Roadmap for evolution towards a Data Driven organization

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Becoming a data-driven organization is one of the most challenging goals for companies today. What is the roadmap to achieve success through Big Data? Find out in this article.

Why is it important to become a Data Driven organization?
A Data Driven organization is one that relies on accurate data to make profitable and intelligent decisions.

Today, the proper use of information has become one of the most important factors in achieving greater competitiveness and maximizing revenue. In fact, the use and democratization of data, technical support and migration to the cloud are keys to success in modern companies.

In this way, companies intelligently adapt to the new behaviors, desires and needs of their consumers , generating personalized and optimized strategies and actions based on data.

Specifically, the information accumulated and afghanistan phone number lead used in favor of the business gives rise to a successful technique where the data is connected to the people of the company and is always available to form part of their usual decision-making process.

The roadmap towards a Data Driven organization
Thanks to examples of Data Driven organizations that have achieved success in their strategies and actions, we can say that there are certain patterns to follow to become one of them.

It is important to mention that data collection and analysis is not only related to the adoption of technology or an innovative project. It also involves the cultural, social and process transformation that makes up the organization.

The Roadmap towards a Data Driven organization is as follows:

Data strategy . It should be designed and defined according to the company culture, future data needs, manipulation policies, internal and external sources, etc. In short, the data strategy should include all angles of data: exploration, management, manipulation and monetization.
People. It is extremely important that company personnel are trained and prepared to make correct use of data. Hiring specialized Data Driven roles is also relevant.
Change management and culture. When company culture is considered, people grow with change and stop seeing it as an obstacle within their work areas.
Business processes. These must be optimized for the business and then integrated into the data strategy roadmap.
Use cases . These should be shaped according to the organization's business objectives. Identifying and assigning a use case person and evaluating their results is also key.
Prioritization . This step involves prioritizing those practices and processes that produce the greatest visibility in the shortest time possible. This prioritization must be based on the rewards and risks associated with them.
Value-oriented roadmap . It consists of focusing on controlled automation in order to adapt to changes, taking into account the values ​​of the company and the existing people.
Proof of value . Establishing a proof of value is central because it allows promoting the business case and those who are aligned with the project. It also allows verifying the result with the required objectives.
Data discovery . This action is deeply relevant and must be built on the basis of the data strategy, processes and use cases. All types of data in the organization and their relationships must be documented and catalogued.
Defining the operating model. This ensures continuity and fluidity in later stages and when it becomes part of operations.
Implementation of the data platform . This allows for simpler approaches and overcomes limitations. Data storage technology and specifications should be implemented following the latest standards that incorporate the above points.
Continuous improvement . It is important to adopt iteration as an industry standard.
As the amount of available data expands, scalability becomes increasingly important for a Data Driven organization .

Establishing a roadmap helps us scale data and make sound decisions by considering users, culture, locations, connections, technology, and the growing complexity of Big Data and Analytics.