It is no secret that data and data analysis is at the center of the economic engine. It connects businesses across seemingly unrelated industries and enables organizations to operate seamlessly. The right data analysis techniques allow organizations to use advanced data science to gain insights into customer behavior, market trends, and competitive research. If your organization is looking transform data into actionable insights, here are some data analysis tools that can prove extremely useful.
Self-service BI
Self-service BI tools provide a low resistance way to graphically interact with large amounts of data. This helps companies to gain valuable insights quickly and easily. Some examples of self-service BI tools include Tableau, Qlik Sense, Power BI, and Domo. For most self-service BI tools, there is some initial IT set up required. Usually, the set-up cost is low and, once the solution is in place, a business analyst can use it easily to graphically study the trends, do a comparison over time, and identify growth opportunities.
Mobile Dashboards
On the consumption end of data analysis, mobile dashboards are a popular way to view and act on data insights. The mobile dashboards are ideal for individuals on the go, such as managers, where ‘near real-time’ information needs to be pushed to wherever and whenever it is needed. Custom mobile dashboards can poll a datastore periodically for updates and push the information to the consumer. They are also popular for raising alerts on customizable events and thresholds.
R Language
R is programming language used in the development of numerical data analysis and machine learning. While it is best suited for data scientists and statisticians, R programming language provides the ability to create reproducible, high-quality analysis. It has all the flexibility and power needed when dealing with huge amounts of data. The language supports a package repository making it easy to share and build upon others’ work. With the increasing popularity of machine learning, there is a wide array of R packages out there to support most of the common data analysis needs.
TensorFlow
TensorFlow, which is Google’s open source framework for machine learning and neural networks, is fast and flexible, rich in models, and easy to run on CPUs or GPUs. The framework is at the heart of commonly used products from Google which reason over vast data on a regular basis such as the applied machine learning APIs for Google Cloud Natural Language, Speech, Translate, and Vision. While the initial learning curve presents a significant challenge to onboarding, once there, the framework provides a scalable and efficient way to bridge the gap from a research to a live system.
Jupyter Notebooks
Jupyter Notebook, aka IPython Notebook, provides an efficient way to transform and ‘clean’ the data, perform numerical transformation, extrapolate trends using statistical modeling and create machine learning models. It has gained popularity with the data scientists and ML researcher communities and is available on commercial big data platforms like Azure.
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