What’s Stopping You from Moving Up the Data Maturity Curve?

Every ambitious company wants to reach a certain level of data maturity where they manage, analyse, and leverage data to improve service, boost the customer experience and meet business goals.

However, becoming a data-centric organisation like Amazon or Netflix takes time, investment, and knowledge. The data maturity curve is a great way to benchmark your progress on this journey, identify ways to improve and highlight challenges you must overcome.

If your progress to data-centricity has slowed, this article will help. We’ll look at each stage of the data maturity curve, how to move up it, and how to overcome challenges that could hold you back.

What is the data maturity curve?

Let’s start with a broader definition. Data maturity is a measure of how well your company collects, analyses and uses data. Like plotting a child’s growth on a doorframe, data maturity can be tracked on a curve known as the data maturity curve.

Your company becomes more data-centric and mature the further along the curve you go. You start at the bottom as an organisation struggling to make sense of siloed data. By the end, data sits at the heart of everything you do.

The data maturity curve acts as both a yardstick and a roadmap, helping companies understand their current standing, set strategic goals, and identify areas for investment.

Let’s look at each of the stages one by one:

Stage one: data silos

Companies sitting at the first stage of the maturity curve don’t have a unified data strategy. They probably collect data from various business and marketing applications, such as Dynamics 365 Finance and Google Analytics, but it remains siloed.

You may have an inkling of your data’s potential, but there’s no clear path to realising it. There are certainly no standardised processes or systems to manage it.

How to move forward…

The good news is that progressing from this stage is achievable with a willingness to embrace data-centricity. You’ll need an initial investment, too, specifically in a tool to centralise data and automate collection.

Implementing a data warehouse or unified data platform allows you to consolidate data into a central repository, making it easier to automate data extraction and improve data quality. Establishing this "single source of truth" is key to moving past data silos.

Stage two: a single source of truth

Data is much more accessible at this stage of the curve thanks to a platform, application or data warehouse acting as a single source of truth. It ingests data from almost every application and transforms it into a single, standardised, audited version.

Data quality issues are starting to be resolved at this level, and the door is open for data to become a shared asset across your organisation. Collaboration and visibility are significantly increased.

Extracting insights from your data isn’t easy, however. Most companies at this stage require developers or data scientists to build specific reporting solutions. Self-service options are non-existent.

How to move forward…

You’ll need to focus on the specific outcomes you want to achieve with your data to progress. This will help you hire the right expertise to guide you up the data maturity curve and make it easier to find and implement tools that let teams access data insights without technical support.

Stage three: self-service

At this stage of the curve, your employees are empowered to put data to work thanks to easy-to-use self-service reporting.

Self-service capabilities allow teams to benchmark performance and make data-driven decisions at scale. Teams can also automate complex data-related tasks like ESG reporting, accelerating the process and leading to more accurate outcomes.

Finally, application integration becomes possible, with teams able to quickly set up connections between two or more different platforms via your single source of truth.

As a result, teams can move beyond applications. They’re not locked into a suite of products and can choose the best solution for their business, irrespective of the vendor.

How to move forward…

You’re almost at the end of the curve now, but you can still move forward by using your data with AI and machine learning algorithms to spot patterns, make predictions and transform workflows.

Stage four: predictive analytics

At the final stage of the data maturity curve, data-driven decision-making is embedded into every aspect of the business. Organisations use data not only to track historical performance but also to predict future trends and opportunities.

High-quality, consolidated data allows companies to build bespoke predictive models, combining data sets with AI and machine learning algorithms to make accurate predictions. Data becomes a powerful tool, guiding companies to improve their workflow, anticipate customer needs and proactively respond to market changes.

The result is a culture where everyone, from leadership to frontline staff, has access to data and can use it to improve outcomes and drive growth.

Overcoming common roadblocks along the curve

We’ve given guidance above on how to move from one stage to the next. But there could be other things stopping your company from moving up the curve.

Here are the most common roadblocks and how to overcome them:

Cultural resistance

Your progress will stall if employees aren’t motivated to adopt data-centric practices or think about how to integrate data into their workflows. Siloed efforts, with each department developing its own data practices independently, can also hinder your efforts. This fragmented approach makes it challenging to progress beyond the initial stages of the data maturity curve.

Solution: Create a unified data strategy and communicate its benefits across your organisation. When you demonstrate how data-driven decision-making can improve individual and team performance, not to mention streamline workflows, it becomes a lot easier to foster a culture that embraces data.

Data quality

Moving up the curve is almost impossible if you aren’t improving data quality. Missing, inconsistent, or incomplete data will make it challenging to derive meaningful insights, let alone build predictive models.

Solution: Invest in tools and processes that automate data ingestion, consolidation and standardisation. Building a single source of truth will increase data quality.

Budget limitations

Strategy and culture can take you a long way. However, there will ultimately come a point where your company can’t become more data-centric without integrating software and platforms like a data warehouse or unified data platform.

Solution: You don’t have to spend six figures or more on bespoke consultancy solutions. Instead, choose cost-efficient cloud-based alternatives like the 5Y Unified Data Platform that provides many of your reporting and analytic requirements out of the box.

A lack of expertise

Data transformation requires specific skills that many organisations lack internally. Without the right expertise, advancing along the data maturity curve or fully leveraging data insights is difficult.

Solution: Partner with external experts or train existing staff. You can hire this-party experts on a monthly basis or for a one-off project. Either way, accessing specialised knowledge can help your team execute complex data projects and ultimately move the company further along the maturity curve.

Moving up the data maturity curve with 5Y

The journey along the data maturity curve is a continuous one. You won’t be stuck at any one stage forever, but moving up the curve requires a clear understanding of your current position and a plan to address the roadblocks in your way. With the right tools, mindset, and strategy, you can progress from a reactive approach to a proactive, data-driven organisation.

5Y is here to help you along the journey. We can assess your data maturity, identify the challenges holding you back and provide targeted solutions to help you advance.

Find out how we can help by speaking to one of our experts or booking a demo today.