What the world needs now is a ubiquitous schema that is without form and can capture everything. That sounds like Hadoop or some NoSQL capture/archive systems. We want these data sources to be more performant so that they can also support analytical analysis with tools like Tableau. Spark will help that along.

What the world really needs is good data that performs – data that is complete and correct without all the attendant time and expense of extract/transform/load (ETL) development. Here’s how the drag-and-drop future of data might get us there:

Machine-Captured Data

It won’t be perfect, but it should be better than human-captured data. Transactional business processes need to be redesigned to ensure granular accuracy and completeness. Accountants need to start closing more than just the general ledger, they need to start assuring the the quality of every detail, not just the general ledger. We need to develop a granular close process (GCP):

This close cycle shouldn’t exceed three days. Major re-thinking is required.

Cloud-Based Transactional Systems

As more businesses move transactional systems into the cloud, the chore to prepare this data should become easier. The SMB market will start the evolution because the cost to install and maintain these systems will be attractive to business owners that aren’t in the business of running server farms.

Easier ETL

While some vendors are making strides, the tools are still too cumbersome for most people to use. Making this task easier is a gigantic opportunity that is growing every year. We have a spreadsheet culture that needs easier-to-use tools.

Intelligent Storage

Technical staff needs help monitoring what is being stored on employee equipment. We need fast, easy, reliable and secure ways to capture and sync data being created on laptops with the network storage platform. Syncing the data securely between the laptop and the DAS in a way that is completely effortless and timely is what everyone needs.

Discovery Tools

When all these things are in place, discovery skills will be even more important than they are today. Data Science is the hype-cycle buzzword of the day. We don’t need more data scientists, we need to create tools that makes everyone a data scientist. The deep and wide knowledge of the few needs to be disbursed to a much wider number of generalist analysts.

Does this sound like a fairy tale wish list? Well, there was a time when the “Dick Tracey” watch sounded like impossible technology, fax machines were cool and cell phones were the size of bricks and cost $10,000. Re-imagine your company’s future or someone else will.