Data Informed Organizations Need Data Literate People

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Per my discussion on Stages of the Data Transformation Journey from data-ignorant to data-informed, it is not a “one-size-fits-all” and is ongoing. However, it is a misconception that a data-informed organization only means having executive buy-in backed by an awesome data science team. Yes, both are important. But, you can be a data-informed organization without the most advanced data science team, or even without a data science team at all.

Instead, data-informed organizations require data-fluent people making decisions, and an empowered, data-informed culture behind it. Data-fluent people meaning people that can apply data, gain insights from data, and tell stories with and around data. Data informed and empowered culture meaning an organization that encourages and empowers data-informed decisions.

For example, a data-fluent project manager may seek out new data he typically does not receive to better ensure his project meets targets. Or a data-fluent product manager may utilize customer data insights to develop the next "big thing" for her organization.

Most decisions are not made by people with VP or CXO in their title. Instead, decisions are made by analysts, associates, engineers, customer service managers, generalists, architects, scientists, and dozens of other titles. It is the culmination of thousands of decisions each day that is the fate of organizations. Ideally, each of these decisions is made by a data-fluent person or persons in the context of customer value. (Look for more to come on the subject of being customer-focused).

“Most decisions are not made by people with VP or CXO titles”

The real value of data-fluent people, though, is not what they do alone. Instead, data-fluent people help drive improvements for the entire organization. And that makes the organization run better. Think what companies like Toyota did on the factory floor — but now it is on the office floor.

In addition to encouraging specific improvements to processes and systems, data-fluent people help drive a data-fluent culture. This is where data-fluent people provide the highest value.

The important question then is, does your organization hire for, train towards, and reward data fluency? If the answer is no, then your organization will not be data informed.

So, the next time an executive asks how do we become a data-informed organization? Respond, “We need to start attracting, retaining, developing, and empowering data-fluent people."

Stages of the Data Transformation Journey

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The data transformation journey moves companies from data-ignorant to data-reactive to data-driven to data-informed and, in fact, eventually to real-time data-informed. The data transformation journey is a process. This journey isn’t straight; it will take some organizations longer than others to progress. Organizations also generally don’t move in lockstep in this journey. But to be clear, companies that don’t progress along this journey will eventually not exist.

That is a lot for one paragraph, so what are these data maturity stages:

Data Ignorant: Data ignorance means your organization doesn’t meaningfully use data. The organization may be getting data but doesn’t understand quality, meaning, and/or context. The organization may have great people, but their true north is their gut or their manager or executives' gut. There are still many successful companies doing this. Leaders with experience and intuition can often add enough value to overcome the influence of data for some time. Just remember that, yes, ignorance may seem bliss, but data ignorance is only bliss until you have competitors that are not.

Data Reactive: Data reactive is just what it sounds like - organizations capture data but don’t strategically use data in their decision-making; instead, they use data to react. Further, they don’t strategically define the data to capture but rely on what others do and copy it. For example, a T-shirt company realizes it lost 34% market share in a market it had once dominated. Now they react and make a salesperson change and a social media ad buy. Data-reactive organizations or departments are still very prevalent. They often have founders or leaders that rely heavily on gut still. They have also realized that complete data ignorance can bring, and they have moved forward to data reactive.

Data Driven: Data-driven organizations understand that data is valuable and almost always good data is better than any one or several people’s experiences. Data-driven organizations understand that they must be thoughtful in capturing data and then have processes in place where people use this data in their jobs to make decisions. Many data-driven organizations are extremely data-sophisticated by most standards. The challenge with data-driven organizations is that they often let the data drive them without a full understanding of the context of the data.

Data Informed: Data-informed means data is thoughtfully understood and processes aligned to maximize data-informed decisions in an organization. Further, data captured and other data sought are strategically determined to add organization and customer value. Data-informed organizations not only make decisions based on data, but they understand the context of data in those decisions. This context is provided from a combination of organizational experience, competitive landscape, industry expertise, and decision impact on various stakeholders. 

Moving along this data transformation journey is not simple and is not one-size-fits-all. It is not something you can just ask how much it will cost to get me to the end of this journey. This journey is constant and doesn't have an end. Needs and technologies continue to change around data. Things like artificial intelligence, blockchain, information security, and quantum computing are currently and will continue to change the data transformation space. Further and most difficult, data transformation is a culture change for organizations.

Look for future postings where we will dive into further discussion on how to move along this data maturity journey. For now, I wish you Godspeed along this data transformation journey, no matter where you are.

Watch out for data science shiny objects

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There are three main ways that organizations use data and analytics in their organization:

  1. Enhancing customer/product experience

  2. Enhancing employee experience

  3. Increasing operational efficiencies

Individual data science efforts will often cross more than one of these areas.

I am writing this post because I see organizations making decisions more often based on increasing operational efficiencies than enhancing customer/product or employee experience. Cutting costs is the shiny object that never goes wrong on an earnings call. But does it add to the long-term value of your product, your organization, and your most valuable assets, your employees? Often, the answer is a short-term yes but a long-term no.

Be a leader that delivers customer and employee value from the power of data and analytics first. Along the way, some operational efficiencies will come along as part of your efforts.