Process

An actionable guide for small and medium-sized businesses to acquire the best practices to become data-driven.

Introduction

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:

  • Strategy
  • Process
  • Data
  • Platforms
  • Analysis
  • Culture and Skills
How Data-Driven is your organisation

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:

  1. Demand Pipeline
  2. Adoption
  3. Reporting Lifecycle Management
  4. Business Processes

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.

2. Adoption

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.

Women_in-data-collaboration 2

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.

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3. Reporting Lifecycle Management

In our data and analytics maturity framework, reporting lifecycle management is defined as:

“The processes in place to add, maintain or remove data and dashboards.”

When data becomes stale, its business value depreciates. An effective lifecycle reporting structure involves maintaining and removing dashboards where necessary to keep everything fresh. That means up-to-date and relevant data, or the removal of data and insights that simply aren’t being used, as well as the continuous review of the most accurate and appropriate data sources.

This last point is crucial when it comes to the performance and speed of your dashboards; as more data and calculations are added, an improperly managed dashboard risks becoming slow, which will deter your teams from using them.

As such, you need to make sure there is someone within your data team assigned to maintain your dashboards, and get rid of anything that’s either out-of-date or unused.

In addition, the navigation of dashboards can get complicated as more datasets and categories are added, so must be revisited to ensure a coherent storyboard is maintained.

Successful reporting lifecycle management can rid your operations of ‘grey’ area reporting, in which the lack of reliable data leads employees to creating their own interpretations when using it. In turn, this will have a broader impact across your organisation, with different teams acting on different datasets, for different purposes.

If adoption is about establishing the use case for data and analytics, then data lifecycle management is about putting the reporting standards in place to support that use case. It’ll keep your data fresh, your insights valuable and your teams invested in getting the most out of your data and analytics processes.

4. Business Processes

In our data and analytics maturity framework, business processes are defined as:

“The quality of documentation of business processes.”

Processes that are documented can be measured. In the most mature organisations, business processes are fully documented across the whole organisation in a consistent manner and kept up to date. To get started on this documentation, you need to understand how data flows through your organisation – who uses that data (and how) and in what order?

A good analogy for the benefit of strong business processes is that of symptom/diagnosis. In order to diagnose and treat a medical condition, you need to understand where the symptoms come from and what they mean to diagnose the problem.

The same goes for your business operations; if you understand how things are supposed to work, and have documentation in place to identify where any issues are arising, you can effectively diagnose and ‘cure’ the issue.

As a specific example, merely measuring the % delivery on time as a metric tells you nothing around WHY late orders are being delivered late. In order to diagnose the drivers behind the late delivery, you need to break-down the the different steps of the order to a more granular level, and track the performance of the component parts of the process. You can then start fixing the process and improve the overall performance of the delivery on time KPI.

Having this documentation in place will enable you to embed analytics into the decision-making process of every department within your organisation. The greater your understanding of operations and workflows, the clearer and more informed those decisions will be.

For SMBs, effective and focused documentation of business processes can mean the difference between an aimless (and ultimately failed) data and analytics approach and a successful one.

Summary

By focusing on building these four dimensions, you can embed data and analytics maturity into the DNA of your organisation, driving adoption and fostering data-driven decision making. Ultimately this will enable you to establish a platform for success, develop a cohesive understanding of how your business works and improve performance through relevant, up-to-date insights.

The tips contained in this guide can be used wherever you are in your data and analytics maturity journey, regardless of the scope of your resources, and will help ensure success in implementing the other five pillars in our data and analytics maturity framework.

If you want to learn more about how to transform your business with data and analytics, check out our maturity framework or contact us so we can have a chat about your requirements.

The Data-Driven SMB

An actionable guide for small and medium-sized businesses, on how to become more data-driven in an increasingly digitalised economy.

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