Data use cases, data privacy, data management and data governance all need to be factored into your organisation’s strategic decision-making, and that’s where a data strategy comes in.
9Yards Senior Consultant Tatiana Konnova, says that while every business is unique, most share similar challenges and concerns around data.
‘A strong data strategy doesn’t just provide a target state view of your data landscape such as data sources, data flows, and data platforms. To be effective, a data strategy must tie in with business goals, describe relevant data use cases, and provide a realistic pathway to get to the target state.’
Why does your business need a data strategy?
Are your technology teams and leaders asking questions about how to:
- implement a ‘right size’ data and information practices
- integrate data to remove silos and fragmentation
- achieve the best possible business value from your data
- approach data analytics and reporting to meet the needs of customers and strategic decision makers
- choose a platform or technology solution that will optimise the quality of customer data
- improve the trustworthiness of our data, to build user confidence
- reduce the requirement for manual processes?
If any of these questions sound familiar, your data strategy is the place to look for answers.
Key components of a data strategy
A good data strategy doesn’t just list platforms or describe data flows. Instead, it considers what your organisation wants to achieve and how to get there from a data and information management perspective.
Some key elements to include in your data strategy are:
- Business alignment – Which specific business goals does the data strategy support?
- Technology impacts – What architectural changes are required to deliver on those goals?
- Data delivery model – Will you use a centralised, decentralised or hybrid model? What are the implications of each?
- People and process – What changes to skills, roles or workflows are needed to support the strategy?
- Industry trends and relevance – What are others doing, and what emerging technologies or use cases could be relevant for your organisation?
Top tip: The data strategy should provide direction without locking everything down too early. It’s a living document that informs decision-making, helps prioritise initiatives, and supports consistency over time.
An effective data strategy doesn’t exist in isolation
Your data strategy should be tightly integrated with other strategic planning assets – making it a practical tool that helps teams make decisions, allocate resources and manage change.
Data strategy and business strategy
A well-designed data strategy doesn’t just acknowledge the broader business strategy; it actively supports and enables it. Test this by selecting specific goals from the business strategy and mapping them directly to data use cases and initiatives in the data strategy.
Data strategy and enterprise architecture
The technology and architecture focus of the data strategy goes beyond the discussion of data platforms and data reference architecture. Consider: What will need to change across your enterprise landscape to facilitate the delivery of the strategy? Does your organisation have an established enterprise architecture and portfolio change management capabilities to enable successful implementation of a data strategy?What are the people and process impacts? Are there skill gaps in any teams that will be impacted by the data strategy?
Data strategy and target state roadmap
In the same way that the technology strategy interacts with the target state roadmap, the data strategy identifies goals that will need to be mapped and prioritised. How will data-related capabilities such as data governance develop to support your future state? The target state roadmap shows how the organisation will evolve over time – and that includes data.
Data strategy and your portfolio of initiatives
Your portfolio of initiatives for the next strategic term requires investment decisions and prioritisation. Stakeholders and business leaders responsible for making those decisions should also be involved in the development of your data strategy to ensure that the data-related initiatives and activities are included in broader business planning and resourcing.
How a data strategy underpins AI
If your organisation is serious about AI, it must also be serious about data. AI relies on trusted, well-managed, and well-understood data. Without a strong data foundation, AI efforts can stall, underperform, or even cause harm.
Your data strategy helps ensure that:
- the right data is available in the right format for AI and analytics
- data governance processes are in place to manage risk and ensure data quality
- ownership and accountability for data is clear across business and technology teams
- AI initiatives align with business priorities, rather than chasing novelty for its own sake.
A data strategy also helps to clarify where to invest effort. If teams are spending too much time cleaning and sourcing data manually, it will never be possible to effectively scale AI.
How 9Yards digital transformation consultants can help
Working with digital transformation consultants like 9Yards, you’ll have access to data strategy expertise that’s tested and reliable as well as insights about new trends and technology. This information could be invaluable for decisions about how you will structure your data team and how you can creatively adopt (or defer) new technologies, giving data the priority it needs, alongside your other goals and initiatives.
Reach out to 9Yards digital transformation consultants to understand how to get the most out of your data strategy.
