The Data Storytelling Toolbox: Data Management, Digital Transformation, and Master Data

The Data Storytelling Toolbox: Data Management, Digital Transformation, and Master Data

By Matt Dallisson, 09/06/2022

Scott Taylor believes in data storytelling. More to the point, he believes that telling a “data story” leads to Data Governance success. Taylor is a consultant and “Data Whisperer” at MetaMeta Consulting, and author of the book, Telling Your Data Story In his presentation “Data Storytelling for Data Management” at DATAVERSITY®’s Enterprise Data World Conference, Taylor talked about the importance of telling the story about data, rather than just with data.

Good data storytelling can contribute to the value an organization derives from Data Governance, Taylor said. In Designing Data Governance that Delivers Value, McKinsey Digital cited securing top management attention and generating excitement for data as two of six critical practices to drive Data Governance excellence. Both require being able to tell a proper and effective data story, Taylor said.

Technology Doesn’t Work Without Data

Data Management is the foundation of digital transformation, and although there are many voices talking about Data Science, data literacy, and other disciplines, the one voice he rarely hears in the story is the voice of Data Management. Data storytelling falls into two camps, he said: the louder voice that supports analytics, and the type of data storytelling that shows the connection between digital transformation and Data Management.

As businesses are moving from analog to digital and from manual to automated processes, everything a company does turns to data. Enterprises big and small are struggling to manage, understand, and derive insights out of all that data, but instead of value, in many cases it’s creating a lot of chaos.

The Data Story

Every enterprise has a data story, and that story starts with the meaning and the purpose of the business. Taylor has found that a universal goal for businesses is to provide value to relationships through its brands at scale, and although businesses have been providing value to relationships through brands forever, the “at scale” part is what’s new. “That’s the part that takes technology.”

To provide value, it’s imperative to grow the business, improve the business, and protect the business, which requires increasing sales, improving operational efficiency, and mitigating risk. To do that at scale, the business needs technology. Technology needs data, and data needs standardization and management, he said. “The success of every digitally transformative customer-facing initiative is inextricably linked to the successful output of your Data Management efforts, no matter what,” and getting that story across is a critical step to success.

The Data Journey

When most people think about a “data journey,”a disproportionate amount of focus, time, and funding is spent on the end of data’s story — deriving meaning from the analytics. Business Intelligence, Data Science, artificial intelligence, machine learning, data literacy, visualization — all of these processes are focused on where data ends up. Taylor believes the story should start at the beginning: “We’ve got to remember where data starts,” he said. “You need that data side first, before you can get any value out of analytics. You need to determine the truth before you derive meaning.”

Digital Transformation by Pandemic

For many companies, digital transformation was on the agenda “someday” in the distant future, until COVID-19 became a wrecking ball, forcing the defining moment. Suddenly shoved into a digital transformation initiative, organizations are realizing they don’t have the data to back it up, and the stakes have never been higher, he said.

Universal Requirements for Digital Transformation

Organizations have more data now than ever before and are scrambling to find new ways to manage it. Despite the overwhelm, that onslaught of data provides possibilities never dreamed of before. Most companies start from a place Taylor calls a “legacy state,” with multiple silos, state and regional sales areas, global markets, and various departments — sales, marketing, finance, operations — all with different enterprise systems supporting those departments that tend to create separate and different data.

To go from this legacy state to an integrated enterprise, with connected, centric, networked ecosystems, there are two universal requirements: authenticated identity, and a common data structure. Both can only be attained if they start with Data Management.

The Classic Challenge

The ultimate goal for any business is to “get the stuff you make to the people who buy it,” he said. Given that most organizations are using disparate data from multiple systems and workflows, varying definitions that lack internal standards, and acquiring growing stores of syndicated or custom data from third parties, how is that possible? The solution, he said, is master data. Reference data, metadata, and comprehensive Data Management provide the foundation to unite all those different sources and silos, so it’s up to data managers to communicate the importance and garner support for master data.

And while it may be tempting to use a detailed architecture model to illustrate how all these disparate elements can be united, a simple chart with broad categories, underscored by a baseline of master data will play much better in the boardroom.

The 4 C’s of Master Data

One of the key benefits of mastering data and managing data is the common data structure that it provides to an organization, and Taylor presented a conversational way to explain structuring data he calls the Four C’s of Master Data: Code, Company, Category and Country.

Of these four, he said that Category can be particularly problematic because so many organizations tend to have a huge category called “other,” with some companies using subcategories called “other, other,” or even “DK” for “don’t know.” By using these Four C’s, he said, “You’ll know where everything is, you’ll know what kind of thing it is, you’ll know who owns it, and you’ll know what’s unique.”

Don’t Lead with “Quality”

As important as Data Quality is to an organization, he said, it doesn’t sell, because Data Quality is considered subjective. Data Quality doesn’t capture the hearts and minds of executives. Rather, it’s what Taylor calls a “big amorphous blob” that doesn’t have the right kind of hook to engender a commitment from leadership. Instead, he recommends opening a conversation about Data Quality with something similar to this:

“We don’t have a common definition and a common structure for our customer data, and we just published in our annual report that one of our key objectives is to become a premier partner of choice with our relationships. We can’t do that because we don’t have the data to back it up.”

Quality is important, he said, but don’t lead with it. Keep the data story conversational.

The 8 -Ates of Data

Taylor presented eight concepts that end in the letters “ate” as another set of tools to help Data Managers illustrate the different ways that data is used in a business.  

Relating is the most important, he said, because a company needs to build relationships, grow relationships, improve relationships, and protect relationships, through its brand, services, and products. “If you don’t have relationships, you don’t have a business.” 

Before having a relationship with data, it has to be validated. Validation entails determining whether the data already exists, whether it’s accurate, whether it’s safe to use, and ensuring that it meets all the criteria necessary for use.

Once validated, data is integrated, along with all the necessary contextual information and metadata, and put in one place, whether physical or virtual.

Most reporting is some form of aggregation that can answers questions such as: How many things do I have? Who owns them? What kind are they? And where are they in my system?

Interoperability is the process of connecting seamlessly on a code-to-code, machine-to-machine basis with the parties and the systems that drive the business, and is a core part of creating a trust network. “I would say ‘interoperability’ is the word of the century here. Things must connect, when and where they should connect.”

Once a foundation is built, the next focus is to evaluate, paving the way for analytics, AI, and machine learning.

Communication makes it possible to provide people with insights that drive effective business activity. Standardized structured data and a shared vocabulary are tools for better communication but it’s essential to focus on the basics first.

Data in motion has value. It must flow — it cannot be stuck in a silo or locked in a PDF. “Get it to as close to the point of decision as possible. That’s where the value is,” he said. “Master Data sitting in an MDM system doing nothing — is doing nothing.”

Data Management’s Extraordinary Role

There are very few departments that can help an organization grow and improve and protect itself all at the same time, and often with exactly the same data. Although marketing, sales, and other high-profile departments tend to get more attention, the pivotal role in digital transformation belongs to Data Management.

Managing the core content for an organization at a time when digital transformation is so critical provides a chance to create real change, he said. “We have this moment, this business reckoning, where we really have an opportunity to drive what we’ve all been working towards for so many years,” and Data Management needs to tell that story. 

This content was originally published here.

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