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Explore how data-driven management boosts smart decisions. Learn the strategies to build a data-driven culture in your organisation.
More than half of business leaders rely on their intuition to decide whether something is going right or wrong.
The geniuses of our time are often credited with enormous intuitive skill. For example, Steve Jobs is quoted as saying, “Have the courage to follow your heart and your intuition; in a way, they already know what you want to become.”
Intuition can be a useful tool, but it would be a mistake to base all decisions on a mere hunch. Intuition can only be verified, understood, and quantified through data.
Below you will find information on the benefits of becoming more data-driven and a series of steps you can take to become more analytical in your innovation and transformation initiatives. Data-driven decision-making will lead you to greater effectiveness.
Data-driven decision-making is an approach in which decisions are collected, analysed, and interpreted based on data from a variety of sources to make sound and informed business decisions.
Data-driven management is a strategic concept according to which decisions are made based on the analysis and interpretation of data. Data-driven management is more than just collecting data. With the help of this strategy, you can obtain consistent data to personalise your products and services as well as the entire customer experience journey. Facts, metrics, and data are used to make strategic business decisions that align with your goals and initiatives.
In the business world, data-driven decision-making can be observed in a variety of ways. For example, a company can:
How exactly data can be included in the decision-making process depends, for example, on your business objectives and the type and quality of data you have access to.
When companies realise the full value of their data, making data-driven decision-making the norm, everyone from business analysts to sales managers to HR specialists can use data to make better decisions.
However, this is not achieved simply by selecting the right analytics technology. Rather, you need to create a culture in your organisation that encourages critical thinking and curiosity. Employees at all levels need to develop, practice, and apply their data skills. This requires that data be available within the framework of security and governance, and that training and development opportunities are provided to give employees the skills to handle and manage data.
Building these core competencies will help to promote data-driven decision-making at all levels of work.
To get the best out of the available data volumes, it is particularly important to know the specialist terminology and to master the various methodological areas of application.
Data-driven decision-making is the use of continuously optimised data, analytics, and software from various sources to make strategic business decisions.
For this method to be successful, the sources of information must be made available to all members of the company.
Data-driven design is the use of data analysis in the design and development of products and services.
In general, it is about designing data about the company’s goals and digital environment (apps or websites), users, and the measurability of the strategy in such a way that successful services and user experiences (UX) are created.
The use of data analysis in the design process enables the creation of customised, efficient, and sustainable products and services.
Data-driven companies are always making data-driven decisions to improve their agility and efficiency. A profound digital transformation enables these companies to use the necessary technologies to optimise the use of data from various information sources.
A characteristic of these data-driven companies is their ability to capture, organise, and share information with all members of the organisation, facilitating collaboration and innovation.
Data-driven marketing is a set of marketing decisions or strategies that are developed after analysing, processing, and using the vast amounts of data that come from users and their preferences.
These substantial amounts of data are collected through interactions with consumers and used to make predictions about their future behaviour. The aim of this methodology is to understand consumer habits to develop more accurate and result-oriented digital marketing initiatives.
With increasing digitalisation, companies are being put in a position to make the best possible use of the huge amount of data they generate. To do this, it is important to develop a data culture or a data-driven mindset that extends to all areas.
A data-driven mindset is not just about incorporating innovative technologies. It is about creating a data architecture that encompasses the entire value chain, from the operational system to management solutions, analysis tools, employees, and corporate culture. Data analysis is at the heart of entrepreneurial activity.
Big data-driven decisions enable companies to anticipate customer needs, mitigate risks, and offer more relevant products and personalised services.
Today’s largest and most successful companies are using data to their advantage to make important business decisions. The success stories of the following companies show how data analysis influences decision-making processes.
Google is focusing heavily on what it calls “people analytics.” As part of one of its well-known people analytics initiatives, Project Oxygen, Google analysed data from more than 10,000 performance reviews and compared them to employee retention rates.
Google used the information to identify common behaviours of high-performing managers and developed training programmes to develop these skills. As a result of these measures, the popularity of managers increased on average from 83% to 88%. 1
Following the closure of hundreds of Starbucks stores in 2008, then-CEO Howard Schultz promised that the company would take a more analytical approach to identifying future store locations.
Starbucks is now working with a location analytics company to determine the ideal locations based on demographic data and traffic patterns. The company also considers feedback from its regional teams before making decisions.
Starbucks uses this data to determine the likelihood of success of a particular location before embarking on a new investment.
Amazon uses data to decide which products to recommend to its customers based on their previous purchases and search patterns. So instead of blindly suggesting a product, Amazon uses data analytics and machine learning to drive its recommendation engine.
