Understand the data maturity model to refine, grow, and scale your data operations.
Many businesses find it hard to use data to make business decisions, even though data is becoming an increasingly valuable asset for driving business growth.
The data maturity model can help you identify the gaps in your data strategy that are stopping you from reaching a high level of data maturity.
In this article, you will learn:
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.
The data maturity model can be viewed as a data maturity assessment tool. It helps you to:
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.
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:
Dell’s data maturity model proposes four increasing stages of data maturity.
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:
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 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.
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:
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 built a state-of-the-art AI in 3 months with Keboola. From having zero data science initiatives in-house beforehand.
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:
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.
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:
The crucial question now becomes: how mature is your organization?
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.
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:
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.