Let’s talk about the elephant on the screen – the cost of managing bad data. An enterprise gets data from various sources, internal and external. For instance, the external data could be in the form of leads from a marketing campaign. And we all have been through the rigmarole at least some time in our professional careers, exasperated at the erroneously filled information, numbers in place of characters and vice versa, and we haven’t yet gone into the quality of data! Similarly, data from internal sources such as store-level sales data, inventory information, data from supply chain pipelines, etc. are all usually not standardized. And every piece of bad data that gets into your insights-generating application ensures that your insights lack the accuracy that they must.

That’s the importance of ensuring Data Quality in the data stack. A recent report states that Data Integration and Quality tools will reach a scale of $1.2 billion globally by 2028. It is our belief that the overall cost savings through such an investment should lead to at least 3 gains for enterprises, if not more. Data Quality cannot survive on its own without the principles of Data Governance. Businesses will need to figure out how their he datasets are managed efficiently, so that insights from them can be generated on-the-fly and not in a week or two.

In fact, more than 50% of time and resources are spent on Data Engineering and Management i.e. getting data ready for insights. With data landing from several different systems at a faster velocity, businesses will need even more support from data engineering to derive insights and value from data. If they don’t do that, the probability of gaining valuable insights from data go down exponentially.

If data is so important, does that mean that data engineering solutions should be built in silos? Absolutely not. Data engineering solutions that encompass Data Operations, Data Management and Governance and Quality provide validated data that help data scientists deliver actionable insights to business users, play huge role in creating business value from data. Moreover, such solutions must be designed in a plug and play manner, so that they fit into the enterprise technology stack irrespective of which stack is being used.

This trend is a great opportunity for enterprises to double down on the sanctity of their data. It is even more important to maintain proper data in fragile macro-economic conditions as this can be the key differentiator when business decisions are taken. Are all decisions data-driven? No, but data wields an important influence over these decisions. If you have an artificial intelligence and machine learning strategy without a data strategy, you are doing an unimaginable disservice to the possible return of investment that you can get from investments in data engineering and technology.