The Missing Link in Managing Data: Business Relevancy

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ILarge organizations today often have years of accumulated data spread among multiple ERP and legacy systems, which places an extreme burden on the multitude of systems as well as organizations’ ability to use them. However, a bigger issue is ensuring that the data is accurate, up-to-date and ready to use meaningfully at any given moment. The business relevancy of data is the number-one most misunderstood data problem that organizations face, as well as one of the most expensive.  

Both IT and data-focused professionals are now beginning to understand that there are significant savings to be had over the life of ERP and other systems, not only during the implementation of a major ERP transformation, through ongoing information governance, including identifying and isolating the business-owned data that is truly used.

When performing massive data transformations and ensuring that data is business-ready, companies typically accept an agreed-upon error rate in order to get their systems up and running or may even take it a step further to achieve an ‘error free’ guarantee. But this doesn’t mean that the data is business-ready—a problem that often translates into costly delays and business interruptions. Understanding what it means to embrace the concept of business-relevant data is the first step toward a business-ready transformation. But how do organizations get there?

Early Awareness and Executive Buy-In

Developing and raising data knowledge and awareness is a critical factor in establishing the concept of business relevant data. The majority of today’s organizations don’t know if their data is accurate and up-to-date and are often maintaining extreme amounts of data, because there is no established process or understanding of how to define and implement a governance model around business relevance. Often, the people who are actually managing the data aren't invested in its accuracy because they haven’t been trained on or may be unaware of the overall enterprise data vision and strategy. Moreover, there frequently isn’t a long-term data advocate or steward to oversee procedures in ensuring the business relevant data concept is fostered and implemented.

Simply put, there is no one to “love” the data! Establishing senior level buy-in on the importance of ensuring business ready data, rather than just focusing on the ERP implementation, is a necessary first step and a foundational data guiding principle. Additionally, organizations should prioritize the appointment of several data stewards and even a Chief Data Officer role to begin to mature the process of information governance that will enable principles like business relevance.

Let the Data Tell The Story

It often takes lifting up the hood of one of the various underlying data systems to uncover data anomalies and data issues that contribute to the larger story of overall data accuracy across the entire enterprise. Today, the common practice of data migration is based on a paradigm in which an ERP system is considered the central integration point for all other systems. Unfortunately, this approach can promote redundancies and allows for the proliferation of inconsistent and incomplete data when multiple systems are part of the enterprise model.

By allowing the data to tell the story via a unified, aggregate method, it is far easier to determine what types of data need to be re-configured and what needs to be cleansed or archived. Through the process of categorizing and prioritizing data, organizations can determine how frequently it is being used and by which systems and users. The process should begin by extracting the data into a central analytical repository. This enables the capability to report on the entire data pool against the ERP system on an ongoing basis from the inception of information governance. Once analyzed in this manner, data can be flagged appropriately for accuracy, business readiness, business relevance and completeness. Further, data that is inactive for several years can be evaluated for either archiving or elimination from the systems, thus increasing the value of not only the data in resident systems, but the organization’s ability to perform in an agile manner. In addition, specific data that is utilized on a continual basis can be prioritized within the overall data pool.

As an example, a company may be sitting on hundreds of thousands of outdated, inaccurate vendor master data records that are burdening the system, causing confusion in usage and inevitably driving up operating costs. After a master data analysis on business relevance, the accurate vendor count may be a mere one-third or less of the data present as ‘active’ within the system.

Shift from Reactive to Proactive with People, Methodology and Technology

Establishing a repeatable process for each system deployment that focuses on analysis, cleansing, construction and consolidation, enables an organization to move from a reactive, isolated data approach to proactively ‘loving’ their comprehensive data sets. Using a business data transformation process that takes advantage of a proven methodology, skilled practitioners and technology, an organization can establish the necessary foundational building blocks to enable business ready data. In the data mapping phase of the information conversion process, old system to new system data relationships are defined. During de-duplication, legacy data is rationalized and duplicate data is identified and removed. Following the de-duplication process, all errors in legacy data are fixed and missing data is added to the system. Finally, all data is accurately and completely loaded into the target system in the data validation and loading phase.

Ultimately, the organization achieves significant efficiencies as each wave of the deployment is executed and experience and confidence is gained. Each wave inevitably yields cleaner deployments, fewer issues, less risk and the ability to execute concurrently. This process can be used as the gateway to achieve a long-term information governance strategy across the organization.

Owners and managers of business data are often reluctant to make changes to their current process of data management, as they believe they have a good handle on their data. By removing the fear of the unknown, those with a vested interest in their data will quickly recognize that taking a holistic, programmatic and methodical look at their data will result in increased efficiencies and business intelligence for their departments and organization as a whole. This ultimately translates to higher business value and greater efficiency in operational readiness. Embracing the concepts of business relevance and business readiness is a solid first step on the journey to achieving best practice information governance.

About the author:

John Danos is vice president – Delivery Services Strategy at BackOffice Associates.