Tying Data All Together

Data virtualization is a possible remedy for B-to-B marketers struggling with information overload

By Michael J. McDermott

Daniel Hertzberg/theispot.com


Is it really possible to have too much of a good thing? In the case of marketing data, the answer is a qualified "yes." The challenge is gaining access to all that data from multiple systems and quickly acting on it to achieve marketing objectives. That usually requires an enterprise to link all its critical data to a single master file.

Times are changing, however. The emergence of data virtualization technology now makes it possible for marketers to get a more unified view of all relevant customer data, regardless of where it resides across an organization. In fact, the technology is sometimes viewed as the holy grail of data management for marketers, although it has not taken hold at most organizations.(See "Data Virtualization Simplified," below).

"It's important to marketers because marketing workflows often touch multiple applications and systems — customer relationship management, marketing automation programs, marketing data warehouses, etc.," says Rishi Dave, CMO at Dun & Bradstreet. "Virtualization allows marketers to access all information from these systems through a technology capability called a 'virtualization layer.' Ideally, B-to-B marketing organizations should deploy both a master data strategy and a data virtualization strategy for maximum impact."

Top-of-the-funnel metrics, such as clicks and impressions, no longer suffice for B-to-B marketers. To truly understand who their customers and prospects are and how they behave, marketers need to analyze every touchpoint throughout the customer journey, says Elle Morgan, marketer and evangelist at Woopra, a real-time customer analytics platform. "This means having visibility into everything, from product engagement data to support requests," she notes.

That breadth of visibility enables marketers to expand their measuring capabilities beyond basic metrics (e.g., which campaigns drove the most new customer signups) to more relevant ones (e.g., how engaged customers are and whether they're long-term customers).

"Data virtualization essentially abstracts complexity from the data migration and unification process," Morgan says. "A good data virtualization solution will seamlessly unify data across your sales, support, product, and marketing teams. It will reveal the interconnected customer experience and illuminate areas for optimization."


Seeking a Holistic Customer View

Bringing information together is just one piece of the puzzle; to attain a holistic customer view, marketers must consider three components: data management, analytics, and activation.

For B-to-C marketers, the analytical and activation components present problems of speed. The Clorox Co., for example, has models that can draw on its brands' multiple data sources to serve up customized content, such as a recipe from Hidden Valley Ranch.

"The problem is, if my load time for the recipe becomes 30 seconds, I end up losing that consumer," says Ashish Joshi, senior director of global data, analytics, and data science at Clorox. "So, it really doesn't matter if my recipe was customized or not. Right now, I think data virtualization has more relevance for B-to-B marketers."

Running an effective marketing campaign that uses a 360-degree customer view also requires data from multiple systems and sources, such as third-party data, digital data from web properties, sales data from CRM, customer satisfaction data, billing/payment information, and device data from the Internet of Things, Dun & Bradstreet's Dave says. "This data is not static; it will constantly be changing," he notes.

Virtualization also enables marketers to source the latest data from each of these systems in near real time, rather than storing a snapshot that could be outdated. As a result, the data marketers use is more accurate and timely, enabling them to make better decisions and take the appropriate actions.


Making It Work at Syngenta

One B-to-B marketer successfully deploying data virtualization is Syngenta, a global agribusiness. The company has developed powerful abstraction layers across multiple data stores, which has bolstered the analysis and interrogation of complex datasets.

The flexibility of the abstraction layers makes it easy for both researchers and marketing teams to access critical data about products and relate it to real-time changes in the marketplace, such as market demand based on weather fluctuations, says Bill Danker, R&D IS domain head for biology and breeding at Syngenta.

Danker points to multiple benefits the application of data visualization techniques provides Syngenta:

  • Less time spent on finding the single representation of data for all uses.
  • Increased agility due to the ease of plugging in additional data sources.
  • Insulation from changes in data, so downstream applications are less affected by source-system changes.
  • Less duplication of data thanks to a common aggregation platform that enables multiple usages.
  • Increased flexibility for specific crops and regions thanks to business-owned and -managed curation rules.

"It's that last bullet point that allows us to virtualize complex data sets, both internal and external, to our marketing and sales teams," Danker says. "It provides them with up-to-date information that can be used to modify marketing campaigns and respond quickly to changes in pest pressures, diseases, or environmental factors."

There are many different types of data virtualization tools available, including SaaS solutions. Dave suggests B-to-B marketers read the most recent Forrester Wave report and Gartner's "Market Guide for Data Virtualization" to obtain more information about which tools might work best to meet their specific needs.

Clorox's Joshi says that many traditional data warehouse tools, such as Oracle Database and Google BigQuery, support limited virtualization through external tables and views, but he emphasizes that the market is evolving.


Multiple Applications for the Brand

Dun & Bradstreet's Dave stresses that companies should consider implementing a full master data strategy to yield the best results. "Data virtualization is a good start to accessing data from multiple sources in near real time," he says. "It has a smaller technology footprint, compared to alternatives."

In addition, Dave says, broader access to data and the ability to leverage real-time information from disparate systems leads to more effective campaigns and better ROI.

The most important thing marketers need to know about data virtualization is how much it can accomplish for their organizations, says Alexander Kesler, founder and president of inSegment, a full-service digital marketing agency specializing in digital strategy, SEO, and demand generation, among other things.

"Integrating sales and marketing has been a key initiative for B-to-B marketers," he says. "Now, imagine integrating data from sales, marketing, and operations, and product usage, and business applications. That kind of information, all integrated in one place, not only makes for better reporting, it allows B-to-B marketers to make smarter decisions about their programs."




Data Virtualization Simplified

B-to-B marketers need to think of data virtualization as a conduit tying disparate data sets together to optimize marketing objectives, says Katrin Ribant, co-founder and chief solutions officer at Datorama, a global marketing intelligence company.

"Because of the crowded nature of today's martech landscape, there are thousands of point solutions that must be evaluated when a marketing technologist decides to build an optimal technology stack," she says. "When the stack is complete, it will leverage dozens of point solutions."

For example, answering a simple question, such as which platform commands the most marketing dollars, requires access to all the spending data from all the different platforms the marketer uses. The spending data then must be normalized into one common amount, which may involve currency conversion and applying computations for fees. The last step is ranking platforms by the normalized figures to determine where the most money is being spent.

To help illustrate the example, Ribant suggests imagining having to listen to 50 international delegates (metaphorical marketing point solutions) at the United Nations. Each has a piece of information marketers need to make a decision, but there are two problems: Marketers only speak English, and they need to make 100 decisions a day based on the information they receive from the various delegates.

"To make this happen efficiently and effectively, you need a universal translator — this is where data is normalized — to bring the information together and have it make sense to you," she says. "Once the insights from your delegates are translated and organized, then you can make a variety of decisions on an ongoing basis."


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