The market is currently awash with BI tools that advertise lofty claims regarding their ability to leverage data in order to ensure ROI. It is evident, however, that these systems are not created equally and the implementation of one could adversely affect an organization.
While consistent multifold increases of the digital universe is ushering in lower costs for data storage, a decline reported to be as much as 15-20 percent in the last few years alone, it is also the catalyst for the the rising cost of data management. It seems that the cause for concern regarding data storage does not lie in the storage technologies themselves, but in the increasing complexity of managing data. The demand for people with adequate skills within the realm of data management is not being sufficiently met, resulting in the need for organizations to train personnel from within. The efforts required to equip organizations with the skills and knowledge to properly wield these new data management tools demand a considerable portion of a firm’s time and money.
The increased capacity of a new data management system could be hindered by the existing environment if the process of integration is not handled with the proper care and supervision. With the introduction of a different system into a company’s current technological environment as well as external data pools( i.e. digital, social, mobile, devices, etc.), the issue of synergy between the old and new remains. CIO identifies this as a common oversight and advises organizations to remain cognizant of how data is going to be integrated from different sources and distributed across different platforms, as well as closely observe how any new data management systems operate with existing applications and other BI reporting tools to maximize insight extracted from the data.
Evan Levy, VP of Data Management Programs at SAS, shares his thoughts on the ideal components of an efficient data management strategy as well as the critical role of integration within this process, asserting that:
"If you look at the single biggest obstacle in data integration, it’s dealing with all of the complexity of merging data from different systems… The only reasonable solution is the use of advanced algorithms that are specially designed to support the processing and matching of specific subject area details. That’s the secret sauce of MDM (Master Data Management).”
The massive and seemingly unwieldy volume is one major concern amidst this rapid expansion of data, the other source of worry being that most of it is largely unstructured. Many data management tools offer to relieve companies of this issue by scrubbing the data clean and meticulously categorizing it. The tedious and expensive process of normalizing, structuring, and categorizing data does admittedly carry some informational benefit and can make reporting on the mass of data much more manageable. However, in the end, a lengthy, well-organized report does not guarantee usable business insight. According to research conducted by Gartner, 64% of business and technology decision-makers have difficulty getting answers simply from their dashboard metrics. Many data management systems operate mostly as a visual reporting tool, lacking the knowledge discovery capabilities imperative to producing actionable intelligence for the organizations that they serve.
The expenses that many of these data management processes pose for companies and the difficulties associated with integrating them with existing applications may prove to be fruitless if they are not able to provide real business solutions. Hence, data collection should not be done indiscriminately and the management of it conducted with little forethought. Before deciding on a Business Intelligence system, it is necessary to begin with a strategic business question to frame the data management process in order to ensure the successful acquisition and application of big data, both structured and unstructured.
Joe Sticca, Chief Operating Officer of True Interaction, contributed to this post.