As all industries are embracing big data, and now quickly leveraging cloud, mobile applications, and IOT, self-service analytics has become an imperative to keep pace with rapid change. Continue reading Free Full-Version Tableau Trial from Knowledgent
Rachel Sholder, Data Science Intern, Analytics and Visualization
Data analysis is the process of collecting, inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and then communicating your results to have the biggest possible impact. According to Merriam-Webster, a process is a series of actions or steps taken in order to achieve a particular end. While the “particular end” of data analysis might be a presentation to a client, my “particular end” I have been working towards achieving is a successful end to my summer internship. Continue reading Data Science 101: Three Takeaways from My Internship
Wondering if you should leverage a data lake? Trying to improve the state of data analytics in your organization?
As this month’s infographic depicts, one of the main advantages of the data lake is the acceleration of advanced analytics capabilities.
After you ingest your raw information, including structured, semi-structured, and unstructured data into the lake, you’re able to transform it for querying and analytics. Next, provide self-service analytics for your users so they can analyse the data they need using the tools of their choice. Your users can then collaborate on analytics, sharing their analytic models with the community.
View the infographic below:
Implement the four best practices in our agile analytics life cycle to achieve your most productive agile analytics environment.
What challenges are you facing with agile analytics in the data lake? Let us know in the comments!
Microsoft continued acquiring state-of-the-art BI capabilities, as it recently announced the acquisition of mobile visualization leader Datazen. Continue reading A Look at Microsoft’s Acquisition of Mobile BI and Visualization Leader Datazen
During the Industrial Revolution, the efforts of engineers produced incredible, measurable improvements in business and human lives. It’s not surprising that, as a result of this, the Engineer became the defining role of the Industrial Age.
According to Peter Drucker, we are now in the transition from the Industrial Age to the Information Age. This is why the #BigData phenomenon has emerged. Organization and even individuals are beginning to see the benefits of mastering information, in a way that we simply have not before. The highest-paid opinion will no longer rule.
What is the analog of engineer in the Information Age? What role will define the potential and risks of data? Continue reading 7 Traits of the Informationist
Over the past few years, capabilities for visualizing data have expanded greatly – and just as the Internet has democratized the exchange of information, cutting-edge visualization tools are democratizing the ability to represent data graphically. But democratization doesn’t guarantee quality; creating a good visualization that conveys information clearly, concisely, and in context requires a lot more than the ability to create a graph with a few clicks.
So, what differentiates a great visualization from a mediocre one? The measure of a good visualization is how well it communicates information quickly to someone who hasn’t previously seen the viz. Here are six visualization best practices to help you take your presentation of information up a notch. Continue reading 6 Best Practices for Data Visualization
Biotechnology organizations frequently outsource early discovery to multiple contract research organization (CRO) sources, generating massive but loosely structured research files to support scientific discovery of next-generation medicines.
Our client, a large BioTech firm, engaged our big data experts to find a solution to the burgeoning internal demand for this data that also facilitated sustainability. The increasing scale and complexity of their data significantly impeded data acquisition, slowing business momentum. Rapid, easy, and controlled access to data was a strategic imperative; it could not be compromised by the needs of traditional operating models nor technologies.