One of the greatest barriers to discovering value in data is the difficulty that many organizations have in connecting data to the business units that might find it beneficial. While there are several approaches to connect business with data and data skills, there‘s still great opportunity for software to connect the right data with high-value business problems.

Data discovery means making connections between available data and the events and outcomes that affect your work. This oversimplifies things a bit, though. Many different factors make this process more difficult than it might seem, and in the real world there are both technological and organizational barriers to connecting data with value.

It’s still common for barriers to exist between the data keepers and the people charged with creating value. Worse, the skills and resources for discovering value are frequently as siloed across the organization as the data itself. Database experts are limited to IT functions, while the business users are (often intentionally) shielded from data.

As a result the path that leads from data to a concrete business value can be difficult and slow. As much as a business group might want to find solutions in new data, they are not close enough, organizationally to the data to determine if it has value. The reverse is true for IT. While they have access to much of the data, they are removed from the business needs that could addressed with data.

The sweet spot for discovering value in data is when accessibility, tools, and skills are connected in the right parts of the business, but there is no clear path for every organization. Some companies find that data science teams are an effective way to connect access and expertise with business units. Others use BI software to pipe specific data to business users. In many cases, an IT group simply acts as a consulting group for responding to ad-hoc requests. In each case there are constraints that limit the business unit’s access to data.

Machine learning software offers an opportunity to flatten an organization’s data. Emcien customers are using Scan to connect the data across data lakes and traditional databases to discover what’s possible with their data. The software functions both as a tool for IT to give business users the data they need and by business users to do their own data discovery across the data resources of the enterprise. With the ability to see these connections, IT becomes instantly closer to the meaning contained in the data. Business units get a view across the available data, discovering what resources they have to accomplish a task.


  • The ODBC connection allows connection across enterprise data
  • Autonomous Machine Learning algorithms connect data automatically
  • No data is transformed or transferred