Tag Archives: 3 Vs

Defining Social Big Data for Human Insight

Social Big Data for Human Insight
Internet map 1024 by The Opte Project is licensed under CC BY 2.5.

DataHive Consulting is focused on the concept of Social Big Data for Human Insight. But what does that actually mean? For us, there are three key components to this phrase: Social, Big Data, Human Insight.

“Social” is about the engagements and transactions that we use to interact with each other. Social media and social networking are the most obvious examples, but we are interested in the entire span of interactions. Email is a social interaction. Telephony call detail records and text messages are also social interactions. Even our interactions with locations and content can potentially be social if two or more people are interacting with the same location. In short, we are focused on personal and shared interactions.

The most important aspect of “social” is in getting good data. In this case, good data is determined by the quality of interaction. Social data will not be useful if employees, customers, and partners are not interacting with each other and getting value. So, from this perspective, the lessons of effective social collaboration are vitally important for maximizing the value of Social Big Data. Garbage in, garbage out is just as true for relationships as it is for data. Because of this, we care about setting up social engagements properly to create business value and to support ongoing social insight through Social Big Data for Human Insight.

“Big Data” is a bit of a marketing play, to be perfectly honest. We do care about the 3 Vs of Variety, Velocity, and Volume, and we work with video, sentiment analysis, predictive analytics, and data mining. But we also deal with the traditional data management and business intelligence tools that can analyze social data. Often, the key to unlocking the value of social data is as simple as connecting the correct data to the correct applications. In cases like this, DataHive will recommend a basic data integration and connection process rather than a Big Data implementation. The goal is to use the correct business intelligence and analytic tools and not to simply add Big Data tools for its own sake.

At the same time, there are specific challenges to Social Big Data as well. Companies trying to gain value from the full Twitter firehose need to have semantic strategies, sentiment tracking capabilities, focused goals, and a strong knowledge of which Twitter fields are more likely or less likely to provide value to those goals. Video analysis may require packet inspection, natural language processing, metadata creation and analysis, video surveillance, and user participation tools to translate gigabytes of video sharing into actual insight. These social data sources are much more complicated than the relational databases and structured data marts that DBAs initially grew up with. When we conduct data-driven work, it is in light of Social Big Data for Human Insight.

“Human Insight” is the most important part of this model. DataHive is not concerned with data analysis for its own sake. In fact, one of our biggest criticisms of the “Big Data” mentality is concept that the supposed “data scientist” would look at data without hypotheses and look for findings. (We know that not all data scientists take this approach by any means, but we believe in an empirical and scientific approach for analyzing data.) In our world, businesses and organizations have specific goals that they are trying to achieve and Social Big Data needs to help them to improve processes, provide guidance, and foment disruptive innovation.

Although social media is typically in the hands of marketing or customer service, DataHive believes that there are Human Insights that go across the entire organization: human resources, sales, research, operations, IT. The insight may be a simple as picking up website complaints which lead to the realization that the corporate WAN is being impaired at a key node. From an HR recruiting and morale perspective, the sentiment for your organization may be much stronger or weaker online than your internal metrics may track. And the pure insight and educational value of key thought leaders online can provide value to every level of the organization if the correct information is used.

But beyond the sentiment of the social media itself is another level of insight held through metadata, social connections, and context provided through sales, talent, and other key business capabilities. Data has a topology and a specific meaning, especially as we try to collaborate together. Social efforts can be pointless if we are not talking to the right people or if we lack a critical mass of users. There is also a bigger world of human insight associated with global challenges, but we are starting with business challenges. Today, businesses, tomorrow, the world!

As we look at human insight, we want to show you how to gain value from all three of these areas both in isolation and in combination. We use our decades of experience in using social media, our use of metadata and relational data, our social science backgrounds in understanding the sentiment and psychology of social data, and our business backgrounds to provide you with greater insight into using social data.

If you have questions on what social data is or how to take optimal advantage of your social data, we would love to discuss your social strategy and social data use cases. Please contact us at connect@datahiveconsulting.com to schedule a free consultation.