Scarcely a day passes by where you don’t see a headline about “Big Data” and how analysis of this big data is going to lead to huge efficiencies, targeted marketing and large profits. Big-data analytics are delivering an economic impact in the organization, but too often senior leaders’ hopes for benefits are divorced from the realities of frontline application. That leaves them ill prepared for the challenges that inevitably arise and quickly breed skepticism. The reality of where and how data analytics can improve performance varies dramatically by company and industry.
Customer-facing activities. In some industries, such as telecommunications, this is where the greatest opportunities lie. Here, companies benefit most when they focus on analytics models that optimize pricing of services across consumer life cycles, maximize marketing spending by predicting areas where product promotions will be most effective, and identify tactics for customer retention.
Internal applications. In other industries, such as transportation services, models will focus on process efficiencies—optimizing routes, for example, or scheduling crews given variations in worker availability and demand.
Hybrid applications. Other industries need a balance of both. Retailers, for example, can harness data to influence next-product-to-buy decisions and to optimize location choices for new stores or to map product flows through supply chains. Insurers, similarly, want to predict features that will help them extend product lines and assess emerging areas of portfolio risk. Establishing priorities wisely and with a realistic sense of the associated challenges lies at the heart of a successful data-analytics strategy. Companies need to operate along two horizons: capturing quick wins to build momentum while keeping sight of longer-term, ground-breaking applications. Hedge funds have been among the first to exploit a flood of newly accessible government data, correlating that information with stock-price movements to spot short-term investment opportunities. Data analytics firms will deliver useful tools and services based on statistical methods, even without deep industry specific knowledge, that offer some value to their customers. But no one has a patent on the most commonly used statistical methods which are decades old or even hundreds of years old. Therefore, these services will quickly become a commodity and prices will race to the bottom. After all, there is really no barrier to entry. Buy a statistical package or two, hire a few programmers in Bangalore, a couple of ex-cheerleaders for sales and you are in business. Any existing software services company should be doing this, if they are not already doing this.
Sources: http://seekingalpha.com/article/441171-beware-the-hype-over-big-data-analytics http://blogs.hbr.org/2012/10/big-data-hype-and-reality/ http://www.mckinsey.com/insights/business_technology/views_from_the_front_lines_of_the_data_analytics_revolution