Three Trends Around Data Quality for Performance Management

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With the approach of the Fourth of July, business executives, middle managers, information technology and knowledge workers in both large and small organizations have specific information needs. Retail industry personnel might want to know how many people made specific purchases of top-selling retail products during last year's holiday weekend. Manufacturers will want to understand the buying trends behind purchases of specific brands of beer, wine or food products to help predict impact on production requirements. Services industry managers might want to better understand historical travel patterns in certain geographies to adjust staffing or tailor promotional offers.

Business intelligence (BI) and performance management (PM) solutions help these companies strategically increase their competitive edge and grow their business by answering three basic business questions:

  • How am I doing?
  • Why?
  • What should I or can I be doing about it?

However, BI/PM solutions are only as good as the data that drives them. If the underlying data that feeds the business information is inconsistent, any retail, manufacturing or services organization that wants to widen the gap between them and the competition on a holiday weekend, or any other pertinent market milestone, will find themselves at a distinct disadvantage.

In the continuous battle to outperform the competition, leading organizations realize that adding data quality to their BI and PM initiatives is one of the ways to widen the competitive gap. Today, there is a growing movement for organizations to implement their BI/PM solution alongside a data quality solution.

BI and PM implementations are quickly becoming the focal point for the most strategic use of business information. The implementations are maturing and involving more people, more departments and more data, and are stretching beyond IT to deeply engage the business community within an organization.

Trend #1: BI and performance management is fast becoming the focal point for the most strategic use of information.

In this world of data, a world growing exponentially, one of the ways organizations can significantly differentiate themselves with a BI/PM solution is to track how they are doing, why their business is tracking in a particular way, and what they should actually be doing to grow revenue and beat the competition. Yet, according to a 2006 DecisionROI report, "Business Traction from Better Decision Action," many organizations attribute poor decision making to a lack of timely, reliable business information.

Because organizations need access to data to answer these questions, and need BI and performance management to turn the data into actionable information, it becomes critical that the data they access is of high quality and can be trusted to be the foundation of good decisions.

Let's look at an inventory management example.

Picture a growing multinational organization with 45 branches. The inventory manager wants visibility to all of the suppliers across all of the branches to create efficiencies in the supply chain. If he knows that he has 10 vendors across the nation for a single item, he can most likely negotiate a deal from a single vendor based on price. Yet, once he engages the performance management solution to provide metrics on costs per branch, brand ordered and quantity, how can he make sure his results are accurate, unless he makes sure the underlying data is also accurate? The old adage "garbage-in, garbage-out" holds true today, and will become increasingly acute as the sheer volume of data grows exponentially every day.

To obtain the business value they desire, organizations are now seeking to address data quality alongside their BI and performance management implementation to help drive confidence in the data collected and confidence to us that data for a competitive edge. So organizations should start thinking of managing data as a strategic initiative if they haven't already.

Trend #2: BI implementations are maturing and involving more people and departments as well as accessing more data than ever before.

As organizations mature in their BI/PM deployments, they move from a department-based, bottom line-focused process to a more collaborative, cross functional, top-line business improvement decision making process. This shift engages more people across more departments who must understand a broader scope of data inside and outside the organization and how that information relates to their work. The additional exposure and less familiarity with the data increases their scrutiny of the numbers and therefore the need to address data quality. For example, if you are a department manager, and the decisions you make effect how my organization runs, you want to be sure you can trust the data you are using as the basis of my decisions. For instance, in HR you want to know how many new sales hires it will take to drive corporate revenue projections, in marketing you want to know my best customers for special offers and in sales you want to know the right mix of sales associates per territory for pipeline optimization.

A lot of organizations also find that their BI or PM implementation becomes pervasive over time as they expand their deployments. Organizations typically will start with a single department like marketing, sales or finance. As the other departments begin to see the value delivered, then they too want to be brought into the BI fold. As these BI and PM deployments expand and become pervasive, even more people are looking at the data to make business decisions. This exposure provides even more of an impetus for the organization to be certain the underlying data is consistent and credible.

Trend #3: Business community as well as IT needs to be engaged if data quality issues are going to be addressed.

The most successful organizations engage the business community as well as the Information Technology team to address data quality issues. This engagement represents a shifting priority in organizations since data quality was previously considered an IT problem to solve.

However, as we all know, IT does not own the data, nor do they want to. Most forward-looking IT teams will gladly work with the business, as the subject matter experts, to build a solution that addresses data quality. Ultimately, the quality of data that is entered by the business is really owned by the business, so business leadership needs to take responsibility for identifying the data quality issues, defining the business rules or processes to resolve them, and establishing the acceptable levels for improvement. Only then can IT take those metrics and rules to put in place the technology required by the business to meet those objectives.

Ongoing collaboration between the business and IT is essential to ensure that data quality is not a one-time clean up task, but a strategic project to continually track the health of the data over time.

Coming full circle

Data quality provides a way for organizations to track the health of their data over time. Given today's reality of reliance on information to drive competitive advantage, it seems obvious that the information businesses use to make decisions should be measured, managed and monitored for good data quality. If information isn't accurate, it won't be trusted, and without trust the business won't use the information to drive their business. When data quality is a business objective, then appropriate plans and actions can be taken to address issues.

Organizations have come full circle on this issue. They want to ensure the data they use to drive the performance of their organization is accurate, but they also want to be able to then turn around and use the performance management solutions they are already familiar with to track the health of their information over time. So they are really looking at a full circle: data quality for performance management and performance management for data quality.

This Fourth of July weekend, those savvy organizations that have put in place data quality initiatives with their BI deployments will widen the gap between themselves and the competition, as they engage both business and IT to use consistent business information as a strategic asset across the extended enterprise.

About the Author

Jennifer Schmitz is a Senior Product Marketing Manager at Cognos, an IBM Company. Her responsibilities include the go-to-market activities around Cognos' data quality initiatives. With seasoned expertise in the field of data quality, Jennifer helps to shape the information management messaging and positioning for business intelligence around "all things data". Prior to Cognos, Jennifer was a senior data quality product manager and was responsible for bringing enhancements and break through data quality software features to market. Her accomplishments include developing and executing strategy for an enterprise data quality suite of products, as well as driving the development of new industry specific applications designed to sit on top of the data quality suite's foundation.

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