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decision makers, decision, management, magazine, books, articles, research, business

Issue No:09/06/1

Business Analytics

Not so long ago, companies found a competitive edge by employing information technology to harness operational efficiency. The sheer cut down on transaction process times fashioned a breeze of success, and ‘paperless' was the buzzword. Bus boys stopped moving around the office, and settled in a cubicle. The first-movers had a perfect edge over the late-movers. Everyone loved the affect on their bottom-line, as they joined a movement to go paperless.

However, with almost every business utilizing their investments in information systems, the pro-actives were looking for other growth avenues. They were looking at their years of transaction data. They had moved from a company of two to too many, and recorded progress on their systems. Their growth, their development: they realized had a story to tell. They realized a potential for knowledge discovery. It was not going to emerge from an impressive Martian, but something that they possessed that could turn to Bullion.

It's a story of companies transforming their transactional data into a wealth of information, and birth and life of a phenomenon called ‘business analytics' AKA ‘business intelligence'. The underlying objective: Discover new and meaningful patterns in data.

The Challenge

Unfortunately, businesses were not experts in data analysis and statistics. They were expert in their particular domains, and were even visionaries per say to have identified the need for business intelligence. However, they relied on data analysts to extract information from data. Business users imparted domain knowledge to the analysts, and then waited for them to derive information. The analysis involved several iterations. Time was required to perform these iterations, but businesses needed to collect, analyze and directly act on results. They required instant decision-making.

Due to this evident gap in relevant analytics, and business needs; analytics meant long cycle times. There was a need for business area expertise that could help them reduce decision delivery times. The necessity heralded a move towards deeper collaboration between business users and analysts.

The Trend

Today, emerging trends in business analytics are more than just challenges and failures. Failures and shortcomings are looked upon as areas for improvement as the industry continues booming. Businesses have learnt constructive lessons over years. The phenomenon has outgrown into an industry. There are indications of immense growth for an industry that is dominated by a business need for knowledge discovery.

Analytic firms have shifted their focus from serving everyone, to serving a particular industry. The idea is very simple: Learn about one specific industry and optimize the analytics offering. As the industry tries to incorporate task-specific knowledge in their analytics offering, the focus is on industry verticals. It helps industry gain domain knowledge that only businesses in a particular industry possessed. It also allows sharing of best practices and integrating cross-industry experience. Domain knowledge has helped realizing the potential of analytics not as an afterthought reaction, but as a preemptive action. The industry has plunged into optimization of inter and intra-business functions.

To allow business users analyze data and quickly gain insight, the industry has moved from reliance on data mining experts to business users. Industry gurus describe it as descriptive analytics, whereas some: predictive analytics. Regardless, of your selection of terminology, the actuality is, business users require models that closely resemble market reality. The need is to adopt comprehensible models that are relatively easy to understand. Employment of visualization to represent evidences on critical attributes has become a need for business users, and industry has reacted to address it.

‘You cannot analyze what you do not collect'. Collection of rich data has become critical. It has resulted in better data collection efforts, and emphasis is rendered to generation and storage of data. The industry has realized that they need to design data analysis into their systems. There is a great potential in overlaying multiple sources and employing a sophisticated IT infrastructure to make timely analysis feasible.

The Future

Business analytics will take information from operational and tactical decision-making input to a level where strategic decision-making feeds on analytics. Value add through analytics would not just be a discovery; it will be a function with results measured and acted upon. The industry will employ multiple data sources to identify information that are not inherent in a single data source. Information systems covering an entire supply chain will replace the need for individual information systems, and inter-business analytics replacing the value-addition rendered from intra-business analytics. Approach towards analytics will move from reactive to proactive and then real-time.

Integration efforts will focus on return on investment with increasing need for analytic results as a starting point towards the critical next steps of action and measurement.

With the tip of iceberg recognized, it's the beginning of new era in business. It appears quantitative not just qualitative inputs will make an organization successful in the market place. Use of relevant data will tell stories of success and failures, and help identify arenas for future growth.

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