How to make "data-driven" more than jargon in your organization

Collecting data doesn’t make you data-driven. Is your organization taking the necessary steps to clean, define, store, and analyze data for customer insights?
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Many organizations today describe themselves as “data-driven,” and it’s easy to understand why: Organizations are producing, and have access to, more data than ever before. It’s considered a competitive advantage and many customers are demanding it.

Further, advanced technologies like artificial intelligence (AI) and machine learning (ML) are more widely available to help make sense of this massive amount of data and improve business processes and functions like customer experience (CX). 

But what does it really mean to be a data-driven organization? To some extent, the term “data-driven” has become marketing jargon, possibly because it’s being used to describe even the most basic data activities. But just because an organization collects data doesn’t mean it’s data-driven.

The bottom line: Being a data-driven organization means digging into the information readily available and making strategic business decisions based on the facts and insights that are uncovered.

Sounds simple, right? Not always.

In order to get to a place where organizations can confidently call themselves data-driven, here are some of the initial steps to successfully leverage data, draw out insights, and as a result, maximize ROI.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Moving from data-producing to data-driven

As our world becomes increasingly connected and digitized, we have a growing amount of unstructured data. The ability to effectively manage and analyze it will be imperative for organizations trying to enhance business processes and innovations.

[ Could you be measuring your data more effectively? Read also: IT metrics: 5 measurement mistakes to avoid. ]

The first and most critical step to successfully leveraging unstructured data is to organize it, which can be daunting. With data stored in a variety of repositories and formats, it’s important to first understand what the data actually is and how much of it is relevant to the business.

As data understanding increases, creating a data map and dictionary of what and where is crucial. Organizing data can be a long process, but by starting with the critical data and working down, your organization will quickly amass defined and trusted data.

Once it is clean and defined, move that data from the “swamp” of messy data to a clean data lake where it can be preserved and stored – a trusted place where the data is ready and available for analysis.

As the data is structured, begin experimenting and using it in existing processes and conducting analysis to inform new business decisions. Start small and work up to bigger, more critical decisions based on the data available. 

It takes time to understand the relationships in clean organized data, and at the beginning it’s best to avoid taking too many risks.

At the outset, avoid swinging for the fences. It takes time to understand the relationships in clean organized data, and at the beginning it’s best to avoid unnecessary spend and cycles by taking too many risks.

This is also where AI and ML come into play. Understood and defined data can be used to train different algorithms and help streamline or automate processes and innovations.

As your organization expands its data-driven initiatives, it is important to mind the unavoidable newly discovered gaps in data. As organizations define and organize data, they often discover they are discarding relevant or highly useful data in old processes.

Every organization I have worked with discovers they are throwing away valuable data out of ignorance or inexperience. In one case, while building a data lake we discovered that we were purchasing data from an external source for a few relevant fields but throwing all the remaining data away. By simply saving the entire files into the data lake, we found much of the remaining data was useful in other processes and innovations. After discarding over 90 percent of the data we purchased for a single process that was built years ago, we made the simple decision to stop discarding and saved all the data.

When gaps in data do exist, successful data-driven organizations know where they are making data-driven decisions as opposed to making suppositions, and they blend the two to develop informed and accurate takeaways while understanding the risks more clearly.

Similarly, it’s vital to understand potential biases in data and recognize how these biases may affect decisions. Without this understanding, it’s difficult to leverage data to guide some decisions because it may simply be less informative. Geographic, racial, gender, and other harmful biases, for example, can come into play when basing decisions for one area of the country on data you’ve collected in a different region. This can lead to everything from biases in news reporting to variations in healthcare.

It’s important to try to understand potential bias before acting on it. If we assume all data is biased then it simply becomes an exercise in discovering how, and this understanding will guide the use.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

Using data to improve customer experience

I’d argue that the most valuable data comes from sources that are currently dark or wildly disorganized. One of the most powerful examples is customer conversations. This is rich data that many organizations simply throw away because they don’t know how to access or organize it. Interactions are recorded and put on a dusty shelf in cold storage and then deleted after a period of time.

[ Read also: 4 books to boost your data storytelling skills. ]

What is the value of knowing what a customer is saying? How are they reacting to products and services? Who are they? Why did they choose your product or service in the first place? 

Understanding customer conversations influences more than just customer service. It has the power to inform and transform every level of your business.

When it comes to customer experience, many organizations believe they have much of the data they need to improve customer loyalty and brand affinity – but oddly, this doesn’t include the actual voice of the customer. Listening and deeply understanding what customers are saying, across every channel, is the key to unlocking these answers. Understanding customer conversations influences more than just customer service. It has the power to inform and transform every level of your business.

Without a clear understanding of your customers’ most important needs and opinions, it’s difficult to determine how to make informed decisions. Discussions about all our brands are happening everywhere, from contact centers to social media platforms, web forums, public messaging apps and more. This unsolicited, indirect feedback can be the most valuable, especially when combined with solicited feedback like surveys.

At the end of the day, nuances and trends captured in customer conversations can be analyzed, assessed, and acted on to improve every business element, including positive customer experiences.

Looking ahead

Organizations today face a near perfect storm of increasingly data-centric environments and AI and ML technologies that are continuously creating new standards for customer service and business results. Regardless of what we call it, embracing the true meaning of “data-driven” will require taking the necessary steps to collect, clean, define, store, and analyze data.

Are you up for the challenge?

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

Richard Britt
As the Vice President of Artificial Intelligence, Rick leads the talented team of Data Scientists and AI Engineers that are as passionate about machine learning solutions as he is. Rick and his team are keenly focused on research that does not replace humans, but augments their skills, allowing them to excel in partnership with AI.

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