This article originally appeared in the November 2014 edition of Dialogue, our monthly e-Newsletter. Click here to learn more and to subscribe.

For many marketers, the science behind Big Data just doesn’t add up. Though they comprehend its value, few have the skills necessary to parse insight from this information. Yet, while data science skills are in high demand, few organizations have the budgets to extend their workforces, leaving many marketers to pick up the slack.

Data scientists use their mathematical, analytical, and statistical backgrounds to solve business challenges, while marketers are accustomed to leading creative endeavors. However, both skill sets must exist in unison, as one area of operations depends upon the other. In response, many organizations are looking to consolidate these responsibilities by empowering their marketers with the knowledge necessary to aid their analytical goals and reach target audiences simultaneously.

Judah Phillips, author of Building a Digital Analytics Organization explains that at the heart of data science lie hard skills, such as fluency with certain programming languages and practical understanding of statistics, as well as softer skills, such as the ability to work with and influence business people who will never exude the same passion for data science as the data scientists themselves. Thus, while it’s likely that marketers will not develop the same drive for analytics, these individuals must exhibit the knowledge and skills necessary to lead or participate in programs and projects of an analytical or quantitative nature, for they have an increasingly significant seat at the decision-making table.

Marketers should learn to not settle into their current skill sets, as their data scientist counterparts are curious by nature and never stop learning. They have the drive to constantly improve and better understand how to solve problems, for they’re always pushing the envelope with the latest technologies to find better, more efficient ways to overcome business challenges. Marketers should also follow data scientists’ targeted approach to Big Data. Too often, marketers focus on collecting as much data as possible, and forget that much of this data doesn’t necessarily translate into knowledge or insights. By streamlining their data collection strategy, they can uncover value faster and avoid becoming overwhelmed with unnecessary information.

Inevitably, in the pursuit of data-driven optimization, the questions are often more important than the answers. But, as data continues to expand and grow more complex, marketers demonstrate an increasing inability to ask the right questions that will lead to the biggest opportunities. To counteract such issues, marketers must adopt the four skills that have come to define data science success:

1. Analyze: Though not all marketers must be data scientists, each must begin their analytical journey by learning how to read and draw insights from the information available. Organizations focus too much on the ‘what’ behind Big Data—volume, velocity, variety, and veracity—thereby deterring themselves from the essence of this insight. Instead, marketers should focus on the ‘why’ of Big Data, enabling them to move beyond conventional knowledge and see past siloed business problems to understand connections. Traditionally, marketers are expected to solve specific problems. But, in today’s dynamic, competitive landscape, these teams must embrace discovery-driven analytics, so they may explore available data for patterns and see where the road leads them from there.

2. Interpret: Data scientists let the data tell them the answers, from which they then interpret the meaning. This allows insights to illuminate unexpected opportunities that may otherwise go untapped. Thus, marketers can use their natural storytelling abilities to bridge the gap between data science and organizational understanding in an effort to explore what the data reveals and how it applies to current and future business practices.

Tom Davenport, professor at Babson College, notes that companies typically look to consumer data to help them find answers to their particular questions, with most specifically seeking evidence that will support their preconceived claim. Therefore, few teams allow themselves to explore the data as it stands and see these insights for what they are. Instead, marketers must interpret the data available, as they willingly let the information fuel their decisions and guide future strategies. Debbie Qaqish, author of Rise of the Revenue Marketer, echoes Davenport’s sentiment by encouraging marketers to let the data do the talking. If marketers are analyzing the right data points for their organizations, they need only look for the story behind this information, as it holds the key to improving business strategies and driving revenue results.

3. Cleanse: Oftentimes, companies fail to grasp the benefits of Big Data because they lack the essential, unified view of their customers and prospects. Behavioral information typically exists in silos across channels, preventing these organizations from generating any actionable results. However, this isn’t the only challenge on the horizon, as even those who manage to connect all the pieces of the customer journey must then establish which data points require attention and which fail to further the company’s overarching goals.

Data must be governed and cleansed, as outdated, incomplete, or inaccurate information skews results, wastes valuable resources, and makes data inactionable. Even more, ‘dirty’ data causes a slow movement of prospect data from lead source through internal processes, providing insufficient data to ensure targeted content or messaging support marketing efforts.

Marketers must learn to parse incoming data to determine which elements require monitoring so they may eliminate the information that clutters their efforts and clouds their judgment. Those who aren’t familiar with data analytics frequently look at every incoming morsel to ensure they leave no stone left unturned. However, they fail to comprehend that they’re doing the organization more harm than good. Therefore, marketers must learn to recognize which insights will drive results and which will distract.

4. Understand: Success comes from the ability to interpret data and apply the lessons learned in ways that improve the customer experience. With numerous channels generating insight, marketers have the chance to develop an extensive understanding of customer behavior and desire. Once they’ve established notable patterns, marketers may then use their knowledge to support and enhance various elements of the customer experience. Companies that have found ways to apply data across marketing channels are leading the way, as they’re able to “cross-pollinate” using Big Data. For instance, marketers can use social media interactions to discover consumers’ real-time interests, which can fuel advertising campaigns and content marketing strategies. In doing so, marketers demonstrate their ability to derive meaning and use said clues to increase relevancy and satisfaction.

Ultimately, however, companies must recognize the difference between turning all marketers into data scientists and empowering them with the skills needed to take part in these analytical projects. Instead of approaching data science as an all or nothing initiative, organizations must create teams that exhibit an array of marketing talents. These members must come to the table with the knowledge that each individual understands how to utilize, analyze, and act upon incoming data in order to improve the customer experience and drive increased revenue.

For companies that are fortunate enough to employ actual data scientists, leaders must develop synergy between these disparate functions to cultivate an effective workforce. Marketers must not come to rely upon these professionals for all their analytical needs. Instead, data scientists must educate marketers to interpret consumer insights, while marketers must help scientists focus their efforts to ensure their work aligns with business objectives. Thus, no matter where the given company stands with regard to data science, every individual has the opportunity—and responsibility—to learn from one another and expand their personal knowledge base for the good of the team.




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