maj 09, 2022
One significant challenge today’s organizations are facing is that the rate of growth in data is outpacing the rise in value their teams can extract from it.
In almost every industry, we are collecting more information than ever before — 90% of the data in existence was created in the last two years — so it should follow that more insights are being gained as a result.
This, however, is not the case for an increasing number of organizations, despite sitting on vast amounts of data and, in some cases, having the tools and technologies available to unlock it.
The crux of the challenge is in the preparedness of the people and processes within the organization.
If they are not optimized to extract real value from data as when they need to, they can’t be part of an efficient, data-driven operation.
If they are optimized in such a way, though, they can start exhibiting the power of a data maturity model that enables sustainable business growth.
The data maturity model
It is by no means an easy task to achieve data maturity as a business, but it’s certainly not impossible.
It’s a process that can be broken down into four distinct stages of data transformation:
Each step must be carefully orchestrated to move a business away from restrictive data silos and toward embracing a data-as-a-product (DaaP) mindset at every level.
Let’s take a look at each stage in more detail to help you pinpoint where you might be as an organization and determine what steps you can take next on your path to data maturity.
1. Realizing: The awareness of the opportunity
This is the stage at which the enormity of the opportunity becomes clear.
You may be sitting on an accumulating amount of data, but it’s difficult for your people to access it or your processes to unlock it.
You may still be working with siloed spreadsheets or self-service tools that are slow and cumbersome, thus restricting the value you could get from your data.
You may only be allowing access to your data for a small number of employees, thus denying yourself the outcomes that can come from interdepartmental collaboration.
The most important aspect of this stage is that there is a mandate to become data-driven somewhere within your organization.
Even if your data strategy isn’t clear, there’s no data governance or the prospect of cloud is a pipedream, both the realization of the opportunity and the desire to take action are crucial.
What steps should you take at this stage?
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Audit your data usage and determine where your weaknesses are, e.g., prolonged time-to-value or lack of communication.
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Compile a list of which tools and technologies might benefit from the likes of automation and wider data governance.
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Analyze the potential of a data maturity model and create hypothetical scenarios to start onboarding data-as-a-product champions.
2. Transforming: From MVPs to scale
The next step is to install a chief data officer (CDO) to transform your organization’s relationship with data and ensure closer collaboration between business and engineering teams.
You’ll recall that the crux of the challenge lies in transforming the ways people and processes treat data, so this stage must be built on creating a change management program to instill a data-driven culture with a product-first mindset.
This way, you can start piloting tightly scoped data products that address user needs and begin the transition to a modern cloud data stack. Desired outcomes:
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Change your operational data architectures for the better.
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Redefine your analytics so information is easily and instantly accessible.
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Design your data-driven CX and product enablement strategy.
What steps should you take at this stage?
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Embed the role of the CDO into your organization by formulating constructive planning sessions between business and engineering teams.
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Implement new data processes and train staff to find, analyze and use the information they need to start scaling from solutions to products.
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Shout about the internal successes of a modern cloud data stack and redefined approach to analytics.
3. Leading: Achieving market differentiation
Once a robust data strategy has been established, an organization can move into carving out market differentiation as a data leader.
It will have a data-driven decisioning culture at every level, enabling cross-functional product and data teams to thrive.
Each new data product will be subject to a product/market fit approach, and monetization will be on the horizon, turning data product creation into a possible revenue stream.
This stage is also where product owners (POs) come in, and a data marketplace is established to allow ease of discovery for every team, every department and even every brand within larger organizations.
This is when data CX reaches the levels of the digital natives and true innovation is within reach.
What steps should you take at this stage?
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Take an agile mindset into your data marketplace to identify new opportunities for data product monetization.
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Maintain cross-functional product and data teams when it comes to finding product-market fits.
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Champion continual improvement to streamline people, processes and technology as a data leader.
4. Innovating: Becoming a data-driven digital native
The last stage of the data maturity model is when a data-as-a-product mindset has been fully adopted and the challenges around people and processes have been overcome. Data is accessible for everyone, and data products are generating profit.
By this point, the organization has advanced to such a state that it is recognized as an innovator. Its data strategy embraces AI and ML across business domains, and its operational and analytical systems are fully integrated and modernized to the cloud.
It’s the optimal state, boasting a CX that’s reached the levels of the true digital natives.
What steps should you take at this stage?
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Strategize a data product roadmap to protect and improve your new revenue stream.
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Build your data-as-a-product mindset into onboarding for new members of your teams.
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Champion data maturity and its impact on innovative customer experiences in your market and beyond.
Which stage of data maturity are you at as a business?
Turning a mandate to become data-driven into an achievable action plan and timeline looks different for every business.
You’ll have various operational obstacles to overcome, from skills gaps to legacy platforms, but the benefits of adopting a DaaP mindset are endless. They can be the difference between evolving into a digital leader and stagnating as a digital laggard in your industry.
To transform your relationship with data, get in touch today with our data and AI experts today to discuss how we can help you take the next steps toward data maturity.
