2. Pack Rats
• Google charges $2 to store 100GB
• Each technology wave has brought in more
users – now Big Data
• Petabytes of data from computers, sensors,
social networking data, & scientific data
• Value is moving from physical assets to data,
patents, copyrights & trademarks
• How do we measure this value?
2
3. State of Art: Serving current need
3
Finding Value in the Information Explosion, Sloan Management Review : Summer 2012
• Gartner Survey of 410 senior leaders
– About 25% quantify the value of their data precisely
– 33% measure the benefits that each type of data generates,
– nearly 25% say their assets are well cataloged and defined.
• In reality, less than 5% calculate the value of their data,
measure its benefits, or properly inventory their
information
• IT within organization is focused on storing, protecting and
accessing massive amounts of data
• Few CIOs and other IT executives were succeeding at
generating significant business value from their data
• Little spent on the business opportunities possible with
such data
CIOs Consider Putting a Price Tag on Data, CIO Magazine link
5. Is it worth processing the data?
5
• If we can get a good market price to cover the
costs of processing, go ahead and drill the data.
• Data has to be prepared for decision making and
that costs money
• Depends on the value we get
6. What we mean by value?
6
Given your decision problem, how much
would you be willing to pay for information
that reduces uncertainty minus the cost of
that information.
Context Matters
9. Needs work
9
Data
Action
Cost to use Value returned
Many Activities
• Capture
• Transform
• Classify
• Maintain
• Discover
• Disseminate
10. Information drives actions
10
Data
Programs Decision
Reduce uncertainty
StructuredDataSemi/UnstructuredData
THIRD PARTY DATA
ENTERPRISE Event DATA
• D&B
• Thomson Reuters
• Nielsen
• SAP & ERP
• Salesforce &
Eloqua
• Engineering
• Mfg./ * Production
• Distribution
• Legacy / Others
CONSUMER GENERATED
DATA
• Weblogs
• Omniture
• Capital IQ
• Social Media
Open DATA
Experiments
11. Friction points for data flow
• Information silos
• Poor infrastructure
• Data integrity
• Meta data & data models
• Data quality
• Technical standards
• People, power and politics
11
Organizational goal Data Liquidity
12. Cost drivers of a data asset
• What investments can we make toward the
management and improvement of the data
asset?
– Cost of collection or purchase
– Governance
– Quality
– Stewardship
12
13. Data Quality
• Completeness
– Are all data sets and data items recorded?
– For each decision, do we have all of the data needed?
• Consistency
– Can we match the data set across data stores?
– Is it consistently represented?
• Accuracy
– Does the data reflect the data set?
• Timeliness
– Is the right data available at the time of making the decision?
• Provenance
– Where did the data come from, how was it transformed?
• Validity
– Does the data match the rules?
13
14. Costs
One time
• Hardware
• Software
• External services
• Internal services
Recurring
• Software maintenance
• Hardware maintenance
• External data
• Help desk/tech support
• Personnel and admin
• Housing/facility
• Data quality
14
Source: Evaluating Return On Information Technology Investment, Karl Westerlind link
16. Questions around Value Disciplines
• Customer intimacy
– Aims at offering the best solution and focus on customers to
maintain long-term relations and growth. Continuously tailoring
and shaping products and services to fit customer needs.
• Operational Excellence
– Aims at offering the best total cost. Optimizing internal and
external processes to minimize costs. The focus in the
organization is on standardization and streamlining of
operations, efficiency and low total cost.
• Product Leadership
– Aims at offering the best product or solution. The focus in the
organization is on R&D, design and innovation. Organization
structure and culture need to be flexible to stay ahead of
competition and offer cutting-edge solutions to customers.
16
"The Discipline of Market Leaders" by Treacy and Wiersema
17. Consider the following grid
17
Internal data
External data
Operational
Excellence
Customer
Intimacy
Product
Leadership
Look for value creation opportunities within each cell
New equipment
purchase
Value Disciplines
New Product
introduction
Software purchase
/development M&A
Customer
Acquisition
cost
Competitor
pricing
18. Computing Information Value
• Value of Information is a concept from decision
analysis: how much answering a question allows
a decision-maker to improve their decision
(minus cost).
• Value of perfect information
• It is about reducing uncertainty in decision
making
• Connecting the right information, to the right
people at the right time.
18
EVPI = EV(perfect information) - EV(current information)
19. Benefits from information
• Improved financial and operations
management
• Increase employee productivity and reduced
headcount
• Improved info organization & access for
decision making
19
Source: Evaluating Return On Information Technology Investment, Karl Westerlind link
20. Techniques to Estimate Value
• Delphi
• Scorecard
• Voting and weights
• Real options
• Statistical methods
• Optimization models
• Information markets
20
22. Why measure value?
• Competitive advantage
– Lower cost for higher value
– Greater quality to cost
– Fastest access to information
– Access to scarce or unique information
• Better M&A valuation
• Security and privacy expenses justified
22
23. 23
My data is more valuable when it's with your data
Use platforms and ecosystems
24. What is an API?
24
Tech definition:
An Application Programming Interface is a structured way for two
computer applications to talk to each other over a network
(predominantly the Internet) using a common language that they
both understand.
Business definition: Capabilities exposed over the internet for partners to
use. A way to get your data to partners and apps.
Essentially a contract.
Amundsen’s Dogs, Information Halos, and APIs: The epic story of your API strategy.
Sam Ramjee Apigee.
25. Why APIs?
• Creates more options
• Allows for third party experimentation
• Provides a control structure
• Pathway to a platform strategy
• Way to collaborate with ecosystem and
partners
25
26. Role of API
2626
Programs Decision
A
P
I
Partner
Programs
Decision
Data
StructuredDataSemi/UnstructuredData
THIRD PARTY DATA
ENTERPRISE EVENT DATA
• D&B
• Thomson Reuters
• Nielsen
• SAP & ERP
• Salesforce &
Eloqua
• Engineering
• Mfg./ * Production
• Distribution
• Legacy / Others
CONSUMER GENERATED
DATA
• Weblogs
• Omniture
• Capital IQ
• Social Media
OPEN DATA
EXPERIMENTS
27. Purpose of API
• Tactics
– Means to an end. Netflix used APIs to get their
application on more devices
• Product strategy
– API is the product. Twillio uses its API to add
communication features to any product.
27
28. What is accessed?
• Data
– These APIs provide access to data. Twitter allows
third-parties to access tweets and provide value
added services
• Process
– These APIs provide access to services. IBM Watson
allows developers to use its process engines to build
their own apps. For example, an airline could create a
virtual travel agent app that recommends the perfect
destination based on customer preferences [Watson]
28
30. Access
• Qualified
– The API provided determines who gets access and
at what rates
• Open
– Anyone can access the API
30
31. Type of process
• Core or high valued
– These are the crown jewels of the provider
• Peripheral or low valued
– The provider has adequate competency with
these processes but they are not core to its value
proposition.
31
33. Planning Cycle
33
Data
Events
A
P
I
Third-party data
Open data
New Plan
What decisions do we make using an entity?
What other information can we collect to support DM?
What is the added value?
How much can we spend on the data?
User Feedback
Requirements
Experiments
A dependency analysis can help identify the critical data elements
Add/Drop information
What to collect?
How frequently?
How much to spend?
Tech/People and Process
34. Conclusions
• Data is an asset
• Many techniques to assess the value
• Operate in feasible zone
• Identify critical data elements
• Create Data plan
• API makes data even more valuable
• Keep increasing the value through quality
improvements
• How much do you value your data?
• What is your data strategy?
34
