Improve Your ETRM System with Advanced Analytics and Data Visualization | Opportunistic LLP

Whether your organization is already running like a well-oiled machine, whether it still has a few tough spots, or is considering a new energy exchange and risk management system (ETRM) or upgrade, establishing a strong capability for advanced analytics and data visualization will immediately increase the value you get from your systems and underlying data.

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From trading and risk control to finance and accounting, putting data to work for your organization empowers your team to understand the wealth of information deeply embedded in your organization’s systems and make better, faster decisions. in complete confidence. The advantage, in terms of value, is immense.

But building an effective analytics platform isn’t easy. To do this, the organization, from the leadership to the front lines responsible for data entry, will need to embrace the value that can be created to put the prerequisite steps in place and build a solid base of high quality data.

(Example of visual representation – Source: Opportune LLP)

Data analytics and visualization are buzzwords in today’s information age, but what are the real world applications in ETRM? Here are some areas of a downstream business that an advanced analysis initiative will have a significant impact:

  • Trade – Everyone knows that there is not a single screen or dashboard that can contain everything a trader wants to see. In today’s digital age, there is more data stream to process, not less. For a trader, useful analyzes must meet at least two main objectives: 1) ease of access and 2) responsiveness. Traders need to be able to quickly access new data feeds as they monitor the markets and determine how one data feed will (or not) impact another. And, when they identify the data they want to analyze, the responsiveness of the solution to deliver the analyzes is critical. Of electricity markets which move minute by minute to crude markets moving at a slightly slower pace, if a trader has to wait for a response then someone else in the market can seize the opportunity.
  • Risk control – Real-time visibility of positions in corporate portfolios gives risk offices the ability to make quick decisions to protect against rapidly changing market conditions. Accurate forecasting of long-term positions, coupled with up-to-date solvency, enables the risk office, finance teams and traders to make sound contractual decisions that meet compliance standards and maximize future profitability.
  • Planning – Planners want to go beyond summary reports and create dashboards to forecast inventory issues, integrating inventory with planned movements, lift trends, and price data to highlight points of potential pinching.
  • Accounting – For transactions with a high number of transactions (trolley sales), viewing the entire data flow from bill of lading (BOL) to cash application (school bus report – make sure all children go up and are all delivered to the correct bus stop) allows accounting to work proactively and based on exceptions.
  • Finance and management – The combination of operational and accounting data via corporate performance management tools allows management to understand which operational levers have a historical impact on results and, more importantly, in the future. This turns the process of budgeting, planning and forecasting into an ongoing process that adapts to changes in the business and the market.

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Sounds good, but the business needs to invest in three pillars – a strong database, accessibility of information, and self-service capabilities – to leverage the data produced by your organization. Let’s take a look at each of these cornerstones in more detail.

1) Data foundation

Begins with the secular acronym GI = GO (garbage in = garbage out). It almost sounds silly to say today, but there are still many organizations struggling with data quality in ETRM and ERP systems. There isn’t a silver bullet that fixes all data quality issues, but there is a common thread for organizations that lead data quality:leadership and responsibility. From trading to the mid-office to the back-office, it is up to the management of each group to stress the importance of data quality and to hold their group (s) accountable.

  • Strong master data management and governance: Master data management must be centralized to standardize data between the different systems used by the business, such as customer data, locations and products. This makes reporting across multiple systems easier and faster by avoiding the repetitive and tedious exercises of data mapping and translation to make data consistent across different data sources. This is reinforced by data governance to effectively maintain data consistency between groups.
  • Data capture: Simple, user-friendly tools should be used by the business to capture data with validation checks to ensure data is captured in the correct formats. This can be done through user engagement web platforms such as Microsoft Dynamics 365 Business Apps, Salesforce, Mendix, OutSystems, etc., which offer modern user experiences to enter or download data when and where a user wants ( for example, native mobility). These solutions can scale quickly with changing business needs through low-code development.
  • Data storage strategy: The democratization of analysis will quickly overwhelm most transactional query systems. A data storage strategy is therefore essential to deliver the necessary data without disrupting day-to-day transactions. This includes determining what data should be available, as well as when and how to replicate it, and how to streamline the process for new data requests.
  • Understand what “real time” means for the business: No one wants to wait for the data to be available, even if maybe not in the analysis system. Each business scenario will have different definitions of what “real-time” data means, and business and IT need to be able to articulate the value of the cost of on-time performance.

FOLLOWING: Software quality assurance and implementation project of your ETRM

2) Accessibility

Modern analysis tools like Tableau Software and Microsoft’s Power BI surface data integrated deep into disparate systems provides greater access to information for business users than ever before. There is power in information. Empower the organization not only to understand the health of the business and how individual business areas contribute to the business as a whole, but also to predict future performance and identify the levers that boost performance.

To do this, the company will need:

  • A corporate business intelligence tool – It is a single platform that serves as a hub for all reporting and analysis needs across the enterprise. Many companies have multiple reporting solutions managed by different groups. This inevitably leads to different and competing versions of the truth, which creates conflict and long reconciliation across conflicting perspectives. Avoid this by establishing a common, centralized analytics platform.
  • A minimalist approach to design – Dashboards and reports need to be clear, simple, and intuitive to reduce friction for users to understand what’s in front of them. Testing new relationships with users and listening to feedback will help identify sticking points when consuming information.
  • Mobility – Whichever analysis tool is selected, it should provide mobile functionality to enable users to access information from their mobile devices while on the go. It usually starts off as a ‘nice to have’ feature until users see the possibilities, and then becomes a ‘must have’.

3) Self-service analysis

An organization should develop an analytical approach that allows users to access reports, dashboards, and information in general, without having to rely on someone else in the organization to run a report or extract data. . It must also establish a path to quickly adapt the analyzes developed by users to the business in order to capitalize on value.

  • Improve the company’s skills – Leadership should equip the organization with access to training and learning materials and encourage a culture of continuous learning so that employees develop new business analysis skills, whether it is to obtain data (e.g. SQL) or analyze data with modern tools (Tableau software, Power BI, SAS) / languages ​​(R). Equipping business units with people who know how to work with data and extract insight from it will open the doors to unrealized value creation. Major analytics platforms often provide high quality training materials or self-service platforms, so access to learning materials shouldn’t be a barrier.
  • Citizen development – Leadership should also give the company the ability to create reports / dashboards with a framework that controls the quality of the reporting artifacts provided to the company, but allows for speed from idea to implementation . Balance is the key here. Too many approval thresholds can hamper the process of creating new analytics tools and discourage future ambitions. In addition, a lack of control often leads to less precision and more reconciliation efforts.

“From negotiation and risk control to finance and accounting, putting data to work for your organization enables your team to understand the wealth of information deeply embedded in your organization’s systems and to make better and better decisions. faster with confidence. “

Conclusion

Turning your organization’s business data into business insights takes work, but with the right leadership, governance, and support capabilities, you can provide your organization with the analysis and visualization to pull all the cylinders off.


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