Using big data to make better financial decisions
Research suggests that many organisations fail to align operational decisions gleaned from analytics with their financial implications. This ultimately affects the bottom line – upward of three per cent of the average company’s profits are compromised by poor operational decision making. Here are two ways to avoid this problem.
1. Align financial strategies with outcomes instead of stakeholders
It’s often difficult to make sound operational decisions when finance-related goals are broad and aligned with general business objectives. For example, how should a team member who is tasked with “increasing revenue” approach the challenge?
A promising alternative is to assign finance-focused employees to individual decisions types. That means giving them a specific aspect of the business to improve (e.g. pricing, inventory, renewals), instead of expecting them to make operational decisions across the whole business.
- How much should you discount prices?
- How much inventory should you hold?
- Which is the best type of marketing campaign to deploy?
Instead of traditional planning and reporting responsibilities (e.g. profit and loss), strategic employees are made accountable for improving specific decision outcomes. The "decision expert" model is reportedly 2.5 times more effective than the business generalist approach at driving financially sound decisions.
2. Leverage advanced analytics wherever there’s a profit to be made or loss to avoid
A data-driven approach can be used to make better decisions in almost every business area, from HR to marketing, supply chain management, sales, manufacturing operations, and risk and compliance.
For example, let's imagine a global pharmaceutical products supplier is struggling to establish a pricing strategy for new products. Instead of relying on gut instinct to set new prices, a three-phased analytical approach could be used to:
- Establish customer segments and personas by collecting customer data from disparate sources.
- Integrate and harmonise data to identify high-performing, profitable product categories.
- Leverage predictive modelling techniques to devise suitable marketing strategies catered to the needs of different customer segments.
Unleashing your most powerful dataset – your people
Measuring workforce productivity has always been a challenge, but the digital workplace, which is sure to expand in the wake of the pandemic (including remote work environments and cloud-based apps), makes establishing and measuring KPIs even more complicated.
Workforce analytics provides a solution to the dizzying complexities of tracking and improving employee productivity. It uses many of the same AI-driven, dashboard-ready techniques as seen in other business areas, but in this case, human behaviour, relationships, and traits are analysed to design outstanding transformation initiatives.
Microsoft’s Workplace Analytics package shows what’s possible. The platform pulls organisation-wide data from the full range of Microsoft collaboration systems (e.g., calendars, emails, office programs) and turns it into behavioural metrics that can be analysed to unearth patterns of success. Findings are then shared across the company through the applications to help people achieve more in their roles.
Learning from challenging times
Data analysis can help us learn from challenging times and put practices in place to help weather them in the future.
- By collecting and crunching regional data, you can discover which are the most important and sensitive locations for your business. This could help you benchmark regional data in future on various situational shifts.
- You can estimate the impact of cancelled in-person events and project associated losses in the future.
- You can deploy new analytics platforms to measure the productivity of your remote workforce. With more digital touch points than ever, you’ll be equipped to make better decisions about which HR processes can be made more efficient.
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