What is a data management culture and how to instill it in business

Main article: Data management

What is a data management culture and when to implement it

A data management culture is an optimized approach to its collection, naming, storage and application. According to research, 73% of companies "waste" the information flows surrounding them "wasted," due to the lack of Data Culture. The concept guarantees the purity and accuracy of information used to make management decisions at all levels.

Over five years, the share of Russian companies implementing data management initiatives has grown fivefold. The very development of Data Culture in Russia began around 2010. A layer of managers who can enter the professional leadership has appeared in the country. They needed a specific tool - data on everything that surrounds the business.

It is necessary to introduce a culture of data management in terms of maturity. Not digital, but managerial. When managers want to correctly configure processes, but do not receive complex tools. This requires information flows, which, in turn, need the concept of Data Culture. So that the data can be trusted, and make the right decisions on their basis.

What a data management culture is for and what it includes

Recently, a report appeared on the website of the Center for Information Management, entitled "Data Management in Russia in 2024." Experts turned to the heads of large organizations for help and compiled a detailed digest.

The question: "What priorities do you focus on when making decisions about the development of data management in the company?," The experts closed as follows:

• 18% - Eliminate costs and improve efficiency
• 17% - improved customer satisfaction;
• 11% - increased sales and business growth;
• 11% - creating products and improving their quality;
• 10% - decision-making based on reporting and data analysis.

The five items presented are the goals of implementing the Data Culture concept. On paper, they may be different, but in practical conditions, enterprises strive solely for such changes.

The DAMA (Data Management Association) wheel helps to understand what is included in the data management culture. The framework includes 10 areas of knowledge Data Management (data architecture, documentation and content management, etc.), as well as another special upper-level area of ​ ​ knowledge - Data Governance.

How to start creating an in-house data management culture

The data-driven approach is a method of managing a data-driven business. It allows you to make decisions based on facts and numbers, without intuition and lengthy assumptions. With its help, companies optimize processes, increase efficiency and increase competitiveness, improving the quality of products.

Of course, it is impossible to implement a data-driven approach in a couple of days. It requires a special culture of data management, erected according to the steps of a multi-stage regulation:

1. Define "target state" - "point B." Develop goals that can be divided into three groups. Improving the quality of management decisions, reducing costs and reducing risks, respectively
2. Design and launch a set of initiatives leading to the closure of previously established missions. Introduction of new positions (from data architect to data engineer), employee training, introduction of methodologies like CRISP-DM.
3. Reconfigure the key elements of the operating model. Work with processes, roles, technological solutions and indicators of the internal KPI system.
4. Engage all stakeholders. Among them are top managers of the company: general and HR directors, technical leaders, heads of business divisions and IT managers (CIO, CTO, CDO).
   
   

Managers who are not directly involved in the work are involved in it indirectly. They become transformation drivers. Define performance indicators, empower field employees, and monitor project progress.

Risks from implementation and lack of a data management culture

In many companies dealing with large information flows, the discipline of data management has already ceased to be considered exotic. Nevertheless, there are many difficulties in introducing the Data Culture concept and using related tools.

Among the main challenges regarding the implementation of a data management culture:

1. Complexity of systems. You need to learn how to administer a large number of heterogeneous business entities and information sources.
2. Lack of understanding of application goals and tasks. Data management solutions are often seen as infrastructure-based, with no reference to specific business value. People do not realize why they are needed, who is their consumer and what missions will be closed later.
3. Scope of coverage. Data Culture permeates the entire infrastructure of the organization: from ERP to CRM to MES. Creating integrations that ensure the coherent and consistent operation of systems is a challenge.
   
   

Plus, data management projects typically require a huge number of workflow changes. They influence the culture of the organization and need a lot of engagement from employees.

If the problems presented are not closed, the company will face unpleasant consequences. Practice shows that difficulties associated with the quality of data have negative material effects.

Among them - recurring costs for the implementation of initiatives; unoptimized costs for redundant storage of information; more work to find/clean data, etc. The organization ceases to be productive and falls into the hands of competitors.

The place of software in the data management culture

The belief that data management is the job of IT is unfounded. IT specialists perceive it as applied, from the position of Data Governance - one aspect of Data Culture. The software ensures the stability of systems, the operability of integrations and the prompt resolution of incidents.

The construction of a management infrastructure built on a culture of decision-making based on data is an exclusively managerial issue. It should be dealt with by business teams, whose employees, in turn, just set tasks for IT specialists.

The author of the article is data management expert Kirill Kuznetsov.