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 sizes and types. Traditionally, data-driven business decision making has been the preserve of large enterprises, or brand-new organisations built on modern platforms designed to collect and analyse data (e.g. Uber, Spotify, Netflix) with deep pockets to invest in the technology and specialist expertise required.
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.
But it’s no good having all these tools without the maturity to use them. We’ve put together a data and analytics maturity framework to help SMBs build the capability to take advantage of data warehousing and analytics tools to grow their businesses. That framework is broken down into six different categories:
- Culture and Skills
This guide provides expert insight and recommendations on how to improve the culture and skills across your organisation, in support of achieving a higher level of data and analytics maturity.
Culture and skills can be defined as the approach to the development of employee skills and best practices with regards to data and analytics.
A data and analytics culture must fit within the wider culture of an organisation. If there is a clash between data culture and organisational culture, this will lead to friction and confusion and an ultimately ineffective data and analytics strategy. As such, the very first thing to remember when trying to build a data-driven culture is that it must be supported at all levels of the organisation.
Skills are defined by the people within an organisation at any given point in time – in the fast-paced world of data and analytics, these skills must continually evolve to help individuals and organisations meet their business requirements.
Culture and skills can be further broken down into four separate dimensions:
This page will explain the importance of each of the dimensions and offer tips to build data-driven culture and skills within your organisation.
In our data and analytics maturity framework, community is defined as:
“A defined and structured approach to creating and managing a people led community to drive adoption and develop skills.”
This approach is important because analytics can only be successful if the people within an organisation can understand and adopt the insights that are served up to them. Therefore, creating a culture of learning and sharing is paramount to embedding analytics in any organisation.
For more traditional organisations – those using legacy systems, for example – the process of transitioning to this culture can take longer and require more support. This can be due to the inherent human nature to reject or resist change, but it’s not impossible and the benefits of a data-driven culture make it worthwhile investing the time to build one.
Having a focused approach to community is also important to data maturity because effective analytics is not a ‘single-shot’ solution to the challenges of business. Building data maturity must be an ongoing process of development and evolution. As such, it’s important to instill a data-driven culture within your organisation that supports everyone in the organisation to interpret, understand and implement data-driven decisions on a continual basis.
In the most data-mature organisations, structured community activities take place on a regular basis, driven by defined community roles, and there is widespread awareness across the organisation of the importance of data and analytics and best practices.
For SMBs looking to develop this level of maturity, there are several ingredients to building an effective data and analytics community. Firstly, don’t be afraid to look externally. Inviting external experts into team sessions to showcase their methods or products, for example, can help to gain a different perspective on your analytics and broaden knowledge.
Equally, collaborating with other organisations can give you an insight into what other people need with regards to data and analytics.
In such an interconnected business landscape, an insular approach to a data-driven community isn’t enough, so be sure to make your business an outward-facing one when developing your community.
Internally, the most important thing you can do to ensure you build an effective community around data is to give employees time and encouragement to develop their skills and understand the part they can play in the overall culture. If you offer employees access to learning and development programmes (more of which later), but ask them to complete these in their own time, you’re unlikely to see the level of engagement required to build community long-term.
Instead, make these activities a part of the working day and emphasise how they can help individuals with their work and improve things for the business as a whole.
2. Best Practice
In our framework for data maturity, best practice is defined as:
“The measures in place to ensure development and quality standards are met and align to predefined corporate standards.”
Best practices are best thought of not as rules but as guard rails to guide your data and analytics community in carrying out their work. This is mainly about having documentation and processes in place for methods that offer continuity throughout the organisation, rather than a prescriptive set of rights and wrongs.
The possibilities in analytics are endless, from the tools you use to carry out certain jobs to the methods you use in data management and how you present the data visually. With no guard rails or standards in place, you can run the risk of data being presented in mismatched formats or inconsistent use of data sources, which will lead to faulty analysis that doesn’t help anyone.
In the most data-mature organisations, advanced processes are put in place to enforce documentation and application of best practices, whether that’s in the use of Artificial Intelligence or the way analysts visually present their data insights and stories.
For SMBs looking to develop these processes, a sensible first step is to develop style guides, including best practice examples, and a range of documentation that sets a precedent and guides your employees in how they should interact with and use the data they work with.
As we already mentioned, the possibilities are endless, which means there will always be a case for doing things a certain way, but if you want to ensure consistency and build the foundation for great performance over time, the establishment of processes is key to ensuring style guides and documentation requirements are adhered to.
For example, for employees or organisations at an earlier stage in their analytics journey, it might be advisable for these best practices to be more rigid. As your team grows more comfortable in their understanding and use of data, they can move away from set-in-stone rules to guard rails that guide your data and analytics community in their work. The standardisation of these best practices will help to ensure consistency and build your authority within data analysis.
However, it’s important to keep in mind that strict rules can sometimes stifle creativity and turn analytics into a factory of report creation. Whatever standards you have in place, make sure they still allow your employees room to think outside the box.
Rules-based tasks will, of course, require more strict governance, which is why a hybrid approach to data and analytics processes is best to enable true data maturity across all levels of your organisation.