Business intelligence is one of the hottest topics among industry leaders. Private equity firms are no exception. However, through our exposure to hundreds of companies, precious few have shown up having successfully rationalized their data assets into a functioning toolset capable of accelerating growth.
Even the smallest companies produce a significant amount of data that represents untapped value. So why don’t more companies exploit the resource?
The Pitfalls of Planning Your Power BI Dashboards Backwards
Many companies get tripped up by the reality that converting data into a business asset is a project that spans both technical and business resources. It generally starts as a business-driven initiative, such as:
- How do we improve our order-to-cash efficiency?
- How can we get our A/R agents to take more ownership of DSO goals?
- How can we create stronger accountability among sales managers and their reps to take advantage of cross-sell and up-sell opportunities?
Meeting business objectives then requires identifying the leading KPIs (key performance indicators) to drive accountability toward results. Then comes mapping those KPIs to the personas who own the responsibility and can have the most impact on them.
The result is a set of business requirements for reports and dashboards that will improve visibility and business outcomes.
The technical requirements (i.e., which data sources to draw from, what data model will best support reporting, what infrastructure will meet performance and security thresholds, etc.) are all a function of the business requirements.
However, most companies put the cart before the horse.
They make the mistake of relegating their BI initiative to the IT department. The ensuing technical work can quickly get out of step with the business objectives. Critical best-practices don’t rise to the top, including:
- How to design reports that “touch a nerve”
- Training and supporting end-users to ensure report adoption
- Mitigating report proliferation
- Maintaining report quality and reliability
The result is that those few companies who make a concerted effort to mobilize their data report anemic results.
Real Private Equity Business Intelligence Examples
In our most recent article, What is Business Intelligence for Private Equity, we outlined the basics for how PE firms and their portfolio companies can use BI tools to mobilize their data for better performance and higher valuations.
As you consider where you can apply data to move the needle, here are several real-world examples of how mid-market companies have successfully done just that.
The following Power BI examples demonstrate how several companies have been able to harness their business information. Each of the companies represented in these case studies are mid-market, following a buy-and-build model.
By centering on business outcomes first (and technical requirements second), they turned their data into a high-value asset that drives performance.
Power BI Example #1: Healthcare Company With Multiple Regional Clinics
Several issues hindered growth and profitability for this organization:
- Revenue consistently fell short of expectations across the organization, with no clear insight into root causes or areas of needed improvement.
- Information was decentralized and mostly distributed via spreadsheets, resulting in inefficient manual effort, high risk of error, inconsistent action plans by management, and low adoption.
- Employees only marginally understood their Revenue Cycle Management Model, causing operational and procedural errors, along with unknown pathways to success.
- Poor insight into the impact of rate- and code-changes was making negotiations with payers and forecasting difficult.
In response, the following reports were developed in Power BI:
Visual Claims Report
- Clear indication (via KPI) of greatest variance by RCM step.
- Heat-mapped flowchart illustrating which steps in the revenue cycle cause the most draw-down.
- From contracted rates to actual payments, pertinent info is grouped by procedure code, payer, and facility.
Expected vs. Actual Report
- Clear indication (via KPI) of greatest variance between Expected vs. Actual revenue by claim step, facility, and payer.
- List of transactions filtered by variance and sorted by magnitude to produce highest-value lists of revenue opportunities.
Rate Management Report
- Dynamic, multi-variable, what-if analysis to forecast the impact of proposed rate changes.
With easy ranking of transactions and clear visual indicators of issues impacting revenue, the company was able to focus their attention on the highest-return efforts and reduce the gap between expected vs. actual:
- Revenue variance and shortfalls improved when leadership and management gained the ability to clearly identify sources of variance at the process step, facility, and payer level.
- Operating efficiency increased and errors decreased after management and leadership began developing operating guidelines and procedures based on highest-impact variables.
