Introduction to PRISM: Skills

In our blog, ‘Adapting to the Times: Thoughts and Practical Recommendations from an Analytics Perspective’, our CEO outlined some practical recommendations to support organisations adapt through the uncertainty we all found ourselves in. 

That uncertainty has driven an acceleration in digital transformation, change programmes and other initiatives that look to optimise the customer experience through better use of data. As organisations increasingly look to make evidence-based decisions through the lens of a customer-centric approach, we’ve talked about the importance of measurement, the importance of data strategy and the importance of analytics tools and techniques to help drive an analytics strategy. 

In this next blog in our ongoing PRISM series, we discuss the importance of people and skills to build that analytics strategy – a colleague-centric focus to support the customer-centric focus that many organisations are striving towards.

Quite often it’s easy to focus on tools, technologies, reports, and data when considering analytics initiatives. Of course, these are all important and crucial, even. However, in order to accelerate the value and role of analytics and to bring to bear a framework that scales to the ambition of delivering data-driven decision-making at all levels of a business, careful thought needs to be given to how to shape and deploy analytics processes and people.

As many readers will know, it’s often a challenge to find and recruit the right people with the right skills and experience to support analytics capabilities in whatever operational model is in place. And whatever the operation model, some key roles probably spring to mind – data analyst, business analytics, data engineer, data scientist or data architect. Each of these roles comes with a staggering list of criteria – from data warehousing and ETL skills, through to programming languages such as Python, R, to data visualisation techniques and critical thinking. And don’t forget the soft skills too from absorbing stakeholder requirements, to determining what stakeholders need from an analytics perspective, relating this to the business / commercial challenge and via a robust communication process, supporting the client’s end goals. It’s demanding work being a part of the analytics community within a business – especially when coupled with the urgency from others – eager to get the report, the dashboard or the key insight!

In the context of our PRISM methodology we consider the impact of analytics across the entire business – how these analytics experts interact with stakeholders and therefore how analytics is embedded and distilled across an organisation. Success or otherwise of initiatives such as promoting self-serve analytics can be heavily influenced by how analytics expertise is distributed across the business.

Putting together the appropriate skills, roles and expectations in an effective analytics operating model is critical to successful outcomes for analytics. Critical also for the acceleration of analytics maturity.

Typically, most organisations fit into one of three broad operating environments for analytics. First, we have a centralised analytics function which sits outside of business units but provides the overall capability and acts as a centre of excellence. Second is the idea of a decentralised network of analytics experts – often aligned within business units, supporting each unit with a distinct lens of support that may or may not join up when thinking horizontally across the business. Last, there is the idea of a hub and spoke model – a more federated approach where there is a matrix organisation providing a hybrid of the first two approaches.

There are of course other ways to embed analytics in a business. Regardless, each of these will have distinct challenges in terms of executing a people strategy that supports the effective and sustained flow of insights to drive the desire for data-driven decision-making. In practice, the ideal model depends on the company’s priorities and the maturity of its analytical capabilities.

Whatever the model, it’s important to realise that alongside a data strategy, alongside a change programme (e.g. digital transformation) – which may bring a pivot to customer-centricity, for example – investment in a people plan is essential to fuel the processes that takes data from point of capture and turns that into valuable insight at the point of need. For example, it might be beneficial to take a service design approach to analytics and think about all the touch-points, all the interactions between departments and business units, and to think about how best to align experts to support that design and scale up the ambition.

As a core part of our PRISM methodology, we unpick all the human interaction points across the data lifecycle and to develop people strategies to support that – including training, role definition and skills development. It is only then that you can connect all of the pieces of the analytics strategy and bring influence to decision-makers. A people-centric analytics strategy to support customer-centric delivery!

To find out more about Lynchpin’s approach to analytics and data strategy, please visit our Benchmarking and Strategy page. And be sure to follow us on LinkedIn and Twitter to catch all of our latest updates.