The emergence of automation has posed a concern for data scientists--will it eventually replace them?

But there is no cause to worry as the eventuality will never happen. Instead, automation will help data scientists to improve the results they derive from analyzing vast amounts of data.

When it comes to the marriage between…

Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “…

Most of us working in business intelligence and data science spend a lot of time creating data models, reports and beautiful dashboards. Even though the models are great and reports look fancy, the true value is in your customers actively using it. How often do you find that nobody is actually running the report?

This hurts, but it also makes sense. People get so much information during a day, they really need to be selective. So any important information should pop out of your report…

In today's hyper-competitive business world, B2B & B2C businesses rely on data to achieve an edge in their markets. However, many companies do not realize that customer data decays rapidly and create challenges in achieving business goals. In the postmodern era, customers simply change base too often, and to maintain a complete and accurate record of their whereabouts is a challenging task. Dirty data not only damages your credibility but also costs you money…

Summary:  Too many solutions.  We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production.  The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.

 …

Summary:  AI/ML itself is the next big thing for many fields if you’re on the outside looking in.  But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.

“The Next Big Thing in AI/ML is…” as the lead to an article is probably the most…

What is RPA?

Robotic Process Automation (RPA) is the utilization of programming with machine learning and AI capacities to deal with high-volume, repeatable tasks, and transactions. RPA is an innovation planned for computerizing business forms. Robotic Process Automation conveys direct productivity and improves accuracy transforming an organization’s workflow. Empowering RPA allows flexibility within the enterprise. Programming robots are easy to…

Summary:  Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place.  Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.

We’ve been trying to get a handle on…

Summary:  Digital Decisioning Platforms is a new segment identified by Forrester that marries Business Process Automation, Business Rules Management, and Advanced Analytics.  For platform developers it’s a new way to slice the market.  For users it eases integration of predictive models into the production environment.

As…

Background

For people working in Artificial Intelligence, the term “Human-in-the-Loop” is familiar i.e. a human in the process to validate and improve the AI. There are many situations where it applies, as many as there are AI applications. However. there are still some distinct different ways it can be deployed even within the same application.

Contact Center Example

Let’s take for example the automation of a contact center. A…

And what we can do about it…

Introduction

Much of the recent AI revolution has been focused on automation through big data and/or sensors and feedback into neural networks. The resulting applications are highly valuable to businesses and consumers. They improve quality of life by optimizing labor and resources. However, these applications fall short when it comes to handling human reasoning. Much of the rationale behind the operation of these systems are implicitly embedded in the data.…

Summary:  What are the real threats of job loss from real and AI enhanced virtual robots?  How do we position ourselves and our children to succeed in this new environment?

Data Scientists Automated and Unemployed by…

Return on Investment (ROI) is defined as the ratio of a return (benefit or net profit) over the investment of resources that generated this return. Both the return and the investment are typically expressed in monetary units, whereas the ROI is calculated as a percentage.

ROI formula:     (Return – Investment)/Investment

It’s typically expressed as a percentage, so multiply your results by 100.…

There has been a lot of activity recently around revenue attribution - marketers want to develop a better understanding of their customer acquisition funnel and be able to measure progress against it.  Most of this attention has been focused on the B2C space. However, less work has been done measuring the performance of B2B marketing activities. 

Certainly the marketing automation segment is very vibrant with a large number of vendors (both big and small) providing solutions that…

All businesses are at the mercy of data quality challenges. From the moment you capture your first lead, you’ll be fighting a battle against data decay. The bigger the database gets, the more problems the business can encounter, and it isn’t easy to single out a…

We return this week for Part II of our blog with astrophysicist and data scientist, Kirk Borne, Ph.D. Formerly a NASA scientist, he’s one of the foremost experts in big data and its applications in business, government and science − from exploration of space to economic growth. Here again he speaks with Anametrix CEO Pelin Thorogood, this time identifying the areas where he thinks business will benefit most from big data.…