The Business Value of Data
We All Know the Issues
• IDC Study showed that through a coordinated data
governance and data management program, it is
possible that a benefit equal to 1% of revenue could be
achieved on an asset that is of significant market value
of the organization.
• While these programs aren’t all about defective data,
Reuters reports that 30% of all operational errors are
due to “bad data”.
Data Principles - Examples
• Principle #1 –Data is a valuable business asset– The first and most important data principle is that data is a valuable asset and should be governed and managed
as such.
• Principle #2 –Use of data– All decisions should be made with as many facts as possible and practical. Data should be complete, accurate
and available to substantiate facts for all decisions.
• Principle #3 - Consistent definitions – There is more value in data that is defined in one common manner (e.g. the whole is greater than the sum of
parts).
• Principle #4 - Appropriate data lifecycle – Data should be created, maintained, and/or purged or retained within the constraints of good business
practices, government legislation, corporate ethics and privacy/security commitments.
• Principle #5 - Quality of data will be measured – Data quality is relative to the purpose to which it is to be applied. Consumers of data not only need access to
data, but more importantly they also need to understand the timing, reconciliation, completeness and accuracy
of that data.
• Principle #6 - Appropriate data access – Staffs are a valued and trusted resource to the business. By default, every member of staff can and should be
trusted to handle data appropriately and sensitively. The default position is that a staff member can access data
unless there is a specific commercial, legal or ethical reason why not.
• Principle #7 - Every data element has one person or role as an ultimate responsible “owner” – While data is technically “owned” by the business, every data element requires a unique and ultimate
responsibility authority.
Areas of Impact• Unified Vision
– Businesses that can break down the barriers between their disparate silos can obtain value by
obtaining unified views of business-critical data like customers and products, as well as apply
existing data in new value adding ways.
• Remove Redundancies
– Remove redundancies and reduce the need for rework and therefore reduce operational costs by
repairing data quality issues that create the need for manual reconciliation and avoid futile
marketing and sales expenditures.
– Abstract data from applications in order to serve numerous users with varying requirements with
the same common data.
• Continuous and Efficient Workflow s
– Continuous workflow with timely data available. This prevents people from waiting for data that
they require to perform their work or make decisions that can cause lost opportunities or added
costs.
• Compliance
– Aid compliance to legislation such as Sarbanes-Oxley because knowing where to find the
information will decrease the risk of non-compliance and allow for greater responsibility. This is
of high significance for financial institutions that increasing need to comply with financial and
regulatory reporting.
• Improve Data Quality
– Poor data governance and data management leads directly to poor data quality. Early indicators of
the business costs of non-quality data include non-recoverable costs, rework of products/services,
workarounds, and potential and foregone revenue potentially as high as 10-20% of revenue or
total budget of an organization. On its own, by halving the costs related to poor data quality, a 10-
15% saving on operating expenses can potentially be achieved.
VALUING THE DATA ASSET
Value of a Business
ValueOrg = Share Price x Number of Shares
Theoretical Value of Data
DataValue (theoretical) = ValueOrg x y
What is your y = 10%?, 20%?
Calculating Data Efficiency
• Qualitative Model
– Use the Capability Maturity Model for Data (CMM-D) to capture the
qualitative values
DataEfficiencyCurrentDataEfficiencyCurrentDataEfficiencyCurrentDataEfficiencyCurrent = (= (= (= (DataMaturityQualityScoreCurrentDataMaturityQualityScoreCurrentDataMaturityQualityScoreCurrentDataMaturityQualityScoreCurrent) ) ) ) ((((DataMaturityQqualityScoreBenchmarkDataMaturityQqualityScoreBenchmarkDataMaturityQqualityScoreBenchmarkDataMaturityQqualityScoreBenchmark))))
DataEfficiencyFutureDataEfficiencyFutureDataEfficiencyFutureDataEfficiencyFuture = (= (= (= (DataMaturityQualityScoreFutureDataMaturityQualityScoreFutureDataMaturityQualityScoreFutureDataMaturityQualityScoreFuture) ) ) ) ((((DataMaturityQualityScoreBenchmarkDataMaturityQualityScoreBenchmarkDataMaturityQualityScoreBenchmarkDataMaturityQualityScoreBenchmark))))
Potential Value Gains
DataValue(estimated)Current =
DataValue(theoretical) x DataEffciencyCurrent
DataValue(estimated)Future =
DataValue(theoretical) x DataEfficiencyFuture
DataValue(estimated)Gains =
DataValue(estimated)Future - DataValue(estimated)Current
Quantitative View
ROI = (GainOfProcessImprovement – CostToImprove)
CostToImprove
Calculating Return-on-Investment
ProcessDataCostCurrent = (ActionTotalTimeCurrent x CostOfResources)
ProcessDataCostFuture = (ActionTotalTimeFuture x CostOfResources)
GainOfProcessImprovement = (ProcessDataCostCurrent – ProcessDataCostFuture)
CostToImprove = CapitalInvestment + (ResourceTime x ResourceCost) +
ExpenseInvestment
Net Financial Impact
Conclusion
• This topic is of critical importance if organizations are
to leverage the potential of their data asset.
• A dedicated program provides a methodology to
measure the business value of the data asset within the
organization, and provide a focus for the enterprise on
achieving an appropriate rate of return.
How Else Can We Calculate Benefit?
• What other algorithms should we be using?