The Data Maturity Model — What is it?
If you’re reading this, you probably know a thing or two about data. Data is one of the most rapidly growing resources in our world, with an estimated 2.5 quintillion bytes created every day. It seems over 90% of data existing today was created within the last five years.
Humans make errors. We make errors of fact and errors of judgment. We have blind spots in our field of vision and gaps in our stream of attention. Sometimes we can’t even answer the simplest questions. Where was I last week at this time? How long have I had this pain in my knee? How much money do I typically spend in a day? These weaknesses put us at a disadvantage. We make decisions with partial information. We are forced to steer by guesswork. We go with our gut.
That is, some of us do. Others use data.
So what is the data maturity model?
The data maturity model in simple terms is a roadmap from point A to point B with milestones in-between to provide a framework that helps organizations understand and improve their data management practices. It is designed to help organizations assess their current state of data management and identify areas for improvement.
In the last 5 years, as the amount of data produced has increased our ability to process and extract information from this data has also increased. With this ability, the gap between Point A and Point B has continued to increase, with organizations building sophisticated data use cases to better support their business needs. With the edge data provides over its competitors, a data strategy is no more a maybe but a must.
The framework is usually broadly classified into 4 or 5 broad categories to generalize this model.
What do the key milestones on the journey from data awareness to data pioneer look like?
- Empower the right people to support your data journey — At this stage prioritize breadth of skills and experience over narrow expertise. Whilst you may need a data specialist as your organization matures, at this stage a data generalist is more important.
- Release your data from packaged solutions like google analytics which do not provide under-the-hood metrics but instead aggregate your data across broad categories. Transferring your raw, un-opinionated data to another platform, you’ll have more flexibility to manipulate data.
- Define the overarching goal of your data journey. You may want to empower an internal team or create a data product for external use. Align your data goals to your business objectives, and plan how to reach them.
- Exploring the switch to a modular data stack. Understanding the limitations of packaged analytics platforms and how building a custom data stack would overcome them.
- Think about data governance as it will only take on a more important as your data matures. This is to ensure that data is understood and trusted across your organization, and is accurate, complete, and compliant.
- Lay the foundations for first-party data collection, anonymous or cookieless tracking which in turn also protects your users.
- Building a single source of truth — It is essential to build a central data repository that will serve as the ‘brain’ of the organization, eliminate data silos, unify teams, and empower analysts to serve data from a single source of truth. This builds trust in the data the teams are working with.
- Breaking free from a packaged solution like google analytics in favor of a modular setup that puts you in the driving seat of your data. You’ll also be able to build your infrastructure around your business, not the other way. Open-source tools are also a great option as they prevent vendor locks and give you flexibility with your data.
- Treating data as a product — Now that all your internal stakeholders are an integral part of your data workflows, they become your biggest stakeholders. Explore what the goals of your internal stakeholder were and build dedicated data products to achieve them.
- Prove the value of data by going past visualization to storytelling — Leverage the power of customer insights to power automation where possible or leverage where applicable.
- Fostering a vibrant data culture such that data is part of day-to-day business decisions. At this stage, multiple teams benefit from a close relationship with data. Focus on making it even easier for data consumers to ditch data, with data catalogs and improve data literacy — and invest in productionizing data to make it ubiquitously accessible.
- Tighten data governance — Leverage automated testing to ensure tracking and validation are working as expected before deploying new tracking. This will further increase your data quality and build assurance that your data is accurate and complete.
- Finally, review and revamp to continuously push the boundaries to innovate with data.
What does all this mean in the context of media?
(I like to talk about media as a lot of my work, implementations and ideas have revolved around media industries).
Media is one of the most competitive industries, organizations are constantly looking to get an edge over their competitors and one such edge has been data.
Examples of media companies making data-driven decision :
- Netflix in their first big original film, Bright, starring Will Smith, cost the company $90M and launched on the platform in December. The purchase of the film concept and subsequent marketing were completely informed by customer data. Bright saw 11M viewers in its first 3 days, despite decidedly poor reviews from rating sites like Rotten Tomatoes. Ultimately, Netflix was able to rewrite the playbook for blockbuster filmmaking using data.
- Readerscope a solution developed by the New York times can be used as a content strategy tool to develop creative ideas for branded content or campaigns by searching a brand’s target audience segment (e.g., millennial women) to understand what they’re reading, either as topics or as representative articles exemplifying those topics. It can also help brands find the right audience or geography for a certain message by searching a topic (e.g., human rights, philanthropy, or travel ) and seeing which audience segments over-index for interest in that subject. Topics are algorithmically learned from The New York Times article archive using state-of-the-art natural language processing, and all of the reader segments are targetable with media on NYTimes.com.
To conclude, with massive amount of data being collected organisation should also continue to improve their capability to process this data and achieved different buisness goals. The data maturity model acts as framework to help organizations assess their current state of data management and identify areas for improvement.