Cesar Ripari, Pre-Sales Director at Qlik for Latin America, highlights the key steps every organization should follow to prepare an efficient plan for data strategy to help them avoid deviations, inconsistencies and improper use of information.
Simply continuing without a well-defined strategy is no longer a viable option. It is crucial to have a strategy and, from that, 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, a data strategy helps foster a data-driven culture and initiatives aligned with the business’s general and specific objectives.
Developing this plan requires special attention to highly relevant factors that will help companies avoid deviations, inconsistencies and improper use of information. To prepare an efficient plan, we can highlight the key steps every organisation should follow:
1 – Align organizational goals
The goals of the organization’s entire data strategy must be aligned. Collaboration between departments is necessary to define the strategy for data usage, specific processes and areas where information can play an important role in achieving the organization’s goals.
2 – Assess the current situation
It is important to conduct a comprehensive analysis of current data assets, processes and competencies by organising an inventory of datasets by business unit, identifying their sources and quality. Additionally, the technological infrastructure for storage, processing and integration must be assessed to identify any necessary adjustments. Information governance methodologies should also be validated to align roles, responsibilities and compliance.
3 – Identify use cases
Engage key stakeholders from various departments to gather insights and discover what they seek from data. At this stage, it is important to evaluate the main business objectives of each sector, which types of data are essential for daily operations and decision-making, how data is managed and used, and the primary challenges (or concerns) related to data currently being faced.
4 – Develop the data governance framework
Prepare the environment for a balanced implementation of solid governance measures, based on People, Processes and Technologies. At this stage, define the core elements of the framework, including roles, responsibilities and policies. All principles and objectives for adopting the measures should be outlined here.
5 – Define data architecture
Data engineers should design a scalable and flexible architecture aligned with the business, considering the growth in data volume and technological advances. It is important to develop a data model and integration strategies for a continuous flow between systems.
6 – Establish data quality standards
Define a data quality process, metrics and standards to ensure accuracy and consistency in analysis. Engage key stakeholders in the data quality cycle for validations, limits on data usage and expectations regarding the information.
7 – Implement security and privacy measures
Identify security and privacy requirements and establish protective measures for sensitive data, possible vulnerabilities and security threats. Ensure compliance with current regulations and alignment with internal policies and procedures. Regular security audits and assessments should be set up to proactively address potential weaknesses.
8 – Define processes for the data lifecycle
It is important to define clear processes for every stage of the data lifecycle, from collection, storage and processing to data disposal, with ethical and governed guidelines. Each stage should include detailed mapping of data origins, usage profiles, transformation and integration, migration between environments and systems, retention and archiving, backup and recovery, access controls, supervision and governance, and continuous monitoring of data quality.
9 – Develop analytics and Business Intelligence (BI) strategies
Develop strategies to leverage all organisational data through Analytics and Business Intelligence solutions. Identifying KPIs (Key Performance Indicators) and defining corporate metrics aligned with the organisation’s objectives are important factors for data-driven decision-making.
10 – Develop Data Literacy Initiatives
Plan educational programmes to promote a data-conscious environment, focusing on data reading, understanding and communication. Identify target audiences and their specific training needs in data usage, including soft skills. A well-defined communication strategy, supported by executive leadership, should be established to emphasise the importance of data in decision-making.
By following these 10 steps, companies can establish a solid foundation for an effective data strategy. However, it is important to note that this should not be viewed as a static project but rather as an on-going process of improvement and adaptation. Reviews and adjustments should be made as business needs evolve and new technologies emerge. This way, organisations can stay at the forefront of innovation, harnessing the full potential of data to drive business evolution.
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