Data Maturity Model: How to Move Up the Ladder

What is a data maturity model?

A data maturity model is a framework that businesses use to analyze their current level of data capabilities, identify areas for improvement, and create goals for moving up the ladder to increase data maturity. 

Each stage of the model has its own characteristics and identifying features. 

Higher levels signal more advanced and sophisticated uses of data throughout the company. Companies at the lower stages of the data maturity model do not make full use of data for their growth. 

Crucially, moving up the ladder on the data maturity model doesn’t just improve your “data maturity score.” It helps grow business as well.

Before diving into each stage, let’s look at the other benefits a data maturity model offers.

Why do you need a data maturity model?

The data maturity model can be viewed as a data maturity assessment tool. It helps you to:

1. Understand your current maturity stage. The data maturity model helps you lift yourself into a bird's perspective and assess your current level of data management by benchmarking your organization against a general standard.
2. Set goals to move to the next stage. Once you know where your organization is on the maturity scale, you can align your data strategy with business strategy and set goals that move your data operations to the next stage.
3. Stay competitive. Your competition is optimizing its operations all the time. The data maturity model gives you a competitive advantage. ensures you are effectively managing and utilizing the data at your company to grow in the right direction.
4. Identify and prioritize areas for investment. The data maturity model also acts as a roadmap. It helps you identify what you need to prioritize to move to the next stage of data maturity. This clarity of execution helps you identify and prioritize areas of investment to maximize your ROI.

These benefits might seem abstract, so let’s look at them in practical terms by exploring an actual case of a data maturity model and its stages.

What are the stages of the data maturity model?

Multiple organizations have proposed different data maturity models - Gartner’s model focuses on transformational changes in large enterprises, IBM sets forth a maturity model for data governance, and Snowplow’s data maturity model focuses heavily on data architecture.

They all have their merits, but we’ll focus on Dell’s data maturity model here for two reasons:

1. It is the most universal, covering all company sizes and areas of data management.
2. It has a proven track record of being linked to business outcomes (increased revenue, lowered costs, improved ROI).

Dell’s data maturity model proposes four increasing stages of data maturity.

Stage 1: Data Aware

Companies in the data-aware stage are characterized by reactive data analysis. 

The data team is jumping from one ad-hoc request to the other, always manually compiling non-standardized reports. They need to create datasets and ETL workflows from scratch at each request since nothing is modeled. 

If this sounds like your organization, your goal is to move from manual to standardized reporting. You can achieve the next stage by focusing your data strategy on:

• Data modeling
• Database design
• Centralizing your data assets (for example, building a data warehouse with standardized reporting tables and pre-computed metrics)
• Integrating your data assets into a BI tool and building reporting dashboards.

Pro tip: Rely on tools to automate all the heavy-lifting of the data aware stage. For example, you can automate the entire ETLT data pipeline with a couple of clicks in Keboola. No coding required.

Heureka Group’s Data aware → Data proficient growth

Heureka Group moved from manual Excel reporting to fully-automated and centralized business intelligence covering 5000 e-commerce shops in their portfolio with Keboola. By automating the manual work and enriching data automatically, Heureka Group netted an additional 450k EUR per year.

Stage 2: Data Proficient

Data-proficient companies have standardized reporting, deployed data assets in a data warehouse, and automated metric computation for the most important metrics.

The challenge for companies at this maturity stage is data alignment. 

Multiple business functions use data, but they are used in silos. Multiple data marts and metric definitions introduce disagreement into decision-making and cause friction between stakeholders and their interpretation of core organizational KPIs.

To move to the next stage, companies need to work on:

1. Breaking down silos between departments and business functions. 
2. Working on assuring data quality by streamlining and aligning different ETL workflows.
3. Centralize reporting of company-wide KPIs into a single source of truth (a single BI reporting tool).
4. Implement a master data management strategy making a single backbone for all data usage, data assets, and data policies. 

