Using data science to enhance decision-making enables businesses to seize the opportunities and avoid the pitfalls of ever-evolving markets and the increasingly complex global business ecosystem. It is possible to optimize business performance in this way rapidly and sustainably, with improvements evident within weeks of starting a data science transformation program. Progress is both rapid and cumulative, with a flywheel effect quickly transferring gains from one function or division to another. The faster a company learns, the more competitive it becomes. Companies starting out on this journey do best when they think big – planning to effect a company-wide vision for data science transformation – but act small and fast, focusing on operations that can benefit first and quickest from forward-thinking data management.
What is data science transformation and why does it matter?
Applying data science to a company’s operations offers leaders the opportunity to transform their business. To achieve this, first they need to bring into their day-to-day operational planning data that details all aspects of the business; data that is clean, reliable, easy to access, and up-to-date. With a comprehensive data infrastructure as a foundation, companies can use this with impact, to improve the health of their business and ensure it is set for long-term, sustainable success.
That said, navigating and thriving in today’s business ecosystem is ever more challenging. Leaders are required to consider and evaluate more data sources and bigger data sets than ever before as they seek to make the right decisions for their operations. There are just too many data inputs for humans – individually or in teams – to ingest and interpret fast enough to have impact on decision-making, as they have historically.
Human intelligence prefers simpler, single-factor explanations of the world but struggles to cope when four, five, or more factors interact. Yet if you can’t measure market dynamics properly, you can’t steer or optimize performance. This is why – at a time of keen competition and global volatility, uncertainty, complexity, and ambiguity – the ability for companies to detect and act on genuine signals has never been more highly prized.
One of the most effective and efficient ways for businesses to make better decisions, faster, and more often is to transform their data science operations. This means putting a good base of usable data in place along with a team that knows how to use it – and then actually using it. Data science expertise and the application of algorithms and tools that accurately model ever more complex markets enable businesses to become increasingly reactive and evidence-based in their response to changing dynamics. By widening the data lens, by increasing the number and scope of factors under active consideration, and by managing data better at scale, leaders can outflank their competitors. Applying data science to business decision-making enables more and more decisions to be made better than chance. When hundreds and then thousands of decisions are made better than chance – through faster, better-informed feedback loops – leaders can identify more reliable signals, at speed, while at the same time ignoring both noise and erroneous signals. Cumulatively, these marginal gains aren’t just additive; they’re multiplicative. The faster a business learns about the shifting dynamics of its market, the more competitive it becomes by an order of magnitude.
Data science transformation through 2 case studies
The two primary functions of a retail bank are to boost sales of loans while at the same time managing risk, ensuring that loans do not become bad debt on which over-exposed customers default. This is particularly important in a period of global shockwaves and unpredictability, from COVID to Russia’s war in Ukraine. Ekimetrics enabled a challenger bank, taken private, to move from retrospective risk assessment a month after the end of each quarter to monitoring exposure in near real-time. By bringing in more external data, by structuring and analyzing the data that matters to the business as-live, we helped the leadership make better sales decisions and grow at pace.
The biggest single marketing investment for consumer goods businesses is in retail promotions, with promotional spend often dwarfing advertising investment. Promotions incentivize retailers to give more shelf-space to brands while at the same time attracting consumers to buy more products, more often. Traditionally, our client created a calendar of promotions at the start of each year based on the previous year’s activity and sales performance. This approach suffered because it was frozen in the past, based on a mix of experience and intuition, and unable to account for competitor activity and rapid changes in consumer behavior. We helped a multi-brand consumer goods manufacturer to adopt a more dynamic way of working with its data. We enabled the company to transform its data science capabilities to gather better-quality market signals quicker, empowering brand teams to make better, more responsive decisions, to significantly enhance the return on investment of their promotional spend, and to create a better-informed new product development strategy.
The intelligent way to find, choose and optimize your private equity investments
Private equity firms are facing a market correction broadly in line with the macroeconomic climate today. Of the three levers that private equity funds rely on to create value within their portfolio companies – multiple expansion, deleveraging, and operational improvement – it is only operational improvement that remains a viable option today. After more than a decade of rising equity multiples, valuations have dropped abruptly since the beginning of the year. Interest rates are also rising around the world, making the cost of debt in private equity deals much more expensive.
The one sector that has remained consistent and so increasingly attractive to private equity investors is tech-enabled business, companies that have data science capabilities baked into their philosophy from first concept.
Private equity firms continue to operate in an increasingly competitive environment, as supply for capital from investors has slowed in the face of rising demand for fundraising. After a long period of growing demand for private equity, investors are becoming pickier and invest only with private equity funds with the highest returns. The opportunities for growth afforded by data science transformation are yet to be realised in most mid-sized, €100m-€1bn turnover companies that private equity firms often invest in. And this is why this topic is of such relevance to this sector.
At Ekimetrics, we help companies get more from their data and we implement pre-packaged AI solutions, so they can combine high impact with long-term business purpose. Our 400 data experts and industry specialists work together in integrated squads. We value specific industry and domain expertise and focus on useful, usable and used solutions.