Kicking the Bad Habit of Bad Data

Posted by Katie Mowery on June 25, 2019

It’s time to face the reality... no more kicking it down the road... your organization just might have a data problem. Maybe it started with a few processes that broke down over time. Maybe someone left and took with them the knowledge of their homegrown data archiving strategy. Maybe manual entry or data manipulation destroyed the integrity of your change management. Regardless of how it happened, it has likely led to some enterprise-wide bad data habits. 

It's no longer enough to just clean up your data - that's like quitting something cold turkey - it will not change the processes and behaviors that led to poor data quality. You need to build a solid foundation of clean data, and ensure it stays clean – because when the input data is bad, the output is unreliable, and when that data output drives your business, the stakes are high for failure or success.

How do you build a plan to address poor data quality?

It’s important to start with data preparation and planning – the “building” phase. An enterprise must understand what business processes are being transitioned to the new system and determine where the data exists and its quality, based on how well it supports the new system and whether it meets the new rules of the new SAP system:

  • Assess the quality of data in SAP, common and shared databases, legacy environments, Excel files and generic delimited files - Wherever you store the data and use the data - it's all important
  • Analyze how data quality affects enterprise processes and performance by discovering, defining and monitoring data quality levels from a business perspective
  • Align requirements for business rules that will be used in your new environment
  • Build data quality dashboards, scorecards and trend charts to provide a bird’s-eye view of data quality and of key data metrics to determine the impact or source of data problems 
  • Employ a strategy to standardize and consolidate widely used data and identify what can be can scrapped, identify outdated or redundant systems and applications and develop a plan for what should be migrated into the new environment
  • Continuously monitor and share the trustworthiness of information to determine whether it is fit for use based on its quality
  • Develop custom business rules, terms and policies for governing data long-term

This isn’t necessarily easy, because this process can’t be automated, but without an understanding of how the data will be used in the new business processes, an enterprise will be replicating the sub-optimal data in its new system. Connecting data migration with data governance is key to ensuring decisions are made on accurate, business-ready data.

The path to clean, standardized data may seem daunting, and many enterprises look to an IT partner to help them navigate it using the latest software platforms and techniques. However, many large SIs will focus on the implementation of the system and its integrations first, and address the large-scale data quality or migration project much later in the process or at the end, after the entire implementation plan has been designed. This approach lengthens the rollout timeline and creates the risk of bad data making it to the end of the process. In other words, it's important to look to a partner that puts data first to ensure only the highest-quality data is being used to drive business intelligence from the new system. 

Because a large percentage of business and analytics processes are based on master data; it’s the core building block of the intelligent enterprise. Ensuring clean, accurate data is a significant first step on the journey to SAP S/4HANA or whichever new landscape you are moving towards. 

Topics: Data Quality, SAP S/4HANA, data migration

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