Bill Franks's Blog: Analytics Matters, page 6

Balancing Analytics Agility and Stability

There have been many science fiction stories (as well as video games!) that revolve around the tradeoffs between powerful, strong, hard to harm combatants and those that are small, nimble, but easy to harm. Both have their merits and both can be useful in different situations. However, the same profile doesn’t work best in every situation.

A New Chapter In The Analytics Journey

As I write this, I am finishing a major leg of my personal analytics journey. As many readers are likely aware, I will be leaving Teradata this month. I had a terrific 14-year run with Teradata where I made a lot of friends, worked with some amazing clients, and got to witness firsthand how the world’s largest organizations have dealt with the rise of big data and analytics. Teradata treated me well and I like to think that I, in turn, contributed a lot to the company. It wasn’t an easy decis...

How Analytics Is Evolving Like the Medical Field

It used to be that a doctor was a doctor for the most part. Even a century ago, unless you lived in a large city, people likely had a town doctor who handled most every type of ailment and guided most any type of treatment. Given the limited medical knowledge and lack of sophisticated treatment options during this time, these generalists could often provide a level of care that was comparable to the best available. Today, that is no longer true in medicine and a similar trend is playing out i...

Moving Beyond Predictions – Second Order Analytics

Last month, I wrote about why simply making predictions isn’t enough to drive value with analytics. I made the case that behind stories of failed analytic initiatives, there is often a lack of action to take the predictions and turn them into something valuable. It ends up that identifying and then taking the right action often leads to additional requirements for even more complex analyses beyond the initial effort to get to the predictions! Let’s explore what that means.

Why Predictions Are Not Enough

Predictions Are The Starting Point…

Almost by definition, advanced analytics or data science initiatives involve applying some type of algorithm to data in order to find patterns. These algorithms are typically then used to generate one or more of the following:

When Big Data Can’t Predict

Most people think that in the age of big data, we always have more than enough information to build robust analytics. Unfortunately, this isn’t always the case. In fact, there are situations where even massive amounts of data still don’t enable even basic predictions to be made with confidence. In many cases, there isn’t much that can be done other than to recognize the facts and stick to the basics instead of getting fancy. This challenge of big data that can’t be used to predict seems like...

Ethical Implications Of Industrialized Analytics

As analytics are embedded more and more deeply into processes and systems that we interact with, they now directly impact us far more than in the past. No longer constrained to providing marketing offers or assessing the risk of a credit application, analytics are beginning to make truly life and death decisions in areas as diverse as autonomous vehicles and healthcare. These developments necessitate that attention is given to the ethical and legal frameworks required to account for today’s a...

Data Preparation: Is the Dream of Reversing the 80/20 Rule Dead?

I recently had someone ask me, “For years we’ve talked about changing analytics from 80% data prep and 20% analytics to 20% data prep and 80% analytics, yet we still seem stuck with 80% data prep. Why is that?” It is a very good question about a very real issue that causes many people frustration.

Breaking Analytics Out Of The Box - Literally

The lines between open source and commercial products are blurring rapidly as our options for building and executing analytics grow by the day. The range of options and price points available today enable anyone from a large enterprise to a single researcher to gain access to affordable, powerful analytic tools and infrastructure. As a result, analytics will continue to become more pervasive and more impactful.

Embrace Analytic Athleticism

I see a strong parallel between athleticism and analytic capability. I also see a strong parallel between learning to speak multiple languages and learning to work within differing analytic environments. I’ll explain what I mean by both of these statements in this blog in the hope that it will help make the path forward seem clearer and less intimidating.