Organization Development

To get structure into the data game

Defining roles and responsibilities is a central component of successful data governance. Although the focus is often on implementing digitization projects, the organization of the data area should not be neglected. A clear structure is particularly important to enable the use of artificial intelligence and to ensure efficient collaboration.

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To the

Case Study

Our process

Analysis

Recording and analysis of the latest organizational structure, roles, and responsibilities and any associated challenges
Identification and description of potential for improvement
Development of a first target image for structures, roles and responsibilities, including reconciling with the strategic objectives and needs of the company

Recommendations and action plan

Create a detailed plan for organizational transformation to enable data-driven decision-making
Definition and description of roles and responsibilities
Identification of the existing staff working for the unfolding is suitable for new roles
Recommendation of strategies for eliminating staffing gaps, including hiring support or continuing education initiative

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Transformation

Ongoing support of the transformation process
Assistance with the creation of job profiles and implementation of job interviews as well as implementation of Coding Challenges to evaluate any candidates
Opportunity to fill interim positions by key personnel such as Chief Data Officer (CDO), Head of Business Intelligence (BI), Product Owner (PO), etc.

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Why do companies need data organization?

Define roles and responsibilities

Who has the responsibility to develop and drive the data strategy? How can you describe the various roles and stakeholders? Once these questions have been clarified, many things can be derived from them, including dashboards and data governance.

Establish data governance

Not everyone in the company has the same view and idea of how key performance indicators are measured and what they mean. Data protection and compliance issues are just as difficult. This requires rules, affiliations and responsibilities. An important aspect of data organization!

Make decisions efficiently

In many other business areas, it is clear exactly who makes which decisions, when they happen and what the processes for this look like. With data, this is often neglected — because the topic has grown significantly in a very short period of time and there was no time to develop strategies. That is why setting up an organizational structure helps to make decisions even more efficient.

Hiring Support and Upskilling

Which skills are not yet available in the company? Which does the company need to take the next step towards data? Especially in the area of artificial intelligence, it is immensely important to find the right people who want to achieve great things together. We support you in selecting and training these personalities — but find out exactly who they need first!

This is how we proceed when optimizing data organization

From data audit to enable — that's how we work at The Data Institute!

Phase 1

Absorbing the status quo — the data audit

As a first step, we like to draw using a data audits On which data organization exists in the company, what exactly the organizational structure looks like, who has which roles and responsibilities and how this structure was created. There is more than just the distinction between centralized and decentralized organizations — data is often collected and used in many places that are not apparent at first glance.

Phase 2

Set target image

The next step is to talk to data stakeholders, product owners and management to find out what the company's goal is. This is not yet about the organization itself, but about the corporate goals that are to be achieved with data. Is it the use of artificial intelligence? Or increasing sales and reducing costs?

Phase 3

Development of the arget organization chart

Now it's getting really fun: We're working out what the org chart should look like in the future. There is, of course, a difference between restructuring or optimizing the existing organizational structure and developing a future goal. In doing so, all roles related to data are considered across disciplines.

Phase 4

Implementation of data organization

We are now helping them to implement this new organization. Responsibilities and access may have to be redistributed, but the development of new dashboards is also often a necessary step that is now necessary and supports employees in their new roles.

Phase 5

Enablement

Simply putting someone in a new role is often not effective. The person must be able to arrive, receive the necessary resources and fully understand the new position, just like the people with whom they work in the new constellation. We help employees incorporate data into their corporate culture, use new tools and thus establish a new corporate culture with a focus on data.

Grafik des Frameworks mit dem Data Institute arbeitet.

Data organization in our framework

We always work with the organization framework, culture and architecture.

Because in our opinion, these three areas are the most important factors for successfully anchoring data in the company in the long term.

Data organization is therefore one of the three pillars that are inextricably linked and interdependent. As a company, you can choose a focus that best supports the company's goals in the short term. But it is important to always keep an eye on all areas and not to neglect any of the three pillars in order to generate long-term impact.

How does our framework work?

The Data Institute — the strong partner when using data organization

We want companies to quickly get an impact and see what they can do with data. At the same time, we have an overall view of the company and want to implement long-term strategies that enable employees to work with data independently.

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What is data organization anyway?

Our framework positions data organization, data architecture, and data culture on one level to ensure a comprehensive data strategy. The basis is an understanding of business processes. While the organization defines who is responsible for which aspects of the data, the culture describes how data is handled and the architecture describes which technical tools are used.

The close integration of these three areas creates use cases that serve as MVPs and make it possible to implement and optimize the data strategy step by step. Our goal is to enable employees to independently implement data-driven projects and thus make a lasting contribution to the company's success.

Who needs data organization?

In fact, a data organization needs every company that works with data and has more than one person who accesses this data.

Because this is where the first responsibilities and roles arise, which then also have an impact on data governance.

That is why it is necessary to set up a data organization even during a start-up; for companies that have already established themselves, it is important to regularly check the organization, identify blind spots and thus not only support employees, but also to act in compliance with GDPR and regulations. The data organization is therefore inextricably linked to the data strategy and achieving goals.

Who is involved in data organization?

The structure of the data organization must, of course, be specified by management. However, every company has unwritten rules, historically developed processes and responsibilities, but also outstanding employees and departments who have previously unseen skills.

That's why we at The Data Institute not only talk to management, but also to specialist departments to understand and assess their needs, abilities and talents. This is immensely important in order to set up and establish a functioning data organization in the company over the long term.