Organizations are increasingly relying on data to drive value and many disruptive businesses have done so very successfully. Whether they are monetizing the data they collect directly like Facebook and Google or using the data to create new products like self driving cars or better TV shows data is playing an ever more important role in businesses across the board.
Marketing has been one of the beneficiaries of increasing amounts of data in todays digital world, gaining a lot more visibility of the consumer. Marketers need data across all stages of the buying cycle before they come up with a strategy or a marketing campaign. They need data related to actions, trends and behavior to name a few. So it’s no surprise that marketers are often seen as professionals whose strategies are driven by data.
The data in question is typically accumulated in a number of ways. Marketers can tap into first party data in the company’s databases, look into results of past campaigns, purchase third party data, work with partners and gather new data through surveys and focus groups. The applications responsible for creating these data sets include the likes of web analytics (such as Google Analytics or Adobe Analytics) and CRM systems (like Salesforce or Microsoft Dynamics) to enterprise application data warehouses, data-lakes, and DMPs (for example BlueKai, Krux). The data is then analyzed and the results put to work in the decision making process.
With a growing number of data sources and silos marketers have had to recognize they are ill equipped to collect, process and analyze the shear volume of structured and unstructured information in this new data driven age. Whether internally or externally sourced the expertise within these marketing teams has evolved to encompass technology, data collection, processing and analysis. There’s significant upside though and a marketing organization with a data driven culture and well put-together data science team can have a far reaching impact across the business. Data engineers and data scientists are just two of the new roles appearing in marketing to help deliver this value.
A data engineer will help collect, sift and connect customer data across numerous structured and unstructured data sources. They will be very comfortable handling large complex data sets and the infrastructure needed to ingest, house and model the data. They will have familiarity with ETL tools and data modeling, Apache Spark, data warehouses and NoSQL / SQL database systems.
The data scientist is the one tasked with extracting the value from the data. It’s a tough role to fill and good ones are increasingly tough to find. (McKinsey predicts demand for data scientists will soar 50% by 2018). To be effective a data scientist needs domain expertise, should be adept at articulating insights, creating visualizations and storytelling. They operate much like scientists creating testable hypotheses, then executing tests on data sets that can be modeled and prototyped. Once new models and prototypes have been validated they must then be “productized” or “operationalized” and deployed to can drive efficiency in day to day operations.