This is the second article in a five-part series providing actionable insight, advice and guidance to small and medium-sized businesses on how to become more data-driven in an increasingly digitalised economy. Complete our live benchmarking tool to compare your approach to data and analytics with small and medium businesses from across the UK.
As we explored in the first article in this series, the ongoing pandemic has raised the expectation for decision-makers to be able to explain or justify their decisions. This has exposed the vital need for business decisions to be re-engineered, from a decision-making process historically based on gut feeling and intuition rather than on data.1 As such, organisations are increasingly leveraging their data to empower business leaders to make decisions based on facts, trends and statistical information.
Through embedding data and analytics into business strategy and digital transformation to create a vision of a data-driven enterprise, organisations are able to quantify and communicate business outcomes and foster business changes fuelled by data.
Achieving such a vision understandably requires a higher level of data and analytics maturity. And, while recent research continues to reveal that small and medium businesses have been investing significantly more time and money into their data and analytics initiatives over the last five years, many of these organisations are still falling short of achieving measurable business outcomes for their investments.2
That said, for organisations aspiring to advance beyond their low-data maturity rut, there are a lot of reasons to be optimistic. With advancements in technologies such as AI, automation, and cloud, organisations will be able to accelerate their data and analytics maturity by overcoming challenges such as data silos, poor data quality, lack of effective governance, and manual dependencies.
For business leaders keen to embark on this journey, the starting point must be to first understand what the necessary competencies are for achieving greater data and analytics maturity, and how to advance these competencies to become more data-driven.
How do I measure the data and analytics maturity of my organisation?
As part of our ongoing efforts to empower business leaders with truth and certainty from their data, TrueCue has designed a comprehensive data and analytics maturity framework to help small and medium businesses (SMBs) confidently navigate to the next stage on their unique maturity journey.
The business value of data and analytics must be based on business outcomes that are quantifiable and measurable, and that impact business results strategically and tactically. In order to optimise this business value, it is critical for business leaders to be as intimately involved in their data and analytics initiatives as they are with their enterprise-level business strategy.
However, organisations with low levels of data and analytics maturity often struggle to harness the value of their data assets, and to construct a strategy that directly connects their data and analytics initiatives to business goals and objectives.
Gartner recommends that business leaders seeking to overcome these challenges should evolve their organisations’ capabilities for greater business impact, by taking simple steps in the areas of strategy, people, governance and technology.3
Given this, and other research, we’ve developed a framework which assesses six key areas of an organisation’s approach towards data and analytics, with each area being interdependent to the other competencies and of equal importance for becoming more data-driven:
Strategy
An effective data and analytics strategy is dynamic – it’s not a rigid, step-by-step technique. Organisations should promote the creation of a well-defined strategy and operating model that conceives data-driven business opportunities, orchestrates organisational action and aligns with the corporate strategy.
Process
The processes you establish underpin all your data and analytics activities, drive best practice across each of the key areas in this maturity framework and support further adoption throughout the organisation.
Platforms
Investing in the right technology stack is key to the success of any organisation’s data and analytics initiatives. From data management platforms to business intelligence tools, building an infrastructure which facilitates a culture of data and analytics across the organisation is integral to becoming more data-driven.
Data
Sourcing, integrating, validating and governing multiple data sources securely, so that trusted insights can be generated, identified and immediately acted upon, is crucial for any organisation seeking to generate maximum value from their data.
Analysis
The success of any data and analytics strategy is directly related to the sophistication of its data analysis. This can be determined by the current level of sophistication of critical activities, such as the level of automation and how insights are being disseminated across the wider business.
Culture and skills
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.
The methodology
Our framework, as shown in figure 1., evaluates the current level of data and analytics maturity across each of these six competencies, based on four dimensions that are unique to each competency and scored between one (low) to five (high), based on the current level of maturity. For example, to accurately gauge an organisations ability in the area of “analysis”, the organisation will be measured across the dimensions of sophistication, automation, dissemination, and permissions and access.
TrueCue Data & Analytics Maturity Framework
An organisation may score highly in the “analysis” area based on its “sophistication”, but also score on the low-end of the scale in relation to its analytics automation capabilities. As the competencies are interdependent on each other, to improve an organisation’s data and analytics maturity a holistic approach should be taken, whereby all key areas need to be improved relative to one another. By doing so, businesses can ensure there is a seamless alignment between data and analytics initiatives, its people and the corporate strategy.
Once an organisation has a clearer understanding of its maturity level within each of these key competencies, it will be able to start prioritising the areas that require immediate improvement.
Data and analytics maturity benchmarking
To support SMBs embarking on their data maturity journey, we’ve designed an interactive benchmarking tool that allows business leaders to compare their current level of data and analytics maturity with industry peers.
Everyone who completes the assessment will receive a free personalised report, which provides insight into their organisation’s current level of maturity and how this aligns and compares with the wider SMB market. TrueCue will then provide actionable next steps on how they can improve their data and analytics maturity.
Data-driven organisations are more competitive, more resilient to external threats and better positioned to achieve their strategic objectives. Our framework offers small and medium-sized businesses a simple way to measure and compare their current level of data and analytics maturity, enabling them to logically navigate to the next stage on their journey to becoming more data-driven.
For more information please click here.
| This is the second article in a five-part series focused on the Data-Driven SMB – the next article will focus on strategy and process. Part 1: The Data-Driven SMB Part 3: Data and Analytics Maturity Framework: Focus on Strategy and Process Part 4: Data and Analytics Maturity Framework: Focus on Data and Platforms Part 5: Data and Analytics Maturity Framework: Focus on Analysis and Culture & Skills Release Date – 16/02/21 |
1 PwC’s Global Data and Analytics Survey 2016
2 Midsize Enterprises Need to Get More Value From Data and Analytics Investments – Gartner, 2020
3 Achieving the Business Value of Data and Analytics – Gartner, 2020