In business, data is everything. It can provide insights, deliver a stellar end-user experience, and shape growth strategy.
Unfortunately, not all companies are as data-savvy as they want to be. The vast majority are sitting on piles of data but with no structured way of exploiting it for competitive business advantage. That’s where data maturity models come in. Used the right way, they can guide a company’s transformation from being clueless about data to taking advantage of its full potential.
Data Maturity, Defined
Before discussing data maturity models, it’s worthwhile to define first what data maturity actually is.
In a nutshell, data maturity measures how well an organization uses and analyzes its data. Does management simply gather data and store it in their database? Or do they actively explore that data to shape their business strategy and operations?
Companies with a high level of data maturity have that data fully ingrained into their operations. Companies like Netflix and Amazon, for example, often employ advanced tools like artificial intelligence and data mining to derive insights and deliver personalized experiences to their customers.
On the other end of the spectrum are companies that are just starting in their data maturity journey. They may be more concerned about a more efficient data gathering or storage process and have not yet fully utilized that data to its full potential. Some may not be thinking about their data at all.
Achieving data maturity is no easy feat, and it shows. In a Gartner survey of over 190 companies, only 9% are in the highest maturity level. The reality is that data maturity requires an overhaul of how you treat data, and this change requires time and constant effort.
However, like any journey, it’s easier to know how to get to your destination if you have a road map. For data maturity, that map is known as a data maturity model.
What is a Data Maturity Model?
Any organization’s goal is to go from being merely data-aware (having a minimal or basic system in place to handle data) to achieving total mastery with advanced data analysis and usage.
However, it can be challenging for management to know where in that journey they’re in objectively. As a result, it can be impossible to know which areas to improve to move on to the next level.
A data maturity model solves this problem by providing a systematic way of evaluating an organization’s level of data maturity. It analyzes a business’s data handling processes and policies, including any dedicated data teams.
Data maturity models are made up of levels or stages. Each of them describes how a company should handle their data if they were at that stage. Consequently, they can look at the higher stages to know what they need to work on to progress.
The Many Types of Data Maturity Models
While the goal for data maturity is the same for most companies, how they get there can be different. There is no standard framework for data maturity; every company uses a slightly different model.
For example, some data maturity models have six levels instead of five. And while the first (basic) and last (mature) are primarily defined the same, the terminology and scope in each of the levels in between tend to vary.
This section will look at three common maturity models and their stages: The Dell Data Maturity Model, Snowplow Maturity Model, and the Gartner Data Maturity Model.
The Dell Data Maturity Model
Goal: standardized reporting
This is the lowest level in the Dell model, characterized by using manual processes to gather and report data. The problem in this stage is that each department or area of the business does its data reporting, with no regards to the rest of the organization. There are no standard processes, and data is not verified, either.
Thus, this stage’s goal is standardization and normalization through database design, a unified reporting dashboard, and data modeling techniques.
Goal: track KPIs using BI
Once an organization has standardized its data and adopted a streamlined reporting platform, the next step is to structure that data.
In this stage, companies still don’t have a unified data handling scheme, with multiple databases scattered throughout the organization. However, companies have already begun tracking their KPIs, and with it, they see the need to manage their data better.
The goal here is to improve the strength of a business’s data. It can do this by using technologies to structure data, creating a data management plan, ensuring fast data access, and having a verification policy in place to assess data quality.
Goal: Successfully use data on crucial business decisions
Thanks to fixing their infrastructure and processes, data-savvy companies are finally ready to use that data as a competitive advantage.
In this stage, management needs to play a proactive role to ensure data-driven decisions are adopted across the organization. That relies on strong inter-department cooperation, where IT and business functions work synergistically.
The goal in this stage is to use advanced technology to leverage data in crucial business decisions. Complete data integration, data mining, and predictive analysis tools are some of the initiatives data-savvy companies must pursue.
Goal: refinement and scaling up
This is the highest stage of the Dell data maturity model, where data takes center stage. As the name suggests, data-driven companies fuel business growth through the effective use of data. In fact, companies at this stage don’t make any crucial decisions without data to back it up.
Data handling is top-notch, and advanced analytics platforms help business units do analytics without direct IT assistance.
But the work doesn’t stop there. The challenge with data-driven companies is to scale their data-driven methodology most cost-effectively. They also need to integrate data analytics as seamlessly as they can to business operations.
The Gartner Data Maturity Model
Level 1 (Basic)
Data is gathered or analyzed only when needed at the basic stage of the Gartner model. And even when they do, it’s not exploited to its maximum potential. The analysis is done manually, usually with Excel spreadsheets using graphs and pivot tables.
Data is often gathered and reported independently, creating separate silos throughout the organization with no interconnection.
Level 2 (Opportunistic)
Data opportunistic companies have the beginnings of a unified reporting system, but it’s still in its infancy. At this stage, IT attempts to spearhead efforts to standardize data, often leveraging solutions to combine them for better insights.
And while analysis is better at this point, it’s still hampered by obstacles that make full implementation especially difficult. Significant contributors include company culture and lack of endorsement from upper management. Data silos also still exist in this stage, which makes analysis very inefficient.
Level 3 (Systematic)
As the name suggests, Level 3 companies are starting to organize their data management efforts. There is more backup from senior management, breaking down organizational barriers to make inter-company cooperation much easier.
IT is also beginning to break down data silos, combining them into one credible source to reduce quality issues. With the added maturity in data handling, the company can also handle external data to improve analysis and decision-making.
Level 4 (Differentiating)
Level 4 companies are starting to take data more seriously. This is reflected in hiring a team dedicated to data, notably a Chief Data Officer (CDO).
Data is now part of business strategy, and most decisions are made only by consulting the data first. Company policies and best practices are implemented for continuous improvement of data handling and analysis.
Level 5 (Transformational)
At the highest level, data takes center stage. Data is more indispensable than ever and is used company-wide. Every employee, from the CEO down to entry-level hires, use data to improve everyday tasks. Data analysis also uses advanced technologies like artificial intelligence to derive and evolve insights.
Snowplow Data Maturity Model
Data aware companies are relative novices when it comes to data analysis and handling. There is an initiative to gather data, and there are analysts in place, but these are scattered throughout the organization. There is no real centralized solution in place.
Data capable companies start to see the importance of data warehouses, together with a company-wide analytics platform to exploit the data. Data evangelists also begin to emerge, which often spearhead data initiatives.
While capable, companies at this level are still hampered by inefficient and incorrect data.
Data professionals in adept companies start to get mature, often formally separating themselves into a new team. Companies use custom models instead of “off the shelf” solutions like Google Analytics. This allows them to derive unique insights into their data further.
Data collection and analysis also start to get more mature in this stage. The challenge now is with maintaining the data infrastructure and compliance with data governance best practices.
With data entirely at the helm, data informed companies start exploiting data to the fullest. They begin to use data to improve their offerings through things like personalization or real-time recommendations.
There are still a few kinks here and there that prevent data informed companies from fully unleashing the power of their data. Sourcing quality data, keeping up with infrastructure changes, and strategy optimization are some of them.
Data pioneers are companies who have tapped data to its full potential. Advanced technologies like real-time machine learning powered by big data give these companies an enormous competitive advantage.
The challenge with data pioneers is finding the right people and utilizing the suitable systems to sustain their data strategy.
Knowing about data maturity models is just one small step in the journey to becoming a data-driven company. You may see where you need to go, but not how to get there. This is where professional advice can help you immensely. Contact us today, and let’s chart your own data maturity journey.