Data and Decision-Making

Frequently Asked Questions

How does data-driven decision-making create value for PE portfolio companies?

Data-driven decision-making replaces intuition and experience-based management with evidence-based analysis, improving both the speed and quality of decisions. It creates PE value by reducing costly errors, identifying opportunities faster, and enabling proactive management. Infosys improved demand forecast accuracy from 65% to 82% on a 30-day horizon, improving bench utilization by 1.2 percentage points — representing approximately $200M in additional billable revenue. Paychex grew revenue 25% from $4.0B to $5.0B partly by using analytics to increase client engagement and platform stickiness. The compound effect is significant: better data leads to better decisions, which produce better outcomes, which generate more data — a virtuous cycle that builds competitive advantage over time. PE firms should evaluate data maturity as part of operational due diligence.

What is measurement and analytics and how should companies implement it?

Measurement and analytics involves establishing KPIs, building data infrastructure to track them, and creating reporting systems that drive action. Implementation should follow three phases: first, define the metrics that matter most (typically 5-10 KPIs covering financial performance, operational efficiency, and customer outcomes). Second, build the data infrastructure — clean data sources, unified dashboards, and automated reporting. Cushman & Wakefield's analytics platform reduced data gathering time by 40-50% for advisors while standardizing the quality of analysis. Third, embed analytics into operating rhythms — weekly reviews, monthly business reviews, and quarterly strategic planning. Leidos used predictive analytics to enable proactive risk management, freeing project manager time by 15-20% from manual reporting. The biggest implementation mistake is building dashboards without changing decision processes — data must connect to action.

How do companies use forecasting and planning to improve performance?

Forecasting and planning translate historical data and market signals into forward-looking projections that optimize resource allocation. Robert Half's AI-driven demand sensing processed 10M+ candidate-job matches annually, improving placement speed by 50% (from 4.2 to 2.1 days) and placement success rate from 68% to 79%. ManpowerGroup improved branch-level demand forecast accuracy from 58% to 76%, increasing talent pool readiness from 42% to 63% and decreasing time-to-fill by 28%. Infosys achieved 82% demand forecast accuracy on 30-day horizons (up from 65%), reducing time to staff projects from 18 to 11 days and reskilling 40,000 employees annually based on predicted demand. Effective forecasting enables right-sizing of capacity before demand materializes, which reduces both the cost of overcapacity and the revenue loss from undercapacity.

What analytics capabilities should PE firms build in portfolio companies?

PE firms should build analytics capabilities in four areas, sequenced by maturity. First, descriptive analytics: standardized dashboards showing current performance against targets. Second, diagnostic analytics: the ability to identify why performance deviates from plan. Paychex's HR analytics platform gives clients business insights that increase engagement and retention. Third, predictive analytics: forecasting demand, churn risk, and market trends. Leidos uses predictive analytics for proactive risk management in defense programs. Fourth, prescriptive analytics: automated recommendations for optimal actions. Robert Half's AI-driven matching automatically identifies the best candidate-job pairings from millions of possibilities. Most portfolio companies need to start at level one or two. The investment is modest — typically $1-3M for a data platform — but the return compounds because each analytics layer builds on the previous one.

How do companies measure the ROI of analytics investments?

Analytics ROI should be measured through specific business outcomes, not technology metrics. Track decisions improved, not dashboards built. Infosys improved bench utilization by 1.2 percentage points through better forecasting — with approximately 300,000 employees, this represented approximately $200M in additional billable revenue, dwarfing the analytics platform investment. ManpowerGroup's demand forecasting improvements contributed to Talent Solutions advisory growing to $800M+ in revenue. Cushman & Wakefield's analytics platform became a key competitive differentiator in client pitches, directly influencing win rates. The most effective ROI measurement links analytics investments to specific operational improvements: forecast accuracy improvement (Infosys: 65% to 82%), time savings (Cushman & Wakefield: 40-50% reduction in data gathering), and revenue impact (Robert Half: 50% faster placement). PE firms should require analytics teams to define expected business outcomes before funding projects.