Data is one of the most valuable assets a business can have.
It’s what allows businesses to understand their customers, make better decisions, and stay ahead of the competition.
However, data can only be used effectively if it is properly managed.
Data maturity is the process of developing and implementing a strategy for using data to its fullest potential.
In this blog post, we will discuss what data maturity is and how you can leverage your data for maximum success!
What is Data Maturity?
In simple terms, data maturity may be defined as the extent to which an organisation is able to use its data in order to obtain useful insights that affect decision-making.
And by data, we mean all of the information your organisation collects such as:
- Your customers; who they are, what they buy, what services they use, what marketing materials they engage with etc.
- Your financial information; business costs, revenue, profits, payroll etc.
- Your staff information
- Supply chain information
However, if all of this data isn’t organised correctly, it’s simply a collection of numbers, names, and text.
Data maturity is not determined by how much data a business gathers; it’s measured in how well it processes, analyses, and uses that information to make profitable decisions.
That said, the more an organisation values its data, and the more sophisticated the methods and technologies it employs to process and analyse it, the greater its data maturity.
The Importance of Data Maturity
The more mature an organisation is in terms of data, the better able it is to perceive possibilities and dangers.
For example, a data-savvy business may use predictive and prescriptive analytics to not only predict what will happen in the future, but also what steps to take.
It can, for example, forecast how many sales of which items it is likely to make in a given month — and thus which consumers to attract with which deals.
In addition, it can evaluate which candidate would be the greatest fit for a new job opening or which supplier to choose for a certain line of parts or goods and how many to purchase to meet a predictable demand.
A data-mature company, on the other hand, can anticipate dangers or problems—for example, which months of the year sales are likely to decrease or when an employee is at risk of leaving the business.
With this data, the company may take proactive measures to direct events in its favour.
The 5 Levels of Data Maturity
Each business will approach their data strategy differently, and while there is no “one size fits all” solution, companies will vary in the maturity and effectiveness of their data function.
You might think of it as a ‘journey,’ from different teams beginning to embrace data technologies, to more advanced pioneers utilising behavioural data to drive the company’s competitive engine.
Here’s a quick step by step guide to help navigate your journey towards data maturity.
A ‘data aware‘ company is on the brink of beginning their data journey.
They understand that behavioural data may be valuable to other departments in terms of marketing and product development, but they have yet to establish a clear data strategy that considers broader business objectives.
At this level, an organisation may or may not have a data warehouse since it doesn’t need to collect and combine data from several sources.
What data it does have is compartmentalised and analysed using standardised reporting software, with spreadsheets and Google Analytics able to handle most use cases.
While there may be a lot of Analysts interested in behavioural data, no special data resource exists to guide the journey forward.
A data-aware business must first clarify what questions it wants to answer with data and what value this will provide to the company as a whole.
A data-capable company has started to draw insights from its behavioural data, but it is still facing significant hurdles.
Although a growing team of analysts is capable of executing some successful use cases, data is still compartmentalised across departments.
This prevents data insights from being shared throughout the company and lowers the likelihood of management’s further involvement.
At this stage, a company has already established a data warehouse, but it’s been used solely for backend or ERP data.
Behavioural data is managed using a packaged analytics platform such as Google Analytics or Adobe Analytics, and the team is becoming more aware of its restrictions.
In many cases, teams are hesitant to rely on data to make decisions due to a lack of trust in the company’s behavioural data.
In the absence of ironclad event tracking and data validation, staff are frequently wary about depending on information for decision making.
A company that is known as a “data adept” has invested in its data department, from a tiny group of data specialists to a centralised group of data engineers, analytical engineers, and analysts and scientists.
The firm is led by the Head of Data, who wants to learn how to get away from pre-packaged analytics solutions and have more control over their data and data infrastructure.
The data team has started the process of building a modern data stack by selecting a data warehouse, business intelligence (BI) solutions and ingestion tools as part of a data platform that can meet the demands of the company as a whole.
Internal stakeholders from marketing, product, and finance teams have been invited to a meeting to discuss their data demands, prioritise significant projects, and evaluate how the data team would fulfil the needs of their front line staff.
A data-informed firm has invested in its data team(s), embedded behavioural data into corporate decision making, and launched one or more “data products,” such as recommendation engines and/or machine learning algorithms.
Behavioural data is now a valued asset in the company, with senior executives recognising its importance and a culture of working together with data fostered among teams.
A centralised data platform allows for real-time monitoring and improvements to the data stack, making changes to data performance and keeping a close eye on data quality.
Data pioneers are on the bleeding edge of the data world.
Companies like Netflix, Spotify, and Airbnb integrate behavioural data into their products so thoroughly that it becomes a normal and expected component of the user experience, as demonstrated by Spotify’s song suggestions and Netflix’s suggested television shows.
At this level, behavioural data is inherent in everyday workflows and it is simple for all teams to access and use to achieve success.
It’s all about keeping the behavioural data flow among internal teams smooth, and coming up with innovative ways to make good use of data – in some cases returning behavioural data directly back to the user on an individual basis.
For the pioneers, finding and keeping the ideal employees is a never-ending struggle. Because they are constantly going against the status quo, data explorers prefer to develop their own rather than purchase data solutions.
Using this, you may be able to boost the performance of your home-brew infrastructure and support antiquated systems.
That being said, the innovators’ control over behavioural data has set them apart from the competition – giving them a significant advantage over rivals and imitators behind them.
Understanding data maturity and how to climb up each step can add a whole new dimension to a business – not just how it uses data, but how it performs overall.
Data-driven decision-making should be a norm in every business, and it all starts with data maturity.