Decision-making depends on several factors, such as market knowledge, experience, professional qualifications, intuition and luck. And often, this process is also supported by the opinions of other people with more experience, in a “follow the leader” strategy.
This may actually work in some cases, but there is a limit to how successful this strategy of basing decisions on what the market is doing can be. Currently, companies already have at their fingertips other tools to make decision-making smarter and more assertive.
The amount of information available to be captured, stored and analyzed is increasing, and the result is that most companies already have enough data in their hands to perform advanced analyses and can guide them on the journey towards safer decisions.
Advanced analytics in decision making
How to optimize business decision-making? A survey conducted by McKinsey showed that 80% of the leaders interviewed stated that their companies are not excellent at decision-making, and 61% said that the process is not carried out effectively.
And this is where advanced data analytics can change this view. Using analytics in decision-making involves using data to improve predictions and generate insights that help businesses.
For example, when a consumer searches for a product on an e-commerce site and receives suggestions for other products that may be of interest to them. Using a simple example, this is one way in which advanced analytics plays a role in decision-making.
Going further, the advanced analysis It allows companies to better understand the factors that impact business, the risks involved, helping to create a business model that reduces these risks and, consequently, improving business decisions, which become more reliable and safe.
Through various techniques, such as data mining, which allows extracting useful information from raw data, and the use of structured and unstructured data, advanced analytics allows companies to better understand their customers, what they want and, with this information, make more strategic decisions.
Furthermore, through predictive analysis, algorithms build forecasting models aimed at assisting in decision-making. On the other hand, prescriptive analysis, based on current information and historical data, allows the visualization of paths that can be followed, suggesting possible outcomes. before a decision is made.
Decision making in the digital age
Today, decision-making is becoming increasingly complex. Although they are still classified as strategic, tactical or operational, the characteristics of each type of decision are changing and the lines between them are becoming increasingly blurred.
Strategic decisions
These are the ones that directly influence business and, in the digital age, they increasingly require real-time insights – mostly used in operational decisions – as there is a great need to capture contextual data and analyze it to guide decision-making.
Tactical decisions
They are connected to the implementation of strategic decisions. In other words, in order to make tactical decisions, leaders are also dealing with a larger number of more complex data that need to be analyzed to generate decision alternatives.
Operational decisions
These are those related to the company's day-to-day operations. Although they have a shorter life cycle, with digitalization, the strategy's success factor is to make the exchange of information between applications easier and to automate decision-making, eliminating silos.
With business complexity increasing, with more and more data available and even fiercer competition, improving the decision-making process is essential for the company's success. And technology needs to be used as an ally. Discover our Data Strategy platform and have access to the information needed to help your company make decisions.