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...
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...
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....
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...
Predictive analytics is this super-complex field that only statisticians and data scientists can understand, right? Well, perhaps it takes some training to do it well but it only takes two sentences to understand what it's all about: In predictive analytics we determine the likelihood of something...
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...
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...
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. "A data scientist is a statistician who lives in...
If you have huge volumes of data, chances are that you can get value out of analyzing that data. But, would you derive significant value from analyzing it in real time? This is an important question because, while there are benefits to real time analytics,...
There exists a chronic confusion as to what the distinction is between your average software engineer (i.e. programmer) and a data scientist. This is totally understandable, considering the fact that both jobs do involve programming and the term “data science” seems so much like the...