Years ago, creating automated dashboards using business intelligence or corporate performance management technology was a chore. The technologies were not scalable, and it required a drove of consultants to automate dashboards. Consequently, many organizations continue to have a manual process for creating management dashboards. The process typically includes downloading information from the ERP system, manipulating the data, loading information into Excel, and cutting and pasting into PowerPoint. The process is inefficient, susceptible to errors, and offers minimal drill-down capabilities.
With today’s business intelligence (BI) technologies, creating automated dashboards is much easier. Tools such as Microsoft Power BI and Tableau have proven to be highly scalable and easy to integrate with any ERP system. With these cloud-based, plug-and-play BI tools and the right data, a dashboard can be stood up in a matter of days. The right data is key, so here are four steps you can take to ensure you collect the right data to build out automated dashboards.
Step 1: Confirm Requirements
This includes confirming the data calculations, data selection criteria, and how the information will be sliced (geography, business unit, timing, etc.) in the automated dashboard. Ninety percent of this already known and simply requires alignment across management, which can be done in a few executive workshops to make sure everyone is on the same page.
We worked with a major E&P client and held a one-hour session with management to get everyone aligned around how they wanted to organize their operating units across the company. Only a few rounds of confirmation emails remained and step one was complete.
Step 2: Validate Data
This simply entails reviewing where the source data exists and tying it to the expected results in the current management reports. A key part of this process is clearly understanding what selection criteria will be used to select data for automation later.
We worked with a manufacturing company to confirm how to extract sales and cost of goods sold data from their ERP system. Using Excel, we found that their system data did not tie to management reports. We discovered certain top-side sales accruals were booked in a separate corporate entity. Therefore, we worked with accounting to change how the accruals were booked to enable the sales details to tie to management reports.
Step 3: Build Wireframes
Wireframes are example dashboards that can be shown to management to confirm how they would like to visualize data. The wireframe development process uses data from the data validation step to confirm if executives prefer columnar formats, pie charts, graphs, color selection, etc.
One of our west coast clients used the wireframe process to hone in on what visual elements would be used for certain graphs to help with visualization of complex data. The wireframe process only took a few weeks and allowed the team to significantly reduce development time.
Step 4: Integrate & Automate
Step four brings all the previous steps together by automating the data calculations (step one) and building the data extracts (step two) into the production wireframes (step three). Since the data has already been validated and the dashboard design agreed upon, this is purely a technical automation step. One of our service company clients was able to complete this step quickly by having an SQL developer develop automation routines to capture data and populate wireframes in a test and production environment.
Providing management with the dashboards and reports they need can happen in a matter of weeks using a non-traditional means of developing executive dashboards. At Trenegy, we use this non-traditional approach to enable our clients to quickly deploy business intelligence applications across the organization. To learn more, email us at firstname.lastname@example.org.