Big Data, Cloud & DevOps

Algorithms have the potential to help us overcome rampant human bias. They also have the potential of magnifying and propagating that bias. I firmly believe this is an issue and it is the duty of data scientists to audit their algorithms to avoid bias. However, even for the most careful practitioner, there is no clear-cut definition of what makes an algorithm “fair.” In fact, there are many competing notions of fairness among which there are trade-offs when it comes to dealing with real world data.

Business intelligence hasn’t lived up to its promise to give users unprecedented access to business insights. Vendors have spent millions trying to improve their user experiences and deliver self-service. But nearly every BI tool forces users to leave their current workflows and open standalone applications to analyze data. Increasingly, application teams are looking for new ways to deliver analytics that encourage user adoption and meet customer demand. Demand for standalone BI is waning.

Data science still carries the aura of a new field. Most of its components — statistics, software development, evidence-based problem solving, and so on — descend directly from well-established, even old fields, but data science seems to be a fresh assemblage of these pieces into something that is new. The core of data science doesn’t concern itself with specific database implementations or programming languages, even if these are indispensable to practitioners. The core is the interplay between data content, the goals of a given project, and the data-analytic methods used to achieve those goals.

Both big data and AI are path breaking technologies in their own right. However, when big data meets AI, the two complement each other, helping us analyze and implement large data sets in unique and unexplored ways. By applying machine learning algorithms, we can make ‘intelligent’ machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them.

Big data analytics is a concept that almost all businesses have heard of in some shape or form. But as with any trend, misconceptions are still at an all-time high. These false truths make big data analytics a solution many businesses are afraid to implement into their infrastructure, leaving companies at risk of losing customers to a competitor, making inaccurate business decisions and missing critical opportunities for growth. Here are 5 myths that still need to be addressed for decision-makers who are still on the fence about implementing a big data analytics strategy.

Topological data analysis (TDA) is a mathematically grounded theory which aims at characterizing data using its topology, which is done by computing features of topological nature. This is why a huge effort is currently devoted in the TDA community to derive ways to process persistence diagrams in ML. Geometry processing is only one out of many possible applications of TDA. This field is very active since it connects different areas of mathematics from algebraic topology to computer science, and more and more people are becoming TDA enthusiasts. 

Online and digital tools and technologies have a lot to offer the modern professional. But entering the digital age and using cloud technologies to the best effect means cybersecurity is now your concern just like it’s everybody else’s — and possibly to an even greater extent if recent European legislation becomes widely popular and continues to change what clients expect of the companies and professionals they do business with. Let’s look at some real-world advantages of building and scaling a legal-focused business using a strong technological backbone that doesn’t sacrifice security robustness for convenience.

Location and spatial data are becoming much more crucial to smooth and successful business operations. Spatial data and analytics are vital to problem-solving and decision-making processes, especially in a local environment. This type of information can be tied back to every other form of data, and it helps to bring a context-heavy tone to everything. Here are three reasons why spatial analytics could prove to be beneficial to the future of your business and, as such, should be a part of your data strategy.