5 Steps to Data Maturity in Marketing

Data is not just a buzzword - it’s the new worldwide currency. Being data-driven is no longer a luxury but rather a precondition to succeed in the market. But here’s the gist: a couple of decades ago companies were suffering from the lack of information, whereas today it’s quite the opposite - businesses are overloaded with data. In order not to drown in this turbulent sea, it’s essential to get the data under control, learn how to manage it, and profit from it.

Table of content

• What is “Data Maturity”?

• How is Data Maturity assessed?

• What are the steps a company needs to take to achieve high Data Maturity in marketing?

What is “Data Maturity”?

Data Maturity refers to the company’s level of proficiency in managing its data. This means not only the technology applied but also the capabilities of the employees and the processes established in the organization. A high level of Data Maturity means the business can make the most of its data and ensure optimal management decisions based on analytical insights.

Why is Data Maturity relevant in marketing?

The entire marketing process is based on data. But what kind of information is relevant here? Marketing is essentially about the customer, and the data used in marketing is also about the customer. It encompasses market research findings, competitor data, information about products and prices, marketing plans, budgets, marketing campaigns, and sales. This means an enormous amount of information that has to be collected, processed, analyzed, and delivered to decision-makers across the organization, not only in the marketing department. The success of marketing - and consequently, of the entire company - depends on the degree of Data Maturity.

How is Data Maturity assessed?

There are several approaches to Data Maturity assessment and achievement. One of the most well-known models was developed by Dell. It classifies companies into four groups depending on their level of Data Maturity: data-aware, data proficient, data-savvy and data-driven. The distinguishing feature of a data-driven company is that data is embedded into all processes, and every decision is informed.

Dell Data Maturity Model

Another famous approach is the Gartner Data Governance Maturity Model which classifies companies into six groups depending on their data management evolution stage: unaware, aware, reactive, proactive, managed, and effective. An effective company does not only achieve information excellence but also uses the information to its competitive advantage.

Gartner Data Governance Maturity Model

The Snowplow Model also applies a similar approach and breaks the journey to Data Maturity into five levels: data-aware, data-capable, data adept, data-informed, and pioneer. The latter group includes such companies as Netflix, Amazon, or Airbnb who are capable of creating personalized experiences for every user based on his or her preferences and past behaviors. These market players are known for applying machine learning methods and operationalizing them in real-time.

Snowplow Data Maturity Model

So what are the steps a company needs to take to achieve high Data Maturity in marketing?

In this case, we are talking specifically about the marketing department, but it’s impossible to reach a high level of Data Maturity in marketing alone if the rest of the organization is not involved in the process. Data Maturity should become the priority for the entire company and the transformation needs buy-in and support from all decision-makers.

Let’s try to summarize all the prerequisites for a company to progress along the Data Maturity scale and achieve the ultimate stage of development in this respect.

Step 1. Identify your data needs

Which data is indispensable for decision-making in your company? What would the relevant types and sources of data be? It’s essential to start with a clear plan, check against the data already available, and note what’s missing. Data needs should be mapped to relevant decision-making tasks, otherwise the data - even if it seems valuable and compelling - will end up tying up the company’s resources without bringing any tangible benefits.

Step 2. Provide the resources and infrastructure

Data is the “blood” of the business, but it cannot circulate without a network of “blood vessels”. A company needs technical resources and infrastructure for data storage and processing, relevant software and methodology, as well as personnel with essential skills to take care of the process.

Step 3. Establish processes

In order for the data management operations to be “ticking like a clock”, relevant processes have to be designed, standardized, and implemented. Which data needs to be collected, how, and when? How does it have to be processed? Which tools and solutions will be applied? Who is responsible for each step? How are the analytical insights presented, at which frequency and to whom? All these questions need a detailed answer. Only then can a meaningful data pipeline be established - from collecting data to making relevant decisions.

Step 4. Run and optimize

Once you have a clear vision of how the data flow should work and inform decisions in your organization, and all the necessary resources and processes are in place, it’s time to test the system, find its bottlenecks and optimize it. This process might take more time than planned and you might run into unexpected difficulties. There might be problems with data quality or specific reports and dashboards might be submitted too late or contain errors. But the effort is worth the while.

Step 5. Make decisions based on data

This was the ultimate goal - providing the basis for more efficient decision-making. Once the system is running smoothly, there are no more excuses. Although intuition is a very important quality for managers, data should be the main argument when important choices are made. A high level of Data Maturity means that data is embedded into the decision-making process and no decision is made spontaneously.

Data management is a process that implies continuous improvement. As the world around is evolving, companies have to adjust and become even more sophisticated in their data management practices. The steps outlined above can be iterated again and again.

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Achieving Data Maturity doesn’t mean simply meeting some standards and becoming proficient in data management operations - it means unlocking a business potential that can only be realized when the company has learned how to benefit from its data assets. Ultimately, it means reaching a whole new level of performance.

Sources

• 9 Types of Marketing Data | Simplicable
• Building a Data Maturity Model + the 4 Stages of Data Maturity | safegraph.com
• Gartner data governance maturity model | LightsOnData
• The Four Stages of the Data Maturity Model | cio.com
• The Snowplow Data Maturity Model | Snowplow | snowplowanalytics.com
• What is Data Maturity & How to Climb the Data Maturity Scale? | softkraft.co