Cesar Ripari, Pre-Sales Director for Latin America, Qlik, outlines crucial points in creating an effective data strategy.
Data is now widely recognized as an organization’s greatest asset, fundamental to support smarter decisions. However, it is evident that many companies still act in a disorderly way with them, without an effective strategy. Those who seek to establish it face numerous challenges. Simply continuing to work without it being defined is no longer a viable option. It is essential to have a strategy, and, based on it, create a structured plan to define how to manage, use and extract value from data, covering policies, processes, and technologies to ensure the quality, security, and compliance of information.
Like a true map, data strategy helps foster a data-driven culture and initiatives that align with the overall and specific goals of the business. The preparation of this plan requires special attention to highly relevant factors that will help companies avoid deviations, inconsistencies and inappropriate use of information. To prepare an efficient plan, we can highlight the main steps that every organization must follow:
- Align organizational objectives: The objectives of the organization’s entire data strategy must be aligned. Cross-departmental collaboration is necessary to define the data strategy, the specific processes and the areas where information can play an important role in achieving the organization’s goals.
- Assess the current situation: It is important to conduct a comprehensive analysis of current data assets, processes and competencies, organizing an inventory of data sets by business unit, identifying their proper sources and quality. It is also necessary to evaluate the technological infrastructure of storage, processing and integration to identify needs for adjustments. The information governance methodology must also be validated to align roles, responsibilities and compliance.
- Identify use cases: Involve key stakeholders from the various departments of the company to gather insights and find out what they are looking for in relation to the data. At this point, it is important to evaluate what are the main business objectives of each sector; what types of data are essential for daily operations and decision-making; how data is managed and used and the key data-related challenges (or concerns) that are currently faced.
- Develop the data governance framework: Prepare the environment for a balanced implementation of solid governance measures, considering People, Processes and Technologies as a basis. Also at this stage, define the fundamental elements of the framework, including roles, responsibilities, and policies. All the principles and objectives for adopting the measures should be outlined at this stage.
- Define data architecture: Data engineers must design a scalable and flexible architecture, aligned with the business and taking into account the growth in the volume of information and technological advances. It is important to develop a data model and integration strategies for a seamless flow between systems.
- Establish data quality standards: Define a data quality process, metrics, and standards to ensure accuracy and consistency in analysis. Involve key stakeholders in the data quality cycle for validations and limits on information use and information expectations.
- Establish security and privacy measures: Identify security and privacy requirements, establish protection measures for sensitive data, possible vulnerabilities and security threats. Pay attention to regulations in force and ensure alignment with internal policies and procedures. Establish regular security audits and assessments to help identify and proactively act on potential weaknesses found.
- Define processes for the data life cycle: It is important to define clear processes at all stages of the ‘life’ of data: from collection, storage, processing to disposal of data, with ethical and governed guidelines. Among these steps, all processes must be mapped in detail, including data origins, usage profiles, transformation and integration, migration between environments and systems, retention and archiving, backup and recovery, access controls, supervision and governance and constant monitoring of data quality.
- Define strategies for Analytics and Business Intelligence: Develop strategies to take advantage of all the organization’s data in Analytics and Business Intelligence solutions. Identifying KPIs (Key Performance Indicators) and defining corporate metrics that align with the organization’s goals are important factors for data-driven decision-making.
- Develop data literacy initiatives: Plan educational programs to promote a conscious environment regarding reading, comprehension and communication through data. Identify target audiences and their specific training needs in the use of data, including soft skills. Well-defined communication must be established, supported by the management levels and emphasize the importance of data in decision-making.
By following these 10 steps, businesses can lay a solid foundation for an effective data strategy.
However, it is important to emphasize that it should not be seen as a static project, but rather a continuous process of improvement and adaptation.
Revisions and adjustments must be made as business needs evolve and new technologies emerge.
In this way, organizations can remain at the forefront of innovation, leveraging the full potential of data to drive business evolution.
