Data Maturity in a Social Business and Big Data World

Dion Hinchcliffe, the executive vice-president of strategy at Dachis Group, in his book Social Business By Design, defines social business as the intentional use of social media to drive meaningful, strategic business outcomes. Companies are leveraging social media platforms and data to drive business, inform consumer engagement, and to enhance and expand the company’s analytical capability. Watching Twitter feeds? Check. Monitoring Facebook and Pininterest? Yep.  Building internal collaboration platforms to more tightly integrate your business partners? Of course.

To harness the transformative power that social business and social business analytics promises, companies need to integrate information from multiple data sources. This includes both structured and unstructured data. It is critical then, to have both a strong data governance foundation in place, as well as an infrastructure that can quickly consume, integrate, analyze, and distribute this new information. Incompatible standards and formats of data in different sources can prevent the integration of data and the more sophisticated analytics that create value.

A company’s ability to strongly leverage social media as a social business will be infinitely enhanced by having a strong foundational data and technology infrastructure, along with data governance policies and processes for integrating social media data sets.

The figure below overlays Hinchcliffe’s social business maturity model (in red, with four of eight community management competencies shown in gold) with a traditional data governance maturity model (shown in blue) and technology maturity model (in orange).

DM Maturity in a Social Business

Implementing cross-channel customer engagement or enriching in-house data with purchased behavioral/lifestyle data WITHOUT already having master data and a master data management system in place would require hours of manual manipulation on the part of employees, leaving little time for the actual analysis of data. Additionally, services such as alerts and recommendations would not be accurately possible (thus potentially risking a privacy violation) without a master profile of the customer. Likewise, an organization’s internal infrastructure (beyond big data clusters) must also be sophisticated enough to move data throughout the organization, when and where it’s needed.

While the rush to social business and big data certainly is on, smart data companies are also investing in foundational data management, data governance, and technology architecture to support their long-term vision.