February 28, 2017


Step-by-step guide to unraveling Innovation through Data Analytics

Proliferation of data within the digital ecosystem has propelled a business paradigm shift in recent years. According to IDC forecast, the size of the digital universe, which was 1.8 zettabytes in 2011, would grow 44 fold to 35 zettabytes annually between 2009 and 2020.

Consequently, aligning business needs and vision to the global digital ecosystem has become the key concern of organizations. They want to derive actionable insights and develop agile business models that respond faster to changing market scenarios, which necessitates the implementation of Big Data and Analytics technologies.

Big Data analytics platforms like Big Data Lake eliminate disparate storage silos by consolidating data into a single shared volume with access across multiple traditional and cloud-native protocols. The creation of an agile Enterprise Intelligence Hub (EIH), built on the concept of a Big Data Lake, can help unlock the potential of an organization’s data assets, leading to agility, improved operational performance, and reduced total cost of ownership.

Big Data Lake can help unlock the potential of an organization’s data assets, leading to agility.

Let us explore the three key steps to deploying data analytics services across the EIH:

Step One: Industrialize Data Acquisition

Although the concurrency of users of a Big Data Lake is generally less than 10, the queries generated are complex and unpredictable. This necessitates a platform which can allow automated data ingestion at any given time, without limitation on format. The Enterprise Intelligence Hub offers a platform to effectively industrialize the acquisition. Data from multiple sources including structured, semi-structured, unstructured, text, and video sources are part of the platform’s capabilities.

To cite an instance, a leading US telecom operator, with legacy systems, needed help with integrating and correlating data from social media to build a next-generation data intelligence system. An end-to-end data analytics platform as well as an agile analytics operating model was adopted to deliver future-ready data ingestion services and enable cross functional collaboration across channels. This allowed advanced analytics to be carried out in a secure manner.

Step Two: Harmonize and Unify Data Storage

A Data Lake is designed to retain all the attributes of data unlike the rigid data marts. It helps to make data future-proof as data is no more bound to the scope defined in today’s scenario and can be used for future decision making. Data analytics services like EIH provides seamless access to an increasing deluge of data sources. It is built on four key concepts –

  • Overarching governance for the data and metadata, providing integration of any data, any time, and in any volume
  • Security to deliver data to any user on any device
  • Standards for staging the raw data needed by all other data platforms
  • Standards for the analysis and transformation environments

A Fortune 500 financial services group was grappling with declining sales conversions despite growing online traffic and needed to gain insights on the same. A migration from traditional clickstream analytics to a Big Data analytics solution was executed to explore consumer behavior patterns across channels, over years rather than quarters, and connect patterns to marketing events and sales promotions. The improved funnel and behavior analysis resulted in an increase in conversion rates, an improvement in the quality of leads, and the ability to create targeted marketing, upsell and, cross-sell offers.

Step Three: Personalizing Data Delivery and Analytics

The final step involves delivering personalized data and analytics services to business users. Analytics services provide the flexibility and agility analytics producers and consumers need to quickly analyze data from any source, including legacy systems, social media, IoT, machine-to-machine communications, and business events and transactions.

A global aerospace manufacturer was seeking to improve customer services and maintenance programs by eliminating manual analysis of service logs, text files, and senor data. The deployment of an EIH helped automate data acquisition and unify the underlying Data Lake infrastructure with advanced analytical tools. As a result, an improvement in higher service reliability was observed across airframes and fleets.

Choosing the Right Approach

Before leveraging analytics services such as EIH in pursuit of a data and analytics driven enterprise, it is crucial to plan correctly and align the technology deployment with business use cases to progressively deliver business value. Here is the Gartner newsletter on how you can look forward to Controlled and Managed transformation with our Enterprise Intelligence Hub . We have successfully created a unique standalone co-innovation lab for a leading investment bank in Europe where business stakeholders, partners, and end–users were able to directly engage in business problems through research, ideation, and rapid prototyping. Our data analytics consulting has resulted in a substantial reduction in the time from business idea to business value.