NoSQL database offer great power to developers and organizations in form of fluidity of data definition and data structure. However like any other transition, moving from relational databases to NoSQL also requires a mindset shift and if you do not stop thinking relational, you may land in more problems than the one you are trying to solve by using NoSQL. This post tries to provide some pointers to catalyze such transition.
Category: Analytics and Insights
Data Strategy is Fundamental for Digital Transformation
Gartner predicts that by 2022, 90% of corporate strategies will include information as a critical enterprise asset. This information propels many companies to embark on their digital transformation journey.
Can Smarter BI Revolutionize your Market Rule?
The red-line between Business Intelligence and Business Analytics has always been blurry. It’s hard to differentiate between BI and Business Analytics because they ride on a similar behavioral pattern.
Advancement in Predictive Analytics for Smart logistics
We can’t foresee the future events and analyze their outcomes, but what we can do is use all of our current digital capacities to gather and analyze past-data to draw insights into the future. In data science terminology, this process is termed as predictive analytics, and it is now widely adopted in sizeable heterogeneous business networks for greater transparency.
Predictive Analytics For Mobility Success
It is relatively reasonable to state that the modern business world primarily builds upon the power of data to bring desired upgrades into their work process. With the world becomes more data affluent, technological evolution would result in enhanced productivity of business data. This in return would also help decision makers to transform their work models into better production engines with data tech acting as its driving fuel.
Can Agile Principles Help to Improve Big Data Efficacy?
Big Data is a vast collection of organizational data, which modern enterprises are turning into decisive insights via analytical means. Every data bit is necessary when it comes to putting big data analytics at work. This data comes from a variety of organizational sources, ranging from clickstreams, social media interactions, operational data points or functional logs. By applying analytical algorithms and automation tools, an organization can use its data goods to derive actionable insights and make informed market decisions.
