In today’s world, data has become one of the most valuable assets for organizations of all sizes and types. With the explosion of digital technologies, businesses regularly generate and collect massive amounts of data. However, simply having access to this data is not enough. Organizations need a clear and well-defined data strategy roadmap to leverage this data for business growth.
A data strategy roadmap is a comprehensive plan that outlines how an organization will manage and use its data to achieve its goals. It helps define the objectives, priorities, and key initiatives an organization needs to undertake to effectively use data to make informed decisions and drive business success. (Download a guide on Building a High-Impact Data Strategy)
A survey by New Vantage Partners found that while 97% of executives surveyed reported having a data strategy in place, only 17% reported being data-driven organizations. There are multiple reasons behind this misalignment. In this article, we will review common challenges to creating an effective data strategy roadmap that would enable the digital transformation of the companies.
Strategy
- Lack of alignment: A data strategy that is not aligned with the business strategy may not get buy-in from key stakeholders. Data strategies developed in a “technology bubble” often fail. To overcome this hurdle, always develop the organization’s data strategy roadmap in close collaboration with business stakeholders.
- Lack of clear goals and objectives: Without a clear understanding of what the organization hopes to achieve through its data strategy, it can be challenging to develop a roadmap. To tackle this issue, organizations should establish a clear vision for their data strategy, set specific goals and objectives, and communicate them effectively across the organization. Begin with the end in mind.
Leadership
- Lack of executive support: Executive support is essential for creating and executing a data strategy roadmap; the lack of which will make it harder to get the resources and funding needed to be successful. Organizations should address this by presenting to the C-suite a business case that clearly articulates the benefits of the data strategy. Communication and education are the key to ensuring everyone understands the importance of the data strategy and their role in its success.
- Lack of organizational focus: Leadership should avoid getting sidetracked by tactical concerns and remain true to the established company objectives when implementing a data strategy roadmap. Organizations need to develop a clear and concise data strategy that articulates the organization’s goals for data, the key initiatives, and the metrics to measure success.
Data
- Lack of data quality: If the data is not accurate, complete, and timely, it is difficult to make informed decisions. A report by IBM found that 60% of organizations are struggling with data quality issues that are hindering their analytics initiatives. Organizations can handle this hurdle by implementing data quality procedures and investing in data quality tools and technologies. Establishing clear data quality standards and processes with regular data quality checks will ensure ongoing data quality.
- Lack of data governance and security: As organizations collect and use more data, security, privacy, and governance become more important. The data should be handled securely and in compliance with relevant regulations. Organizations should establish clear data security and privacy policies, invest in security tools, and ensure ongoing compliance through regular monitoring and review. Clear communication and education can also help staff understand their role in maintaining data security and privacy. It is also highly recommended to have a data governance and security officer or team to oversee the data governance process.
- Siloed data: Siloed data within different departments or business units can make it hard to develop a cohesive strategy. It’s important to have a unified approach that encompasses all relevant data. Organizations should establish a data architecture that enables data sharing across departments and business units. This may involve breaking down silos, establishing data governance processes, and investing in data integration tools.
Technology and Resources
- Lack of technical expertise: Developing a data strategy requires technical expertise in data governance, data architecture, and data management, along with the right tools and infrastructure to collect, store, and analyze data. It requires evaluating the current data technology landscape, identifying the gaps, and developing plans to fill the shortcomings. Organizations should invest in hiring or training staff with the required technical skills, establish clear roles and responsibilities, and acquire the necessary technologies. It may also be economical to work with external consultants or partners to fill any gaps in expertise.
- Limited resources: Implementing a data strategy roadmap can be resource intensive, requiring the right people, tools, and processes in place to be successful. This may include hiring new employees or training existing employees on data management and analytics. If getting additional resources is not an option, organizations should prioritize the initiatives, establish a realistic budget, and allocate resources effectively. Collaboration with partners can also help to leverage external expertise and resources.
People
- Resistance to change: Data strategy often involves significant changes to an organization’s processes, systems, and culture. Resistance to change can be a major barrier to implementing a successful roadmap. To overcome this roadblock, organizations should gain buy-in for the benefits of the data strategy and involve cross-functional teams in the planning and implementation process. Clear communication, education, and incentives can also help to encourage adoption and overcome resistance.
- Lack of data literacy: A certain level of data literacy is essential for understanding and using that data to make informed decisions. Meaningful insights can be derived from data only when one has a clear understanding of its nuances and characteristics. Organizations should invest in data literacy training for employees to equip teams with the skills to create a culture of data-driven decision making.
Processes
- Inability to measure progress: Without establishing metrics that measure progress toward the organization’s data strategy goals, it can be hard to identify the needed plan modifications. Establishing clear metrics to track progress, investing in analytics tools to monitor and analyze data, and regular reporting and review can ensure the roadmap stays on track.
- Poor change management processes: Change is a constant, and so change management is essential for implementing any new initiative. Preparing employees for the changes should be part of the plan. A comprehensive change management plan that covers communication strategies, training programs, and support mechanisms is necessary to facilitate the successful adoption of a data-driven culture within an organization.
- Lack of time: Implementing a data strategy roadmap can take time, and patience and persistence are critical for successful outcomes. Breaking the data strategy roadmap into smaller, more manageable projects and setting realistic timelines and milestones for each project helps with timely completion and deployment.
Summary
While many organizations recognize the importance of data strategy, they face significant challenges in implementing it effectively. To avoid failure, organizations need to take a holistic approach to data strategy focusing on building a strong data culture, establishing clear goals and priorities, developing a robust data governance framework, and leveraging advanced technologies to support data management and analytics initiatives. With the right execution and a clear understanding of the value of data, organizations can unlock the full potential of their data assets and drive business success in the digital age.
Navigating the hurdles for successful outcomes of a data strategy requires time, effort, and resources. So, organizations often turn to thought leaders such as QuaXigma to assist with developing and implementing a data strategy that aligns with business objectives. By applying its deep expertise in data strategy and digital transformation, QuaXigma helps companies of all sizes extract value from their data.
