When it comes to predicting the future, companies have their own crystal balls in the shape of “what-if analysis”, which allows them to predict the outcomes of specific events and concepts, without the need to execute them first. In its core “what-if analysis” shows, by combining both real-time and historical data, how changing one or more variables will affect the outcome of a certain scenario. It can help you to be ahead of your competitors by letting you distribute your resources better, reducing cost overruns, forecasting bias, and other inaccurate estimates and risks. For instance, it empowers you to run complicated data analyses across your projects and see the outcomes on costs and labor, based on numerous variables.
By transforming their corporate data into valuable insights to drive their decision-making processes, companies can evolve in such a way that truly seems like magic. Yet, a survey by Gartner, showed that 91% of the participating companies have not yet reached a “transformational” level of maturity to embrace their data and use them as a compass to navigate their future strategy and not just to argue decisions based on their intuition.
Nevertheless, as any other “magic” we need to be aware of its dark side. Although data is objective and, in many cases, reliable, it can bring as much insights to the users as they can draw from it. As a result, we must not follow it blindly but identify, which data and metrics will bring us value. In order to achieve, that we can employ several criteria such as applicability, showing if the data set, can be applied in all practice cases; relevance referring to the level of consistency between the data content and the area of interest of the user; integrity ensuring that the data has not go through any accidental modifications from its original source.
Besides, for data-driven strategy to be effective, it must flow through the whole organization and its members need to have a high enough data literacy to interpret it correctly. This will ensure that they do not use data inaccurately to support and validate decisions, made purely on instinct. High data literacy does not mean that we should expect all our employees to be specialized in the same way as data scientists, as by utilizing data storytelling we can communicate the information behind the data in understandable terms to the main decision makers.
For companies to unleash the true magic of their data insights, they must transform into data-driven businesses, where data is at the very heart of the organization. Thus, they need to recognize the great responsibility, related to the impartial evaluation of results and their impartiality in the name of predictability, assessability and business growth.
Related Articles: The Magic of Data Insights Part I
Author: Stoyan Terziev