Here, Jonathan Wilkins, marketing manager of obsolete industrial automation components supplier European Automation, discusses how data analytics can really help manufacturers grow and become more efficient.
Since the concept began being applied to manufacturing, big data has been all about machines, networking and intelligent equipment. In fact, the stuff it covered in industry was so hard for the human mind to comprehend that new data extraction software and cloud storage solutions had to be designed to manage it.
In contrast, small data is all about people, context and individual requirements. It is more about immediacy and being pro-active, rather than accumulating incomprehensible amounts of information.
Small data could well be the next big step for marketers and engineering technology vendors. It's a consumer-style, more responsive social and collaborative tool. It offers insights in a way that is accessible, understandable and actionable for the user's everyday tasks.
The biggest difference between this David and the data-Goliath is that they cater to different audiences and thus appeal to different levels of understanding. If for machine to machine (M2M) 2.5 exabytes of information was considered the daily processing norm in 2012, for a human our limitations make this impossible. More importantly, as a person you would be interested in the social and emotional connotations of the data you come in contact with.
Small data takes into account information from social media outlets such as Twitter, Facebook, Pinterest and LinkedIn. This is an obvious benefit for businesses selling to consumers but also for more sophisticated engineering firms. Small data can offer transactional, CRM type, information, online data such as web reports and also social data. The latter is extremely important in marketing and can be gathered via text and sentiment analysis.
Although not infallible, text and sentiment analysis have been around for a while and so far they are the only ways of turning attitudes and emotions into numbers. It becomes clear to me that this approach is based on a user-centric view and collaborative business intelligence.
The rules of small data are very different from those of big data, where the more information we accumulate, the better the final result will be. For small data gatherers, the pre-requisite is to be simple, smart, responsive and social.
Simple:
The approach here is to make things as easy and visual as possible. This translates to delivering a pro-active message to the buyer or sending out a notification text to say that the order has been dispatched.
Smart:
The smart bit is relevant to the way in which vendors use predictive analytics, proactive services and contextual understanding. This is where sentiment analysis can be used to indicate the next step in the buying process.
Responsive:
The essential quality of small data is the fact that it is dynamic, agile and easy to interrogate. This is the very thing that makes it localised and mobile-friendly. Bring Your Own Device (BYOD) and wearable computing are the things small data needs to cater for in order to observe the responsiveness rule.
Social:
Small data is founded on collecting social information and sentiment. Apps need to integrate social media channels seamlessly. It is all about user experience attitudes and communicating with other individuals. In order to tap into the information exchanged through social networks, small data specialists need to be timely and keep engaging with users.
Small data and big data both have their unique uses in today's engineering environment. Although at times they overlap, it is the human factor that makes them distinct. Companies that want to stay ahead need to look at both and make sure they integrate information in a way that helps build an accurate image of both customers and end users.
