It's impossible to overstate the importance of data in the realm of business. Regardless of the organisation’s size, industry, or business model, digital marketers can't plan for optimal growth without discussing data.
While data is widely discussed in organisations today, there's a world of difference between basic reporting, utilisation of data, and sophisticated approaches like predictive analysis and machine learning. Currently, most digital marketers are operating somewhere in between.
But what exactly is data maturity? What are the benefits? And how do you reach a higher level?
Here is a step-by-step model to help you reach optimal data maturity to advance your marketing team to the next level.
What Is Data Maturity?
Businesses collect an enormous amount of data from their customers, employees, marketing surveys, and other organisations. What differentiates a success story from an average business venture is how the organisation uses the data.
Data maturity is a metric of how sophisticated a company's data analysis is. A high level of data maturity indicates data is deeply embedded in the fabric of the organisation. It has been incorporated into every decision that the company makes.
The Benefits of Optimal Data Maturity
Data maturity isn't an absolute concept. There are different levels of analytic maturity. Organisations across the globe are talking about data analysis and are on different levels of analytic ability.
Improving business maturity is good for your business. Data-driven companies are 23 times more likely to acquire customers, 6 times more likely to retain customers, and 19 times more likely to be profitable.
More than 40% of brands plan to expand their data-driven marketing budgets, and 64% of executives strongly agree that data-driven marketing is essential. Many companies are already looking forward to an AI-powered future. In fact, leading marketers are twice as likely to master the use of machine learning than industry laggards.
Marketing leaders may understand the importance of data analytics, but it's still too easy to get lost in a sea of tools and terminologies.
Only after you understand the stages of data maturity and your stage among them, can you refine your strategy and the technology you would need to support it.
If you think your organisation has a sound data strategy, it's a good idea to gauge the state of data maturity, because a higher level of analytical maturity results in a higher chance of growth.
The 5-Step Approach to Optimal Data Maturity
Data is an asset, but only when a business gathers, stores, and analyses it within a defined setup and structure. To derive meaning from the collected data, the company needs to function at optimal data maturity.
The below stages show the way a company can introspect its data processes and ask difficult questions regarding how to make the most of its data.
You should also use this approach as a roadmap. You can see where your company sits on this roadmap and use it to make long-term plans, which will help develop your data team into a powerful resource.
1. Business Reporting
Business reporting is where your data journey begins. SMEs and startups are on this level. At this stage, data is collected and reported in silos.
Businesses on this level have recognised the need for data analytics but have not built any kind of structure to do serious analysis of the data. These organisations export their Salesforce or Google Analytics data into a series of spreadsheets and store it on a local device.
There's no blending of data for cross-functional analysis on a data platform.
2. Business Intelligence
This is the stage when you start blending data into a single warehouse. By now, you should have a more holistic view of your data, and you are seeing a bigger picture emerge.
At this moment, you wouldn’t have different sources for your Salesforce and Google Analytics data. You would have one source for all your data.
3. Ad Hoc Analysis/Insights
Ad hoc analysis is the stage where organisations start gaining autonomy in the analytical question and answer process.
In the previous stages, you could get answers from the data but couldn't ask unique questions. Now you can. At the ad hoc analysis stage, companies must have an independent data team sophisticated enough to use SQL, Python, or R to create their own models.
4. Hybrid Centralised Data Teams
By the time you reach centralised data, you should have already internalised data usage into decision-making across teams. The demand for data is high. It's time to find and create ways to prioritise data requests to make the most of the data teams' resources.
At this stage, there's a shift in the organisation’s structure to a hybrid data collection model. Your centralised data team should be responsible for collecting data and building sound business models.
The company will start having individual analytics embedded in each business function. It will be in charge of answering function-specific questions. At this stage, an organisation's data team will be considerably large.
5. Predictive Analytics and Machine Learning
Predictive analytics and machine learning are the most advanced stage. Companies at this stage have the technologies and tools to answer questions other businesses aren't even thinking about.
Predictive modeling is all about real-time analysis of information to make decisions about future products, markets, customers, staff, and more. Organisations using predictive analytics are investing a hefty sum in technology and people.
They are looking at making fundamental improvements to the company based on sophisticated data models.
Data maturity is a must to derive meaning from the information you collect. This 5-step data maturity metric can help you understand if your company has mastered data maturity.
And the companies that are reaching a higher level of data maturity, are the ones that will be able to provide their marketing teams with the data they need to create highly effective marketing campaigns in the future.