Despite 54% of CMO’s citing marketing data and analytics’ inability to meet expectations of influence within an organization, it still continues to be a focus area as 44% of analytics teams expect to grow in the next two years in support of more advanced analytics capabilities, and achieve greater impact on business outcomes. One contributing factor for analytics’ lack of outward influence is the inability to turn data into insight and useful recommendations. This calls for a wider perspective which necessitates robust contextualization of marketing data within analytics teams, ultimately spotlighting the need for a Data Interpreter role.
The Data Interpreter possesses a multi-dimensional skill set that gives weightage to both core technical skills like data modeling and domain knowledge along with other areas of expertise to help contextualize marketing data within analytics organizations, as observed in Gartner’s report on the topic. These skills are crucial to the success of marketing organizations as analytics teams shift from focusing on product and channel insights to an emphasis on customer journey mapping in order to achieve a comprehensive customer view. As customer journey mapping is a more progressive endeavor that consists of more touchpoints and a high degree of interrelation between these touchpoints, it is strongly recommended that marketing analytics teams make room for a role focused explicitly on interpretation and visualization. That said, the Data Interpreter’s hybrid skill set also supplies an enhanced degree of modularity to the analytics team, allowing it to be dynamically staffed to suit project needs.
The Data Interpreter role has become a priority for marketing analytics teams in 2019, as compared to other technical roles in sectors Data Science and Data Engineering. This indicates that many organisations have sufficient data sources and volumes to meet their analytics goals. With the help of a Data Interpreter, actionable recommendations and insights derived from this data can help CMO’s realize the influence of analytics more clearly when making business decisions while also meeting the expectations of the evolved consumer in a more multi-dimensional way.