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Information Strategy:
Improve Data Quality

  Column published in DM Review Magazine
January 2006 Issue
  By Jane Griffin

In my columns this year, I thought it might be interesting to develop a common theme and to base each column on one facet of that theme. As I discussed the concept with my clients and colleagues, one issue emerged as the hot topic. That issue - a question, really - is: How can you make better use of IT resources and initiatives in order to consistently deliver tangible value to the company?

As I discuss various approaches to providing value to the company through IT, I'm not going to advocate a standard approach or methodology because there isn't one. Each company - along with its people, processes and technology needs - is unique. Accordingly, the process of providing more value through IT will be unique to each company. However, there are certain initiatives any company can undertake and customize for its needs to start on the path to delivering more value with IT. The first initiative is one to improve the quality of corporate data.

Data quality (DQ) is not an option. Yet at many companies today, DQ is still deficient. Poor DQ has a damaging effect on the business. It creates difficulty in sustaining compliance with legislative and regulatory mandates, such as the Sarbanes-Oxley Act (SOX). Performance analysis, planning and forecasting are ineffectual because there is no single version of the truth for the company's information.

Virtually every company faces barriers to adequate DQ. Business processes and IT systems are often unnecessarily complex and fragmented. Many companies have difficulty in performing analysis, planning and forecasting functions. Others may have difficulty analyzing customer behavior and reducing customer churn. These barriers are your DQ pain points.

The first step to improving corporate DQ is to identify your particular pain points and determine why they exist. For example, assume you've identified your number-one pain point as the inability to close out monthly or quarterly cycles quickly because financial reports from multiple business units must be reconciled manually. The underlying cause may be that the reports contain different terminology for the same data elements. This is most likely a master data problem.

Once you've identified your pain points and their underlying causes, you can take concrete action to fix the problems. In our example, the clearest course of action would be to implement a master data management initiative to consolidate and standardize master data elements across the company. Each company will have different pain points. It is important that you identify them and take action to fix them. It is also critical that you make an ongoing commitment to information excellence.

This commitment begins by changing the way you think about corporate data. Start by promoting DQ at the strategic level and clearly assigning accountability for data and DQ to process owners. In keeping with DQ as a strategic priority, evaluate strategic initiatives vis-a-vis how those initiatives will affect corporate DQ.

Long-term DQ strategy should focus on continual improvement. Begin by embedding accountability for DQ in the corporate culture. Also, encourage increased collaboration between IT and the company because the business side of the house is where corporate data is aggregated and analyzed. Sure, the data is collected by IT systems, but business processes necessitate that collection in the first place. Thus, the company has critical knowledge about business and analytical requirements. Leverage that knowledge to improve corporate DQ.

Another long-term strategy for DQ improvement is to stop viewing compliance requirements as something you must do. Rather, turn those requirements into opportunities for improving the business. If you use the laws as vehicles to improvement, you should be better equipped to meet present compliance mandates, and your improved DQ levels should help avoid recurring information problems.

The value that can be delivered to the company by improved DQ is enormous. Access to timely, accurate, reliable information provides the same answers to the same questions, no matter where in the company those questions arise. Improved DQ can also lead to enhanced ROI on IT investments, revenue growth fueled by better customer service (thus less churn), and easier regulatory compliance and sustainment. Really, who wouldn't want that kind of value for their money? 


For more information on related topics visit the following related portals...
Data Quality.

Jane Griffin is a Deloitte Consulting partner. Griffin has designed and built business intelligence solutions and data warehouses for clients in numerous industries. She may be reached via e-mail at janegriffin@deloitte.com

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