Why Deep Learning is Important for Big Data

27 May

Why Deep Learning is Important for Big Data

Over the last few decades, huge amounts of data has gone into data warehouses in various forms.  Historically, businesses of all sizes focused on storing data securely.  Security can be defined in a variety of ways, the most common and fundamental of which is protecting it from data loss, theft, ransom, as well as hacking and of course issues around privacy.

Modern data warehouse, expensive to run and what does it all mean?

If your organization has for example, 200+ databases that have built up over a long time, in the past you probably felt good about your investments into the usual brand names for database systems.  The thinking was that as long as your data center (like the one above) kept everything secure – you were good.

Today though, more and more organizations are asking new questions, such as what does all this data mean and what can it tell us about how to perform better?  How do we leverage this information in real time to meet our organizational objectives?

The rise of Deep Learning was fueled thanks to recent achievements that allow the packaging of algorithms able to accurately determine meaning from unstructured data.

Suddenly we are now able to look into decades worth of stored data and use it in whole new ways.

Thanks to Deep Learning, what should matter to you now is more than just keeping these things secure.  The issue you really want solved is what does all this data mean and how can you use it?  Until now, the obstacle in your way to answer this question has been complexity.

So what is complexity?  A long time ago, when your organization only had 1 database – it was relatively simple compared to today.  Back then, when you received reports from your simple database system, it was easy enough for a human being to conceptualize the reports and do something about them.  However, as time marched on, and the number of your databases grew, it became impossible for a human being to even read all the reports and more so decide what to do about about them in an actionable way.  Problems like this lead you and your organization to paralysis as well as spiraling IT costs. You invest further and further into more reporting systems and new versions of databases.  This is like trying to put a fire out with gasoline.

Putting Gasoline on Fire by solving complex database issues with more databases

So where does Deep Learning come into all this?  The answer is that Deep Learning solves complexity.

How do we do that?

Imagine reading every document in an organization in a matter of seconds, accurately count and associate every pattern, occurrence of words and statements, the context in which they occurred, and how they relate to a topic in which you are interested. Deep Learning makes this possible and its a game changer for organizations that maintain large amounts of data.

In order to get this new value, you’ll need those packaged Deep Learning algorithms in an easy to use way.  Before Deep Learning came along, we already knew that there was risk associated with software projects, with 68% of IT projects failing according to ZDNET.  In the world today there are only some 10,000 or so AI developers, which means the odds of failure for a software project that is taking on Deep Learning has an even higher risk due to lack of available AI talent.

This is why we developed Text to Software Deep Learning and SageTea Link Deep Learning.  Our low code approach is designed into our products to lower the risk associated with traditional software development.  Text-to-Software Deep Learning and SageTea Link Deep Learning enable organizations with big data to take advantage of Deep Learning without a big need for code.  Click here for details.