Defining Data Scientists & Their Tools
There’s no shortage of opinion in the blogosphere on the relative merits of SAS, R and Python for data science.
Income Inequality, Political Polarization, and Big Data
What does all this have to do with big data? Well, big data software enables us only to identify correlations between variables. It doesn’t tell us whether a causal relationship exists, let alone which direction the causal relationship runs.
The Fan Experience Matters: Data Is a Strategic Asset
Fan insight is the Holy Grail of fan experience. Sports and entertainment organizations invest a lot of time and resources to better understand who their fans are, what they like and don’t like. Data is a key ingredient for gaining better and deeper fan insight. So it plays a critical role regardless of the sport, event or the size of the organization. Analyzing fan insight is like solving a jigsaw puzzle. Individual pieces (data sources or systems) in “disconnected” states won’t tell the whole story.
What is Data Science – Again?
A data scientist to my thinking has business knowledge and is skilled in both computation and statistics/machine learning. I kind of like the saying that data scientists can program better than statisticians and have more statistical chops than programmers. Many in the industry, though, equate a data scientist with a business statistician.
Driving Third Wave Businesses: Ensuring Your Business Has The Right To Win
In a recent interview, Toffler said that, “given the acceleration of change; companies, individuals, and governments base many of their daily decisions on obsoledge—knowledge whose shelf life has expired.”
Everyday BI: My Stocks
In the last installment of this series, I described the three key steps that everyday business intelligence (BI) users typically go through when they consume data: Observation, Perspective, and Insight. These steps often take place in an ad-hoc manner without the same degree of precision and requirements that one expects in corporate BI environments. Nevertheless, everyday BI users follow a similar process to achieve the same end goal—insight through data for better-informeddecisions.
Mobile BI Design Framework: The Art of Performance
In mobile business intelligence (BI) design, performance is one of the most critical elements of the mobile BI success formula. High quality content, reliable data, andmobile purpose are a must. However, none of that matters if the performance is poor—mobile users tend to be less patient about performance. Think about it for a moment. Unlike a PC users who may be chained to a desk, mobile BI users typically access mobile BI assets on the go and with less time to spare.
The Fan Experience Matters: Data Is A Strategic Asset
Sports and entertainment organizations collect tremendous amounts of data on the fan experience, such as attendance, ticketing, merchandise, etc. These data troves can provide invaluable opportunities for growth and profitability. That is why I called sports and analytics a “perfect couple” in my Sports & Analytics series.
However, having all the data doesn’t do much good if we are not asking the right business questions — or don’t have the right analytics platforms to answer them.
Accounting for Point of Sale
There are a lot of solutions available to help retail businesses get business done. From touch screen technology to mobile credit card and payment processing, retailers have many choices when it comes to selecting the right technology for the business. But even the best point of sale system can lack the critical element that makes…
Surveys Generate Data But Don’t Necessarily Replace Hard Data
Surveys, questionnaires, and polls generate data, but survey data and hard data aren’t the same thing. I often see them treated in the same light in the context of answering business questions or delivering actionable insight, and with equal zeal and qualification. But there are definite differences.