Enhancing Your ETRM System With Advanced Analytics & Data Visualization

Opportune LLP
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Whether your organization is already running like a well-oiled machine, still working out a few rough spots, or considering a new energy trading and risk management (ETRM) system or upgrading, establishing a robust advanced analytics and data visualization capability will immediately boost the value you’re getting 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 allows your team to make sense of the wealth of information embedded deep in your organization’s systems and to make better, faster decisions with confidence. The upside, value-wise, is immense.

But building an effective analytics platform isn’t easy. To do this, the organization—from leadership to the front lines responsible for data entry—will need to buy into the value that can be created to put in legwork upfront and establish a solid foundation of high-quality data.

(Sample visual representation – Source: Opportune LLP)

Analytics and data visualization are buzzwords in today’s information age, but what are some real-world applications in the ETRM arena? Here are a few areas of a downstream business that an advanced analytics initiative will have a high impact:

  • Trading – Everyone knows there’s no single screen or dashboard that could contain everything a trader wants to see. In today’s digital age, there are more data streams to process, not less. For a trader useful analytics must meet at least two main goals: 1) ease of access, and 2) responsiveness. Traders need to quickly be able to access new streams of data as they monitor markets and determine how one data stream will (or won’t) impact another. And, when they identify the data they want to analyze, the responsiveness of the solution to provide the analytics is critical. From power markets that move by the minute to crude markets that move at a slightly slower pace, if a trader must wait for an answer then someone else in the market may seize the opportunity.
  • Risk Control – Real-time visibility into positions across portfolios of the business gives risk desks the ability to make quick decisions to hedge against rapidly changing market conditions. An accurate forecast of long-term positions, in tandem with up-to-date creditworthiness, allows the risk desk, finance teams, and traders to make sound contract decisions that meet compliance standards and maximizes future profitability.
  • Scheduling – Schedulers want to go beyond run-down reports and create dashboards to predict inventory issues—integrating inventory with planned movements, lifting trends, and price data to highlight potential pinch points.
  • Accounting – For high transaction count deals (truck rack sales), visualizing the entire data flow from bill of lading (BOL) through cash application (school bus report—make sure all the kids get on and are all delivered to the right bus stop) allows accounting to work proactively and exception-based.
  • Finance & Management – Combining operational and accounting data through enterprise performance management tools allows management to understand which operational levers impact the bottom-line historically, and more importantly, into the future. This transforms the budgeting, planning, and forecasting process to be a continuous process that adapts to changes in the business and marketplace.

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That sounds great, but the business must invest in three cornerstones—a solid data foundation, information accessibility, and self-service capabilities—to reap the value of the data your organization produces. Let’s look at each of these cornerstones in more detail.

                           

1) Data Foundation

Starts with the age-old acronym GI=GO (garbage in equals 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 one silver bullet that solves every data quality problem, but there’s one common thread for organizations that lead in data quality—leadership and accountability. From trading through mid-office to back-office, it’s incumbent on the leadership of each group to stress the importance of data quality and hold their group(s) accountable.

  • Strong Master Data Management & Governance: Master data management needs to be centralized to standardize data across the different systems the business uses, such as customer data, locations, and products. This makes reporting across multiple systems easier and faster by avoiding repeated tedious data mapping and translation exercises to make data consistent across different data sources. This is reinforced by data governance to efficiently maintain data consistency across groups.
  • Data Capture: Simple and user-friendly tools should be used by the business to capture data with validation controls to ensure data is captured in the appropriate formats. This can be done through web-based user-engagement platforms such as Microsoft Dynamics 365 Business Apps, Salesforce, Mendix, OutSystems, etc., which provide modern user experiences for inputting or uploading data when and where a user wishes (e.g., native mobility). These solutions can evolve quickly with changing business requirements through low-code development.
  • Data Storage Strategy: Democratizing analytics will quickly overwhelm most transactional systems with queries, so a data storage strategy is key to providing the data needed without grinding daily transactions to a halt. This includes determining what data needs to be available, as well as when and how to replicate it, and how to streamline the process for new data requests.
  • Understanding What “Real-Time” Means To The Business: No one wants to wait for data that’s available, albeit maybe not in the system for analytics. Every business scenario will have different definitions of what “real-time” data means, and both the business and IT need to be able to articulate the value of the cost of the timeliness.

MORE: Software Quality Assurance & Your ETRM Implementation Project

2) Accessibility

Modern analytics tools like Tableau Software and Microsoft’s Power BI surface data embedded deep in disparate systems provide broader access to information for business users than ever before. There’s power in information. There’s power in equipping the organization with the ability to not only understand the health of the business, and how individual business areas contribute to the business as a whole, but also predict future performance and identify the levers that drive performance.

To do this, the business will need:

  • An Enterprise Business Intelligence Tool – This is a single platform that serves as a hub for all reporting and analytics needs across the entire business. Many businesses have multiple reporting solutions that are maintained by different groups. This inevitably leads to different and competing versions of the truth, which creates conflict and time-consuming reconciliation across conflicting perspectives. Avoid this by establishing a common, centralized analytics platform.
  • A Minimalist Approach to Design – Dashboards and reports should be clean, simple, and intuitive, reducing friction for users to understand what’s in front of them. Testing new reports with users and listening to the feedback will identify points of friction when consuming information.
  • Mobility – Whatever analytics tool is selected, it should provide mobile capabilities to allow users to access information from their mobile devices on the go. It generally starts as a “nice to have” feature until users see the possibilities, then it becomes a “must-have.”

3) Analytics Self Service

An organization needs to develop an analytics 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 dig up data. It also must establish a path to scale user-developed analytics to the enterprise quickly to capitalize on value.

  • Upskill the Business – Leadership must equip the organization with access to training and learning materials and incentivize a culture of continuous learning so that employees will pursue new business analysis skill sets, whether it be how to get 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 doors to unrealized value creation. Leading analytics platforms often provide high-quality self-service training materials or platforms, so access to learning material shouldn’t be an obstacle.
  • Citizen Development – Leadership must also give the business the ability to create reports/dashboards with a framework that controls the quality of reporting artifacts delivered to the enterprise but allows for speed from idea to implementation. Balance is key here. Too many approval thresholds can impede the process of creating new analytics tools and deter future ambitions. Additionally, a lack of control often leads to lower accuracy and more reconciliation efforts.

“From trading and risk control to finance and accounting, putting data to work for your organization allows your team to make sense of the wealth of information embedded deep in your organization’s systems and to make better, faster decisions with confidence.”

Conclusion

Turning your organization’s business data into business information takes work, but with the right leadership, governance, and supporting capabilities you can provide your organization the analytics and visualization to fire on all cylinders.

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