Data Quality Scorecard

The classification of the different areas of impact of poor data quality resulted in four different impact areas. Within each impact area several different parameters of impact can then be determined to generate what is called a data quality scorecard which contains complex data metrics. These can then be analyzed using three different view points.

The sub-categories for each different category of data quality flaws are described below:

1) Financial

Direct Operating Expenses – direct costs such a labor, raw material that can be used for fulfilling obligations related to contract
General overhead – rent, maintainance, asset purchase, utility, licensing, administrative staff and general procurement
Staff overhead – those required to run a business like clerical, sales, management, field supervision, bids and proposals, recruiting and training.
Fees and charges – bank fees, service charges, legal, accounting, penalties and fines, bad debt, merger and acquisition costs
Cost of goods sold – design, raw materials, production, cost of inventory, inventory planning, marketing, sales, customer management, advertising, lead generation, promotional events, samples, order replacement, order fulfillment and shipping
Revenue – customer acquisition, customer retention, churn, missed opportunities
Cashflow – delayed and missed customer invoicing, ignored overdue customer payments, quick supplier payments, increased interest rates, EBITDA
Depreciation – property market value, inventory markdown
Capitalization – value of equity
Leakage – collections, fraud, commissions, inter-organizational settlements

2) Confidence and Satisfaction

Forecasting - staffing, financial, material requirements, spend vs budget
Reporting – timeliness, currency, availability, accuracy, reconciliation needs
Decision making – time to decision, predictability
Customer satisfaction – cost of selling, retention, buys per customer, items per customer, sales cost, service cost, time to respond, referrals, new product suggestions
Supplier management – optimized purchasing, reduced pricing, making acquisitions simple
Employee Satisfaction – recruitment costs, hiring, retention, turnover, compensation

3) Productivity

Workloads – reconciliation of reports
Throughput – Increased time for data gathering and preparation, reduced time for direct data analysis, delays in delivering information products, lengthened production and manufacturing cycles
Output quality – reports not trusted
Supply chain – not in stock, delays in delivery, missed deliveries, replicated costs

4) Risk and Compliance

Regulatory – reporting, protection of private information
Industry – processing standards, exchange standards, operational standards
Safety – health and occupational hazards
Market – competitiveness, goodwill, commodity risk, currency risk, equity risk
Financial – loan default risk, investment depreciation, noncompliance penalties
System – delays in development, delays in and deployment
Credit/Underwriting – credit risk, default, capacity, sufficiency of capitalization
Legal – legal research, preparation of material

Viewpoints for Reporting

There are three different viewpoints by which the data in the complex data metrics can be reported i.e. validating data, thresholds of conformance as well as ongoing monitoring and process control.

While validating data it is important to note that the rules are to be created for each data set based on the range of values that they can assume. This is commonly achieved by profiling of data, parsing, standardization and cleansing tools. The best of the vendors provide for this mechanism of integrating data quality within their tools. By tracking the number of times the levels are violated one can determine the percentage of violations and conformance to defined rules.

The thresholds determine if the number of violations conform to user expectations. Typically different colors are assigned to different thresholds and the data is displayed according to that format. This is because different data flaws have different impacts and represent different levels of business criticality. The levels of conformance might be different and the thresholds vary according to that.

One also needs to monitor and control the data quality as system modifications and updates have impacted data quality in different ways. The historical view of the data quality showed how the data has evolved over time given different levels of conformance.

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Viewpoints for Reporting