Introduction

The amount of data that organizations create, collect and use is increasing exponentially. This huge volume of data and its consequent complexity, coupled with the fact that organizations are relying more and more on data in their operational activities and decision-making processes make data management a necessity and a fundamental concern[1].

Data is used to run and/or to improve business activities. Organizations use transactional data to run day-to-day operational activities and they take advantage of historical data to improve the ways that they achieve objectives. Such a valuable asset must have a process at work to ensure its sufficient quality. However, despite the fact that data is an important asset to most organizations, its quality is not as well maintained as the other assets[2].

Flaws in data when it flows in and out of an organization are usually recipes for disasters and they all fit right into the blacklist of losses caused by data flaws or data mismanagement:

  • missed opportunities
  • large losses of time and money
  • failure or delays in delivering accurate data to decision makers
  • transactions reworks
  • implementing new systems
  • supply chain uncertainties
  • poor customer service

The costs of poor data quality can be around 40% of an organization’s operating revenue [3]. Sadly, such losses are not obvious before they occur and cause phenomenal damage to short- and long-term organizational objectives. That is the big lesson many organizations learn the hard way.

Database management systems have undergone tremendous development and transformation. These developments have improved all aspects of data storage, protection, and recovery systems. Yet, not much has been done about the data itself. Data quality technologies still lag behind database management technologies [4]. Poor data leads to harmful, flawed and unmanageable processes that cost organizations time, money and opportunities; one reason for poor data quality is the lack of control and governance over it [1].

Data that come from diverse sources have disparate forms, formats, structure, definition, and attributes. Such scattered data must then try to represent one business concept. This universal situation, with serious consequent losses, has led organizations to a desire to be able to apply a consolidation, integration and quality layer on their data at the same time. A system should be at work to constantly employ a quality filter onto the incoming data pipeline, and ultimately convert the data flow into a unified, integrated collection that is commonly understood by users across the organization. This reliable system unfailingly functions toward fulfilling operational activities and gaining actionable insights for decision makers.

That is where data centralization come into play as a set of best practices for managing and controlling data quality risks. It facilitates closer control and governance over data and provides the ability to constantly oversee the data from end to end. This is the solid ground upon which tactical and strategic decisions can be built. [2].

In a centralized architecture, operational and historical data are evaluated, controlled and refined based on the business applications and rules such that they are directly in line with their business and IT requirements. Leveraging this consolidated data on operational and analytical processes also assures the integration of associated data and its accessibility across the organization and onto downstream systems. Such data along with their associated definitions, attributes, and connections are used by and shared among its different users in a common repository. The repository first and foremost ensures that the data meet user’s requirements, and in doing that it checks if the data is timely, complete, and correct. This is followed by making the high-quality data secure, relevant, accessible, and trusted. This centralized, consolidated, orchestrated, consistent and coherent repo constantly allows technical oversight, proper governance, timely and accurate evaluation and reaction to flaws in constructing its high-quality datasets to prevent recurrence of poor data quality, inappropriate application and analysis of data.

Conclusion

The power to govern implications and synchronization is a fundamental step toward achieving organizational objectives and a substantial return on investment. And that is the power gained through ZEMA. Contact us today for more information.

Bibliography

  1. J. E. Olson, Data Quality: The Accuracy Dimension. Morgan Kaufmann, 2003).
  2. P. Jain, “To Centralize Analytics or Not, That is the Question,” Forbes.com, Feb 2013. [Online].
  3. L. P. English, Information Quality Applied: Best Practices for Improving Business Information, Processes and Systems, Wiley, 2009.
  4. Y. Lee, Journey to Data Quality. The MIT Press, 2009.