Tracking advances in BI & Analytics

By Jay Shah, ERP and BI Head, Nihilent Technologies


Jay Shah, ERP and BI Head, Nihilent Technologies


The industry is abuzz with leveraging analytics for uncovering hidden revenue opportunities, improving relations with customers and business partners, better risk identification and mitigation, empowering employees with right information for tactical and strategic decision making and more. Today, we are experiencing data explosion. Amidst this, Business Intelligence and Analytics play a central role in being able to extract relevant information and using it for corporate decision making. 

A quick note on how BI has come to be. We are well aware of the universal adoption of Enterprise Resource Planning (ERP), which in effect, is an Online Transaction Processing System (OLTP). While OLTP systems are capable of providing good operational reports, it hits a performance snag when it comes to complex analytical reports. To overcome this challenge, the data from OLTP systems is restructured to make it conducive to analytics, also known as Online Analytical processing (OLAP). This is the essence of Business Intelligence. The restructured data, housed in a Data Warehouse, permits analysing the data from several perspectives providing deeper and multi-dimensional insights which enable better decision making.

With increased adaption of Business Intelligence, the need for presenting the output in visually attractive graphical formats has grown.  A host of presentation tools that deliver dashboards, drill down reports for top down analysis, adhoc analysis, self-serve BI and more is available in the market.  This has provided easy visibility to multiple business KPIs to track progress vs. plan, identification of operational issues, risk management and more. It is now possible to represent sales data, cash flow data, among others, in the form of trends. Tools further can extend the trend and incorporate logic/algorithms to arrive at future estimates. Thus BI is now predictive. Going forward, BI is expected to combine such predictions with possible actionable options and prescribe actions. This way, BI is expected to be prescriptive thereby taking analytics to yet another level.  

The buying patterns of today’s consumer have changed. They rely more on collaboration and social media tools. Their preferences, dislikes are communicated via videos, tweets, free text on Facebook etc. which, in turn, are source of information for their buying decisions. Such “Big Data”, termed so because of its sheer volume, variety and speed at which it is generated, presents an opportunity to extract relevant information, analyse consumer preferences, and leverage it to redesign offerings, reach out to prospects early and for gaining competitive advantage.

One of the most recent developments is the introduction of In-Memory computing devices. This has enabled analysing data in real time. It is yet another step forward. In-Memory computing devices has helped in fusing the OLTP and OLAP platforms into a single platform thereby simplifying the BI landscape. Thus, with the promise of real-time analytics and prescriptive solutions, digital technology is driving faster realization of corporate goals.

As BI systems continue to evolve, the advances introduce levels of complexity. This has led to CIOs exploring the option of outsourcing BI operations.  The advent of cloud based BI systems has further supported this thought process. At the same time, considerations and regulations around confidentiality need attention.   

Formulating a BI Strategy

BI is all about tracking business performance. The scope of BI should be driven by business objectives and should include addressing strategic, tactical and operational reporting needs.

  • Define Business Requirements: This step is essential to ensure that the BI strategy delivers to business goals and enables better decision making. Work with business to identify the measures and the metrics that are routinely used to monitor performance and track progress,  prioritize requirements and create a roadmap.
  • Create a BI Architecture Blueprint: The components of a BI architecture typically include the sources of data, ETL tools that are required to extract select data from the sources and push them into a data warehouse, the data warehouse itself, presentation tools and finally the consumption services which could be the desktop or the web portals or mobile devices. An additional consideration is On-Premise Vs. Cloud.
  • Planning the implementation:  The approach to implement the solution should be to think big, start small and deliver value as you go along. Primarily it is essential to ready the business for this important step ahead. And equally important is to have stakeholder involvement.  Choosing the right tools, right implementation partner, overcoming poor data quality, setting up project governance team and the change management process.

Future of BI and Analytics

BI systems are growing in their ability to provide complex analysis by reporting on the data that exists within your systems as well as big data. It is able to provide information in visually attractive ways. It is moving from being informative to predictive to being prescriptive; and it is now delivering analytics in real time. The bar is constantly being raised. The marriage of big data to business intelligence and analytics will lead to a marked change in corporate culture and produce numerous next generation models to help businesses enhance their competitive advantage. How companies use the data that's available to them from internal and external sources shall be a key competitive differentiator.

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