Sampath Putrevu posted on 29th January 2018
With Business Intelligence tools fuelling self-service analytics and simplifying a data scientist's job, businesses may soon look at algorithms that yield quicker and simpler results.
By the year 2019, self-service analytics and business intelligence tools could possibly produce more reliable outputs and data analysis than data scientists, says a report by Gartner.
Self-service analytics and Business Intelligence tools automate several functions in data mining, currently being done manually by data scientists across the world.
Gartner Inc is a leading research and advisory company that has expertise in Big Data and analytics.
Surveying more than 3,000 Chief Information Officers (CIOs) from across the world, Gartner reports that top-performing officials consider Business Intelligence alone as the next big thing for the future, in terms of data mining. It says organisations are increasing embracing self-service analytics, and business intelligence to bring these capabilities to business users of all levels.
On a daily basis, data scientists work on several Machine-Learning techniques, and data mining mechanisms without using BI tools, deciphering abstract patterns, coded correlations, and customer preferences and other essential information.
However, data analytics companies may not always choose to employ Artificial Intelligence over data scientists due to the larger investment involved with the former. Besides, human intelligence and supervision are necessary after a machine mines large amounts of data to make an appropriate analysis of the data.
If application of Business Intelligence turns out to be fruitful, its scale of implementation can catch organisations by surprise. In large organisations, popular self-service initiatives can rapidly expand to encompass hundreds and thousands of users.
Carlie J Idoine, Research Director at Gartner said,
"If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don't work out well. This is because the experience and skills of business users vary widely within individual organisations. Therefore, training, support and on-boarding processes are needed to help most self-service users produce meaningful output."
Robyn Rap, Business Intelligence Analyst at indeed.com told Innovation Enterprise,
"Self-service tools are just tools. Using them effectively depends greatly on the person who's using it, their judgment, and their intuition to dig into the data and its quality. Tools are going to come and go, and you can teach people how to use them. It's much harder to teach someone how to approach data appropriately."
Nevertheless, Gartner recommends high focus on four areas to make self-service analytics and business intelligence reliable.
1. Align self-service initiatives with organisational goals and capture anecdotes about measurable, successful use cases
2. Involve business users with designing, developing and supporting self-service
3. Take a flexible, light approach to data governance
4. Equip business users for self-service analytics success by developing an on-boarding plan.
Despite several challenges and complexities, self-service analytics and Business Intelligence could open up a fresh world of possibilities in terms of technological advancements, lesser the burden on humans, and amplify employment opportunities.