Forty years ago, very few businesses developed “well-thought-out” strategy plans, let alone using Business Intelligence (BI) for informed decision making.1 Decisions were taken based on the experience of decision maker(s) and intuition – gut feeling. Today, even smallest of the companies use some form of BI in strategic planning. According to a Gartner report, by 2017, most business users and analysts in organizations will have access to business intelligence and analytics.2 However, according to another Gartner report, 30 percent of organizations who have invested in big data, only eight percent have taken it into production.3 Some of the reasons include:
- Inability of organizations to demonstrate value
- Failure to evolve into existing enterprise information management (EIM) process
- Miscarriage in cultural and business model changes adaptations
The quantum and variety of data being generated is growing exponentially every day. For example, the telescope installed at New Mexico, under the project Sloan Digital Sky Survey, collected more than 140 terabytes of information in a decade. Its successor, the Large Synoptic Survey Telescope scheduled to come online in 2016, in Chile, will gather that much of data every week. However, only 0.5 percent of total data generated is being analyzed, as mentioned in an IDC report.4 About 96 percent of the zettabytes of data produced is locked behind firewalls of corporates.5
To wade through such an ocean of data, organizations need a platform that can process huge amounts of data on a real time basis and provide meaningful insights simultaneously. They need tools that will leverage data and analytics for decision making at speed and scale. This will help determine new products and services to be developed and launched in the market at a pace that is faster than competition. Some of the characteristics of such a platform include:
- Agility: The tool should be able to process all kinds of data – market research, customer, social, location, short shelf-life, etc., making real-time analysis more meaningful.
- Have multiple layers: This will allow multiple analysis of data and project trends. Helps present valuable insights in an easy to understand format for the intended audience.
- Business user-friendly: The tool should have an intuitive interface that allows a business or non-technical user to explore data without much training or expertise in any analytics tool.
- Have a strong foundation for enterprise data security: It is important to define data within an enterprise, and help them to identify and locate sensitive structured and unstructured data. This prevents anomalies.
What do you think? Share your views with me.