Ever heard of the phrase ‘data-rich, but insight poor’? Lack of insight is precisely the challenge businesses are facing today.
Research shows that 4.4 zettabytes of data generated per day globally in 2019 skyrocketed to 44 zettabytes in 2020? Here is an even more overwhelming projection. By 2025 the whole world will be producing 463 exabytes of data each day!
Businesses need to leverage these mind-blowing trends in data volume, to derive insights for data-driven decision making, which will increase agility, boost customer experience and satisfaction, and cut costs.
The success or failure of any business hinges on its ability to unleash the power of data. They need robust data management infrastructure and resources to manage, analyze, and interpret enterprise data for potent decision making. Research suggests that up to 80% of all data, continues to be unstructured while less than 1% is analyzed.
Chief Information Officers (CIOs) and Chief Data Officers (CDOs) of the biggest global enterprises are struggling to tame the data beast. They battle with issues in aligning and integrating data efforts, as well as optimizing data quality, security, fidelity, integrity, and accessibility. Sparse quality data gathered from disparate systems and diverse teams result in low-level, short-term, and poorly construed insights, affecting the bottom line of businesses.
Businesses need a complete overhaul of the sporadic data management and transition to a consistent, centralized data strategy. The centralized data source provides a trusted data point to organizations. Hence the decisions made from this single source of truth will be trustworthy, enabling businesses to gain a competitive advantage over their peers.
New to the data game? Follow the six best practices discussed below for implementing a centralized data strategy in your organization.
Aligning data strategy with business strategy
The centralized data strategy should be defined within the business context, aligned with organization hierarchy, decision types, and stakeholder needs. It must align with business strategy to prioritize areas that require more strategic, tactical, and operational decision making at different ranks such as top executive, senior management, and associate levels. An effective data strategy should streamline data collection and management for product design and delivery, operational and process improvements, and compliance.
Focused investments in MDM
Companies must invest in a robust Master Data Management (MDM) system to support business initiatives and build an effective data strategy. Providing a single point of truth for business-critical data, MDM eliminates data silos and synchronizes enterprise-wide data from large databases. MDM helps harmonize and manage data related to products, customers, assets, locations, suppliers, employees, references, and materials. Effective Master data management implementation will simplify linking and syndicating information across digital and physical assets.
Boosting accountability, access control, and role-based security
Data governance and stewardship plays a crucial role in providing accountability, managing access control, optimizing role-based security, and ensuring regulatory compliance. The enterprise data strategy should be equipped with robust data governance to reduce the risk of improper access, data tampering, and leakage of sensitive information. Data stewardship ensures that data is stored, archived, and backed up to secure against internal issues, data theft, and cyber-attacks. Businesses should remember that data governance and stewardship must enable greater transparency and efficiency, as opposed to confusion and bureaucracy.
Consistent checks to improve data quality
Though data needs to be visible and accessible to the right people on demand, data management teams must ensure that frequent access does not alter its fidelity and integrity. A strict control framework in place checks and monitors data quality regularly for inconsistencies. It organizes and categorizes internal controls through stringent policies and procedures to create optimal business value. Establishing Interim data checkpoints helps steer the organization towards its business goal by ensuring that the data strategy is accessible, scalable, and actionable.
Setting up cross-functional collaboration
For the centralized data strategy to deliver value, businesses must ensure cross-functional collaboration between product, technology, and business teams. Building scalable multidisciplinary teams with diverse, complementary skillsets and experience will promote data competency and data-driven culture across the organization. Creating a centralized hub or dashboard will enable the relevant teams to access data, share intuitive analytic tools, and generate insightful reports. This succinct communication empowers organizations to uncover actionable insights and allow data literacy across functions.
Measuring the impact of the data strategy
Businesses must measure the performance of the data strategy to manage it effectively. Defining proper metrics is critical to understand the efficiency of the data strategy implemented. It helps re-evaluate and refine data management processes and procedures to ensure optimal data management.
Success is 20% skill and 80% strategy! Businesses should take a holistic approach to adopt a centralized long-term data strategy for continuous growth. As data strategy is the cornerstone of business strategies, it requires long-term vision and efficiency. The best practices deliberated above will lay a foundation for a robust, long-standing centralized data strategy.
Latest posts by Dinesh Kesavan (see all)
- Product Data Syndication with Product MDM for Retailers and Distributors - October 22, 2020
- Why data unification is the only alternative for delivering seamless experience - September 29, 2020
- Transform your Business with Best Practices in MDM Implementation - September 15, 2020