Data maturity is the journey a business or organisation takes towards increasing capabilities and improving its use of the data that it generates, essentially it is a measure of how advanced data capabilities are. An organisation with high data maturity would be characterised by having data deeply entrenched into the organisation’s core, incorporated in every decision making process across all different parts of the business - from product development to finance to marketing to service delivery.
In today's modern world you would be surprised to understand that whilst data is viewed as important by business leaders it is not always prioritised as much as it should be in the manu circumstances.
Why is it important for any business to examine and understand how mature its data strategy, management and utilisation?
Technological advancements have meant that organisations now gather and process more data than ever before - but more data does not always mean more insight and better decision making, in fact the opposite is true. Quite often more data means less understanding as it is harder to use the data in order to gain a true perspective on what is happening in different parts of the organisation. This means that data maturity is quite strongly correlated to company financial performance.
Data is an asset, but only when it is gathered, stored and analysed within a defined setup and structure - some of the key areas that should be assessed by organisations when looking at data maturity are:
Strategy & Vision - becoming data driven does not happen overnight and there will be bumps in the road, many failures before there are successes. For any data strategy to be a success it must be supported by business leaders - it is vitally important for this to be in place and for data to be part of the overall company strategy rather than an add on that is viewed as “nice to have” rather than “business critical”.
Governance - this is not a glamorous topic area, but without effective data management and governance procedures in place an organisation will struggle to gain meaningful insight from its data. To ensure data quality business must ensure that the data collected is relevant, flexible and accurate. Standardised data management is required across the business so that you can enable comparisons to be made & real insight achieved.
Data Assets - This is a very crucial part of the company’s decision making process. Data assets refers to any information regarding the company’s products, services and overall operations. Capturing, transforming and enriching these data assets helps a company identify patterns, processes and opportunities to improve performance and make better more effective decisions.
People - One of the biggest challenges & opportunities that data leaders highlight is people. People can make or break the success of your data strategy. I have lost count of the amount of times I have heard data leaders say “the tech is not the issue, but the people usually are”. This is not because they want to be problematic, but changing an organisation's mindset to become data driven is not as easy as just saying you want to be data driven. It takes a cultural change that must be bought into by all parts of the business.
Skills - This focuses on the specific skills within a data team, an organisation’s ability to support data management lifecycle and delivery ranging from data architecture and integration, project management, data science and analytics.
Architecture - When it comes to architecture in an organisation’s data journey, this refers to alignment of infrastructure and systems such that they can support business applications and data management in a secure, flexible and scalable manner. To reach higher levels of data maturity organisations will invest heavily in architecture as this acts as the foundation for success.
The Development of Data Maturity
From the basic level to the highest levels of data maturity modelling, data management is absolutely imperative to being successful with your data strategy. Many organisations do not place a value on their data which can make it tricky to justify investment in data management solutions.
However, forward thinking companies and those that have success with data have all been seen to prioritise the management and governance of the data that they collect. All companies regardless of their revenue size, industry or competitive environment rely on their data to help them make effective decisions. Put quite simply, you are only as good as the quality of the data that you gather.
Data management means that a company has to establish roles, procedures and responsibilities that ensure the acquisition, maintenance and disposition of data. Each of these relies on a combination of resources: people, technology and processes. Whether an organisation is just now embracing data management or is at the height of its data journey then it is important to ensure that data management & governance standards are mature. When organisations are starting their data journey this starts with combining data sources in one place and then gradually proceeds into running advanced queries in a language such as Python and also using predictive machine learning models.
The majority of data strategies start with spreadsheets - this may sound basic, but most beginner organisations store the majority of their data in excel or other spreadsheets, so this it makes sense that this is the starting point. Next comes to the targeted presentation layer where dashboards are created to disseminate information in ways that are easily digestible and this is where decision making can really start to be improved. After this, an organisation can add machine learning discoveries as an added improvement to the self-service dashboards. Finally, the highest level is automation where the organisation automates discovery and hence, achieves immediate installation into real-time processes on the data.
In a nutshell, organisations that develop the use of data and improve their use of advanced analytics to develop data maturity. These characteristics of an organisation’s data journey are identified as the five stages of data maturity where the organisation looks at their data processes and figuring how to improve it as well as make long-term plans to turn their data into a formidable resource in a bid to be industry leaders.
These six stages include:
Business Reporting
This is the beginner stage of a company’s data journey. This is where early-stage startups and SMEs start and involves collecting data for their own records. During this stage the organisation has recognised the need for data, but have not built a structure to do serious analysis of the said data because they don’t really need to. Hence, sales, purchasing and marketing data are often in a series of spreadsheets on a local device instead of blended together in a data platform which helps improve cross-functional analysis.
Business Intelligence
This stage of the data maturity model is where an organisation starts to blend data together on a single platform. This process helps to give a more holistic view of the data allowing them to see the emergence of trends and the bigger picture. It enables the organisations to ask better questions and make more effective decisions to create better outcomes leading to improvement of company performance.
Ad Hoc Analysis
When it comes to this stage of the data journey organisations start to gain more autonomy, in the previous stages organisations get answers from data sources but are unable to ask their own questions. However, in this particular stage, companies can start to really challenge the data to deliver, this stage will require an independent team that creates their own data and the team will have to be able to utilise SQL, Python or R.
Centralised Data Teams
When an organisation reaches this stage the data analysis is sophisticated, such that it becomes an ingrained part of every team’s operations across the organisation. Each team or department is able to make use of the data resources and data starts to be viewed across the business as vital to the overall operations.
The organisational structure becomes hybrid such that while there is a centralised data team to collect information into a single truth well as create data models, there is also the presence of individual analysts in different business functions and departments within the company who can be answer questions regarding that line of business and how data is collected and analysed to improve specific functions. This stage requires a bigger investment in personnel, technology and tools and also involves governance and engineering requirements that were previously not required.
Machine Learning and Predictive Analytics
This is the final stage for data operations which is the highest level of data maturity models for the most forward-thinking companies. At this stage, there is a huge involvement in cutting-edge tools and technology both for decisions as well as for business forecasting. Companies who reach this stage are definitely asking questions that other companies may not be considering yet, analysis of information and data is used to make decisions on future markets, products, customers, personnel and more. At this stage, data is not just a way to run the business well but all data operations are geared to make great improvements to the company and its performance and are based on sophisticated data models.
To reach this stage, a company has to make significant amounts of resources available across all parts of the business. Data operations are more sophisticated and precise and to achieve this, there is a need for a heavy investment in people and technology. This can result in a big payoff for the company and its financial performance, however, return on investment can take a long time and therefore, companies at the higher levels of data maturity should manage their expectations and keep a realistic mindset.
Advancement in Data Management
With data journeys, it is impossible to skip a maturity level and therefore, attaining momentum as well as retaining is key to the success of the organisation’s data journey. Though, the data maturity evolves based on how the company remains committed to its data driven success. This means that not only is it important to create projects that utilise the advanced levels of data maturity, but also utilise progressive elements of the data maturity model to deliver wins by correlating data with product availability and risk management among other factors. Achieving the highest level of data management is not an easy task especially because it involves changing processes and managing strategies and technologies. However, treating data as a crucial strategic asset will help the company invest in great data management operations which continuously keeps the business more competitive.
If you are interested in finding out more about how leading organisations are utilising data to drive transformation across their business then please join us at Data & AI Sofa Summit on 21-22 July 2020 - https://www.sofasummits.com/data-and-ai
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