- Standard Operating Procedure compliance increased through employee use of the heat-mapped, revenue-cycle flowchart because it provided a clear visual depiction of the Revenue Cycle Management Model.
- Profitability increased through code optimization and payor negotiations, as enabled by the Rate Management report. With the included what-if analysis, management was able to calculate the cost/benefit equation of proposed payer terms.
Power BI Example #2: Manufacturing company
Although a very different industry, this company shared similar frustrations around poor visibility:
- Executive Leadership and Plant Managers had poor visibility into whether orders were going out on time across the company and from individual plants.
- Divisions and locations had developed disparate data “camps” and varying calculations for the same performance metrics, making it difficult to aggregate and compare performance, and creating inefficiencies due to operational silos.
- Plant managers needed better visibility into their backlogs throughout the month in order to maintain consistent production by coordinating the job queue.
We started by addressing the low-hanging fruit. Initially, delivery performance, an issue to which their bottom line was particularly sensitive.
On Time Delivery Report
- Calendar view with clear indication of OTD (on-time delivery) performance to goal by day and month-to-date.
- Trend lines to identify production patterns and trends by location.
- Increased competition across plants via the "Highest Ever Rank by Date" visual.
- Ability to filter by year, month, business unit, and plant.
Production Efficiency Report
- Calendar view with clear indication of production performance.
- Bar charts to clearly identify top and bottom performing areas of the operation by production efficiency.
Production Efficiency Report
- Visual indicator of machine utilization as a function of operating hours, standard machine hours, actual quantity produced, and standard production quantity, with variance metrics for each.
- KPI and ranking of employee productivity in terms of time and machine utilization.
For the first time, management was able to see a normalized view of on-time delivery and employee/machine productivity across plants and up to the current day.
- Employee productivity and machine utilization improved as employees were able to see in real-time how they were performing against goals, and in comparison to their peers.
- Management efficacy improved as managers were able to coach employees on addressing performance gaps based on empirical feedback, rather than reacting to the latest issue or micromanaging to overcome a lack of accurate insight.
- Management efficiency increased due to automatically generated and distributed dashboards that delivered clear visibility into consistent and timely performance metrics.
- Shipping staff improved order delivery times as a result of increased, measured accountability to goals, and a clearer sense of direction from internal benchmarks across locations.
- Profitability increased by identifying under-performing locations through a comparative analysis of divisions, enabled by the normalization of measures across systems.
Power BI Example #3: Manufacturing Company
Another manufacturer we worked with leveraged business intelligence to address issues related to margins and P&L trends.
- Management struggled to control material costs and maintain margins.
- They lacked granular insight into margins at the parts level, and were unsure where changes needed to be made.
- Executives lacked clear insight into P&L trends, and had little ability to perform comparative analysis versus prior periods.
To solve these issues, reports were created to provide clear margin analysis, with drill-down to the individual part level, and to accurately highlight trends across the P&L.
Material Margin Analysis Report
- Comprehensive visibility into material margins and gross margins.
- Clear indication of relationship between material costs and margins.
- Ability to drill down to customer and part levels using report slicers.
Profit and Loss Report
- Multiple levels of analysis, including monthly, MTD, and YTD with ability to compare current year to previous year for each.
- Phase-switching for eight key metrics, including: TTM per case, TTM per each, MTD actuals, and MTD % of net sales.
- Management improved margins by optimizing COGs at the part level.
- Material margins and gross margins improved through better cost control.
- Overall profitability increased with leadership’s ability to perform trend and comparative analysis across the P&L.
Power BI For Private Equity Takeaway
Business Intelligence is now more accessible than ever as a way for private equity firms and their portfolio companies to get a read on how to truly move the needle. As illustrated by the above examples, when companies rationalize their data, they develop a functioning toolset capable of transforming the organization and accelerating growth. By centering BI initiatives around business outcomes first, organizations can overcome the common pitfalls experienced by others, and turn their data into a high-value asset.