how to start implementing a culture of data management? / Hebrew

how to start implementing a culture of data management? / Hebrew

By 2025, the global field of data management will be five times more than in 2018. The demand for the implementation of Data Culture projects is only gaining momentum. Companies that have managed to invest in the development of such an architecture are reaping the rewards: reducing costs, increasing sales, and growing business.

Let’s talk about how to start implementing a culture of data management and what pitfalls can be encountered along the way. Let’s go!

Prerequisites for using Data Culture

You can understand that a company needs a data management culture based on two factors:

  • the organization has accumulated a large amount of information (73% of which often are wasted);

  • the firm already makes decisions based on analytics (like Excel spreadsheets or partially automated reports).

But the main prerequisites indicating the company’s readiness to work with Data Culture are:

  1. It is critical to make management decisions based on correct data.

  2. The presence of many people who support the project of implementing a new approach.

It is important that the idea of ​​transition to work with Data Culture is supported by the first persons of the company. Without their participation, the project can be implemented, but with little probability and for a long time.

Conditional work scenario

There is no universal scenario for the application of data management culture. Each company is in a different degree of readiness for this project. Plus, the experience and motivation of management differ.

The concept of Data Culture is to start making decisions based on information that has passed quality, integrity and completeness control. Only such data reflect the actual state of affairs within the company.

Let’s give an example. It often happens that the management accounting of the company is very different from the accounting accounting. The first is aimed at forecasting and regulated by law, and the second reflects the facts of economic activity and is conducted based on laws. This leads to contradictions that prevent correct decisions.

It turns out that the company needs an accounting methodology that ensures consistency of information. But its creation is complicated by two typical factors:

  1. The organization’s management does not understand how to organize work with large data sets.

  2. Part of the employees sabotages the transition to transparent handling of information.

You can implement the concept of Data Culture on this example or in other situations according to the following regulations:

  1. We determine the degree of readiness of the company to work under new scenarios. We see if there is a critical need to use data management tools, as well as support from top managers.

  2. We localize the starting position. We answer the questions: “What do we already have?”, “How do we manage data?”, “What reports do we receive?”, “What information do we use?”.

  3. We are forming a project plan for the implementation of Data Culture. It is important that it foresees movement from simple to complex, in short sprints, and also includes metrics that are understandable to the participants of the process.

  4. We define goals, deadlines, limitations and risks. We allocate resources and budgets, assign roles and mark the reporting structure within the team.

  5. We start making management decisions based on data after the first sprint. Even if the project is far from complete, and the analytics tools work well. This will help identify errors and correct them with minimal damage. Plus, it starts forming the habit of working with data for everyone, and the management makes decisions based on it.

Poor data quality is a problem faced by various companies. It is possible to protect the company from related financial risks and loss of competitiveness with the help of Data Culture.

Possible problems and risks

The positive effect of implementing a data management culture comes quickly after the first sprint. But it is important to understand that it is impossible to integrate Data Culture initiatives without costs. A large layer of impending problems will be related to information about poor employee performance or cases of abuse of authority.

Therefore, at the start of the project, the manager should determine the policy of applying incentives and sanctions. It is better to use the whip at a minimum, otherwise the risk of sabotage will increase. Employees will begin to hide the objective picture, not implementing initiatives and not adapting to life in new conditions.

In addition, there is a risk of encountering costs of a reputational nature. If the disclosed information becomes public, competitors will get access to it. Therefore, in parallel with the use of data management culture, the practices of dealing with trade secrets are also integrated.

Involved employees

Before starting the project, it is worth listening to all interested parties, but with the understanding that many opinions will slow down the process and prevent a quick decision. Finding a balance between “listening to everyone” and “listening to no one” requires a role model.

Each person on the project is assigned one of four roles:

  1. Driver – the one who implements the project at all stages. He manages stakeholders, allocates resources, builds business cases and monitors metrics.

  2. Approver – a person who approves various decisions. He is ultimately responsible for the outcome of the project and sometimes vetoes decisions.

  3. Contributors are business and IT experts. Provide context for achieving goals and consult on important issues.

  4. Informed are the people who will be directly affected by the project. They work without the right to vote, but turn to the approver for consideration of opinions.

As a rule, at each level of working with data, these roles are performed by different people. For example, it will be better if the position of “driver” or “approver” is taken by the head of the company. It’s great when there are several drivers – then the work includes not only top management, but also a group of line managers.

Deadlines and budgets

The duration of Data Culture application depends on the state of affairs within the company and its size. Let’s understand the terms using the example of a conditional organization that has the following parameters:

  • number of staff – ±30 people;

  • some management decisions are already made taking into account analytics;

  • the team understands the purpose of the project;

  • there are many “drivers” in the team, but few “informers” (everything is fine with ideas and desires, but there are not enough hands).

Then the project to introduce a culture of data management in the company can be implemented within about a year (provided that the company breaks down complex tasks and moves in short sprints). If the company is large (with 30,000 or 500,000 employees), the implementation of the project will take much longer, and for such projects it is necessary to create project offices and involve consulting.

Now that we’ve dealt with the deadlines, let’s move on to finances. The project will pay off immediately after the completion of the first sprint. For example, thanks to the automation of those processes that used to last for weeks (data collection, data cleaning, report generation, etc.).

All other economic aspects (budgets, resources or man-hours) differ from company to company. They are predicted only on the basis of a detailed description of the application project.

Business evaluates costs in terms of efficiency and financial return. With the achievement of this result, there will be no problems in the project on the implementation of the concept of Data Culture. The initiatives will pay off due to the fact that the company will stop making mistakes that were provoked by the disparity of information.

Resume

Implementing a data management culture is a complex task for the IT and business departments of the organization. Having dealt with it, the company will receive a series of positive effects, and its managers will begin to make informed decisions based on reporting and detailed analytics of incoming information.

Related posts