Data-driven decision management (DDDM) is a corporate governance strategy that prioritises decisions that can be supported by verifiable data. That is, any critical decision is made only based on data processing results according to the specified parameters.
In fact, this is a representation of the old wise saying: "He who owns the information - owns the world." Indeed, the decisions made in the data-driven concept are much more objective and practical, taking into account long-term strategies. In the material presented below, you will learn about how the data-driven approach works and whether it can be implemented.
The Essence of DDDM
A thorough analysis of information allows you to create an idea of the effectiveness of the business, as well as outline the main directions of its development. At the turn of the 2000s, a new approach to making strategic decisions based on the study of data emerged. Over the past two decades, data-driven decision making has become one of the basic principles for the work of business leaders, marketers, managers and other professionals.
Data-driven decision making eliminates any possibility of building a strategy and calculating steps based on intuition, unsupported guesses and assumptions. Instead, accurate information and hard figures, the only valid basis for finding business solutions, are put at the forefront.
It would seem that what is innovative about this approach is that business people have always been distinguished by the ability to analyse the current situation and draw conclusions based on positive and negative experiences, both their own and competitors. However, the risk of succumbing to emotions and, as a result, misjudging the state of circumstances unique to a person frequently considerably affects a business's effectiveness. The desire to outperform competition and take risks to earn more without supported calculations leads to the downfall of many entrepreneurs.
A business that has made data-driven decision making the main principle of its activity is more protected from a subjective approach to assessing the situation and, accordingly, makes decisions responsibly, favouring a strategy that is most likely to be successful.
What are the characteristics of such an approach?
There are several of them:
Understanding the need to incur additional costs associated with the implementation of data-driven decision making. Information will need to be collected, stored, processed, and this is the work of specialists who must be compensated.
Analytical skills to get the most out of your data. The figures in the report are not an end in themselves; they are only tools for making strategic and tactical decisions.
Flexibility in business planning, the ability to rely on the collected data, but at the same time trust the opinion of experienced professionals and find a compromise. At the same time, it is important to keep in mind that facts and figures are the main guidelines for making a final decision.
Data-driven culture in the company
Suppose the leadership of a commercial organisation believes that it is best to be guided by accurate data when developing a strategy and making current decisions. In that case, the company adheres to a data-driven culture.
In practice, this looks like the introduction of the following principles into the activities of the business structure:
Planned investments in working with data, namely in their collection, storage, analysis, interpretation, and preparation of recommendations to help make the right decision.
Focus on accurate information in the process of determining the company's strategy: numbers are more important than emotions; they are the ones that guide organisations that employ a data-driven approach.
Careful study of the collected data and the formation of the only correct conclusions. Business decision-makers need to have analytical thinking skills to interpret charts, tables and graphs competently. It is essential not only to focus on numbers but also to be able to justify the rationality of the proposed solutions.
Maintaining a balance between the results of analytical calculations and the experience of managers making decisions. It is worth considering criteria that are not reflected in reports and charts but are worthy of attention: suppose the data analysis was carried out before the onset of an important event that affects the current situation. The optimal decision, in this case, will be made in conditions of cooperation and mutual trust between analysts and managers.
Data-driven culture takes root without problems when the initiators of the implementation of this approach are the founders of the company themselves. However, making decisions based on the results of studying data complicates the workflow and requires additional labour costs. Therefore, without convincing the business owner of the need for such expenses, it will not be possible to make data-driven decision making the foundation of the organisation's activities.
DDDM & Marketing as the best combination
A strategy based on the study of data allows us to solve the main task of successful marketing at the present stage – to personalise communication with the client. The most effective targeting campaigns result from careful collection and analysis of information about the target audience and building individual relationships with each consumer.
The high level of competition explains the choosiness of users: in surveys, the vast majority say they do not want to stay on the site if the content does not meet their needs. To retain and grow the customer base, companies are forced to pay more and more attention to collecting and analysing data on potential and existing customers.
Data-driven tools make it easy to personalise the approach to every customer. Today, addressing a user by name in an email is no longer enough to stay competitive. Instead, a seller who wants to outperform his competitors must know what and when to show any of his customers. In other words – which banner ads and pop-ups will work more effectively at one time or another.
Data-driven marketing pays off when a company can gather detailed information about its audience and target markets. Of course, careful data collection, as well as subsequent analysis, will require a significant investment. Still, this investment will pay off due to an accurate understanding of the needs of different customer groups.
It would be a mistake to think that working with data can be a one-time deal. On the contrary, you will need to build data-driven marketing in such a way as to ensure that the information is up-to-date. Data becomes outdated quickly, and a strategy developed five years ago may be ineffective in the current environment. This is crucial because the most effective decisions are based on data.
Using data-driven principles in web development
An inevitable consequence of global digitalisation is the constant updating of data, which also serves as the basis for creating new software. Developers are forced to collect up-to-date information so that their product meets current needs and realities.
Another reason for regularly analysing information about user behaviour, their reviews of digital products, and error reports is the desire to create software that people will not have problems using. To assess the quality of the services provided, development companies regularly monitor information about traffic, downloads, internal issues of the service and the number of users.
As a basis for improving the software product, the results of the evaluation of the code and the consistency of its blocks are used. All these actions show the company's desire to adhere to the principles of data-driven business development.
Data-driven analysis is integral to working on any product or process today. By collecting and processing data, engineers receive information based on which they can optimise the outcome. However, without well-established consumer feedback, it is difficult to count on a complete hit in their needs and, therefore, on an industrial enterprise's success.
Data-driven Decision Making Tools
To collect and study large amounts of data, unique tools have been developed:
Business Intelligence tools – allow you to use all the data that business and marketing platforms accumulate by integrating them into one solution. With BI tools, a company has everything it needs on one dashboard, which simplifies decision-making and can minimise the time it takes to accomplish tasks, enhancing productivity.
CRM-system – allows for automation sales and gives an impartial assessment of the results of marketing activities. With a CRM solution, companies obtain acces to each client’s full profile that includes personal information and demographics, a purchase history, interactions with the company, etc. A CRM boosts lead generation, customer acquisition and retention, allowing the company to grow its business with satisfied and loyal customers.;
Market Research - the king of all data sources, providing the most accurate and valuable first-hand data. It allows a company to analyse its competition, formulate an optimal pricing strategy, understand the user experience, improve current products, etc.;
Attribution Modelling – allows you to understand better how each optimisation contributes to the success of your business. This technique measures conversions across communication channels and allocates value based on their participation in the transaction. This information helps you recognise how much each streamline contributes to your company's performance.;
Website Analytics – a priceless tool that lets you know about your customer’s behaviour. This data reveals which content works well and keeps clients interested, as well as which pages have a significant churn rate.
In closing
Some of the immediate benefits of the DDDM approach include increased profits, reduced costs, improved pricing, minimised bias, better problem-solving, and happier customers. Transferring the company's activities to a data-driven decision making approach can be the beginning of a breakthrough in its expansion. The basis of strategic and tactical decisions will be only real numbers obtained from a thorough study of a large amount of data. This approach minimises the risk of going down the wrong path, prompted by intuition or experience.
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