The COVID-19 pandemic is rampaging throughout the world with unseen consequences. Countless researchers across the globe are using their expertise in their scientific fields to counter this virus. Recently, there has been a breakthrough by a team of researchers at the Massachusetts Institute of Technology (MIT), who have developed an AI model that distinguishes asymptomatic infected people from healthy individuals through forced-cough recordings. They’ve created a model that correctly identified 98.5% of the coughs of people who were confirmed positive. However, the model’s real strength, and purpose, is distinguishing asymptomatic coughs from healthy coughs. This casts a new ray of hope on the search for faster test methods.
Besides the fact that this is a breakthrough finding, and a potentially life-saving invention, it also highlights the importance of data velocity. By simply coughing into a microphone, an asymptomatic person can discover whether he/she is infected within several seconds and take the necessary measures accordingly. In essence, this application – analysing data at a rapid pace in order to make decisive decisions based on the results delivered - highlights an issue companies and their decision-makers often struggle with. Already many times, slower reaction, misalignment of priorities between cross-functional teams, disagreement among managers, and administrative burden have hindered decision-makers to take advantage of opportunities faster than their competitors.
Most people equate data velocity with the speed at which high volumes of data are being generated and flowing into organisations. However, data velocity also encompasses the required processing and subsequent decision-making pace associated with the data. Many types of data have a limited shelf-life and quickly lose their value due to the constantly changing market environment. Working with high-velocity data involves capturing data from millions of customers or electronic sensors and transforming and storing the results for real-time analysis on dashboards and making decisions while events are happening. Therefore, in order to derive in-time insights, companies need to act on data quickly and process them at an even faster rate than ever before. The current COVID pandemic reminds us of the importance of a quick analysis and outcome delivery. In our opinion, this aspect of data velocity is often underestimated, and it has a much larger impact on a company than one might suspect.
Today, companies and executives often emphasize real-time reporting. This emphasis is justified, but they usually forget that real-time reporting and real-time data require speedy execution and effective decision-making to be valuable. When, for example, a company wants to apply sentiment analysis to social media data to gauge the response of customers to a new product, they need to act on the gathered information immediately. The quick processing and analysis of the data should consequently result in swift decision-making and execution. Therefore, you need more than just the obvious technological aspect – you need an integrated view on data and decision-making and a data-driven company culture. These are the competitive assets of any company in these digital times and act as leverage to extract value from the data.
It is our view that the CFO should act as a major catalyst in helping organisations to become more effective and agile in their decision-making. After all, the job description of CFOs has changed dramatically in recent years, with an increasing emphasis on strategic decision-making and strategy execution. Furthermore, the CFO’s position within the firm provides him/her with access to internal and external critical data flows, ranging from financial information to sales data, marketing, supply chain, and general business performance data.
All this makes the CFO the right person to help organisations to transform towards a more agile and effective decision-making entity. That is to say, in their role, CFOs need to think through how they will connect the various existing data streams, align the view of the different stakeholders and report to leadership teams how to move forward and exploit the different opportunities. Of course, the CFO should then maximally embrace the changes in the technological landscape (and how these might help him/her in smarter decision-making) and support the building of a data-driven, agile and safe decision-making culture. Such a data-driven culture in turn will then also allow companies to learn faster. Learning fast in decision-making and investment choices is actually about creating long-term success – by learning from the continuous knowledge gathered at every decision point, CFOs will help organisations to adapt more swiftly to their future choices as a result. True digital CFOs thus help companies to fully exploit the abundance of data and instigate a data-driven culture that encourages firms to optimize their processes and act faster than their shadow. Velocity as such is not only a measure of how fast the data is coming in, it also involves the speed at which data is processed and leads to improved decision-making.