The involvement of executive decision-making in the data processes means data is not the sole responsibility of the IT department or the data team. Data management becomes business driven.

Harri’s Data proficient → Data savvy growth

Harri built a state-of-the-art AI in 3 months with Keboola. From having zero data science initiatives in-house beforehand.

Stage 3: Data Savvy 

Companies in the data-savvy stage make critical business decisions based on data and key initiatives are data-driven. 

A defining characteristic of data-savvy companies is that they move beyond reporting and lagging metrics to use data for predictive analytics and product development. For example, companies embed data analytics into their core products or use data science initiatives to drive advanced predictions with big data and machine learning algorithms.

To move to the next stage, companies need to:

1. Automate and streamline their data integration processes. For example, by automating external data sources acquisition or driving down integration costs with the right tooling.
2. Make information-rich datasets available for key initiatives. The IT department can provision a data lake that supports unstructured data for machine learning operations.
3. Run key initiatives not as ad-hoc experiments or one-off product features, but as a streamlined process, where project management resembles the quality standards of more traditional projects, such as software development.

Rohlik’s Data savvy → Data driven growth

Rohlik, the Czech e-commerce unicorn, uses Keboola to put data at the foundations of every business operation, from reporting to recommender engines and advanced analytics. The Czech unicorn automated 3500 analytic jobs with Keboola’s platform, a feat which would’ve been impossible without the right tools.

Stage 4: Data driven

The final and highest level of data maturity is to be fully data-driven. Data-driven companies have permeated their company culture with data.

If there is no data, no decision is made. Since only data-driven decisions can drive a company's goals.

A key characteristic of data-driven companies is the democratization of decision-making beyond executive stakeholders. Everyone at the company has the tools (an advanced analytic platform) and know-how to make decisions with data and answer their own questions with data-driven approaches.

The goal at this stage is to optimize the company’s data processes: 

• maximize the business areas covered with data-driven decisions, 
• minimize data operations costs, and 
• scale data initiatives via automation and the right infrastructure.

The crucial question now becomes: how mature is your organization?

Assess your company’s data maturity

The data maturity model can be viewed as a framework that can be leveraged as a data maturity assessment tool. That is, as a diagnostic tool for figuring out where your company’s data maturity is at.

Unfortunately, the data maturity model does not provide a one-size-fits-all identification methodology. It showcases a roadmap that helps you assess how to progress, but not identify your current stage.

Partially, this is because every company operates at multiple maturity levels.

For example, you might have a couple of data scientists working on big data projects (stage 3: data savvy), but at the same time have issues with metadata management and cannot trace data lineage through its lifecycle (stage 2: data proficient).

The best assessment strategy is to pick the least mature aspects of your company and set their level as your current operating baseline.

In the example above, your company maturity level would be at stage 2 - data proficient.

Once you identify your current state, it is time to increase the overall maturity of your model by filling in the gaps that are missing to progress to the next stage.

Move up the data maturity ladder with Keboola

Cultural change and digital transformations can be hard. 

To make data management successful and increase the overall maturity of your company, you need to change policies, architectures, tooling, and the mindset of your coworkers.

Luckily, the right tools can help you speed up your data maturity growth via automation.

One such tool is Keboola - the data platform as a service - that takes care of the heavy lifting in the background with automated data operations.

Keboola helped multiple clients scale up the maturity of their operations in weeks instead of months: 

• automated 5000 e-commerce shops for Heureka Group, 
• streamlined 3500 data engineering jobs at Rohlik, and 
• build a state-of-the-arts AI product with Harri.

These are just some of the many successful companies that have used Keboola to move up the data maturity ladder in a couple of months.

How do you step up the ladder and become more data mature? By relying on tools like Keboola, that streamlines all the operations with automations, speeds up growth with pre-built templates, and integrates your dream data stack of tools with a couple of clicks.

Curious about how Keboola can help your company along your digital transformation journey? 

Get in touch and we can jump on a call to speed up your company’s growth. 

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