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Role of Analytics & BI Platforms in Digital Transformation

This is the era of “wanting to be engaged and needing to be informed”. For ages, lagging indicators such as customer sales and customer problem solving were the driving factors for customer satisfaction. However, using predictive analytics to understand valuable customers and appropriately engage with them is a key aspect of business intelligence, as pointed out by Adobe. Companies like Disney and Amazon have embraced the power of analytics to understand their customer activities and create packages that their customers cannot refuse.

Predictive Analytics Methodology

Key Factors for Analytics

Key factors for analytics include agility, real time streaming, and processing unstructured data. In addition, BI technologies that are cloud-friendly play another key role for both companies who are in their journey to the cloud and or looking to implement a hybrid environment. Yet another key factor for analytics is the role of data lakes, which can store data in a raw format. The Internet of Things is another important consideration, which can integrate all data from anywhere in any format.

To demonstrate the competitive advantage of analytics, companies that are collecting tons of data about their customers, partners, employees, and other data domains can capitalize on new business models offering “data services” as a SaaS to their corresponding customers as a subscription based service. Yet another example is the concept of BlockChain offering ledgers across the enterprise as a data service. The next evolution of analytics that I can predict will be the ability to receive picture data (such as streams from drones and self-driving cars) in a commercially available format and affordable to the common marketplace.

Things to Think About When Building Customer-Obsessed Analytics

Business agility is a critical factor of transformational businesses, such as Amazon’s, ability to thrive in this e-tail business. Here is what you need to think about when building customer-obsessed analytics:

  • A strong business case to drive Big Data projects
  • Edge computing and predictive analytics with Big Data to predict and proactively address your customers’ needs
  • Real time streaming analytics using Apache Spark, Kafka, and other Hadoop distributions
  • Data quality and data profiling (Garbage In, Garbage Out)
  • IoTe data for listening and tapping into the “voice of the customer” and benchmarking

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