The overarching principle of a data strategy should be to enable your business to put data to work wherever it has a clear role to play in driving growth or optimising your operations. This covers all types of business data from operational data, customer data, web data to transaction data.
What are the components of a data strategy?
There are various key components that a good data strategy should contain in order for it to deliver on it’s objectives.
The components focus on:
Insight and Analytics
The first principal component of a data strategy is to develop a comprehensive understanding of the businesses insight and analytics requirements. This is the how and why the business wants to use data, analytics and data insights.
This step is important to ensure there is a centralised understanding of what needs to be created and how it will be used. Where data is concerned it is critical that this understanding is clear across all teams so everyone is using the same data and avoiding multiple versions of the truth.
Taking business-wide requirements and shaping them into a shared vision for data and analytics that the business can get behind is a good way to frame the findings into an actionable plan for the future.
The third and fourth components go hand in hand; they relate to the Data & Analytics Business Plan and the Quick Wins. The business plan needs to demonstrate to the business how the investment required in data will pay back and provide a roadmap of financial objectives that the business needs to be working towards along the data journey.
We believe that key to successful data strategy is when a plan recognises the importance of delivering Quick Wins and successes along the way. These can be identified through the requirements gathering phase and their benefits incorporated into the business plan.
Design and Development of Data Operations
The remaining four components are the more detailed definitions and plans for the design and development of the core aspects of capability needed for your data operations. These are:
Design and development of the Data Environment & Tools that will be needed. This is a huge area covering everything from architecture design, data security and governance design and implementation, data warehousing and management processes and tools and customer data management and enrichment.
The planning and set up for Analytics Requirements including the toolset selection, analytics process development and core strategic analytics developments such as the SCV, foundational analytics and processes around the customer data management and their analytics.
Organisational Requirements such as identifying the data leadership and operating model; talent planning, training and recruitment and the communications strategy across the business.
Finally, a core element is the design and implementation of Measurement and Reporting to support the data strategy and needs to include the KPI framework, executive and frontline reporting and visualisations.
Why is data strategy important?
It is unusual, except in a start-up situation to be defining a data strategy from scratch and far more likely to be a case of needing to coalesce a number of ongoing initiatives and streams of activity into a coordinated plan.
A critical role is therefore the ability of the data strategy to reflect and accommodate pre-existing data efforts and investments and secure the buy in of multiple different stakeholder groups.
This is important not just to ensure the business does not waste investments made to date but equally so that the teams already heavily invested in the frontline of data do not feel alienated or side-lined.
The Data Strategy also plays a crucial role on a number of other fronts. The coordination of data management and its analysis and subsequent use across different teams not only helps to ensure the investment in data is put to effective use and teams are not duplicating effort, but also protects the business from the downsides of poor data use.
These downsides can range from poor decision-making through inaccurate or misleading insights or reporting created through poor data governance and usage through to far more serious implications such as those that come from poor data governance and security.
The penalties both reputational and financial of breaching GDPR regulations are becoming more and more severe as the consumer becomes increasingly digital and there are no exceptions made for breaches through poor ill-defined governance and policies around customer data management.
Equally, a good data strategy plays a valuable part in helping the business to better understand and take advantage of big data analytics.
A strong and developed data strategy can guide teams through their journey across delivering powerful strategic analytics through to automated decisioning and targeting to drive multi-channel digital experiences for their customers.
How data science is used in data strategy:
Data Science can play a really key role in the development of the data strategy and in many ways can make or break a good strategy.
Quite often going around a business and gathering data requirements can uncover variations and wide gaps in understanding of what data is and what is possible. This can make the requirements gathering challenging to complete.
This however is a great opportunity for the data scientists to demonstrate the art of the possible through developing some initial analytics stories that show people things about their customers and their business operations that they didn’t know before and demonstrate what could be done.
These same analytics can then be used to identify and check the feasibility of potential Quick Wins and provide valuable inputs into the Business Plan.
How to create a data strategy:
A data strategy is much more than a consulting exercise with a start point and a finite end, it is a programme of work and a journey that if executed to plan should become business as usual as your organisation becomes truly data-driven.
As such creating a data strategy is as much, if not more, about people and how data can transform the way they work and deliver your customer experience.
This means creating a data strategy is predominantly about building agreement on the role and value of data to your business and the right journey to get there.
Ensuring common understanding of data, securing buy-in to the approach and continuous engagement and championing of successes along the way are all vital to achieving the right outcome.