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© 2019 by smarbl

 

Smarbl is all about Data-Digital-Analytics !

 

In the current age of Data & Digital transformation, most organizations generate volumes of information about their customers, transactions, channels and interactions - which only when properly leveraged can transform the information into valuable insights (or ‘Intelligence’), enabling better decision making for the organization i.e., effective customer servicing, better product placement, proactive problem resolution etc.

For organizations to embark on their ‘Digital Transformation’ agenda, it is very crucial to understand how ‘Information-to-Intelligence’ journey plays along the various ‘Customer & Process’ journeys.

Guiding principles of a Digital Transformation:

  • Clear’ Value definition & ROI tracking mechanisms

  • Agreed’ Target Operating Model

  • Right’ team to perform Agile delivery

One of the key areas of focus for any organization trying to master the ‘Information-to-Intelligence’ journey is – Data Management. Companies that do not understand the importance of data management are less likely to survive in the current ‘Age’. Your data is your most valuable asset. To create a truly data-focused organization, it is essential to understand the ins and outs of data management.

Data Management comprises of the below areas:

  • Data Acquisition / Ingestion

  • Data Profiling

  • ‘Practical’ Data Warehouse

  • ‘Exploitation’ Ready

While there are quite a few things that we have to take care of while setting out on a data management journey, the first one involves establishing a comprehensive data acquisition, ingestion and collection framework which can support a wide variety of data acquisition from both internal as well as external sources.

Data profiling plays a critical part from here to understand the various data quality issues, confirming the data to applicable standards and reporting any recurring quality issues. This coupled with a robust Enterprise Master/Reference data strategy helps address questions around single view across various dimensions along with data governance mechanisms. Having a robust data quality and Reference Data framework paves the way for establishing a solid Enterprise Data Warehouse (EDW) – what we call a ‘Practical’ Data Warehouse. A mature warehouse sets the stage for the data exploitation, Advanced analytics, data mining and APIs. With the ever-growing data monetization theme capturing the imagination, having the right and accurate data and being able to convert them to meaningful insights unlock vast opportunities.

There are various tools, capabilities and reference models available in the market to help organizations in their Data Management journey.

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