Data is the most valuable asset a company can have. But simply collecting and storing data is no good unless we really transform into some real useful information. Only when it is extracted and categorized properly to identify and analyze patterns, it can be used for business gain and productivity.

Data Analytics is the process of examining data to draw conclusions about the information they contain. There are several tools and techniques that can be used to collect, transform, classify, cleanse and convert the data into easy-to-understand information. Leveraging the power of analytics helps us locate, refine and connect data more effectively and efficiently.


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With data increasingly recognized as a key element of the digital age, the focus is on how data should be collected, leveraged and managed.  For a long time, enterprises relied on relational databases but these were soon replaced by big data, mainly due to the velocity and variety it brings, and data lakes that bring all data in one place, available 24*7, to the right set of people.


To make meaning out of data and find insights, AI is used to create meta data, do governance without manual intervention. The ultimate goal is to make data available as knowledge – where to find data and how to make it available 24*7. To make this happen, Mphasis has certain differentiators and interventions –


Intervention 1: AI in data (replacing ‘people’ with AI to create content which can be used by downstream systems)


Intervention 2: Big data in Cloud (replacing two systems - Systems of Records and Systems of Analytics with one system in cloud - Hybrid Transactions and Analytics)


Intervention 3: Streaming Data (replacing traditional analytics with Kafka and Spark that analyses even the transient data)


Intervention 4: Serverless data management (moving from traditional platforms to cloud)

Mphasis Bi2Ai Approach – Journey from Business Intelligence to Artificial Intelligence


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Mphasis works on data migration and integration, preps and organizes the data, and analyses this prepared up data for downstream system. This means once the data is ingested, prepared and organized, it can be analyzed at various points, for several purposes.


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