In a DATAVERSITY® interview, Donna Burbank, the Managing Director at Global Data Strategy, said, “The difference between Data Management vs. Data Strategy is almost in the definition of the two words. Think about your money. If you’re just managing it, you’re sort of just getting by, but if you’re thinking strategically, you’re really thinking of future and trends and how to best manage it strategically.”
Yet, it’s more than that, because in practice, the maturity of your organization’s Data Management determines how nimbly your company can strategically implement new business ideas or models. When you use a Data Strategy, “There’s so much more opportunity than just doing what you do better. Entirely new business models can be created through well-managed data and a solid Data Strategy. But you can’t do the strategy unless all the people are aligned, and there are processes for how to manage the data,” she said.
Data Management vs. Data Strategy Defined
According to the DAMA International Data Management Body of Knowledge 2.0 (DMBoK2), Data Management is:
“The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.”
As stated by Burbank:
“I think we’ve been doing that for a long time. If you’re just doing Data Management, your databases might be running and they’re optimized, and they’re backed up and you’re doing the day-to-day management.”
The DMBoK2 definition of Data Strategy:
“Typically, a Data Strategy requires a supporting Data Management program strategy – a plan for maintaining and improving the quality of data, data integrity, access, and security while mitigating known and implied risks. The strategy must also address known challenges related to Data Management.”
Burbank agreed, but added:
“It’s the opportunity to take your existing product line and market it better, develop it better, use it to improve customer service, or to get a 360-degree understanding of your customer. Data Strategy is driven by your organization’s overall Business Strategy and business model.”
Burbank shared an example about a client, a consumer energy company that moved their focus from managing their data to focusing on the strategic use of the data they managed. They discovered that the data they had about their customers’ energy usage was their primary asset, and if they let customers see that data and understand their usage, “And see that maybe I’m using more energy in the daytime, and from my cellphone, I could turn down my thermostat because I see the data, and through a data-driven Internet of Things, I can control my own usage of data,” she said.
“On their wall was ‘We are now a data company,’” Burbank said. “That was a data-driven whole business transformation, and I know that tends to be a buzzword, but companies are really doing that. ‘How do I take my data and create a whole new business model and a whole new revenue stream from data?’”
A Framework for Understanding Data Management vs. Data Strategy Needs
Burbank shared a five-level framework she devised to help her clients understand the relationship between Data Strategy and Data Management, as well as illustrating areas where their organization may need to mature to use data in the most strategic way possible, as shown in the figure below.
Level 1: “Top Down” Alignment with Business Priorities: Data Strategy
The most important place to start is to align Business Strategy with Data Strategy, for example:
- Example: Business Strategy Drives Data Strategy
“I want to switch to all online sales of our product. How do I manage my data to do that?”
- Example: Data Strategy Drives Business Strategy
“Wow, we have all this data about X. We could do Y with our business.”
Level 2: Managing the People, Process, Policies, and Culture Around Data: Data Governance
Data Governance provides a framework for managing the people, process, policies and culture around data. The maturity of an organization at this level – or the lack of it – can determine the options you have for using your data strategically, as well as the timeline for putting it into practice.
“It’s the strategy that helps you focus on which pieces of the management you need to do, and the Data Governance holds it together. If you don’t manage the people, the process, the quality and culture around that, it’s not going to work. You need to get everyone working together.”
Level 3: Leveraging and Managing Data for Strategic Advantage
The third level encompasses the various Data Management practices that help leverage data for strategic advantage, such as Data Quality, Master Data Management, data warehousing, and others.
- Example: “A client did this great trend analysis on social media, but when they mapped this to their own databases, they had six versions of ‘John Smith,’ and one was a CEO and one was in bankruptcy, so that ‘old school’ data quality still was important.”
- Example: “The energy company I mentioned – they wanted to start using the Internet of Things, and smart metering, and all of that, which they did, but to get there, they realized that even getting basic bills to their customers was difficult – they didn’t have names and addresses right. So how do you do that ‘next generation’ stuff if you can’t do the basics of your business, of getting bills to people? Data Quality is critical.”
Level 4: Coordinating and Integrating Disparate Data Sources
With data integration comes many different questions that need to be asked and answered: Where are all those data sources? What is the inventory of all those sources we need about our customers? How do we know where it is and where it should be? How do we integrate all the different formats? How do we understand it and get the lineage through metadata? What is the meaning and context of a customer’s first name, last name, address? For example, is that the email address or the mailing address?
