Accurate price determination for commodities means that data must be gathered, processed and analyzed. What, then, are current best practices for data management?
“Organizations are beginning to recognize their current solutions are no longer meeting current needs,” said Michal Peliwo, Vice President of Business Solutions at ZE PowerGroup. He was the third and final speaker at a webinar on August 24, 2016, sponsored by the Global Association of Risk Professionals, titled “The Price is Right? Strategies for Market Discovery & Optimum Pricing Challenges.”
Peliwo described the fragmented world of data management as it exists at most companies. “MS Excel is still the tool of choice,” he said, “even though it has a Jekyll-and-Hyde personality.” There are “data boutiques” for weather, currency, outages, and so on—but there is no one-stop all-inclusive data place, even though collection should be done in holistic fashion.
Markets are becoming more complex—and more granular. “Data is becoming a commodity in itself,” he noted. In such a highly competitive environment, with everybody looking at the same sample, the question becomes: “who can turn data into information?”
Some organizations have a hodgepodge of subscription services, with overlap and redundancy. Peliwo cited one example of a billion-dollar company that continues to use Excel as the technology to close out the end-of-day trades. “It introduces extensive risk,” he said.
Manual data management leaves an organization vulnerable to several common varieties of risk: intellectual property (IP) risk; security risk; audit risk; user risk; and performance risk. For example, there is a danger of the knowledge residing with only one or two select individuals (IP risk). There is a danger of data getting overwritten or corrupted (security risk). A company might not know why, when, and by whom changes were made (audit risk). There is a danger of input errors due to “fat fingers” or when cut and paste is incorrectly done (user risk). There is a danger the source data formats might change (performance risk). All in all, a company should try to minimize the manual component of its data management.
To reduce risk and increase efficiency across the organization, he said companies need to integrate the four pillars: (1) data management and acquisition in a centralized data warehouse; (2) unified data access and analytics; (3) transformation, automation, and workflow management triggered by data events; and (4) integration with an eye to downstream distribution.
Peliwo emphasized the business challenge of all the different data formats, granularities and locations. Ideally, the organization collects all input data into a “single version of truth” for all models, calculations, and data processes, into a single integrated data flow. That is the ideal. ª
The figure is in this posting is used with permission from Michal Peliwo’s presentation.
ZE PowerGroup Inc. is the developer of the “ZEMA Suite”, an Enterprise Data Management system intended to automate the end-to-end data flows within an organization.
Click here to read about 17 Tips for Workflow Automation, a posting from Cloudwards.
Click here to read about the first speaker’s presentation.