Question

Define and describe the concept of Data Explosion Discuss Thomas Davenport’s assertion that analytics are source of sustainable competitive advantage. Provide examples from your research or experience of organization’s that have realized competitive advantage through analytics

Answer 1

The data explosion is the rapid increase in the amount of published information or data and the effects of this abundance. As the amount of available data grows, the problem of managing the information becomes more difficult, which can lead to information overload.

Customer expectations have reached an all-time high and industry competition is ever increasing -- putting businesses under constant pressure to increase efficiency and improve results. At the same time, the amount and type of available data have grown exponentially -- companies can now collate this information from across their organization and across broader industry sources. Access to this growing pool of information creates a significant competitive advantage and provides a unique opportunity to conduct in-depth market research -- offering rich insight into recent sales trends, critical business improvements and gaps in the market to exploit.

As a result, analytics has become one of the most important tools at an organization’s disposal. When data and analytics work in tandem, the benefits become obvious. Companies can leverage data to improve cost savings, redefine processes, drive market strategy, establish competitive differentiators and, perhaps most importantly, build an exceptional and truly personalized customer experience.

Today, most corporate leaders understand the value of data to their organization. A recent study (registration required) by IDG found that 47% of chief information officers (CIOs) predict their spending will increase most in the areas of analytics and business intelligence over the next 12 months. Yet, while analytics can provide real-time insights into multidisciplinary business functions and improve overall decision making to achieve better results, many organizations struggle to leverage data in a truly meaningful way.

Example:

Dell was founded on a configure-to-order (CTO) business model, where customers chose the various components of their PC and the custom PC was then assembled and shipped. Using CTO, Dell grew to become one of the top PC brands. But this business model began to tire about 2007 and Dell decided to expand to new channels including retail, and value-added networks in the emerging nations. This channel expansion required Dell to specify a product line of “off-the-shelf,” pre-configured PCs, which they could have designed experientially. But instead, Dell turned to prescriptive big data analytics. By accessing their billions of records of customer-purchase history and using advanced analytics that evaluated millions of combinations of possible product configurations, Dell analysts were able to define a product line of pre-configured PCs that (allowing for some trade-ups) captured almost three quarters of Dell’s notebook and desktop sales.Today, these configurations make up about half of Dell’s sales.

This is an example (there are many others) of big data and analytics informing a major business decision. It was a strategically important, one-time decision that was made by using proprietary data, where the combination of data and advanced analytics contributed to a great decision. The competitive value of the data and analytics appears in the resulting product line, which is likely sustainable.