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The systematik data maturity framework helps organizations better understand where they are in their data journey and where they want to be in the future. The guide below shows what your results mean and how our experience can improve and increase your data capability.
There is no shame in being at the foundation stage of data maturity as it shows you are acknowledging it as something you need to invest in and develop. As you don’t have complex architectures and bad practices in place, it can be far easier to fix the problem.
Organizations in the foundation stage do not typically have a robust strategy for integrating data with existing business functions and UTM. Like many businesses, data is stored across disparate platforms, creating significant barriers to data collection and generating accurate insights. Without a strategy in place, there can often be a lack of governance around data processes. For example, google tag manager (GTM) tags may not be configured appropriately, naming conventions are not standardized, and formatting rules are not followed.
Many data processes, such as CRM data entry and extraction into excel from content management systems (CMS), are still manual. The average error rate in manual data entry is 1%, but that can increase rapidly with more complicated processes. Any error can result in inaccurate reporting, leading to poor and wrong business decisions.
When a business produces data that lacks accuracy and is prone to error, senior teams find it hard to trust the insight for decision-making. Instinct is trusted over data meaning they will either be lucky or wrong.
“Most entrepreneurs make decisions by either guessing or using their gut. They will be either lucky or wrong.”
– Suhail Doshi, chief executive officer, Mixpanel.
Organizations in the foundation stage can be slow to produce insight as reporting is the responsibility of a single person and the process to cleanse, manipulate, and analyze data is tedious. As geoffrey moore rightly says, “without big data, you are blind and deaf in the middle of a freeway.” Investment in technology and people to manage the data processes must be forthcoming for future success.
The data does not flow into a central store or warehouse at this stage, meaning a “single source of truth” is not always viable, resulting in a lack of trust in the data.
The systematik team of experts can offer a 100% free no-obligation thirty-minute strategy session call to start guiding you along your data maturity journey. Contact the team today to see how we can help get your organization from where it is today to where you want it to be in the future and turn your data into a more valuable business asset.
We are not marketers who dabbles into data analytics. Data is all we do.
We build and advise on end-to-end data solutions. Our team comprises measurement specialists, data analysts, data engineers, data architects, and data scientists.
As with anything in life, you get what you pay for. Sure, you could hire a freelancer, an agency from a third-world country, or a marketing generalist who dabbles in a bit of everything. In fact, most of our clients tried one of these solution before contacting us.
Here’s why we think you should save yourself the hassle and hire us directly.
Yes, we will work as extended part of your team. You can let us know about your processes and we will just white-label the service package for you.
Yes, this is exactly the type of work we specialize in. We’ve built over 25 modern data pipelines. Consolidating data from more than 100+ data sources.
Think of it like this: all-in-one solutions in data are a bit like those Swiss Army knives. They promise to do everything – cut, screw, open bottles. Sounds great, right? But when you actually need to do a serious job, like screwing in a tight screw or cutting a tough material, that tiny knife or screwdriver just doesn’t cut it. You need specialized tools.
Now, apply that to data solutions. If an all-in-one is super flexible, it’s like a Swiss Army knife with a hundred tools. It can do a lot, but it’s so complex that you need a specialist just to find the right tool. And specialists aren’t easy to come by.
If it’s too simple, it’s like having a Swiss Army knife with just a couple of tools. It works fine at first, but as your needs grow – say you need to manage more data or your analyses get more complex – that simplicity becomes a limitation. You’re stuck trying to do a professional job with a basic tool.
And what if the all-in-one doesn’t have the exact tool you need? You end up having to get additional tools anyway and find a way to make them work with your Swiss Army knife.
Plus, these all-in-ones are often like fancy gadgets where you can’t see how they work inside. If something breaks or doesn’t work as expected, good luck figuring out why without a detailed manual or an expert.
And to top it off, they’re like high-end gadgets that cost a pretty penny, especially as you start using them more and more.
In a nutshell, that’s why many companies prefer building custom data pipelines. It’s like having a tool belt with exactly the tools you need, chosen by you. Sure, it takes time to assemble and you need a range of tools, but in the long run, it’s more efficient, scalable, and tailored to your specific needs.
