The Top 5 Challenges for Data Management
In order to make timely business decisions, companies spend a lot of resources creating viable decision support systems. In the same way that fuel quality impacts engine performance, the supply and quality of data directly influences a company’s decision-making engine and affects the outcome of business operations.
In a recent interview with Financial News, Michael Cole-Fontayn, Executive VP of BNY Mellon, called data a “significant asset” from which BNY Mellon generates insights and solutions to help their clients. But Cole-Fontayn also expressed that the biggest challenge concerns how data is captured, stored, distributed, and analyzed. So, in order to optimize the intelligence gleaned from data, here is a list of the top five challenges companies should address.
Data can come from many sources: internally within the organization or externally from data vendors. This creates multi-faceted datasets with each stream of data supplying its own set of standards, including different formats, acceptable ranges, and granularities (publication periods). Handling multi-faceted data with different standards in an efficient manner has become a major challenge in our fiercely competitive world. However, it gets even more complicated when the required data doesn’t get published when it is supposed to, creating a tragic time lag. For these reasons, data automation is essential as it allows data to be captured in a timely fashion with a minimum amount of resources.
Automation is a more reliable way of managing data when compared to manual process, since humans are inherently error-prone. Providing accurate yet standardized data is a complicated endeavor; but if done properly, data automation, can revolutionize the way companies do business.
According to an IDC study, more than 1.8 trillion gigabytes of data were created globally in 2011, and this figure will grow 50x by 2021. The more data is collected, the more monitoring and validation would be required. Without sophisticated, intelligent data management systems, this data environment will be a wasteland of disordered, fragmented information. But, equipped with the right tools, more volume means more intelligence. The right data management system is capable of harnessing huge quantities of data and distilling it into relevant, actionable information.
- Format Changes and Revisions
One of the main challenges with data vendors is inconsistency. As data vendors evolve and transform aspects of their business, they tend to change their data formats or stop publishing periodically due to maintenance and other reasons.
Many format changes are announced with very short notice while others are not announced at all. Additionally, vendors may revise the data from time to time if the initial value was not correct. But how does one capture revisions, and how far back should the data be checked? One immediate solution is to keep checking the source for any changes that may have occurred; but doing so repeatedly can get you blocked by the data vendor. Once an organization’s name ends up on the watch list, it can be difficult to get any data from that source again. To manage all of these variables and get accurate data consistently, an experienced data management platform is the only viable solution.
The false knowledge that could result from analyzing inaccurate or inconsistent data could be devastating for analysts, managers, compliance officers, and traders within an organization. By over-analyzing the errors, their decision-making engine would at best be useless and at worst be destructive, since it could take the organization in the wrong direction. Data is often late, nulls are often valid, and data volume keeps changing, all of which is very taxing on sub-par business intelligence systems. In rare cases, experts can catch data errors by detecting abnormal patterns. But the manpower needed to accomplish this at scale would be very costly and ultimately unfeasible. But systems that can create meaningful analyses from complex data can save organizations a great deal of time, money, stress, and uncertainty by allowing them to reap the benefits of clean data.
Organizations face the ultimate challenge when all of the gathered data from different sources must meet their internal system requirements. The final goal is to feed the collected data into trade and risk applications, business intelligence tools, billing and invoicing software, custom applications, or operational systems. A viable data management solution should provide a wide range of integration points, so that organizations can utilize the technologies that make the most sense to them.
ZEMA for All Your Data Challenges
The ZEMA solution is a well-recognized enterprise data management solution with a proven record in dealing with data challenges for some of the largest Fortune 500 companies. Having years of experience of dealing with data vendors and improving data hygiene, the ZEMA solution can impeccably collect, validate, administer, analyze, automate, and provide reports on your data.
 “Views from the Top: the Biggest Data Management Challenges”, Slide 18/28, Financial News, accessed February 10,2015. http://www.efinancialnews.com/gallery/sibos-2013-views-data-management/18?mod=gallery-thumb
 Lucas Mearian, “World’s data will grow by 50X in next decade, IDC study predicts”, Computer World, June 28, 2011, accessed February 9, 2015, http://www.computerworld.com/s/article/9217988/World_s_data_will_grow_by_50X_in_next_decade_IDC_study_predicts?taxonomyId=19&pageNumber=1https://blog.ze.com/the-zema-solution/the-top-5-challenges-for-data-management/https://blog.ze.com/wp-content/uploads/2015/05/ThinkstockPhotos-608524590.jpghttps://blog.ze.com/wp-content/uploads/2015/05/ThinkstockPhotos-608524590-300x300.jpgThe ZEMA SolutionAnalytics,Big Data,data,data management,decision making,integrationIn order to make timely business decisions, companies spend a lot of resources creating viable decision support systems. In the same way that fuel quality impacts engine performance, the supply and quality of data directly influences a company’s decision-making engine and affects the outcome of business operations. In a recent...Ryan ArianRyan Arianryan.firstname.lastname@example.orgAdministratorBlogs by data management Experts & Analysts | ZE