Four Steps to Make Your Company "DataSmart"
In the News
Matt Kinsman, Connectiv
Digital-first has evolved into "data-first" for many information companies. But does your organization really understand the data opportunity that it has, as well as the tools to effectively leverage that data?
Christian Ward became SourceMedia’s first chief data officer in September 2018. Ward has been driving data innovation for 20 years as an executive with financial services, digital marketing and digital media companies, including as chief data officer at Infogroup and as global head of content innovation at Thomson Reuters.
He is also a published author (Data Leverage: Unlocking the Surprising Growth Potential in Data Partnerships) who coined the term “DataSmart,” an approach to understanding the value of corporate data. At the upcoming Connectiv Executive Summit, Ward will share how information companies can avoid data strategies that are either “dreamily opportunistic or dreadfully restrictive” to prioritize what assets are most important, simplify the process around leveraging those assets and start monetizing.
Here, he offers an overview of the DataSmart Method as well as the first steps information companies—both large and small—need to take to assume control of their data.
Connectiv: Could you offer some background and context for the DataSmart approach? What does it mean for an information organization to be DataSmart in 2019?
Christian Ward: If you speak with executives at a multibillion-dollar industry leader and then speak with small-business owners, you’ll notice some striking similarities. They all know that there is an opportunity for a more proactive data approach, but they are convinced that they aren’t properly positioned to take advantage of it. This pattern of thinking is common: you’ll see it not only in your own company but also in many of the businesses you deal with. Highly successful companies, even ones with global operations, are sometimes paralyzed by a lack of a data strategy from the top down.
The DataSmart Method is an approach to understanding the value of your corporate data and prepares your company to create a growth strategy around data partnerships. This method was designed from two different perspectives; one from a data entrepreneur and the other from a data privacy litigator. We have found in our research that most discussions on data are either too dreamily opportunistic or too dreadfully restrictive. The reality lies somewhere between the perspective of the most starry-eyed entrepreneur and the most battle-hardened business litigator. Over the years, my brother and I would often debate how companies lack a cohesive strategy to their data partnerships, both internally and externally.
Being DataSmart in 2019 is all about understanding the data you have so that you may maximize its value while minimizing its risk. While GDPR, and preparation for it, drove the dialogue in 2018, the world is now following Europe’s approach and demanding a better approach to data privacy and usage. California, Illinois, Washington, New York, Massachusetts, and Tennessee each have privacy laws either on the books or in the legislature under review, and they won’t be the last. Being DataSmart in 2019 will require companies to evaluate their approach to data, not merely to comply with the new laws and regulations, but also to maximize their competitive advantage.
Connectiv: There are four steps to becoming DataSmart. What are they?
Ward: The four parts to the DataSmart Method are: Identify, Value, Structure, and Protect.
Thriving companies can, and must, identify their data assets. Put briefly, identification of data assets means understanding what information you have, and why it’s useful. For some companies, this may be as simple as collecting customer lists, while for others it is a multifaceted inquiry into terabytes of data. But how companies identify data assets is a question of degree, and not activity. The task is often harder and more complex than you may expect, because your company may have preconceived notions about its data or may perpetuate fiefdoms that silo off data assets from each other. But with the right framework, you can simplify the process dramatically and bring some order to the chaos.
Once you have identified the company’s data assets, you can begin the process of valuing them. Valuation is complex and subjective. You must properly tailor the process to whatever value criterion is ideal for your company. For example, many companies value their customer database above all else, even when someone could cheaply purchase almost every detail about a customer from a third-party vendor. What is the real value of data about your own customers? We’ll describe that in some detail, because in many cases you can unlock the value not through the identification of a data asset, but by combining that dataset with additional, and often external, sources of information.
After a company has defined the data and put a preliminary value on it, it must create data partnership structures that will allow it to confidently utilize these assets in different strategies. Whether you are seeking to buy, sell, share, or resell data assets, you must consider some fundamental timing and legal approaches. The use or misuse of data often generates significant government, industry, regulatory, and even consumer perception risks; every company must understand each of these risks and how they affect partnerships. Equally important, the timing of when to utilize certain partnership structures is highly dependent on situational constraints, and so we will outline where and when to apply different tactics.
The final step in the DataSmart Method is to protect your data assets. Data is dangerously transferable, readily stolen or copied, and easily manipulated to disappear. We will talk about some of the more painful examples of this: not just external hackers attacking from the outside, but also the realization that a partnership you purposefully and excitedly signed somehow opened a backdoor for your data assets to walk out, never to be seen again. Focusing before and during a partnership negotiation on reporting requirements and other techniques will help uncover these risks before they ever go into effect.
Connectiv: What are your observations around the data opportunity for publishing information companies? How far is the gap between that potential and realizing that potential? What should be the first priority?
Ward: Great question, and one that I am constantly evaluating, particularly in my role with SourceMedia. The issue facing publishing companies tends to be one of data glut, not dearth. Data gathered, stored, and ranked by publishing companies, regardless of their industry, has been swelling for years. At this point, publishing platforms are trying to track, cookie, subscribe, email, Facebook, retarget, tweet, store, pattern, and target millions of people every day. This race to know-thy-audience has become all-consuming for some publishers and is a direct result of business pressures on advertising models and low subscription rates.
If our goal, however, is to know-thy-audience, I would argue we are missing the opportunity. With hundreds, if not thousands, of data vendors and platforms looking to sell publishers their wares, the constant growth of data models comes at a significant cost. First, the more data we store on every customer, the harder it is to properly segment and target them appropriately. Second, the more data we keep on each individual subscriber that isn’t actually necessary for our business creates a direct conflict with the principle of data minimization, as outlined in the GDPR. In short, we are missing the opportunity because we have too much data that we can’t efficiently leverage.
Don’t get me wrong, data is the lifeblood of this industry and always will be. However, by concentrating on maximizing the value and the accuracy of a few dozen data points, instead of creepily tracking thousands, publishers may seize upon the opportunities their audiences provide while also enabling them to be more nimble in that pursuit.
Great publishers have always held their audience through outstanding content, focused on their audiences’ needs. They have created experiences, both online and in-person, that engage their customers but also enable their customers to expand and grow their own business interests. When the pursuit of 1,500 columns of attributes about every customer is the focus, we as publishers risk losing that trust because we are looking to “propensity models” for that which we should already know… our audiences’ goals.
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