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?