Sharing is caring: the rise of Data socialization
The “share” button is needed in data too. As data-driven insight becomes increasingly necessary to gain a competitive edge, so does the need to share data to the right people so meaningful change can take place. What if I told you we can share data-driven insight within a company in the same way we share a Tweet, Facebook post and LinkedIn update? From the chief decision makers down to customer-facing staff- all should know what is going on in the business and what they can do to change it for the better. Data socialisation is the newest big data project.
Big data and our unique ability to draw insight from it has built a sturdy foundation for so many new developments. Data analytics, machine learning, the internet of things and behavior science are just a few new disciplines which are connected to and rely on data. The field continues to evolve. We are still learning and uncovering new ways in which data can be used to produce meaningful change.
Socialising data ensures that insight does not sit on the analyst’s desk or within the IT department. It is instead shared across all departments in a meaningful way.
Why do we need data socialisation?
A major obstacle to data-driven transformation within companies was the rigid company hierarchies and silos. Data-driven insight was, more often than not, the chief concern of the I.T department. Data analysts are concerned with complexity and vast access to data, yet this can often alienate any person in the company who does not have the skills to navigate complex reports and dashboards. Most of the pioneering data analytics developments have arisen from university computer science and statistics departments, or R&D divisions of large tech companies. The complexities of data analysis is not common knowledge and especially not knowledge shared across the company.
Executives, unaware of the complexities involved with tapping into data resources, were not seen or communicated with effectively. In 2017, a Gartner study estimated that 60% of big data projects fail. Companies abandoned big data projects, naming them ineffective. What they did not realise was the insight could be used better if it was shared more effectively.
Big data had the potential to cut costs and increase revenue but its many insights were often miscommunicated. Cue the rise of data socialisation, the vehicle allowing data analysis to create positive change.
The rise of data democratisation
Data democratisation is the systematic breakdown of rigid company silos and a key ingredient of data socialisation. Data analytics should be entrenched throughout the company so all departments can access and act on data-driven insight. It is often more productive than a traditional top-to-bottom, centralised approach. For example, within a retail chain, the I.T department has insight that sales are low for a high-profit item. This gets communicated to executives. Executives communicate information to area managers. Area managers communicate insight to sales assistants, asking them to push this item forward. This approach is not timely. Each communication takes time and often action is taken when it is no longer needed. Giving all immediate access to the insight which they are most concerned with is more productive and timely.
Data socialization in action
Datawatch’s newest Monarch platform provides a social media-like space for data preparation and experimentation. Members can “like” and “share” particular insights, causing it to rise to the top of any given feed throughout the company. All can therefore have access to popular and, most likely, meaningful insight.
New developments involve using virtual assistants like Alexa, where executives can ask questions and hear feedback. No longer will they have to navigate complicated dashboards. They can ask what information they need and the platform will spit it back for them. Anybody, no matter their proficiency in data analytics, can receive meaningful data-driven insight.
Sharing insight is just as important as finding the insight itself. Rather than create another big data project that fails, use data socialisation to break down traditional silos so timely action can take place.