What is Data Discovery?
Data discovery is the process of uncovering relevant data insights and getting those insights to the business users who need them.
One of the biggest problems in business intelligence is the mismatch between those in the company who know how to prepare data for analysis, and those who need to use the insights from these analyses in their ongoing work..
Data discovery tools solve this pain point by making it easier for non-IT staff to access complex data sets and draw out the information they need. This process of knowledge discovery can be performed by anyone, without the technical know-how that was required in the past.
These tools are getting more and more sophisticated all the time. As Rita Sallam, Research Vice-President at Gartner (who coined the term ‘data discovery’), puts it:
“Data preparation is one of the most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms. However, data preparation capabilities are emerging that will provide business users and analysts the ability to extend the scope of self-service to include information management, and extract, transform and load (ETL) functions, enabling them to access, profile, prepare, integrate, curate, model and enrich data for analysis and consumption by BI and analytics platforms.”
So… What is Governed Data Discovery?
Data discovery tools are great, but they aren’t always able to keep up with the pace of change, especially when new data is being added to the back end all the time.
That’s where the idea of governed data discovery (GDD) comes in.
It’s really just a more complete and extensive approach to data discovery, which shifts power over data preparation and analysis from IT to the business side of the enterprise.
Governed data discovery systems need to do three things:
Offer Centralized Self-Service
There should be a central, built-in interface that allows users to manage business intelligence and run analyses at will, on a self-service basis.
Provide Data Governance
It’s not just the tools that need to be centralized. All data used by the company should be held in a central location that can be accessed, updated and shared by colleagues throughout the organization. This provides a “single version of truth” across the entire business, ensuring that insights are up-to-date and valuable and that there is a data governance strategy in place.
Most of your team are hitting their targets, but profits are stagnating. When you take a look at your KPI dashboard, you see that renewal rates are falling, eating into your profits. You realize your team is too focused on new sales at the expense of nurturing relationships with existing customers, so you play around with your KPIs to place more emphasis on securing renewals than bringing in new clients.
See an example:
Data discovery can be handled in a number of different ways. Here are the two main processes for understanding your data:
Manual data discovery
Manual data discovery means that all the slicing and dicing is done by analysts. Approaching the process manually means that prepping the data for comparative analysis requires an enormous amount of work by data scientists. Non-technical users like marketing managers and sales heads have to rely on manual reports from templates that don’t always supply the answers they need in the timeframe they want
Smart data discovery
Smart data discovery involves automating the onerous parts of the knowledge discovery process, to speed up the time to insights and make them accessible to all business users. And with technical advancements like artificial intelligence, machine learning, and natural language processing, BI tools have made data analytics easier than ever.
Just because you’re making data available to employees across an organization doesn’t mean you can take your eye off the security ball. Confidentiality issues are paramount, and you’ll need to keep data safe from hackers and cybercriminals outside the company, as well as protecting it internally, too.Start Free Trial Back to Glossary