TADA Insights

We’ve written previously about how the volumes of data that businesses generate can quickly become siloed without the proper tools.

Predictive analytics in manufacturing is a hot trend right now.

In this post, we’ll take a look at why that is and detail three strategies for using predictive analytics in your organization.

1. Collect Quality Data

We’ve already mentioned the vast amounts of data that are generated every minute. With more than 65,000 Google searches every minute, to use just one example, the last thing we really need to do is to collect more data we won’t analyze.

In fact, most data is ignored. According to Forbes contributor Mary Meehan, enterprises only use between 27 percent and 40 percent of the analytics they collect. (Full disclosure: The link she cites as a source from Forrester is no longer available, but the statistic has been cited in other, reliable publications.) 

Predictive analytics has a number of benefits for nearly any organization.

However, predictive analytics seems like a no-brainer in the healthcare industry. The data generated by hospital systems and electronic health records are literally matters of life and death.

While this post looks at some unexpected uses of predictive analytics in healthcare, we need to address a more common one right off the bat: saving money.

If you’ve struggled to achieve full visibility into your supply chain, you’re not alone.

In fact, only six percent of businesses have, according to Andrew Allen at Supply Management in an analysis of a GEODIS survey released last year.

But, it doesn’t have to be that way.

That’s where TADA comes in.

TADA enhances collaboration among your team members by giving them the information they need to drive end-to-end problem solving.

Google is a common word today.

It’s as synonymous with search as Frisbee is to flying discs, Xerox is to photocopies, and Band-Aid is to adhesive bandages.

Whether they intended to is irrelevant—these brands changed the world.

In fact, these brands were so transformational that they became household names and words used to describe everyday things or actions.

We mostly use these terms in a generic sense, but they all began as distinct brand names with aspirational missions.

You may have come across terms like “deep learning,” “machine learning,” and “neural network,” but you might not fully understand what they mean.

In this post, you’ll learn more about them and why they should be important to you.

First, consider the brain. “The brain works like a big computer,” according to PubMed Health. “It processes information that it receives from the senses and body, and sends messages back to the body.”

It’s no surprise that companies expect data analytics to bring results.

These range anywhere from bettering the ability to make strategic decisions, gaining a better insight into customers, and reducing costs, according to the Business Application Resource Center.

This post explores how TADA improves data analytics for your company for many of those same factors.

Data is the lifeline of an organization.

The idea is that the more data an organization has, the better insights it can glean. As a result, data creates a competitive advantage one organization has over another.

To generate data, an organization must have a place to store it. That begs the question: Where does all this data go?

A better customer experience.

A better way to manage inventory.

A better understanding of your supply chain.

With mounds of data being generated by machines, sensors, and people, every organization is capable of reaching the untapped potential in these areas. We’ll take a look in this post at the three areas that offer benefits for retailers using predictive analytics.

Predictive analytics is an idea that’s garnered much attention over the past several years.

In fact, Google Trends shows that interest in the term has increased 76 percent since January 2013.

Predictive analytics is a combination of data analysis technologies and statistical techniques such as data mining, artificial intelligence, and machine learning. The discipline relies on existing information to recognize patterns and make predictions about the future.