Businesses are teeming with data. As a result, business leaders are building up their data science capabilities to help make sense of their data. Our research showed that an effective approach to optimizing the value of that data is to build data science teams rather than relying on a single data scientist. In today’s post, I […]
Author Archive | Bob Hayes
Understanding Customer Survey Data: Descriptive, Predictive and Prescriptive Analytics to Improve Customer Loyalty
Customer relationship surveys play a major role in helping improve the customer experience and increase customer loyalty. By gathering customer perceptions of their experience as well as their likelihood of engaging in different types of customer loyalty behaviors, companies are able to use these data to get insight about how to improve the quality of the […]
The Stability of Customers’ Sentiment, Satisfaction and Recommendation Intentions
Businesses assess the attitudes of their customers using customer surveys. The purpose of these surveys, typically conducted annually, is to help companies maintain or improve the quality of the customer relationship. The quality of the customer relationship is typically indexed by a few key questions, each measuring something important about the health of the customer relationship. These […]
When Does Education Level Matter in Data Science?
Data scientists are highly educated. In our study of data scientists, we found that over half of them, both men and women, hold either a Masters or PhD degree and about a quarter of them hold a 4-year degree. The level of educational attainment is related to proficiency in data science skills (more advanced degrees […]
The Hype of Big Data Revisited: It’s About Extracting Value
Over a year ago, I tested the claim that Big Data was the most hyped technology ever. Using Google Trends, I compared the term “Big Data” with “Web 2.0” and “cloud computing”. It turned out that the Web 2.0 and cloud computing were more hyped than Big Data (as measured by number of searches on the topics). At […]
Maximizing the Impact of Data Science Using the Scientific Method
We live in a Big Data world where everything is being quantified. As a result, businesses are trying to make sense of their ever-expanding, diverse, streaming data sources to drive their business forward. If your competitors have access to the same type of data (CRM, ERP, weather, etc.) that you do, how can you keep ahead […]
How IBM is Transforming Data Science
I am at the IBM InterConnect 2016 event in Las Vegas. While this event, IBM’s largest (estimated 24,000 attendees!), is billed as a cloud and mobile conference, sessions focused on a variety of related solutions around analytics, security, DevOps (which I learned is a methodology) and Watson Internet of Things. I was invited by IBM as […]
For Data Scientists, Big Data is not so Big
In our study of data scientists, we found that only about a third of them possessed skills needed to handle big and distributed data. These results are in line with findings from other studies that find that data scientists typically analyze small data sets. We examined the proficiency of data scientists across 25 different data science skills. […]