Why Data Management remains a challenge in the Data and AI-first era. What challenges companies face with data management and how to begin tackling them
Data is the fuel of the modern organisation. As it’s proliferated across the enterprise, more people are integrating it into their business and operational decisions. This means that having a robust data management strategy and infrastructure is critical for the success of every data-driven business.Nevertheless, data management remains a fundamental challenge to solve even as we are moving towards data-first and AI-driven organisations. Companies can’t progress towards data innovation and AI deployment if they haven’t taken care of the fundamentals of how they are going to manage their data. In this regard, below we are going to explore what are the most pervasive challenges and how organisations are tackling them.
With some of the persistent hurdles like legacy systems and lack of domain-specific capabilities, organisations are impeded from deploying and scaling their AI initiatives. Working with legacy data and systems is especially a problem in enterprise organisations where data is stored in disparate siloed systems, it’s hard to find and aggregate in a universal data platform in order to accelerate data-driven decisions.When it comes to domain specific-capabilities, BARC survey reports that companies seriously feel the lack of external knowledge and the skill gap present on the market. Sourcing the rights skills is a general challenge, as companies data management needs cannot be fully met by engaging external resources.What’s more, data management can’t be done right without having clearly defined data governance policies and rules that govern that use of data and data operations, something a majority of organisations are still fumbling with. Here, we cannot omit to mention the first and foremost enemy of a data-driven company, which is poor, inconsistent and poor data quality. As the amount of data organisations collect has increased by a great degree, ensuring data quality has become harder because of the diversity of data sources, the various types of data that are difficult to integrate, the sheer volume of data, as well as the rapid pace at which data changes.
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