As companies come under increasing pressure to measure long-term value creation, the main challenge is how to draw meaning from their data.
T here was a time when an annual report demonstrated a company’s value to its shareholders via a strong order book, a healthy balance sheet and a history of attractive dividends. But the landscape has changed in recent years, and companies now face the need to express corporate value on a broader level.
There is an ongoing shift from shareholder capitalism (where the overriding priority is to protect value for shareholders) to stakeholder capitalism, where there is a focus on demonstrating to a much wider audience – including customers, employees, investors and regulators – that the company is creating long-term value.
Whether it is caused by the millennial generation forcing a change in values toward a more responsible and socially conscious approach to business, or a consequence of high-profile corporate collapses, the focus has changed. It is no longer just about maximizing profits; transparency, sustainability and inclusion are increasingly part of the overall measure of success.
Failure to factor in these elements when assessing long-term value could impact on investment, recruitment, reputation and, ultimately, productivity and profit. We live in an increasingly engaged climate where stakeholders demand more information across a broader spectrum. And in a digital world, they want this data to be up-to-the minute and readily available.
Levels of data
According to the Embankment Project for Inclusive Capitalism (EPIC), key performance indicators (KPIs) to measure long-term value will increasingly be based around four key areas: talent; innovation and consumer trends; society and the environment; and governance. Stakeholders want clarity on how these KPIs can inform strategic planning, risk management, executive compensation, sustainable operations, business growth and long-term corporate value.
However, one of the challenges facing companies attempting to report on long-term value is the huge amount of data available and how to draw meaning from it. To comprehend the scale of this challenge, consider that data in our global digital universe, is doubling in size every two years1.
In this context, artificial intelligence (AI) could prove to be the game-changer, with the ability to make sense of this data and identify meaningful indicators. Through AI, the analysis of colossal amounts of information could be both instant and bespoke.
Data-mining can help to analyze large volumes of data and classify information. For instance, it can be used to analyze a large volume of patents and classify them into “qualitative clusters”: incremental, adjacent, disruptive and so on. Predictive analytics could also help to analyze market and consumer behavior and provide trends and forecasts.
The fact that companies should be able to use this technology to measure aspects of their activities such as financial value, production, staff performance, cost management and sustainability in real time represents enormous progress. In the past, it was hugely costly and time-consuming to measure value. Data provided to analysts was of mixed quality and there was often no structure or framework to the process. In future, companies should be able to use real-time KPIs, resulting in real-time outputs.
AI as enabler
But AI is not the “silver bullet”; it is purely an enabler to support the generation and analysis of new metrics.
Moreover, companies are still at a very early stage of working out their approach to metrics for long-term value. They must strike a workable balance between tactical and strategic KPIs; operational and financial KPIs; and KPIs that effectively capture the moment while anticipating the future.
As it stands, there is a question as to how well equipped companies are to employ new metrics for corporate governance, consumer trust, innovation, talent and the environment, among other things. Many organizations don’t yet have the appropriate “plumbing” in place, the data sourcing and aggregation capabilities, or the reporting systems required.