In the era of digital transformation, data production is increasing rapidly. Companies use different methods and technologies to improve speed and efficiency in storing, processing, and sharing large volumes of data.

In today's world, data has taken its place among the assets owned by companies. Data is now at the center of decision-making mechanisms, supporting innovation in companies. This change in perspective has started to show itself not only at the management level but also at all levels of the teams, with increased awareness and the use of high technology.

On the other hand do all organizations today have the appropriate infrastructure in place to use data in decision-making? How can existing technologies be used more effectively?

Decisions made with experience and insights are not enough to make the right decisions in a digitalized world. It is very important to use data as well as experience and insights at the decision point to make the right decisions.

The increase in data sharing with digital transformation creates a need for the growth of artificial intelligence projects aimed at gaining value and meaning from data and the effective use of these technologies. The indispensable condition for artificial intelligence projects and other analytical studies is to work with accurate and high-quality data. Otherwise, the outputs of analytical studies may even cause harm beyond benefit. At this point, companies need a defined data strategy to ensure the quality, timeliness, and integrity of data and to create a sustainable structure. This data strategy should be structured to support the business goals and strategies of companies. As Doğuş Technology, we carry out data strategy and roadmap projects to ensure quality requirements within the life cycle of data and to ensure the correct and effective use of data in decision-making mechanisms. We determine data strategies in line with the business strategies of our company, our group, and other client and develop data roadmaps together.

What is a Data Roadmap?

A data roadmap is the determination and prioritization of the projects required for companies to have a data strategy in line with their business strategies and the determination of the roadmap to be followed by positioning them on the schedule with short-medium term plans. When decisions are to be taken within the company, it is necessary to benefit from data roadmap planning because many seemingly small data have the potential to turn into big business opportunities that will affect the right decision-making.

It is important for every company to determine its current business strategy and short-medium-term goals prior to the data roadmap. A data roadmap is a plan that outlines the steps a company should take to achieve its data-related goals. It is a strategic document that outlines the data-related initiatives, projects, and activities that need to be completed to achieve the required results as quickly as possible. Data governance, data capabilities, data processing, analytics, technology & infrastructure, and data architecture can be listed as the areas covered by the data roadmap.

What are the Advantages of Creating a Data Roadmap?

Creating a data roadmap has many advantages, such as communication between management and employees, tracking of processes, as well as effective achievement of company strategies.

Looking at the advantages in detail:

• The data roadmap ensures that the data governance projects required for the company are prioritized and progressed in accordance with the schedule.

• There may be different responsibility assignments for employees within the roadmap. For this reason, managers can quickly be informed about the performance of each employee and strengthen communication between them.

• Within the scope of the data roadmap, business units and corporate employees take ownership of data governance roles (data steward, data translator, etc.). With the establishment of an operational model for data governance, data-specific decisions become supportive of the holistic data strategy.

• On the way to the strategic goal, the most efficient and correct processes are tried to be planned. The roles and responsibilities of the employees who will take part in these studies are defined, as well as who is efficient at which point and who is the decision maker.

• A data roadmap enables different departments to come together to make a unanimous decision for data and to create collaborative projects for data-driven business development. It supports data-driven cultural transformation by increasing the collective consciousness of the company.

• Data governance processes ensure holistic management of data across the company. In this way, it provides many benefits, such as preventing financial losses, reducing repetitive operational work, and increasing the quality of the data owned. It also helps to use resources correctly and effectively.

How to Create a Data Roadmap?

In line with international studies, the DAMA organization can be shown as a source reflecting data governance standards. In particular, the data governance framework published by DMBoK is shown as a resource for companies. As Doğuş Technology, we took this source as a reference in our data roadmap studies. We prepared our original data roadmap framework by enriching it with our experiences in different sectors and institutions of different sizes. We apply the following process to create roadmaps for institutions.

•Define the data strategy:

The first step is to define the data strategy and goals to create a data roadmap. The data strategy and objectives should be such that they define which business goals they will support and which ones they will not support.

•Data sources and data competencies:

Once the data strategy is defined, data sources are analyzed. Requirements are defined for the data to be of high quality and usable. Collected data from data sources should be strategically valuable.

•Data technologies and infrastructure:

By analyzing the adequacy of the technologies that will support the data strategy and provide infrastructure for analytical studies, the data architecture that will enable the accomplishment of future goals is defined.

•Analytical scenarios:

Analytical scenarios and sample data that will enable the data to accomplish the KPIs and goals of the organization are determined by expert data scientists and data architects.

•Data governance:

A structure is designed to ensure the operational continuity necessary for data's effective and secure usage.

•Data roadmap:

Once you have planned your goals, data sources, data flow, and data processing, your data roadmap is constructed holistically.