Being data-driven can help organizations improve their processes, drive better decision-making, provide better customer experience and eventually enabling improved business outcomes. Data-driven strategies use data to allow an outcome-driven collection process, and the insights are used for decision-making purposes. But many companies struggle with managing large volumes of data that they collect from various touchpoints and often fail to use them for meaningful insights or fail to use them to improve operational processes and better business outcomes. A data-driven strategy can be used in many areas across the operations like arriving at proper segmentation, modelling user behaviours, modelling various treatment options for recovery, modelling agent performance, arriving at the right customer contact strategy etc. Data-driven strategies use both historical and real-time data. Historical data includes data like customer data, transaction history, credit history etc. Real-time data includes real-time customer consumption patterns and usage data. This data can be used to build different prediction models and to enable decision making.  Examples include building models to predict customer delinquency, using data to create a customer’s risk profile, using data to understand user behaviour, and accordingly creating a personalized contact strategy, etc.
Some of the key challenges in implementing a data-driven strategy:
Challenges in implementing a data-driven strategy could be multiple, and it can span across technology and process.

  • Technology challenges include systems that enterprises use to collect data and to store data. Modern technologies like the cloud help us resolve this problem to an extent. Cloud also gives us a cheaper option to process this data.
  • Process challenges include how employees do their work, how they collaborate, how they view success etc.

So, this means implementing a data-driven strategy is both a technological and cultural change. This change needs to be appropriately managed at both levels for the desired outcomes.
Below are some of the key challenges that organizations encounter in implementing a data- driven strategy.

  • Having a master data management (MDM) that can give a uniform view of customers, products etc., across the enterprise is one of the biggest challenges of achieving a data-driven strategy. Often data lies in functional silos with duplicate and redundant information residing in multiple systems. Each functional unit has its own systems to collect data, analyse data, and make sense of data from a functional viewpoint. With this siloed approach, the enterprise is missing a consistent view and meaningful interpretation of data.
  • The second challenge is integrating the data residing in individual functional silos in different systems like marketing, sales etc., to give a uniform view of underlying data. A uniform view of data across the enterprise is essential for arriving at meaningful insights and recommendations. And this is also important for providing a consistent customer experience as part of the customer contact strategy.
  • The third is identifying the patterns and correlation in the data and building suitable prediction models. Here it’s essential that there is a right hypothesis for testing and proper correlations are identified.
  • The fourth challenge is building a reporting capability that can deliver insights and present using data visualization tools for decision-making purposes.

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