As part of our PRISM blog series, we have talked about the importance of business culture (Perspective) and Reporting as key enablers for an effective analytics strategy. In this next blog, we will discuss Information as a core component of the analytics strategy jigsaw.

Indeed, whether the need is tactical – for example, to build out more effective dashboards or to build out a complex analytics roadmap that enables a data-driven marketing automation, the need for a data strategy to sustain analytics maturity is self-evident.

Research has shown that less than 50% of corporate strategies explicitly reference data and analytics as an enabler – however that is expected to change significantly in the next 5 years as leaders recognise data as a core and vital workstream in securing competitive advantage. Indeed, other research shows that 87% of executives feel that they are not taking advantage of customer data today.

Business leaders will look to a data strategy to seek commercial advantage. Employees will want to understand how effective data strategy can bring productivity improvements and empower teams and individuals to success. Whatever the requirement, there are some basic and consistent things to consider that will help to identify and prioritise those initiatives that will assist in securing quality and value across data sources.

Data as an asset

Data is fundamentally an asset, an asset whose value is recognised only when it is of sufficient quality and can be turned into relevant knowledge for business decisions to be based upon. Unlocking that value is core to a successful strategic approach to analytics and sits at the heart of each PRISM dimension. But without the right data architecture and infrastructure in place to enable analysis and execution across channels, its value can become highly constrained. Or, even worse, insecure and badly managed data can represent a looming business risk. Ensuring that data is an asset will mean businesses allow that data to flow across the business seamlessly to point of need. Removing departmental silos and removing any friction around vendor lock-in are key tactics in a wider strategy.

Deciding what to capture is vital

Choosing what to capture, where to capture it, and how and where to process the data can fundamentally enable or disable future sales, marketing and customer experience capabilities. And that is even before the myriad of technology solutions and regulatory requirements enter the fray. Today, there are enough technologies of such quality that businesses can accelerate value creation by connecting data capture to business process and outcomes, so that analytics and insight can drive tangible benefit – for example through unlocking addressable customer segments or enabling a measurement of friction points in a sales process. Having a well-articulated measurement framework based on business and commercial needs will help to inform data capture and data connections.

Resilience is paramount

Correctly anticipating future business requirements is critical to making the right technology and architecture choices. Without the right focus, it is easy to simultaneously under-invest in the right things and over-invest in the wrong things.

So, before you embark on a data strategy, here are 4 key considerations:

  1. Identify current and future use cases for data infrastructure, in highly practical terms that are aligned with commercial objectives, (for example improving customer experience), and supporting the efficiency of internal teams.
  2. Always look at total cost of ownership of different architectural and technology approaches to meet those needs, focusing on time to market and value. Initially costs may not always be obvious.
  3. Build a detailed and commercially prioritised roadmap ready for practical execution, whether you are starting afresh or migrating and evolving an existing approach.
  4. Integrate compliance and security from the outset as an embedded way of thinking, rather than a bolt-on. This makes adherence to regulation, such as GDPR, central to the proposition rather than an afterthought.

How we use these considerations to build up a robust data strategy can often be a challenge. The hidden choreography ‘behind the scenes’ to balance the interactions between data modelling, data governance and data quality always needs to be prefaced with a business vision that recognises the value of data both for the business needs of today and the strategic needs of tomorrow.

Governance, models and quality all need to interact in the right way to enable success – planning the delicate steps in an effective process is something that every business needs to consider.

In our next PRISM blog, we will discuss people and skills and why investment here is essential for analytical growth. To learn more about Lynchpin’s approach to analytics and data strategy, please visit our Benchmarking and Strategy page.