Big Data, Cloud & DevOps

Big Data, Data Science and Analytics is the art and science of deriving business value from data. It was understood that if utilised correctly, data could remarkably elevate business’s ability to make insightful & strategic decisions. 

We may want our data scientists to be superheroes, but all superheroes have their kryptonite. It would seem that the main flaw with being a data scientist is the one thing data scientists cannot change – the fact that they are human. Even Mister Fantastic was only human, after all. There is no way around this, so what is the solution? Do we throw all our data scientists out onto the street and replace them with machines?

Ready to learn Data Science? Browse courses like Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab.Databases have come a long way since its predecessors were first created more than 40 years ago. With recent advances in digital strategies directing an increased focus on the customer, the onus has fallen

Ready to learn Data Science? Browse courses like Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab. A decade ago, a major challenge in healthcare was the capture and storage of patient data within the central repository known as the electronic health record (EHR). Today, with many hospital systems regularly

While big data has dropped off the hype cycle, it’s not going away; in fact it will only get “bigger”. Likewise, the Hadoop ecosystem has matured significantly and will continue to do so with all of the big distributions offering data science capabilities. The desire amongst enterprises to migrate big data to the cloud will continue to increase with managed services gaining momentum. There are many challenges that come with all of these things – but with the right strategy and foresight, enterprises can truly maximize big data’s value.

With an almost endless list of sources – including map and satellite data, catchment areas, service points, building and customer locations, land use data, urban data, and communication pathways – spatial data is a valuable global commodity which comes in many forms.

Technology solution should be tailored to fit organization needs, to diagnose and address ethical and compliance risks and to ensure your business can manage these risks in a comprehensive, cost-effective manner.

Combining sensor data with SRM can pertain to any commercial, industrial or manufacturing asset. Organizations with these assets can take a cue from transportation companies, which have begun successfully turning their big data into deep and highly actionable data, which allows them to keep their operations running efficiently.

For a business owner, marketing is vital to the growth of the business. One of the biggest issues in marketing today is that it is hard to get the attention of customers. Getting the attention of customers is crucial to having success with your marketing efforts. One of the best ways to connect with customers is to advertise to them in a way that is going to get their attention. This is where big data can be a huge benefit to your business. 

There is a huge diversity of tools and techniques for approaching data work, and if you half-learn a lot of different techniques you won’t be able to fully understand or accomplish any one technique. Pick one or two people who work in the field and who speak in a language you understand and try to emulate them. It’s good to try to develop depth of knowledge, rather than breadth, because when you know one thing very well you can usually apply that knowledge to other areas. A shallow understanding of many areas won’t help you tackle advanced problems in a specialized area.

Ready to learn Data Science? Browse courses like Data Science Training and Certification developed by industry thought leaders and Experfy in Harvard Innovation Lab. We wanted to follow up our previous piece about how to grow as a data scientist with some other skills senior data scientists should have. Our hope is to bridge the gap between