES AND COLLECT DATA
BUSINESS PERFORMANCE MANAGEMENT
M.I.S REPORTS, ANALYTICS, DATA
WAREHOUSES, DASHBOARDS
BUSINESS PERFORMANCE STRATEGY: DATA
QUALITY, GOVERNANCE, KPI
DEVELOPMENT, BALANCED SCORECARDS
STRATEGIC USE OF DATA: DATA PRODUCT
DEFINITION, DATA DRIVEN PROCESS
TRANSFORMATIONS, DATA VALUE CHAIN
EXTENSIONS
WHAT THE BUSINESS IS DOING HOW DATA SUPPORTS THE BUSINESS

6. Where is our
data?

7. Digital
Transformation
What does it mean
to you?

8. Digital
Transformation
“Adapting business
models to be effective
in a world that is
growing richer in data”
Gam Dias

9. The Strategic and
Tactical
Application of
Data

10. What is your
definition of Data
Strategy?

11. Googling Data
Strategy

12. What is Data
Strategy?
In 2015 I asked Quora. The answers had the following 3
themes:
1. The management of data to generate business value,
control I.T. costs and ensure compliance
2. To ensure that data is proactively managed to create the
best platform for analytics and data science
3. The technical management of the complete data lifecycle to
maximize availability to the business processes
https://www.quora.com/What-is-Data-Strategy
BUSINESS FOCUS
TECHNOLOGY FOCUS

13. From HBR

14. Data Strategy
“A strategic plan for
treating data as a
corporate asset”
Gam Dias

15. Is data a strategic
asset?
Assets that are needed by an entity in
order for it to maintain its ability to
achieve future outcomes.
Without such assets the future well-
being of the company could be in
jeopardy.

16. Simplified
Approach to
Data Strategy
1. Baseline
2. Opportunities
3. Data Sourcing
4. Data Preparation
5. Analytics Enablement
6. Socializing
7. Feedback Loops
8. Governance
9. Transformation

17. Data strategy
stories from the
field

18. General
Approach
DATA
STRATEGY
REPORT
PRIORITIZED
ROADMAP
EXECUTIVE
PRESENTATION
BUSINESS
STAKEHOLDER
INPUTS
SENIOR
EXECUTIVE
INPUTS
TECHNOLOGY
EXECUTIVE
INPUTS
GATHERING FACTS
AND FIGURES
ANALYSIS

19. General Insights
from our Data
Strategy
Practitioners
• Business stakeholders must own and sponsor individual
projects from inception to adoption into the business process
• Obtaining data feeds is always more difficult than anybody
expects, so start this process as early as possible
• Projects should be short-cycle to deliver business value within
a 4-6 week timeframe, create iterative cycles wherever
possible for larger scoped projects
• Data science and analytics is a business function supported
by M.I.S., not the other way around
• Data politics must always be a consideration – certain
analytics render processes and business problems
transparent, this may cause resistance or obstacles in certain
cases

20. Client’s
Executive
Insights:
Mining Company
• Follow the company’s ‘Value Chain’ – different stages require
different tactics, for a mine this is:
Exploration: huge CAPEX, so the analysis will determine the life of a mine,
use of AI to determine predictions, crowd-source the analysis
Build: making sure that the mine has a complete digital twin
Operate: Importance of technology to democratize the analysis, this was
better given to the business rather than a centralized IT.
• When operationalizing any data in the organization, there is a
parallel journey where the organization needs to mature in its
acceptance and use of data
• We come at these problems from a 1’s and 0’s perspective,
yet mining is a dusty physical business. Dust gets everywhere
and renders the data invalid. Keep a foot in the real world
• We are dealing with multi-variate, multi-process data across
organizations, a ‘trader mentality’ can see through the fog

21. Client’s
Executive
Insights:
TV Network
• We developed a better method for determining television
viewing by leveraging the rich data being collected by the
company’s own set top boxes
• We were able to prove that this was a more accurate metric
than the global industry standard TV viewing panel
• Yet, the job of the advertising sales team was to sell to
advertisers, a group that relied on the industry standard metric
as they bought ads’ on other TV networks
• Despite being more accurate, as the incumbent provider had
the market sewn up, it was impossible for one company with
better technology to penetrate
• Since the project was completed, the global industry standard
provider has gone on to acquire set top box data and has
developed similar products to the model we built

22. Client’s
Executive
Insights: Invoice
Automation
Startup
• In AI, maintaining your competitive advantage is hard, ML
models will allow you to increase cost efficiency but there
is a limit
• Building AI models may be very interesting, but what really
matters is having better data than the competition
• Use the machine learning models to keep acquiring more
data
• And start using that data to provide more insights to your
clients
• For combination of data, determine the value of that data to
the client’s business
REF: https://www.kdnuggets.com/2019/01/your-ai-skills-worth-less-than-you-think.html

23. Data Strategy
Co-Creation
Class Exercises

24. Live Workshop
Exercises
#1: Opportunities in your own organizations
What opportunities do you see for leveraging data better inside your own
organization?
How will your organization benefit if you could act on those opportunities?
#2: Obstacles to realizing those opportunities
What are the obstacles that you know of or expect to making something
happen?
Do you understand why those obstacles exist? Are they legacy thinking or
are they infeasible because of other reasons?
#3: Alternative paths around, over or under the obstacles
If they are immovable obstacles, how can we find a creative solution to
overcome or to turn the obstacle into an advantage?
#4: Inter-company Data Sharing
If you could share data with another external organization, what data
would you share and what would you or they do with it that would be
beneficial?

25. Reading
Recommendations

26. 26
info@firstretail.com
Thank You