Your Data Isn’t Alive, But Maybe You Should Start Acting Like It: An Introduction to the Data Lifecycle
The cycle of nature is a given: things live and die and feed new life. Matter and energy are not destroyed but continually reused and repurposed. The old makes way for the new.
This cycle is essential for life, but why do so many of our digital systems not get treated the same way? When was the last time you cleaned out your old data and archived it to make it easier to use and access your newer, more relevant data? How often do you think about the ways caring and nurturing your data will affect its usability later?
We’re not saying your data is actually alive. But at Komodo we turn to this metaphor a lot to help people think about how to improve the actionability of their data, and in turn the success of their organization.
Just as a living organism requires proper care and nurturing at each stage of its life to thrive, an organization's data needs to be properly managed and maintained throughout its lifecycle to maximize its value. This is where the concept of Data Maturity comes in: it's a framework for measuring how well an organization leverages its data to drive growth, innovation, and decision-making.
In the tech industry, there is a lot of talk about collecting and securing data, but how do we actually treat it? And what can we learn about our organizations from how we treat our data? By examining our data practices through the lens of the data lifecycle, we can gain valuable insights into our organization's Data Maturity, and identify areas for improvement.
How Can Data Be Alive?
Life has the ability to grow, change, consume energy, undergo processes, and eventually die and decompose. Data isn’t organically alive, but just as life requires the input of energy, pushing data through its lifecycle requires human effort and investment of resources.
There are many different ways to think about the data lifecycle. A quick internet search will give you articles on the 4 steps, the 8 steps, and even the 12 steps of the data lifecycle. The number of steps isn't actually that important. Every organization has different processes that can be defined in various ways. What's important is the broader concept: the stages of the data lifecycle are interconnected. What happens at each stage affects what will happen at later stages.
The 5 Stages of the Data Lifecycle
At Komodo, we think of the data lifecycle as having 5 major steps:
Create
Deliver
Store
Process
Maintain
Create
Data is born when digital systems do something, or more specifically when programmers create a record of a system doing something - a new user registration, a password reset, a sale. Depending on your organization’s data maturity, data creation could be manual (i.e. data entry) or automated. The care taken into how that data is created is important later for actionability and scalability, the most important thing at this stage is that we are capturing a record of something happening.
Deliver
After data is created, it is delivered elsewhere for use. This is a deceptively simple yet crucial aspect of the lifecycle. Any modern workplace has systems that store data, but data is not always delivered to the system where it is needed most. In a less data mature organization, the Deliver stage might involve a manual data upload, or data delivered on a schedule. More data mature organizations have systems in place to automatically capture and deliver data in real time.
Store
At the Store stage, we think about where the data is being delivered to. Most business processes require the input of data from more than one source, so where and how we store our data is critical to support complex business logic. This is where we hold on to that data energy, where it builds before it can be metabolized. At this stage, an organization becomes more data mature as its storage processes become more automatically structured and normalized, and as access controls are put in place to limit who has access to different types of data.
Process
The process stage is where data truly starts to feel as though it is alive. This is where data inputs from different sources are combined, where that data changes, becomes metabolized and transformed into something new. An organization can assess its data maturity at this stage by thinking about how automatic its data processes are and how easy it is to add newer, more complex processes.
Maintain
Eventually, data needs to be maintained, either in storage or after processing. Data maintenance involves deciding how long to keep data and how long to make it available. It’s very cheap to store data indefinitely, but a data breach can be very expensive. Deleting data can be scary (what if you need it later!), but thoughtful maintenance can drastically reduce organizational risk. If a piece of data has been archived and no one has accessed it in a certain time frame, it may no longer be relevant to your business today.
Pushing data through this process and maintaining your data properly helps your users find and access the most relevant data. Thinking about what to do with the data we no longer need in turn helps us think about what we need from our data creation in the future. An organization with lower data maturity is not archiving data it doesn’t need, or doing a manual, ad hoc archiving process. In a more data mature organization, data access and retention rules are structured and enforced automatically.
Why It's Helpful to Think of Your Data as a Living Organism
Moving data through the lifecycle stages - and maintaining it properly at each stage - is essential for extracting maximum value from your data. How data is treated at each stage of its lifecycle profoundly affects its usefulness later on.
Neglect your data, and you'll end up with a disorganized mess that hides valuable insights and creates risk. Nurture it with intentional processes, and you'll have a thriving asset that empowers your entire organization.
Ultimately, your ability to effectively shepherd data through its lifecycle is a direct reflection of your company's data maturity. Is it important to always be improving your data maturity? It depends! Investing in the people, processes, and tools to properly create, deliver, store, process, and maintain your data can drive innovation, efficiency, and competitive advantage. But improving your maturity requires money, time, and effort. Depending on your business, you might be very comfortable operating at your current level, or it might not be the right time to focus on getting to the next level of data maturity. Every organization is different, so it's about balancing what you need, what you can do, and how fast you want to get it done.
Data maturity is itself a cycle - a constant process of assessment, adjustment and optimization. If you're not sure where your organization stands, a data lifecycle assessment is a great place to start. Or check out our course on Data Maturity & The Data Lifecycle!
At Komodo, we geek out about this stuff all day long. Nothing excites us more than helping companies level up their data capabilities. If you're ready to take your data to the next level, we'd love to chat.
Reach out anytime for a free consultation. Together, we can help your data grow from a helpless bunch of molecules into a thriving organism - and unlock its full potential to supercharge your business.