As societies and corporations around the world wrestle with disruption from the economic downturn, data and analytics appear more vital than ever to delivering enterprise value.
But on which fronts do organisations stand to gain the most from forward-looking insights achieved through a fusion of big data and digital analytics?
In recent years, more and more business owners have begun to recognise that machine-led insights are essential for achieving maximum ROI(Return on Investment) throughout the value chain.
The challenge is keeping up with the sheer volume and variety of data streaming in from across the organisation. After all, how do you map and model processes you barely have sight of?
The answer is: let machines do it for you.
Process mining is an emerging field that combines multiple data science techniques to extract insights from event logs created by any enterprise controlling system (e.g., customer care, CRM, ERP). Analytical techniques powered by machine-learning then go beyond basic process modelling by:
Process mining can be useful in any organisation that has a complex process map. For instance, in manufacturing:
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.
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:
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.
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