How Do You Measure the Financial Value of Your Data?
The rise of big data has transformed data and analytics into opportunities with real economic value. But how do you account for the value of your data?
Learn how to maximize the economic value of data and analytic assets across your organization with an effective assessment framework.
The value of data is staggering
What's the global economic valuation of data?
Try again!
Try again!
McKinsey Global institutes estimated the potential economic valuation of data at nearly $3 trillion. 1
And, given the explosion of data volume in the intervening years and how data has been used to develop new sources of revenue, it’s clear that its value is growing.
If correctly accounted for, data and analytics can play a critical role in determining an organization’s financial valuation
When Facebook launched its 2012 IPO, it valued its aggregated user data on its balance sheet at nearly $6.6 billion. 2
AT&T accounted for their customer relationships at nearly $2.7 billion on their balance sheet in 2011. 3
Recognizing the potential value of data, organizations are increasingly counting data as a “corporate asset”
20% of large firms are accounting for data
Accounting for data as an asset brings new challenges
Data as an asset exhibits unusual characteristics—while most corporate assets depreciate with usage, data actually appreciates or gains value over time.
As an organization uses its data across use cases over time, the data becomes more refined – it gets more accurate, more complete, more usable – which increases its value.
DEFINING DATA AND ANALYTICS
2.5 quintillion bytes of data are being generated every day 5
Data sources within the organization are not limited to structured or relational databases—with the rise of unstructured data, almost anything can be a data source.
There is also a growing wealth of publicly available data.
And, the Internet of Things (IoT) is already unleashing a tidal wave of machine-generated data.
Is your organization ready for the growth of unstructured data?
Organizations that make data-driven decisions have 4% higher productivity and 6% higher profitability 6
Four key analytics capabilities help organizations make data-driven decisions:
1. Descriptive
Monitoring current state performance or results.
2. Diagnostic
Understanding (quantifying) drivers of performances.
3. Predictive
Forecasting likely outcomes.
4. Prescriptive
Recommending actions for future decisions.
Has your organization implemented all four types of data-driven capabilities?
Recent research looked at how three economic concepts could help quantify the economic value of an organization’s data and analytics
ECONOMICS OF DATA
Data as an Asset
Data as a Currency
Data as Monetization
To apply these concepts to the valuation of data and analytic assets, organizations need a framework
Introducing the Collaborative Value Creation Framework
Identify and prioritize your highest potential business use cases
Business use cases provide a basis point for estimating the value of intangible data and analytics assets.
What are business use cases?
Business use cases are clusters of decisions around a common subject area.
Acquiring more customers
Reducing customer churn
Improving the quality of care
Improving customer satisfaction
Reducing cybersecurity risks
Reducing maintenance costs
1. Identify a Targeted Business Initiative.
A business initiative is a cross-functional plan or program that is typically nine to 12 months in duration, with well-defined financial or business metrics.
2. Calculate the Financial Value of the Targeted Business Initiative.
The process of calculating the financial value of the targeted business initiative should be a straightforward financial accounting exercise.
3. Identify Supporting Business Use Cases.
Identify the use cases that support the targeted business initiative.
4. Estimate Financial Value of Each Use Case.
To estimate the financial value of each use case, each impacted business function (i.e., product development, field marketing, operations) creates a financial scenario for that use case. Each use case will have different financial scenarios tied to the number of business functions impacted by the targeted business initiative.
5. Estimate the Value of the Supporting Data.
Have each business stakeholder rate the relative value of each data source with respect to each use case – how important is data source #1 to use case #1, how important is data source #2 to use case #1, etc. One can use a rating scale of 0 to 4, 0 to 10, or 0 to 100, but the finer the granularity of the data rankings, the more precise the data valuation determination.
Build analytic profiles to facilitate analytics capture and reuse
Analytic Profiles help “operationalize” insights by capturing them in an organized fashion so that they can be reused across multiple use cases.
What is an Analytic Profile?
An analytic profile consists of data inputs that help provide insights about an individual entity.
Data inputs
Insights
Individual entities
Metrics
Predictive indicators
Segments
Scores
Business rules
Behavioral insights
Performance insights
Behaviors
Preferences
Propensities
Inclinations
Tendencies
Interests
Associations
Affiliations
Customers
Patients
Students
Athletes
Jet engines
Cars
Locomotives
CAT scanners
Wind turbines
The analytic concept of scores is a practical way to create analytic insights. Scores are actionable, analytic-based measures that support the key decisions your organization is trying to make. For example, the FICO score measures the likelihood of a borrower repaying their loan.
See the following for more details on the role of scores and the Decisions-to-Recommendations-to-Scores mapping process:
Thinking Like a Data Scientist Part III: The Role of Scores
Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part II
Key parts of an Analytic Profile
ANALYTIC PROFILES: CUSTOMER
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1. List the decisions needed to support the targeted use case.
2. Identify or brainstorm the recommendations that need to be developed to support the decisions.
A recommendation is a suggestion or proposal, developed using prescriptive analytics, for the best course of action.
3. Identify the scores, and the metrics that comprise the score, that support the recommendations and decisions.
Scores are a rating system that aids in comparisons, performance tracking and decision-making. Scores are used to predict the likelihood of certain actions or outcomes.
Identify and prioritize your highest potential business use cases
A data lake helps facilitate the capture, refinement and reuse of data and analytic assets across multiple business use cases.
What is a data lake?
A data lake is a data structure that holds large amounts of structured and unstructured data in its native format in order to support the data science processes of refining the data into actionable analytics to create financial or economic value.
Data provisioning
Data governance
Data quality
Metadata management
Security and privacy
1. Data
Unlike a data warehouse where data is stored in a predefined relational structure, no schema is required to load data into the data lake.
2. Data Services
Data services facilitate the capture, refinement and reuse of the organization’s data and analytic assets (using a key-value store for your Analytic Profiles) across multiple business use cases.
3. Analytic Insights
The analytic results captured in the Analytic Profile are now ready to be refined and shared across other use cases, increasing the economic value of the analytic results and addressing the “orphaned analytics” issue.
Start Accounting for the Value of Your Data
Data and analytics are powerful assets in which to invest, but organizations struggle to assign these intangible assets their appropriate economic value.
Use this recent research from Hitachi Vantara and the University of San Francisco to help foster new ways to re-think how to value your organizational data and analytics from an economic and financial perspective.
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