What is the right decision? It's harder for leaders to find answers today than it used to be. Markets are volatile. Developments are uncertain. Companies operate in an increasingly complex environment. Companies that rely on data-based decisions have an advantage. They offer security where logic and experience reach their limits.
Definition: What Are Data-Based Decisions?
Modern data-driven decisions combine human intelligence with the power of data analytics systems and artificial intelligence to anticipate future developments and achieve more advantageous outcomes.
The terms decision intelligence and data-driven decision making have become established for the process of IT-supported, data-based decision-making.
Data-Based Decisions - What Are the Benefits?
There is a lot to be said for data-based decisions. With technological progress, the applications for this are now available to companies of all sizes. Costs are no longer a reason to ignore developments.
Transparency of Decisions
Instead of strong rhetoric, facts decide. Those who make data-based decisions shorten discussions and internal power games. At the same time, employees can better understand why they are working towards which goals. In longer projects, analyses help to maintain motivation and serve as a corrective to avoid undesirable developments.
Replicability of Results
The quality of decisions depends less on individual managers. When employees change, the quality of decisions remains constant because they are made on the basis of data and not according to personal preferences.
Agility and Foresight of Decisions
When data is analyzed using the right tools, it is possible to make valid forecasts and anticipate developments earlier than a decision based purely on experience and human reasoning would allow.
Speed and Determination
Data-based decisions give managers the confidence to break new ground and take even perceived risks. It is also easier to win over the management for innovative strategies if data speak for a high probability of success.
Cost Reduction and Sales Increase
The introduction of data-based decisions alone will not reduce the costs of a company. However, tools and data can be used to identify potential savings. At the same time, customer feedback and sales data can be used to develop targeted new product ideas and product extensions for which there is certain demand, so that high sales can be expected.
A New Approach: 5 Steps to Data-Based Decisions
Traditionally, decisions are made in a vertical chain of causality: strategic decisions influence tactical decisions, tactical decisions influence operational decisions - the process of decision-making ran in isolation in each department. Decisions lasted until strategy specified new requirements. In the highly volatile business world of the digital age, however, more flexible structures have a better chance of success.
The best decisions should take into account the context in which they are made, should be made collaboratively, and should be continuously adapted to changing conditions and levels of knowledge.
Data-based decisions help to meet these criteria. They are not per se better than decisions based on gut feeling. Data can confirm or refute intuition. However, companies only benefit from the data advantage when they systematize their decision-making processes.
It is recommended to establish a decision cycle consisting of observing, testing and learning. In each phase, humans and computers work together. Digital systems collect data, prepare it according to instructions and provide simulations. Findings from previous phases flow into the following phases.
- Observe: Companies collect information about the process in which a decision is to be made. This includes both internal data and market data.
- Analyze: Users evaluate the data and prepare it in such a way that a clear direction of action can be derived.
- Modeling: Employees design alternative courses of action. They consider causalities and dependencies that can lead to different developments.
- Ranking: Responsible persons obtain the perspectives of different stakeholders for the options for action in order to decide on an option despite imponderables.
- Execute: Departments implement the decision made or, this can also be a decision, do not act. This is followed by the observation phase, in which managers monitor the effects of the decision in order to take action again if necessary.
Examples of Data-Based Decisions?
The decision cycle can be applied in any department and for decisions of any scope. Two examples from corporate practice.
How much should we discount summer fashion to empty our warehouse? With the help of intelligent pricing software, companies can make decisions based on clear data. The application monitors the market, alerts employees when competitors adjust their prices. Now companies can make their own data-driven decisions about how to respond. If desired, the application also adjusts prices automatically according to previously defined rules. Companies set up the rules according to their experience and continuously adjust them.
Opening up New Markets
Expanding into new markets can carry a high financial risk. A data-based decision can minimize this risk. When companies have discovered lucrative opportunities through market observation, they should enrich their impression with figures, data and facts through discussions with potential customers and business partners as well as digital analyses. Based on the findings, they can design a business case and simulate its development again with the help of suitable applications. How does the profit change when competitors lower their prices or penetrate the targeted niche? Based on this data, various stakeholders are consulted before the final decision is made and the company expands into the new market.
Data-Based Decisions Need Reliable Data Quality
Data-based decisions do not replace human thinking. Which analyses are carried out and how the results are to be assessed must still be judged by humans. The quality of data-based decisions depends significantly on the quality of the data basis used.
In many companies, the digital infrastructure is not yet sufficiently developed to exploit the knowledge available in the data. Data is stored in departments and is not consolidated across the board. Analyses then lack a holistic perspective. In other companies, data is not systematically maintained and checked for errors. Strategic data management is therefore the first step if companies want to take the next step in their digital transformation and establish data-based decision-making processes.
Frequently Asked Questions Around Data-Based Decisions
In most companies today, a lot of data is collected and a huge treasure trove of data is available. However, the connection to the higher-level corporate strategy is often missing. Important data is then missing because it is not relevant for the collecting department. In other cases, data is collected but there are no processes in place to pass it on to decision makers. An often underestimated problem: Data is used for decision-making, but not sufficiently and regularly cleaned, so that the data quality decreases over time.
Artificial intelligence (AI)-based applications are better able to oversee large amounts of data than humans can. Their advantage over traditional software is that they learn from their experience with growing data sets which influencing factors are relevant for optimal results. AI applications can also make forecasts for future developments much more accurately because they do not simply extrapolate past data, but take into account market changes that are not yet visible to humans and can derive consequences from their database.
In the English-speaking world in particular, decision intelligence refers to the integration of digital systems into corporate decision-making. Managers are often no longer able to oversee the causal effects of a decision in view of the complex contexts in which they operate. For this, decision models are designed, which are then executed by digital systems. They visualize the effects of decisions and thus support executives in acting in the interest of the company's success.