The new age big data analytics, with the popularity of self-service tools in the market, attempts to let anybody, especially business decision makers, to prepare, process and produce highly refined results from data in a jiffy. Gone are the days when organizations stuffed large volumes of data with a core analysts’ team to process handling traditional BI tools.
Analytics not just for Analysts
At Gartner’s Business Intelligence & Analytics summit 2015, analyst Dan Sommer declared that Big Data Discovery is a steadily rising trend as it combines the strengths of data discovery, advanced analytics and big data. He also concluded by stating that big data discovery is the missing link to enable big data to go mainstream. Gartner had also predicted that by 2017, big data discovery would’ve evolved into a distinct market category.
As predicted, this category of advanced analytics is now more mainstream than ever before and advancements in self-service tools are enabling the traditional enterprises in adapting tools over their existing Big Data processing systems. For example, several advanced self-service data preparation tools can be made to work on top of big data platforms like Hadoop. This way, the users can start reaping self-service capabilities right from searching for relevant data to generating reports including cleansing, integration with other data sources and standardization procedures in between.
Now that we are clear about the direction that the future of big data & analytics is heading towards, it is important to ponder how. Taking data to the masses is easier said than done because even when the tools are likely to be distributed, the methods of collection from cloud or on premise apps and their interpretation still need some expert-level handling.
The businesses, from now on, have to tread on the slippery slope of entrusting non-data analysts to come up with key data derivations and ensuring that the decision-makers are still making the informed choice.
Building Individualized User Experience- The essence of Self-Service Capabilities
When people think about self-service tools, they often visualize programs that would let them install and boot all by themselves so that they can start working with it immediately. When organizations put forth the idea of implementing self-service data discovery routine for say, a data analyst A and a business decision-maker B, they would want a tool that could address and fulfill the needs and challenges of A and B separately, yet empower both of them equally to perform complex big data discovery functions.
Creating room for individualized user experience might seem like the most basic thing that one would expect out of self-service tools, but more often than not it gets limited just to dashboard design. With the market bustling with numerous self-service big data discovery tools, user experience would be the key to separate the leader from rest of the pack. Here are some of the important factors that define individualized user experience:
- Defined Roles
To begin with, enterprises look for individualizations right from the launch pad. They would seek unique and refined ways with which A and B can set their profiles up and can find their way around the tool. The role definition and the subsequent processing should be aimed at improving the overall efficiency in seeking valuable data patterns as B need not be bogged down with the innate complexities and A need not go through the very basics.
- Data Processing
One of the biggest differences between the traditional and self-service BI tools is that the former requires the involvement of several stake holders like analysts, programmers and IT to process data into meaningful patterns. This method, however laborious it may be, handles complex data sources and problems effectively as every interpretation is a collaboration of SMEs.
Self-service tools have to somehow pack all the complexities of the traditional BI at the backend and present a key decision-maker with a simple, intuitive UI that can break down complex patterns to simple charts, graphs and data points. A self-service data discovery tool that is focused in offering individualized user experience should let B feed complex data sources without any hassles of handling the back-end processes and still allow A collaborate with SMEs to address complex tasks and problems.
- Customized Result Reporting
For many organizations, the ultimate aim of opting for self-service capabilities is to enable their key decision-makers, like B, to take informed, important decisions in crucial junctures, say an important board meeting or a client interaction. The data discovery tool’s ability to simplify complex data patterns from multiple data sources to create customized reports is absolutely crucial. Individualization would enable different stake holders to gather and hold the same set of data reports in different light to arrive at much more refined results.
One of the main goals of computerization of the modern world is to take lightning speed processing capabilities to the masses and empower them to perform complex operations right at the comfort of their office spaces. Self-service Data discovery tools are all set to push forward that agenda in the digital era where crucial data decisions, derived from mere raw data, can make or break businesses and revolutionize markets.
Latest posts by Bhargavi Seshadri (see all)
- Hybrid Cloud Management with ServiceNow - November 3, 2017
- Accelerate Margins with JDA Retail Planning Solutions- EP and Allocation - November 1, 2017
- Future of Customer Experience with Machine Learning - August 23, 2017