Digitalization and digital transformation are inseparable from the use of data for decision-making. But how can you use data to make decisions?
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In total, there are now 4 strategies for using data for decision making:
No-Data
Data-driven
Data-informed
Data-inspired
And the most common request and thesis in Russia: “We are Data-driven.” But what is hidden behind this, and why are the world's IT giants moving away from this strategy as a single one for all situations and areas of activity?
No-Data
No-data - management and decision-making without taking into account data and analytics. This is the most common approach in small and medium businesses. It is used by a dominant number of managers and organizations.
Data-driven
Data-driven - management and decision-making exclusively quantitative based on data and metrics
First they get numbers/metrics, and then make decisions based on them. The resulting numbers are the first thing they look at when deciding where to move next. Great for justifying decisions to senior management/shareholders.
Data-informed
Data-informed - management and decision-making based on data.
Metrics provide additional information that can be useful in decision making. However, the final decision is made taking into account past experience, expertise, intuition, etc.
Data-inspired
Data-inspired is an approach in which analyzing the market and trends, searching for non-obvious connections in disparate data serves to make strategic decisions and search for new opportunities. The key thing is that we do not rely on experience and analysis of events, but on a vision of the future and research.
Allows you to build a strategy, but has the greatest risks
Let's look at what weak points does Data-driven have?
Lack of creativity
Data-Driven gives no room for creativity and different points of view
Narrow Focus
Data can only answer qualitatively those questions that were initially raised at the design stage
Elimination of Expertise
Data-driven approach does not take into account expert knowledge and experience
Bias
It is almost impossible to create an absolutely objective set of metrics; in any case, the manager’s view will be reflected in the set of metrics
The difficulty of digitizing people's reactions
Data-driven does not take the human factor into account: emotions and feelings, subjective opinion
Demanding on data quality
Data-driven is demanding on the quality of source data and the development of regulatory and reference information
At the same time, it is working with data and data quality that is one of the key reasons for most problems in IT projects
What problems exist in working with data, and what problems do they lead to?
Now, let's answer a few questions:
Is it possible for our companies to create solutions with ideal data?
Is it possible to create objective metrics without overloading managers?
How realistic is all this in conditions of constant change and uncertainty?
What matters most in times of uncertainty?
To do this, let's turn to the Kinevin model, which allows us to determine the order of actions depending on the complexity of the situation
As a result, Data Driven is poorly suited for decision making under conditions of uncertainty and low quality of source data.
And trying to create an ideal system of metrics can lead to an infinite cost of growth for each solution, overload of managers with data and critical errors
At the same time, Data-driven is great for making small daily decisions or decisions in stable conditions, in simple systems
Data-informed helps you navigate situations of uncertainty and combine expert qualities with data analysis and plan for the near future.
Disadvantages of the approach:
not suitable for making strategic decisions and searching for new opportunities, as it is based on experience and analysis of events and facts
It is more difficult to justify your decisions to management and stakeholders because they are not entirely based on quantitative data and largely depend on the point of view and picture of the world, experience and expertise
there is a risk of being influenced by cognitive distortions
the problem of multiple choice - there is too much input data, they will have to be prioritized, look for correlation, and the output information may be contradictory.
Data-inspired helps you build strategies and work for the long-term future, and find new solutions.
Disadvantages of the approach:
not suitable for operational and tactical control
decisions based on this approach are the most risky
forms abstract ideas and assumptions
it is even more difficult to justify your decisions to management and stakeholders
depends entirely on the qualifications of the person analyzing the data
Summary
There is no “silver bullet” – there is no single approach for all situations:
Data-driven is an excellent tool for solving everyday/operational problems or in stable conditions, i.e. for making 80% of all decisions. It is an operational management and planning/control tool
Data-informed is necessary when creating new products, working with people and planning the near future. It is a tactical management and planning tool
Data-inspired helps create strategies and find new solutions, working for the long-term future. It is a strategic management and planning tool.
An example of what tasks each strategy can be used for is in the table below.
Data-driven | Data-informed | Data-inspired |
A/B tests Evaluating the performance and stability of new features | Development roadmap Prioritize the development of new features | Strategy Search for new opportunities |
People still remain the key element of any approach. He can only spend his experience and expertise not on routine, but on intellectual work
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