Machine Learning

Preparing your business for machine learning and big data

Companies are buried in mountains of big data.

Big data keeps growing bigger every day. Between the IoT (Internet of Things), social media, mobile devices and cloud computing, the mountain grows higher and higher. Machine learning offers companies a way to transform the ever-growing mountains of data surrounding them into actionable insights.

But scaling that mountain is easier said than done.

A recent report from Gartner looks at the tricky situation facing businesses who want to conquer their data using machine learning. The report lays out the challenges of machine learning and the need to take proactive steps to prepare for it.

Let’s start at the beginning.

What is machine learning?

Part of the field of artificial intelligence, machine learning uses statistical techniques that give computer systems the ability to progressively improve on a specific task. Or, said another way, these computers can “learn.”

Although machine learning has technically been around since 1959, its importance has increased as businesses find better ways to understand big data.

What’s driving the adoption of machine learning now?

Big data is a tantalizing ideal, with 74 percent of companies saying they’re trying to be more “data-driven.” But the dream of big data has fallen far short of reality.

Many companies are simply overwhelmed by the structural and staffing challenges of managing big data.

The primary challenge is simple. How do you sift through huge amounts of data to get actionable insights? Currently, only 29 percent of companies able to connect data to action. Machine learning offers a key to unlock the insights buried in data.

It’s all about analyzing data to make accurate predictions.

What are the challenges of using machine learning with big data?

The Gartner report calls attention to a specific issue facing IT analysts. When analytic architectures and end-to-end data aren’t set up to work together, the disconnect creates an underlying problem.

In order to conquer that challenge, it’s ideal to have a data scientist on your team. At the very least, you need access to someone who deeply understands machine learning.

But learning the algorithms is difficult without a specific mathematical background. The way businesses integrate data also becomes more complex. In addition, infrastructure must be addressed.

How should SMBs prepare for machine learning and big data?

The challenges are different for small and midsize business than at the enterprise level, where there are more resources. According to the Gartner report, there are many ways to prepare for machine learning. But the leadership needs to come from the IT department

The people who understand the technical challenges of the issues simply must be involved.

Being proactive is the key to deriving the benefits of machine learning. Here are some of the specific recommended steps:

  • Examine the computer clusters and storage infrastructure needed to support machine learning
  • Select a machine learning platform that works with different machine learning frameworks when planning to work with off-the-shelf algorithms
  • Make sure the data organization is updated in an end-to-end analytics architecture in order to support machine learning algorithms
  • If you’re planning to build custom machine learning algorithms, put a development life cycle in place capable of supporting learning models
  • Take note of new frameworks being packaged with AI and machine learning solutions, which provide seamless solutions
  • Go to the cloud if you don’t have appropriate engineering infrastructure and staff to support machine learning, as it’s flexible enough to scale

The next big thing in big data

Machine learning is what’s next for dealing with big data. Companies should be building for the needs of machine learning by understanding the problems they’re trying to solve.

That means going to the IT support experts who uniquely understand machine learning issues. There’s simply no other way to be prepared for what’s next.