The Data Maturity Model
How to accelerate your data journey.
This guide is designed to help you identify where you are on your journey towards data maturity and how to progress to the next stage.
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Any data practitioner today can attest to the challenge of working in data operations, but there is no ‘one’ way to run a data function.
Each organization will approach their data strategy differently, and while there’s no perfect recipe for success, companies will vary in the maturity and effectiveness of their data function. You could picture this as a ‘journey’ – from disparate teams beginning to grapple with data tools; to advanced pioneers harnessing behavioral data to power the competitive engine of the organization.
Whilst each company is unique, the data challenges they face are often comparable.With the support of our customers and industry experts, we’ve created a step-by-step guide to help you navigate your journey towards data maturity. At each step, we’ll explore the key challenges you’ll face and the innovative tools, processes and strategies to help you solve them. With each leap forward, you’ll get closer to your full data potential.
The digital landscape has never been more competitive, making it crucial for organizations working to drive value from behavioral data to strive for constant improvement, exploring opportunities to level up their data function wherever possible.
Level 1: Data Aware
A ‘data aware’ organization is at the start of their data journey. They recognize that behavioral data can bring value across their marketing and product teams, but are yet to establish a clear data strategy that takes account of wider business goals.
An organization at this level is unlikely to have a data warehouse as there’s no real need to store or combine data sets from different sources. What data it does have is siloed and analyzed through standardized reporting tools, with spreadsheets and Google Analytics able to handle most use cases.
Whilst there may be a number of Analysts interested in behavioral data, there’s no dedicated data resource to drive the journey forward. To get started, a data aware organization needs to clarify what questions it wants to answer with data, and what value this will bring to the business as whole.
Level 2: Data Capable
A Data Capable company has begun to glean insights from its behavioral data, but is still coming up against some fundamental challenges. Although a growing team of analysts (often led by a ‘Data Evangelist’) is able to execute some successful use cases, data is still siloed within departments. This prevents data insights from being shared across the organization, and decreases the chances of further buy-in from management.
A company at this stage has also started to use a data warehouse, but usually only for backend, ERP, or CRM data. Behavioral data is managed through a packaged analytics platform like Google Analytics or Adobe Analytics, and the data team is becoming increasingly aware of their limitations.
Another common issue is a lack of trust in the accuracy or completeness of the company’s behavioral data; in the absence of ironclad event tracking and data validation, teams are often hesitant to rely on data to make decisions.
Once we switched to Snowplow from Google Analytics, we saw we actually have 5% more page views and 3% more unique users than we thought.
Head of Data, Welcome to the Jungle
Level 3: Data Adept
An organization that can be referred to as ‘data adept’ has invested in their data team, building out from a nominal team of a few data practitioners to a centralized group of data engineers, analytical engineers, analysts and scientists.
Driven by the Head of Data, the company is exploring how to step away from packaged analytics tools and consider ways to own their data and data infrastructure. Evaluating different tooling options, the data team has begun the process of assembling a modern data stack – selecting a data warehouse, business intelligence (BI) solutions and ingestion tools as part of a data platform that can serve the needs of the wider business.
The data team has invited internal stakeholders from marketing, product and finance teams to discuss their data needs, prioritize key projects and assess how the data team might meet the requirements of their frontline teams.
Level 4: Data Informed
A data-informed company has invested heavily in their data team(s), has embedded behavioral data in decision making across the business and has deployed one or multiple ‘data products’, such as recommendation engines and/or machine learning algorithms.
Behavioral data is now a recognized asset within the organization, a priority for the C-suite and a culture around working closely with data is fostered in each team. Enabled by a centralized data platform, it’s incumbent on the data team to monitor and build upon their data stack, making tweaks to data performance and keeping a close eye on data quality.
Data practitioners, led by the Chief Data Officer (CDO) strive to narrow the gap between the company’s data asset and the operational teams that need it, perhaps looking to reverse ETL and specialized tools to facilitate data democratization at scale.
At Airbnb, we love data, and we like to think that analytics belongs everywhere. For us to be data-driven, we need data to be fluid, fast flowing, and crystal clear.
Data Engineer at Airbnb
Level 5: Data Pioneers
“Data pioneers” are at the cutting edge of the data world. Companies like Netflx, Spotify and Airbnb build behavioral data into their products so tightly that data becomes a familiar and expected part of the user experience, for example: Spotify’s song recommendations and Netflix’s suggested tv shows.
Internally, behavioral data forms a natural part of everyday workflows; is easy for all teams to access and use to drive success in their endeavors. For the data team, it’s about keeping the flow of behavioral data to internal teams smooth and introducing innovative ways to leverage data – in some cases delivering behavioral data right back to the user on an individual basis.
For the pioneers, recruiting and retaining the right people is a constant challenge. Since they are breaking new ground all the time, data pioneers tend not to purchase data solutions, but build their own. This can introduce its own challenges in terms of optimizing home-brew infrastructure and maintaining legacy systems. That being said, the pioneers’ mastery over behavioral data has separated them from the pack – giving them a clear lead over competitors and imitators behind them.
Whether that’s a product decision or a business decision, we want employees to be able to get insights from the data, without having to be an analyst or an expert.
Engineering Manager at Strava
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