How to do more than just technical jargon in your company “data-driven”



Many companies today refer to themselves as “data-driven,” and the reason why is easy to understand: companies produce and have access to more data than ever before. It is considered a competitive advantage and is in demand by many customers.

In addition, advanced technologies like artificial intelligence (AI) and machine learning (ML) are more widely available to understand this huge amount of data and improve business processes and features like customer experience (CX).

But what does it really mean to be a data-driven company? The term “data-driven” has become marketing jargon in a way, possibly because it is used to describe even the most basic of data activities. But just because an organization collects data doesn’t mean it is data-driven.

The bottom line: Being a data-driven company means grappling with the information available and making strategic business decisions based on the facts and insights uncovered.

Sounds easy right? Not always.

To get to a place where organizations can confidently describe themselves as data-driven, here are some of the first steps to successfully harnessing data, gaining insights, and thereby maximizing ROI.

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

Transition from data-producing to data-driven

As our world becomes increasingly networked and digitized, we have a growing amount of unstructured data. The ability to effectively manage and analyze them is essential for companies trying to improve business processes and innovation.

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

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

As the understanding of data increases, creating a data card and dictionary of what and where is critical. Organizing data can be a long process, but by starting with the critical data and then working down, your business will quickly accumulate well-defined and trustworthy data.

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

With the data structured, start experimenting and using it in existing processes and performing analysis to make new business decisions. Start small and work your way towards bigger, more important decisions based on the data available.

It takes time to get the hang of things in neatly organized data, and it’s best to take too many risks in the beginning.

Avoid swinging for the fences at the beginning. It takes time to get the hang of things in neatly organized data, and in the beginning it is best to avoid unnecessary expense and cycles by taking too many risks.

Here, too, AI and ML come into play. With the understood and defined data, various algorithms can be trained and processes and innovations can be rationalized or automated.

As your company grows its data-driven initiatives, it’s important to consider the inevitable new data gaps that are discovered. When companies define and organize data, they often find that they are discarding relevant or very useful data in old processes.

Every company I’ve worked with finds that valuable data is thrown away out of ignorance or inexperience. In one case, while building a data lake, we found that we were buying data from an external source for some relevant fields, but discarding any remaining data. By simply storing all of the files in the data lake, we found that much of the remaining data was useful for other processes and innovations. After discarding over 90 percent of the data we purchased for a single process created years ago, we made the simple decision to stop discarding and save all of the data.

When there are data gaps, successful data-driven organizations know where to make data-driven decisions rather than guesswork, and they combine the two to develop deeper and more accurate insights while also understanding risks more clearly.

It’s also important to understand potential biases in data and how those biases can affect decisions. Without this understanding, it is difficult to use data to make some decisions because it may simply be less informative. For example, geographic, racial, gender, and other harmful biases can all play a role when basing decisions for one region of the country on data gathered in another region. This can lead to anything from biases in news coverage to variations in healthcare.

It is important that you try to understand any potential bias before reacting to it. Assuming all the data is skewed it becomes simply an exercise in figuring out how, and that understanding will guide use.

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

Use of data to improve the customer experience

I would argue that the most valuable data comes from sources that are currently obscure or completely disorganized. One of the strongest examples are conversations with customers. This is a lot of data that many companies simply throw away because they don’t know how to access it or how to organize it. Interactions are recorded and placed on a dusty shelf in the refrigerator and then deleted after a while.

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

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

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

When it comes to customer experience, many companies believe they have much of the data they need to improve customer loyalty and brand affinity – but, oddly enough, that doesn’t include the actual voice of the customer. Listening to and deeply understanding what customers are saying on all channels is key to these answers. Understanding customer conversations affects 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’ key needs and opinions, it is difficult to make informed decisions. Discussions are happening everywhere about all of our brands, 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 such as surveys.

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

looking ahead

Organizations today are facing a near-perfect storm of increasingly data-centric environments and AI and ML technologies that are continually creating new standards for customer service and business results. Regardless of what we call it, realizing the real meaning of “data-driven” requires the necessary steps to collect, clean, define, store and analyze data.

Are you ready for the challenge?

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



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