Most organizations have now realized that data is their most valuable asset. But although data holds incredible value for businesses, it is perhaps also one of their most underused assets too.
Even the world's biggest CRM firm Salesforce has estimated that only 1% of a company's data is used by its CRM system. As so much enterprise data is unstructured, what can be done to open up more of an organization's data, so they can get more insight from it? The answer lies in addressing who uses that data and equipping them with the right tools to do so.
The tools of the trade
When responsibility for data analytics lies with the data scientists within an organization, it immediately puts restrictions on how data is used. Putting data in the hands of business users, or citizen data scientists, is a much more effective way of ensuring an enterprise gets maximum value from its data. To do so though, they need to be equipped with the right tools.
Smart dashboards -- this is an intrinsic tool for managing and analyzing data and a good user interface is, in theory, one of the most straightforward things to provide. But because such tools were initially designed for use by data analysts, they can be complex and more difficult to manage than they could be.
With business users accustomed to using tools such as Microsoft Office, a data dashboard must offer similar levels of user-friendliness.
In terms of data, developers hold all the cards. (Image: Ilya Pavlov, Unsplash)
Powerful visualization tools -- in today's world, people think in an increasingly visual way. A list of a company's highest performing locations carries the required information, but a smarter way of conveying that information would be a map of those locations that allowed the user to zoom in and out to get more in-depth data and information -- far more digestible and useful to a user.
Furthermore, with business increasingly global, the ability to tag data with coordinates to create a dynamic and interactive geographic map is a highly valued capability, allowing a user to demonstrate results and trends in an effective and visual way.
Effective data loading -- with many different users across a business managing data, running their own analyses and reports, then it stands to reason that data must loaded and ready to use whenever that user requires. This requires an effective data pipeline workflow.
Data must be loaded and enriched much quicker, and data from different sources should be processed in different and highly customizable ways, according to the requirements of that particular user. This will deliver more powerful results and give the user full control over the data loading workflow.
A recommendation engine -- for anyone with a sales or account management role, a key part of their remit is to approach clients with deals and opportunities. But account handlers within large enterprises are time-pressured and often dealing with such vast volumes of data that going through that data and sourcing leads is a time-consuming and costly exercise.
So for users to become an effective citizen data scientist, a smart recommendation engine is essential. It should look at data from internal sources (CRM systems, call notes) as well as external (premium data sources, public RSS news feeds) and use that to identify opportunities for a client. It should also score and rank every opportunity and provide actionable recommendations to users, so they can contact clients confidently with the best deal for them.
Intelligent machine learning capability -- the concept behind machine learning is the ability for intelligent automation to create an opportunity to handle more complex processes, incorporate unstructured inputs, adapt to changes and exceptions, and perform better over time. Using traditional data modelling, business users could use untrained data sources and it would generate good results for them. Over time though, that performance will drop off, or certainly level out.
But what if a data source could be trained? What if multiple models could be run at once to constantly evolve and offer improved performance? This machine learning has previously been the preserve of more technical staff, but making it accessible to business users will be an important factor in boosting insight and understanding on an ongoing basis.
The entire company should be able to access data, in order for it to be used most effectively. (Image: Rawpixel, Unsplash)
Cognitive search capability -- people search for information in a different way than they used to and finding the right information within the multiple platforms and silos of a large enterprise is a major challenge. Traditional search is proving unfit for purpose in the knowledge economy, and cognitive computing is becoming more widely deployed.
Cognitive search goes far beyond traditional search, by recognizing context, relevancy, intent and interest to deliver vastly superior search results. Instead of the user looking for the most relevant information, it comes to them automatically from a system that cognitively understands their interests and matches them with the data available.
The true value of data only comes when each employee can benefit from the insight it delivers -- the democratization of data. By opening up data to business users and giving them the tools to get the most insight from that data, enterprises are empowering their teams to do better and more effective work. The business itself benefits and the workforce is happier and more motivated. It's time to take data analytics away from IT and give it to business lines.
— Dr. Dorian Selz, CEO, Squirro