Leveraging Data to Aid the Finance Community
energycioinsights

Leveraging Data to Aid the Finance Community

Leah Ramirez, Director of EG Analytics and Decision Support, Hewlett Packard Enterprise

Leah Ramirez, Director of EG Analytics and Decision Support, Hewlett Packard Enterprise

30 percent of actual data needed for forecasts is not available in time”, “lack of integrated systems drives lots of manual effort”, and “40 percent plus reporting time spent on data gathering to build ‘cuts’ not available in systems”, were just a few of the problems that plagued our beleaguered finance function in 2014. The proliferation of offline financial reporting tools due to lack of integrated systems led to an increased work burden for the various finance teams. The solution, stated very simply, was to create a single source of the truth and develop a common reporting mechanism that was recognized, trusted and accessible to the finance community. However, what tools could we use that deliver consistent, timely and quality intelligence to a matrix, highly complex business? What could provide nimbleness, security, accessibility, and be user friendly? Moreover, many have wonderful visualizations, but lack the ability and flexibility to map the data movement of millions of transactions daily. Our business was a mature one, and had its complexities. Order management systems provide some, but not all the data to deliver the information needed to track performance from the pipeline to the channel.

"We shifted the value proposition of the organization from data mining to analytics, and provided timely intelligence for optimization and risk management"

First, our team developed an approach to the solution: define the taxonomy, align the organization, and build the process and tools to deliver revenue and margin information daily without bias. Next, we tested, in one geographic region, the mapping of revenue by the many dimensions of our value chain (route-to-market, product category, geography, with supply chain alignment) using SQL a server, transactional data from an enterprise data warehouse and mapping tables built on the requirements gathered from finance stakeholders in these business units, financial planning and analysis (FP&A), and regional finance teams. In addition, we built this proxy logic into the mapping tables, and use excel to develop daily and reports. As we scaled this solution worldwide, it quickly became apparent that the server could not store all the data and our front end tool, excel, would not be enough to provide quality, timely reporting. We moved to a hardoop/vertica solution with Tableau as a front end, and this tool, although great at visualizations, could not adequately model the data–although great at if the data is already shaped. We needed a tool that could allow us to massage the data and model it. Eventually, what we have found is Azure and Power Business Intelligence (PBI) has provide us with the ability to house the huge transactional data set (history and actuals), model the data, and build dashboard visualizations and extracts to serve our finance community. We have the capability to provide detailed extracts of sales order data for 8 quarters to the finance community and provide timely daily dashboard for executive consumption. This tool has become the life’s blood of the organization and has provided valuable transparency into our performance intra-quarter and intra-month. 

In summary, the solution to fit our needs took some trial and error, but understanding the system functionality, containing initial scope requirements, and having a tool that is nimble, robust and lay/non-technical user-friendly helps an organization solve its problems. We did not have the luxury of waiting years for a new ERP tool to deploy or a new Enterprise Architecture to emerge–we were able to use these tools on top of our existing systems, structure it, and provide insight to our business. In the end, it shifted the value proposition of the organization from data mining to analytics, and provided timely intelligence for optimization and risk management.

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