The hype around Big Data is finally starting to die down. Big Data Analytics is no longer a “holy grail” that people are searching for, rather it is a discipline that many, though not all, companies are now doing.
Before the Big Data Hype started, Analytics Tools existed and They still exist today although they have evolved to meet the demands of Big Data and the Demands of an online world. However, for those who have not used Analytics tools before and are unlikely to ever go near a Big Data Hadoop Cluster, there is still much confusion about Big Data and Open Analytics.
The question that comes up for those that have never used open analytics tools before is “If the Big Data team can answer everything, then why do I need to be able to run analytics tools myself or put them in the hands of my staff?”
If you have used any kind of business analytics tool before, you will already know the answer.
Analytics tools may pre-date the Big Data Boom, but they have certainly not been made redundant. In fact, the advent of Big Data has breathed new life into analytics tools and made them more valuable than they have ever been before.
The way Big Data works is it looks for and finds “needles in haystacks”. You have millions of pieces of information and you use Big Data technologies to find correlations across that data. Once you find a correlation, you don’t need all the other information in the Big Data Cluster. What you need is to extract the data you found and use a different kind of tool to do more in depth and specific analysis on it. You also need to be able to create visualisations that tell you at a glance what the data is saying. Sometimes you need different teams to work on different “chunks” of that data and collaborate as they do so. Open Analytics tools are deliver these capabilities.
Not everyone in an organisation needs to do Big Data, but everyone can benefit from being able to better analyse the data that matters to them. Whether that data comes from a Big Data Cluster or from your old legacy order processing database actually doesn’t matter. Open Analytics tools enable you to analyse that data, understand your business more deeply and make informed decisions as a result.
Great Analytics products can assist you in many ways including:
- Integrate different data sources easily
- Share live and interactive Data and Reports
- Conduct your own analytics often with natural language questioning
- Provide Visualisations of data to better understand at a glance
- Mobile functionality to enable insight on the go
- Enable collaboration to share and analyse data in groups
SAS is a perfect example of a company that has taken a heritage in analytics and embraced current day advances to deliver incredibly powerful tools that puts data in the hands of everyone across any department. Their latest web based analytics platform, SAS Viya, encapsulates everything needed to put data driven insights in the hands of everyone in your organisation. The analytics they have built into the Viya platform is based on 40 years of heritage experience brought forward into a mobile web based big data driven age. Viya empowers anyone and everyone in your organisation to derive insight from data, even enabling predictive analytics.
If you have not put analytics capabilities in the hands of your staff already, it is really worth doing so You don’t need Big Data to make it worthwhile. Even moving the data, you hold in your spreadsheets into an open analytics platform can be a revelation. However, the real benefit comes when an organisation decides to become data driven putting both the data and the appropriate analytics tools collaboratively across all departments.
For more information please download DSA Spotlight on SAS Viya
- March 2018(64)
- February 2018(65)
- January 2018(80)
- December 2017(71)
- November 2017(72)
- October 2017(75)
- September 2017(65)
- August 2017(97)
- July 2017(111)
- June 2017(87)
- May 2017(105)
- April 2017(113)
- March 2017(108)
- February 2017(112)
- January 2017(109)
- December 2016(110)
- November 2016(121)
- October 2016(111)
- September 2016(123)
- August 2016(169)
- July 2016(142)
- June 2016(152)
- May 2016(118)
- April 2016(60)
- March 2016(86)
- February 2016(154)
- January 2016(3)
- December 2015(150)