Danger, Will Robinson! (AI, Analytics and the Invisible Dangers)

While we may yet be a long way from the threat of truly sentient computers such as Hal 9000 and ARIIA, the current capability of AI technologies still offers plenty of power for those who wish to harness it for malicious purposes. These types of crimes go beyond the grey ethical areas that we discussed […]

What’s The Difference Between Business Intelligence and Data Science?

Tools and techniques for management reporting and analysis have evolved since computers first came out. One can argue that the first management reporting tool was COBOL (COmmon Business Oriented Language). It allowed business people to get data out of systems created by computer people. COBOL was eventually replaced for reporting by tools like Information Builders […]

Step 1: Collect data. Step 3: Profit. Step 2: Hire data scientist?

Data science is, and has been, in vogue.  Every forward-thinking company wants to have a data science program because by now it is conventionally understood that it will improve profitability and efficiency across the business.   To some companies with troves of data waiting to be ‘data scienced,’ his means hiring data scientists willy-nilly even […]

In Defense of Relational Databases

As you may know, many big data technologies are defined as schema on read. What this means is that you can throw whatever you want on the disk and then, when you need the data, you tell the data store what that data means (e.g. the second column contains price per unit). Traditional relational databases, […]

The Shifting Role of IT in Analytics

In the distant past (circa 2015), IT was responsible for providing data consumers with data, tools, and development expertise. Today, the landscape is shifting, especially in larger organizations. Nowadays, IT provides some of the data – the certified, clean data. Consuming departments fill the rest of the needs – tools, analytic experts, even non-certified data. […]

What does it mean to hash data and do I really care?

Hashing is simply passing some data through a formula that produces a result, called a hash. That hash is usually a string of characters and the hashes generated by a formula are always the same length, regardless of how much data you feed into it.

Data Science and Predictive Analytics Explained in Two Sentences

In data science and predictive analytics we determine the likelihood of something by looking at data about it. We do this simply by looking for similarities between that data and data from past cases where we actually know the outcome.

Pitching Your 2018 Data Science Budget

Last week we offered some suggestions on how to attack your data management initiatives in 2018 according to your organization’s level of data expertise.  This week we follow up with some tips on how to pitch those new data science technologies and initiatives to increase your chances of getting resources allocated to your projects.   […]

Planning Your 2018 Data Science Budget? Here Are Some Logical Projects

It’s that time of year again!  With budgeting for 2018 at the forefront of your agenda, you may be wondering where to head with your data science efforts.  Investments in big data are often expensive, but when planned correctly you can manage costs and also ensure that your new systems and employees generate data-driven profits. […]

The Difference Between an Actuary and a Data Scientist

The hot new thing, data science, isn’t so new after all. Since the advent of modern actuarial science in the late 1980s, insurance companies have relied on actuaries to use math and statistics to anticipate the future.