A Simple Guide to Outsourcing Data Analytics in Healthcare
The global healthcare industry, more than any other, suffers from the “data rich but information poor” syndrome. While it finds itself on the brink of a major makeover with the advent of Big Data and analytics, it lags considerably compared to other industries. Many researchers have raised this red flag.
How Natural Language Processing is Helping Democratize Business Intelligence
A minuscule percentage of those in the business of providing BI solutions have adopted NLP and adapted it to generate results for Enterprise clients. While the figure may be small today, advancements in the field are bound to push the number up.
The State Of Natural Language Processing Today
Readers of this blog may have realized that Natural Language Processing (NLP) was missing from our ‘5 Data Analytical Trends To Watch For in 2018’ post. Our in-house team of predictive data analysts say it lost out to the other trends by a narrow margin. But that in no way takes away from the importance of NLP and its growing influence in the world of big data analytics. The loser by a whisker surely deserves an honorable mention, hence this 2-part post.
5 data analytics trends to watch out for in 2018 – A slideshow
2017 has been an eventful year for Big Data and data analytics. So what can we look forward to in the new year?
Fashion, Weather and Predictive Analytics
Wondering how the words, fashion, weather, and predictive analytics are connected (fashion weather and predictive analytics)? Here’s a poser – what is one of the biggest challenges before the global fashion industry today? Weather. You wouldn’t have guessed it, right? Pic
Human Behavior Prediction using Machine Learning Techniques
Customers leave behind an incomprehensible amount of data while they go about shopping. Making sense of that data and reacting in real time are the two things that will keep companies one-step ahead of their customers (and competition) in the present-day customer-centric world.
From Data To Decision: Why Companies Fail to Benefit from Such A Long Journey
Can you name that one big challenge that businesses which have deployed data analytics face? It’s not failure to get qualified data scientists or IT professionals, nor is it finding the right analytical tools. The problem confounding companies is – how to implement actionable insights derived from analytics. For businesses that have started deploying analytics, the journey starts with data, moves on to its collection, analysis and visualization, finally ending in a decision that has to be then implemented. Yet, like a decathlon athlete who fails to clear that last hurdle, many companies falter at the last mile.
Building an effective What-If Analysis Model
Data analytics need not stop at just looking in the rear-view mirror. 'What-If' analysis offers organizations a way of forecasting the future, with historical facts as the base. But your scenario modeling must be made efficient for best results.
Top 3 Reasons Why Data Governance Strategies Fail
Top 3 reasons why Data Governance strategies fail? As far as organizational strategy goes, the one that has been the focus of late is data governance. Ever since data analytics as technology and technique was added as a major weapon to an Enterprise’s competitive armory,