Database administration key to achieving big data and BI goals
As enterprises are under increasing pressure to make improved, data-driven decisions, both big data governance and business intelligence (BI) analytics will be critical factors in making improved, information-driven decisions.
Computer Weekly reported that Ted Friedman, Gartner analyst, recommended that organizations approach big data initiatives with centralized management.
"Do not make your big data implementations siloed. Make them part of the overall strategy for BI," he advised. "Link to stuff you are already doing. Don't make big data a standalone thing."
Friedman explained that he is concerned by the way in which many businesses have tacked on big data solutions, keeping them separate from other analytic solutions. Very often, firms might deploy big-data technologies for a particular marketing campaign without using data integration tools relating to existing information. While the increasing variety of data sources and types can be a challenge, Friedman asserted that combining these diverse sets can provide enhanced analytic value. For example, the ability to make sense and derive meaning from unstructured data, such as social media, and relate it back to structured transactional data, provides enterprises with considerable power.
Information Management reported that while big data initiatives have been implemented for the last 25 years, experts estimate that up to 65 percent of these projects are unsuccessful. Steve Dine, contributor for the news source, attributes this failure to poor data quality and inadequate business involvement. According to Dine, the consequences of these failures are costly: Aside from wasted project investments, there is a potential for revenue loss and increased operating costs.
Integrated architecture enhances analytic value
In order to make big data and BI projects successful, enterprises need to start by designing an overall strategy for data storage, management and integration to ensure reporting and analytics are accessible and accurate. Dine explained that this can be a challenge because enterprises are accumulating a growing number of applications and volume of data, and it can be difficult to relate records due to siloed storage. Fulfilling these objectives is only possible after integrating data from disparate sources and stores, which reduces labor demands from data collection and results in more efficient analysis.
By empowering businesses to manage data from a central storage system, the information is processed and analyzed more effectively. Dine asserted that leaving the application of a BI solution to IT often results in a poor data integration architecture, which renders it unmanageable. Further, without remote database support, IT departments may spend 80 percent of their time on data maintenance, which prevents them from focusing on innovation and new developments.
By giving BI and big data analytics context through data integration, information gains new value and can be better aligned to achieve specific goals.
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