Enterprises of all sizes and sectors have invested significant time and funding into big data projects, but research has shown that many firms are disappointed with the results. Industry experts suggest that the problem lies in a lack of strategy and expertise. For these initiatives to be successful, firms need support from database experts as well as the most transparent analytics solutions that can integrate with existing business intelligence (BI) tools.
The Financial Times reported on a multitude of conflicting studies that have demonstrated big data successes and failures. Many of these initiatives show great promise, but the source noted that Accenture found only 22 percent of companies are satisfied with analytics programs and just 39 percent say their data is relevant to the business strategy. According to the source, Accenture attributed this to the fact that enterprises often measure too much data that doesn't matter as opposed to focusing on the information that does. In order to improve the outcome of big data projects, experts agree that enterprises need to invest in new capabilities and intensify the application of analytics. The Financial Times explained that, for instance, businesses can analyze information to answer questions such as how many customers have been lost in a given month. But to gain deeper insight into why they have left, and furthermore, predict when they might leave in the future, is something only big data can provide with proper tools and IT management.
A new approach
Big data can be especially useful because it incorporates external information, such as weather patterns or social media trends, with internal business data, like customer transactional histories. EWeek asserted that this capability has been offering enterprises an "odd reality": firms can find a new market, product or price level that was previously out of reach using intuition-based decision-making.
However, to achieve this, companies need to re-approach big data analysis. EWeek contributor Eric Lundquist revealed that asking a simple query over a large, real-time set of data is a more accurate and effective way to gain value from these projects than creating sophisticated algorithms for smaller samples.
This process can be applied to a number of indicators, including customer sentiment, weather forecasting, sports predictions or financial services. As long as firms take a new approach to business analytics, almost anything is possible.
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