60-year-old food processing company improved data quality to become Analytics ready
As the era of advanced analytics is upon us, organizations have realized that data can provide them a competitive edge. However, not all organizations are well-quipped to become future-ready. As per the Gartner Study, poor data quality is a primary reason for 40% of the data initiatives failing to achieve their targeted benefits. Our client was moving from SAP ECC to S/4 HANA but their data quality issues were creating bottlenecks in their supply chain, customer relationship management, finance and other business processes, and were hindering their migration to S/4HANA. They wanted to proactively establish a robust data management process with clean, reliable and accurate data that would enable better analytics and decision making.
Why should organizations consider data quality as a business need?
High quality data is a pre-requisite for making valuable business decisions. By now, we all are aware of the “Garbage in, garbage out” scenario with data. Data Quality signifies the condition of the data in terms of various metrics such as accuracy, completeness, consistency, reliability and also, whether it’s up to date. Low scores on these metrics make the organization susceptible to poor decision making.
Higher Processing Cost:
As per the HBR study, it costs ten times as much to complete a unit of work when the data is flawed than when the data is perfect. According to one Gartner estimate, poor data quality can result in additional spend of $15M in average annual cost.
Low Confidence and Trust in Reporting:
With the advent of embedded analytics, users are more inclined to embed analytics in their workflow to assist them in better decision making. Under such circumstances, with poor quality data users lose confidence in the analytics and reporting that is derived from the data.
Compliance Risk:
Complying with regulations is a necessary requirement for organizations nowadays. Data needs to be correct, complete and easily accessible during any compliance audit. Otherwise, it can delay the process and businesses may end up in legal trouble as well. A poor data governance set-up also contributes to such unwanted complications. (Read more on Applexus Data Governance)
While migrating to S/4, our client wanted to make sure that the data is correct and business ready to be leveraged for decision making in their day-to-day operations. With time, legacy data often gets crippled with dummy values, incompleteness, multipurpose and contradicting fields and reused primary keys. Hence, it was imperative to migrate accurate and clean data to S/4 for its effective future usage.
Applexus Approach to ensure Data Quality for the client
Our domain experts at Applexus crafted a solution keeping the business needs of the client in mind. Our solution had three core elements to it.
Measure what Matters:
Business needs, objectives and goals are specific to a customer and the data needs to draw actionable insights for these business users specific to their context. As legacy data is often plagued with business rules violations, duplicated or missing values and non-unique identifiers, data needs to be parsed into the target system keeping the business requirement in mind. Otherwise, the target system may end up with data that isn’t able to assess the business needs for a specific context. Our team engaged with the client to provide missing data attributes and throughout multiple mockloads and validation ensured that the data is business ready.
Expedite with Pre-Built Solution:
Applexus’ unique RunningStart methodology for data cleansing involves solutions with pre-built data-quality reporting capabilities that uncovers data quality issues, data correctness and completeness, conforming to SAP standards and cross functional issues. Pre-built jobs for data profiling, data scoping and relevancy, de-duplication, data harmonization and address cleansing, leveraging SAP data services further expedites the data cleansing process.
Define Future State:
A successful data quality/cleansing needs to have a future state of the data defined to ensure that the quality is maintained. We engaged with the client to establish a robust data governance and ownership model for the master data. This future state analysis and recommendations also enabled our client to have roadmap towards a culture around analytics and data-driven decision making.
