Whether you are in Energy, Commodities, Agriculture, Mining, Shipping or a Market participant within a data-driven organization, you often find yourself overwhelmed by large volume of data that needs to be managed and analyzed. Further, the data is continuing to grow at an exponential rate; for example, the natural gas market alone can produce more than half a million data points on a daily basis.
Taking the time to manage vast amounts of data isn’t easy. It’s onerous, costly, and although it is not a core business focus, it is very time-consuming. Many organizations don’t have the resources required to undertake the task of managing high volumes of data. On the other hand, some organizations are expending valuable resources downloading market data from public and vendor source sites manually through custom ad hoc or segregated processes, normalizing this data, and distributing it to the right users to make informed decisions. All of these processes must occur before decision makers can even begin making sense of the data.
In order for data to be useful, market participants must be able to trust the quality of data they receive and quality is difficult to ascertain when data is collected and stored manually. System inefficiencies, interrupted flows of data, and human error can completely undermine the analysis process.
The Data Management Challenge
In order to get a handle on your business’ data needs it is important to establish what data you require from the outset. For example, if you need to create forward curves, you may need to access historical spot prices or exchange prices to complete the task. Ensuring that you’re working the correct data from the beginning will save you more time when it comes to making sense of the curve.
Another data challenge facing many businesses involved with data analysis is that they typically gather vast volumes of data from disparate sources in a myriad of formats; data may be published as a URL, PDF, CSV, or Excel file, for example. Some sources are made available to the public, while others must be purchased from private vendors. Different sources require different extraction and formation methods. The ability to collect data from any source in any format and granularity, validate this data, and integrate it with end users’ systems is the Holy Grail for business analysts, traders and risk managers alike.
Not the least of these data management concerns is that in many cases, the brightest minds in an organization may be spending hours collecting and normalizing data rather than analyzing it. Resources are typically very costly. Without centralized data, all data generated from varied systems becomes overwhelming and practically unusable in its raw form. Storing data long-term can be challenging; most normalized data ends up in data log files discarded on a regular basis.
Developing a database that ensures data is centralized and easy to access throughout the organization is essential for businesses with cost reduction and efficiency in mind. However, most organizations lack the necessary IT bandwidth or resources to perform such a task.
How can your company reduce the resources spent locating, collecting and normalizing data, and instead focus more energy on using that data to guide business critical decisions?
The Powerful, Integrated Solution
With the right data management solution, you can overcome the challenges associated with managing large amounts of data. At ZE PowerGroup, we have developed an award winning data management and analytics solution, ZEMA™. ZEMA is an enterprise data management and integration platform that offers advanced capabilities to meet specific data management requirements for analysts, traders and risk managers. ZEMA’s powerful automated data capturing application, Data Manager automatically captures, schedules, standardizes and stores tremendous volumes of data from multiple data providers across all industries worldwide, and pushes this data into one centralized database. ZEMA collects data from any source, in any format, and under any schedule.
ZEMA ensures the integrity of data is known by assuring data being collected is timely and accurate through automated data checking and validation procedures, enabling end-users to make rapid, consistent, and effective decisions.