10 best practices for data-driven companies

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Discover some of the main good practices of data-driven companies
Data-driven companies are those that have found a way to focus their management on data and manage to convert all this information into a valuable contribution. Thanks to this, they obtain a
360-degree view of the business and can respond to the needs of a highly competitive environment with strategic decisions in real time.


In general terms, we could say that turning data into zalo database is the best investment to increase profitability in a short time. However, what is needed is to achieve a comprehensive transformation focused on technology, business culture and data governance , the pillars of a data-driven company.





86 % of consumers are willing to pay more for a better customer experience

Source: Super Office


10 best practices for data-driven companies
Below are the main good practices to achieve an intelligent data-driven company:



1. Establish a clear vision: A data-driven company needs accurate and timely data to gain effectiveness, competitiveness and vision. To do this, the vision must be clearly established to clarify possible doubts. Only a data-focused culture can efficiently involve the entire company.

2. Seek participation: It is important to have other people committed to the process, sharing the vision and participating in its implementation to make the desired change process effective. Promoting professional development within this model, for example, is an interesting way to facilitate and consolidate the change process.

3. Learning from others' successes: Learning from the experiences of others can be a great help in driving the transition to a data-driven model. From learning from other people's failures and successes to discussing possible doubts or simply getting ideas on how to address general and specific issues.

4. Review data infrastructure: Critically reviewing existing technology infrastructure is a time-consuming task, but it is key to do it thoroughly to assess data needs in order to speed up the change process.

5. Work on data quality: data must be well documented, organized, formatted and error-free, and quality dimensions must be controlled (completeness, conformity, consistency, accuracy, duplication and integrity). It is essential to designate someone responsible for these types of issues.