Article Evaluation 4
- 1. Advanced Analytics ARTICLE BY DOMINIC BARTON AND DAVID COURT JANUARY 2018 PREPARED BY: ANSHUMAN RAINA Work for You MAKING
- 2. WHAT'S HAPPENING NOW Big Data and Analytics have skyrocketed over the past decade and now increasingly, companies are integrating it into their System. The Recent successes of companies like Google and Amazon have garnered widespread applause from Executives around the Globe.
- 3. DATA BOOM GOES THE Big Data thus, is seeing an unprecedented expenditure investment by technology leaders like IBM and Hewlett-Packard. This triggering in companies with such widespread changes was last seen in 1990s, when organizations redesigned their core processes.
- 4. Even though many organizations have pioneered for this change, some view it with Skepticism and compare its likeness to what happened with CRM Systems, which led to a huge uproar and hype with too less a future as well as huge losses. Nevertheless, we believe that the time has come to define a pragmatic approach to big data and advanced analytics—one tightly focused on how to use the data to make better decisions.
- 5. FULLY EXPLORING BIG DATA REQUIRES THREE MUTUALLY SUPPORTIVE STEPS: 1. COMPANIES MUST BE ABLE TO IDENTIFY, COMBINE, AND MANAGE MULTIPLE SOURCES OF DATA. THEY NEED THE CAPABILITY TO BUILD ADVANCED ANALYTICS MODELS FOR PREDICTING OUTCOMES MANAGEMENT SHOULD TAKE UP TO TRANSFORM THE ORGANIZATION SO THAT THE DATA MODELS ACTUALLY YIELD BETTER DECISIONS
- 7. The universe of data has changed vastly over the past few years. The ability to see what was previously invisible improves operations, customer experiences, and strategy. But mastering that environment means finding deliberate ways to identify usable data you already have, and exploring sources of information.
- 8. SOURCE DATA CREATIVELY. Often companies already have the data they need to tackle business problems. But managers simply don’t know how the information can be used for key decisions. Companies can impel a more comprehensive look at information sources by being specific about business problems they want to solve . For Example: In a TED Talk , the speaker noticed that TED was an independent data set in itself and analyzed it so as to give The "Ultimate" TED Talk.
- 9. IT SUPPORT GET THE NECESSARY Legacy IT structures may hinder new types of data sourcing, storage, and analysis. But Slowly and Steadily integrating technology to equipment can be successful. This process will be time and cost dependent, but the technology required for analysis and visualization is a one time investment needing constant upgradation.
- 10. BUSINESS OUTCOMES BUILDING MODELS THAT PREDICT AND OPTIMIZE
- 11. C O M P A N I E S S H O U L D A S K HYPOTHESIS-LED MODELING “ W h a t ’ s t h e l e a s t c o m p l e x m o d e l t h a t w o u l d i m p r o v e o u r p e r f o r m a n c e ? ” PRACTICAL DATA RELATIONSHIPS
- 13. UPGRADING THE ORGANIZATION Develop business-relevant analytics that can be put to use. Embed analytics into simple tools for the front lines. Develop capabilities to exploit big data.
- 14. IN CONCLUSION The era of big data is evolving rapidly, and companies should decide whether to act or not. But rather than changing drastically, slow yet simple changes should be integrated at first. Clear, short-sighted goals should be targeted till the time everyone becomes comfortable The transition should be slow and steady so as to give everyone a time to adapt. As more companies learn the core skills of using big data, analyzing customer needs may soon become an essential and decisive competitive asset.