- Example: “We’re talking to a customer named Charles. Where do I have all the information about Charles? Maybe I want to know what he’s saying on social media about his phone service. Maybe he’s angry and tweeted after he called. How do we know that it’s the Charles that called about his cellphone, and that he’s been a premium customer for the past ten years, rather than another Charles, who is new and pays late? All that information is in your relational databases. “
Level 5: “Bottom Up” Management and Inventory of Data Sources
What makes Data Management complex, especially today, is that you have many disparate data sources: relational databases, big data, unstructured data, XML, documents, voice, and media, so how do you make sense of that? These disparate sources can be used to inform Business Strategy, for example:
- Example: “We have all these videos. How could we use that?“
- Example: “Our customer support call logs could be mined for insights.”
A Framework for Strategy in Practice
Burbank stressed that it is important to assess the current level of Data Management maturity, and how this aligns with business goals and strategy.
A framework can help a company recognize deficits in key areas that need to be addressed before moving forward. “Even the biggest company in the world shouldn’t try to bite off all of this in equal efforts. So, based on your maturity, what’s the quickest win?” Burbank says that the key is finding “the fastest thing to do with the biggest benefit,” which will be different for each company. She also suggests working with a consultant.
“There’s art and there’s science, right? I think the science is a lot of the management part. The ‘art’ is how to strategically get those quick wins. A consultant can help show, where your technology is, and where your vision is, and how to get there in the quickest way.”
The Insurance Company and the Coffee Shop
Burbank showed how to use a framework to assess whether a company was at a level of maturity necessary to support their strategic goals.
A company that wants to use technologies like predictive analytics or artificial intelligence (AI), for example, should have an idea about why they want to go down that road. “Is it because it sounds like a neat thing to do? Is it because you’re an insurance company and you want to better predict risk in a certain area, and we understand there are new tools that could help us?”
If the insurance company already has an existing team that is “very analytics-driven, and all we’re going to need to do is basically augment their existing models, because they are already doing Data Science,” that’s a different scenario than a coffee shop down the street and “they’re managing everything with Excel spreadsheets.”
Focusing on the insurance company and traveling down the framework, she asks,
“How are the people in the process? Well, they have a bunch of data scientists that really get the data. Check! How are they at the bottom? Do they have the right data? Actually, yes, they’ve got databases, and they’re looking at unstructured and open data, it’s integrated. I’m thinking it’s a good scenario.”
From a Business Strategy standpoint, using AI to help with risk assessment for the insurance company makes sense, she said. “It could be a quick win because it aligns with your business goal, you have the people and the process. You have the data; you just need to augment what you have.”
Alternatively, the coffee shop wants to use analytics to predict how many people could come in to get coffee. Using a framework and assessing the people available – a group of baristas and part-time accountant – she said, “I’m not sure that’s the best Business Strategy. Their maturity level is very low because they’re just using spreadsheets,” and they don’t have the skills or the Data Management infrastructure to succeed.
In between the insurance company and the coffee shop, there are companies that have some of the necessary pieces in place, and “It might make sense, but how long does it take me to get there?” she said. “That’s why this framework is very helpful, because you can check the box: does it make sense?”
Maturity Determines the Pace
She likened maturity to running a marathon. “Anyone can run a marathon. It’s just what you want to do to get there.” Is it something you really want to do? Will running it meet your goals in life? What’s your existing fitness level? “If you’re already running a four-hour marathon and you want to get to three and a half in a year, well, that might be doable,” she said. If you’re sitting on the couch and want to do it next month, that won’t work, “But we could do it next year, and we could run a 10K in a few months.”
“I’m still getting to my goal, but first, I ran a 10K. I ran that 10K in a faster time and then I did a half-marathon, and so we sort of do that with data. Is it a valid goal? Are you trying to run an elite marathon or just trying to make a better marathon time? Or are you still on the couch and need some training?”
Burbank sees a growing number of businesses that recognize the value of using well-managed data strategically. “We want to play with the big kids now and we realize, if we want to be an enterprise company, we have to start treating our data that way,’” which is good, she said.
“Twenty years ago, it was only the big companies – the banks and the insurance companies – that really got data. Now, smaller companies are coming to us because it’s more the norm for things to be more data-driven and seeing data as an asset.”
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