McKinsey estimates that 35% of Amazon customer purchases could be linked to the company’s recommendation system.2
A data-driven corporate culture works well in any company. Solid planning that is based not only on hunches and intuition but also on thoroughly collected and analysed data leads to a successful future. This form of decision-making is also of immense value for strategic decisions.
The data-driven approach offers companies several advantages, in particular the following:
Technologies that help organisations make better use of their data are easy to acquire and deploy. However, the harsh reality is that the transition to a data-driven decision-making framework requires more than just technology.
Some of the challenges and what can be done to overcome them are:
Getting actionable information in real time requires more than just a strategy. Many data teams are buried under aspects of business strategy. They lack sufficient resources and authority to support data operations and gain new strategic insights, whether at the team, departmental, or corporate level. Data analytics and resources are often pushed to the back burner in favour of other short-term initiatives that are deemed more important.
In this respect, the data strategy should clearly define which priorities apply. It is important to establish sensible rules for data governance and to trust the data team and the collected data.
Both digital migration and data transformation are important activities on the journey through the world of data, whether it is to grow a business, attract new customers, or increase operational efficiency.
You can implement the best technology in the world, but if your data is poor in quality, unlinkable, and undiscoverable, or if you are not collecting the right data to answer questions relevant to your business strategy, you will not get the most value from your technology investment.
Data analytics is still seen as the work of a few, with only certain teams owning and using data to drive the rest of the business forward.
But anyone who works with data should understand data ethics.
Without proper training, situations can arise where information is duplicated; the correct source of truth is not known, quality issues arise, or data is not managed ethically because people do not know how to deal with it.
To anticipate these challenges, leaders and managers need to frame the challenges with hypotheticals, demonstrate how tactics and strategy are connected, reinforce the idea that data and technology literacy are critical to professional outcomes and impact, invest in skills, policies, ethics, and data management, and exemplify data stewardship.
And finally, data must also be findable and linkable so that it brings maximum benefit to your strategy.
Driven by the hope of better satisfying customers, backing up innovative ideas with solid evidence, streamlining operations, and clarifying strategy, companies have spent the last decade collecting data, investing in technology, and spending heavily on analytics talent. However, data is rarely the universal basis for decision-making: a strong data-driven culture must also be created. Experience has shown that this is the bigger challenge.
Why is this so difficult?
Our work across a wide range of industries shows that the biggest barriers to building data-driven businesses are not technical, but cultural.
It is easy to describe how to incorporate data into a decision-making process. But it is much less easy to make this the norm, even automatic. It is a huge challenge for employees to change their way of thinking.
That is why we have put together ten commandments for data to help create and maintain a culture centred around data.
Companies with a strong data-driven culture have leaders who exemplify that decisions should be data-driven, that this is normal and not new or exceptional. They lead by example. This is a good example from the top of the organisation that can lead to significant changes in company-wide norms.
Managers can have a strong influence on behaviour if they are smart about what to measure and how to measure it. For example, companies can benefit from closely monitoring competitors’ price movements.
Data scientists are often isolated within an organisation. However, analytics cannot exist and deliver value if it operates separately from the rest of the organisation. Leaders must recognise and promote the importance of a data-driven culture.
Data analysis is not possible if there are difficulties accessing the required data. Consistently ensure that the data is reliably available. Implement processes and use tools that facilitate the collection, analysis, and use of data. Check the completeness and quality of the data.
Absolute certainty is impossible. Ask your team to also state the uncertainties explicitly and quantitatively. This has three strong effects.
Develop proofs of concept where feasibility in production is an essential part of the concept. Start with a simple implementation and construction, and increase the level of sophistication later.
Basic knowledge should be part of the basic training. Training employees in special analytical concepts and tools should only take place shortly before they are needed.
Data literacy can play a significant role in employee satisfaction. Make sure that data literacy is not only demonstrated but also tried out and practised. Employees can then implement data literacy in their work.
It is easier and more efficient to work with standardised metrics and programming languages and in a standardised way than having to constantly clarify discrepancies and translate collected data. Use universal methods to save time and effort.
For most analytical problems, there is rarely a single correct approach. Data scientists approach problems differently, choose from several alternatives, make compromises, and decide on the selection. All of this leads to a deeper understanding of the approaches and an open decision-making culture.
A data-driven corporate culture is about creating an environment in which all employees understand the value of data and use it to optimise their work. It goes far beyond using data to make business decisions.
Such a culture promotes innovation and the exchange of ideas and improves overall company performance.
A data-driven culture is critical because it enables organisations to be more agile and proactive, respond quickly to change, underpin their business strategy with sound data, and achieve their goals effectively.
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