As a leading data management company, ZE is committed to learning about new developments in the data management space. This month I was fortunate to attend the 2012 Enterprise Data World Conference (EDW) in Atlanta—the business world’s most comprehensive data and information management educational event. Over the course of the week I, along with three of my colleagues, attended sessions relating to emerging trends and technologies in the world of data management. The hot topics were definitely NoSQL and Big Data and how these technologies will power the emergence of the Data-driven Business. However, there were also excellent lectures on solving present day data issues surrounding data governance, data quality, data modeling and data integration.
Investigating data governance and data quality solutions was one of my key objectives because I’m involved in related projects at ZE. As a company offering a leading data management solution, ZE is continually trying to beat its own data quality benchmarks. It was no surprise that learning more about this topic was one of the primary purposes of many of the attendees, given the increase in focus on corporate governance following the financial crisis of recent years. It was interesting to note that data governance appears to be driving a lot of the major master data management projects that have surfaced during the last few years. One interesting technical space that looks like it is getting traction is the data score card. These score cards have the ability to measure not only data quality but also provide stakeholders with a BI tool that enables them to gain insight to the data and its use cases. In the future I can see this tool being utilized to measure the ROI on Data or “Return on Data,” a measure that would have the potential to drive future data projects and ultimately the Data-Driven Business.
I also came across an interesting technology that could improve enterprise-wide trade and risk decisions—particularly when aggregating data across asset classes. Semantic data models are designed to represent the real world as accurately as possible, and organize data in such a way that it can be interpreted meaningfully without human intervention. The key value proposition of this technology is removing the time consuming data mining tasks associated with large sets of data. Until recently semantic data models were primarily used as a data management tool; however, this technology is beginning to be leveraged by business intelligence applications giving end-users access to a vast amount of non-normalized data across an enterprise. Over the last couple of years corporations have leveraged semantic data models to tap into millions of customer interactions recorded in thousands of applications—the end result are real-time customer experience dashboards that executives can monitor around the clock. This integration model could be applied to trade positions across every trade desk at an entire enterprise. With the power of semantic data models, organizations could tap into the silos of information contained at each trade desk across an enterprise to gain real-time access to each desks trade data allowing for real time risk management
Overall, EDW was an exceptional event that provided excellent lectures and an exceptional educational experience, along with great networking opportunities. I would like to thank Tony and his team at Dataversity for their hospitality and the extra effort they put in to introduce the ZE team to some of the key influencers in this space. I look forward to meeting everyone again at future Dataversity events.