Data Quality Assessment Initiative, Find a Right Strategy

Business Intelligence and analytics are CIO's top technology priorities in 2016. In fact over the past few years, CIO's has consistently top ranked BI as a technology priority. No wonder BI represents one of the essential component in organization's growth especially in the data integration and IT arena. Enterprises have finally come to realize data as a valuable asset they cannot afford to ignore anymore and have launched major BI initiatives to make most out of the data insights. But most businesses are failing to get the most out of the insights locked in the data.

The big question is why so many BI projects are failing

According to Gartner fewer than 30% of BI initiatives meet the objectives of business. So the big question is why so many BI projects are failing to take off or remaining as non-starters. The explanation lies in understanding the fact that data need not necessarily mean information. Clear distinction should be made between data, intelligence and actionable insight. Just having access to data doesn't necessarily mean it can be readily put to use.

Good data equals good decisions, this concept has never been more pertinent than today. That said the garbage in, garbage out (GIGO) concept holds equal importance. It makes sense that making informed decisions is all about having access to actionable data. But actionable data need not necessarily mean accurate data. That explains the importance of getting data quality right. Thankfully the focus on data quality is changing the way businesses gather and use data.

Data quality is a rising concern for data driven organizations

Organizations are feeling the pinch of compliance costs with the rise of regulatory compliance requirements. As a result data quality what was once a nice to have, has now become a business priority thanks to CIOs growing interest in the data governance. Data quality holds key to the success of business intelligence initiatives and data warehouse. Data driven organizations are increasingly becoming concerned about data quality issues. It is not without a reason, information intensive applications including Business Intelligence and other such decision support systems can help business users in their decision making only if they have access to dependable, complete and accurate data. Bad quality data can have a significant impact on a business.

Business impact of poor data quality

According to the Data Warehouse Institute (TDWI), poor data quality costs American businesses $600 billion a year. That's a staggering amount. Data quality issues can lead to decreased ROI, increased compliance & regulatory issues, back firing of strategies, missed sales opportunities, erosion of confidence to name a few. That's not alone enterprises are now realizing that data quality has a significant impact on their major strategic business initiatives aside from sales and marketing, if Gartner is any indication.

Data quality just doesn't happen on its own

It is important to understand that the data quality in data warehouse just doesn't happen on its own, it must be managed. Resolving the problem of bad quality data begins with understanding the need to invest in data quality assessment. In fact a number of organizations that have invested in data quality initiatives including data sourcing, profiling, cleansing, matching, data mapping, finding end to end enterprise lineage and impact analysis capabilities etc. have already started to reap the benefits.

Conclusion:

Many enterprises are attempting to improve data quality by investing in legacy COTS (commercial off the shelf) tools but as it turns out they are fast becoming obsolete. Data quality assessment is the need of the hour. Data quality assessment offers many benefits businesses can no longer afford to ignore.