The explosion of data in the age of networked intelligence requires new approaches. That’s the key point of a new report from The Tapscott Group, which proposes a variety of strategies and approaches for dealing with the vast amounts of data collected through social business

Called "Rethinking Analytics for the Social Enterprise," the SAP-sponsored report makes the case that social businesses are in a new generation of analytics,. In this generation, data is collected, analyzed and often used socially -- through the connection of collaborating human brainpower -- with new, real-time mobile platforms.

Social Analysis

“The contours of a new paradigm are emerging,” the report states. It's one that goes beyond Big Data and beyond a narrowly focused view of analytics.

One of the characteristics of the new paradigm is the social collection of structured and unstructured data from sources both inside and outside a company, including interactions with customers and prospects. That data is analyzed socially in a collaborative fashion throughout enterprise, using mobile tools and generating results that are more visual, more current and immediately actionable.

The report says that to become a social business that can effectively utilize this new management of data, make better decisions collaboratively, and lower transaction costs, management should encourage a culture where analytics is core to the organization’s mission and is actively used to drive decision-making. This encouragement includes executive support, adequate funding and performance metrics that match the analytics platform.

The report points out that a business should also conduct an organizational audit to determine what data sources are already available in customer records, financial data, supply chain information and government statistics.

Clean Data

To manage large amounts of data from multiple sources, robust technology needs to be chosen, and efforts should be taken to make sure that data (and the data collection operation) is as clean as possible. “Acting on bad data can seriously damage an enterprise,” the report reminds readers.

Another step is to building an effective team to conduct analytics. Other important steps include building, testing and retesting scenarios that can get the benefits of predictive analysis, which the report cited as the most important benefit of an effective analytics program. Predictive analytics can be improved by incorporating data from social networks, the report noted.

Examples of this new age of data analytics include using granular data about the health of large populations over time, analyzed both by individuals themselves and across large populations. Mining companies can generate millions of dollars in savings while improving governance by having employees generate useful, customizable reports through their own dashboards.

For retailers with both brick-and-mortar and online stores, customer data distributed to workers throughout the enterprise can help maintain relationships with the growing number of customers who comparison shop in multiple channels.