Data drives business. There’s no way around it. Handled correctly, data and analytics can deliver multiple avenues for competitive advantage, from improving operational efficiency to delivering better, more targeted products to customers. It can also help businesses to understand the marketplace in which they are operating.
The ability to handle, interpret and use large volumes of data is more important than ever, and that applies to businesses of all shapes and sizes. Traditionally, data-driven decision-making has been the preserve of large enterprises, or start-up organisations built on modern platforms designed to collect and analyse data (e.g. Uber, Spotify, Netflix). Inevitably, deep pockets were required to invest in the necessary technology and specialist expertise.
Luckily for small and medium-sized businesses (SMBs), times are changing. Data and analytics technology is more accessible than it has been in the past and, as such, SMBs can access enterprise-grade solutions and potentially transform their organisation’s digital operations.
But it’s no good having all of these tools without the maturity to use them. With that in mind, we’ve designed a data and analytics maturity framework to help SMBs build the capability to take advantage of data management and analytics tools to grow their businesses. That framework is broken down into six key categories:
- Culture and Skills
Process is fundamental to the overall data and analytics maturity of an organisation. Without defined processes in place to drive the adoption and use of data, and documentation to ensure best practices, a data and analytics approach can become fragmented. In turn, this will lead to confusion and a loss of faith in the value that data and analytics can bring.
In our data and analytics maturity framework, the process category is broken down into four dimensions:
This page will explain the importance of each of the dimensions and offer tips to implement robust, value-adding processes within your organisation.
1. Demand Pipeline
In our data and analytics maturity framework, demand pipeline is defined as:
“The processes in place to receive, filter, prioritise and fund incoming analytics requests.”
These processes are extremely important because they establish how your analytics team deals with incoming requests. Once you’ve established a data and analytics team, and established a data-driven culture, the team is going to start fielding requests from all different areas of the business – some small, some large, some internal, some external – and the team is going to need some way to manage them.
Whatever the size of your organisation, analytics capability is a finite resource. As such, you need to make sure you have a methodology in place to understand, manage and prioritise demand for analytics from across the business.
In the most mature organisations, there are processes in place across the organisation to receive, filter, prioritise and fund incoming analytics requests on a frequent and regular basis.
Think about the quality and availability of data, as well as team resources, when choosing which incoming demands to prioritise. Your team may need to take time to collect additional data in order to handle certain requests, which will lengthen the timeline to execution of certain data projects.
The demand pipeline processes you put in place should ultimately keep ROI (return on investment) in mind, too. That means that, when filtering requests to allocate resources & funding, the two most important factors to consider are feasibility and value, i.e. how achievable is it and how much value will it deliver to your organisation.
Demand pipeline management is the first step in transforming the data you collect into tangible value. By instilling a purposeful, focused approach to how you use your data resources, you will give your team a foundation to succeed on an ongoing basis.
In our data and analytics maturity framework, adoption is defined as:
“The ways in which usage of analytics is driven into the organisation’s DNA.”
Adoption of analytics is crucial for every organisation, because quite simply, data and analytics insights that don’t get used are ultimately worthless to your business. As such, you need to encourage the use of these insights across your organisation, driving it into your company’s DNA so that it becomes almost second nature.
The first key-step to driving successful adoption of data and analytics within any organisation is to involve your end users in the development and build process. Ultimately, the end-users of data insights – this could be your marketing or sales teams, or even a potential client – need to be able to see the value those insights can bring to them.
Once you’ve consulted with the end users you can further drive adoption by creating detailed user guides in a variety of formats – text, video, gifs or infographics. Whichever format you decide on (and again, this will be determined by the needs of your end user), these guides must seek to educate the reader on how best to interpret and use the data insights they’re going to receive.
In the most mature organisations, detailed user guides are delivered via meetings and are accessible online, supported by on-demand videos/gifs.
Adoption is measured for all dashboards with clearly articulated success criteria. By measuring the adoption of data and analytics on an ongoing basis – tracking who is interacting with the data and how – you can further target and improve on your ROI.
As with any strategy involving data and analytics, successful adoption is top-down; you have to make sure those in executive positions (whether that’s a CEO or Managing Director) are using data and analytics in meetings and are allowing the insights to drive their business decisions. This will set an example to department heads and cascade adoption down throughout the organisation, making best practices standard practices.
By showcasing the true value of data insights and how they can really add to your employees’ roles, you’ll create the excitement and buy-in necessary to create a truly data-driven organisation.