One of the key issues facing businesses in 2014 is utilizing the large quantities of complex data collected across various channels. Most businesses see the need for relevant social integration and understand that social listening is key to evolving with changing paradigms. The big question now is what to do with this information.
The answer lies in translating big data into little or everyday data and making it as real time as possible. In order to make the most of the large quantities of data being collected, its vital that companies make the data more manageable through classification and categorization. 40% of business executives say that variety and complexity of data rather than the quantity of data are their biggest issues. Often data, especially social data, is collected across multiple platforms and in various forms (i.e. text, video, image) making the data hard to analyze and difficult to integrate with the internal processes already in place. In fact, businesses spend 80% of their time on data engineering rather than on analysis. This only slows down the process of turning big data into actionable success.
In order to integrate the data more successfully try this approach:
- Collect the Data across platforms:
- Separate the “Noise” around your brand from the “Signal” you’re sending out
- Focus on what message being pushed to the audience across multiple channels
- Analyze, Categorize Channels and Classify Meaningful Responses
- Provide context for responses, who is posting this, Are they an Influencer? Does it reference churn? Is it a potential customer?
- How does the data Enhance, Explain, Validate, or Redirect the information gained from traditional sources of information? (internal, 3rd party, Customer)
- Focus and Filter into Actionable Insights
- Utilize data to better trace the full life cycle of user experience
- What actions elicit positive or negative sentiment?
- How does this behavior correspond to trends across key demographics?
- Operationalize data into Actions
- Define actions to be implemented aimed at measurable business outcomes
- How does the data and actions compare to competitor analysis?
- How quickly can these actions be put into place and see results?
While this process gives a framework for tackling big data analysis and integration, it requires one key ingredient: knowing and understanding the needs unique to the business unit. Before the process can truly begin, understand what the desired business outcomes and goals are and only focus on the data that addresses these areas. If the data doesn’t speak to the issue, to use it. With greater understanding of the end game, work backwards into defining what actions affect this goal, what user information is available regarding these actions, and utilize that data ONLY. Once this process becomes continuous and streamlined, make the goal to be as “real time” as possible with data analysis and operational implementation. This is the final step toward the ultimate end game: Prediction.