What is Hashing? 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. For example, the MD5 formula always produces […]
What does it mean to hash data and do I really care? Read More »
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 by looking at data about it. We do this
Data Science and Predictive Analytics Explained in Two Sentences Read More »
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. As
Pitching Your 2018 Data Science Budget Read More »
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.
Planning Your 2018 Data Science Budget? Here Are Some Logical Projects Read More »
Actuary, data scientist, or just a guy in the rain? 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. What Actuaries and Data Scientists have in common
The Difference Between an Actuary and a Data Scientist Read More »
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 are also real costs. To help you decide whether
Do you need real-time business analytics? Start by asking yourself these 4 questions Read More »
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 term “computer science”. However, the two differ in some significant
So, what is the difference between a programmer and a data scientist? Read More »
People in the tech world love to speculate on the future structure of the corporate IT department. While we cannot know precisely how this change will manifest, there are undeniable trends towards a more engaged and decentralized IT presence in organizations. This may very likely end in the elimination of the IT department as we
The Sad Future of the Corporate IT Department Read More »
I know I have data, but is it big data? Companies often find themselves wondering whether or not they have big data and questioning whether they even need business intelligence and predictive analytics to improve their business strategy. The reality is that you probably have more data than you think you do, but that the
Is My Big Data Giving Me Big Value? Read More »
It is important to remain aware of the fact that making data work for you is as much about people as it is about technology. Dataspace has learned a lot about end-user engagement with predictive software and business intelligence infrastructures over the past two decades. After all, it is not the products but the user of
Driving value through end-user engagement Read More »
