More and more companies today are adopting business intelligence at the organizational scale to enable all their departments access, analyze, and visualize data efficiently for more evidence-based business decisions. However, the process of implementing enterprise BI should start with an important foundational step, such as building a robust and holistic data strategy to establish organized and secure data flow throughout your organization.
In this article, we explore the concept of data strategy, highlight its key components, specify the importance of having such a strategy for BI adoption, and give a few pointers on how to build a more efficient one.
What is a data strategy, and what does it include?
Data strategy is a long-term plan outlining how an organization should manage, process, and utilize data to meet its business goals while ensuring compliance with corporate data policy and external regulations. In the enterprise BI context, having such a strategy is particularly vital, as it ensures that the organization’s data is accurate, accessible, and secure.
Since data requirements in different organizations can vary, there is no general consensus regarding what components such a strategy should include. But if we look through the real data strategies followed by modern organizations, we can see they cover many common aspects:
- Data governance
Data governance, fundamental to any data strategy, defines general rules for how a business should manage data to ensure its quality, accessibility, and usefulness across an organization. It encompasses policies and practices that an organization should include in its data management processes, including protocols for data gathering, transfer, cleanup, validation, and other activities. Data governance also defines the roles and responsibilities of all people involved in data management activities, including data owners, data stewards, and data stakeholders.
- Data architecture
Data architecture is another critical component of data strategy which documents and maps how data flows and distributes across an organization’s IT infrastructure. Data architecture also defines how users should access and utilize the data to comply with an organization’s data management policies and standards.
- Data security
Data security mainly focuses on the measures that an organization should apply to safeguard its data against external and internal cybersecurity threats, such as malware, DDoS, and social engineering attacks. This data strategy component typically defines corporate policies related to administrative and technical data security features and covers access controls, data backup, incident response, and other security-related procedures.
- Data analytics
Data analytics is another essential data strategy component that specifies how organizational data should be analyzed and used in decision-making. It typically includes the organization’s analytics toolset, such as data visualization, data reporting, and business intelligence tools and specifies how employees should utilize these tools to derive impactful business insights based on data.
The benefits of having a data strategy
- Increased operational efficiency
Implementing a data strategy based on a robust data governance framework can help an organization standardize and automate time-consuming BI operations. By eliminating bottlenecks associated with manual data management, an organization can enable employees to access and share necessary data faster, which can enhance overall work productivity. 36% of business leaders participating in the 2025 Outlook: Data Integrity Trends and Insights survey by Precisely note that improvements in data governance helped them accelerate access to relevant data, while 57% state they could improve employee collaboration.
- Enhanced data security
Even a single data breach can lead to significant reputational and financial losses for an organization. The Cost of a Data Breach Report 2024 from IMB reveals that the global average data breach cost has already surpassed $4.9 million. By implementing a data strategy that incorporates encryption, user authentication, and other data protection measures, a company can significantly minimize security risks and avoid related expenses.
- Improved decision-making
According to the abovementioned survey conducted by Precisely, 67% of respondents state they don’t fully trust their organizations’ data for decision-making because of its poor quality. Some common challenges to getting high-quality data include a lack of adequate tools for automating data quality processes (49%) and inconsistent data formats and definitions (45%). By implementing a data strategy prioritizing data quality and reliability, an organization can increase trust in data and subsequent BI insights among decision-makers, helping them take better-informed actions.
How to create an efficient data strategy?
- Promote collaboration between CDOs and business teams
Typically, chief data officers (CDOs) and members of their teams are responsible for the development of data strategies within organizations. To build an efficient data strategy, a company should ensure that its CDO office works closely with business teams and considers their feedback from the very start of the strategy creation process to identify the most common data usage scenarios.
- Incorporate employee training on data reliability practices
We already said that an efficient data strategy should promote data reliability and foster trust in data across the organization. 92% of data professionals who participated in the State of Data Observability 2024 survey from CDO Magazine agree that data reliability is core to data strategies.
Among the ways to foster data reliability the most efficient is to support it with a training plan for a data collection team. After all, their work directly impacts data quality, which largely determines data trustworthiness. The training plan should be based on measurable and achievable objectives, provide multiple training methods and formats (including webinars, workshops, or mentorship programs), and contain efficient evaluation models (such as Kirkpatrick’s Four Levels of Evaluation or Kaufman’s Evaluation Method).
- Engage third-party data experts in the strategizing process
To create an effective data strategy that would remain useful in the long term, data offices must have solid expertise in data management, data engineering, statistical analysis, AI, and many other domains. Unfortunately, practice shows that even well-equipped in-house teams do not always possess all the competencies required to develop a robust data strategy. For instance, 48% of respondents to Informatica CDO Insights 2024 survey think their teams lack relevant skills in AI and machine learning and require upskilling or reskilling.
If organizations face specific knowledge or skill gaps across their data offices, they can consider resorting to third-party data experts to assist with data strategy development. If needed, experts can help an
organization audit its existing data assets, design tailored programs for data governance, architecture, security, and analytics, build an end-to-end data strategy, and then implement it smoothly.
Final thoughts
Enterprise BI enables different teams across an organization to easily access data, visualize it in an easy-to-understand format, and analyze it efficiently to make data-driven decisions on both tactical and strategic levels. To succeed in enterprise BI adoption, a company should first establish secure and efficient data processes throughout its organization, which is why implementing a robust data strategy is essential. If you’re looking to build a winning data strategy that will support your business growth years ahead, enlist the help of a reliable data consulting partner to ensure your project’s success.