Big Data: Here is what enterprises need to know

BANGALORE, INDIA: In Big Data, most organistions err in choosing the right set of data and deriving the right value from analyzing the patterns. That is where Cigniti Technologies comes in. Adopting the new concept of Testing in production (TIP), it guides organisations in performance and security.

In an interaction with Deepa Damodaran of CIOL, Raj Neravati, COO, Cigniti Technologies, tells why the company see high potential in social media platforms, retail industries, and insurance industry.

CIOL: What are Cigniti's offering for Big Data?

Raj Neravati: Cigniti is an IP-led testing services company. Cigniti is also embracing Big Data. We have created a service offering around Big Data known as Big Testing. Similar to Applets and Servlets in the Java 2 JEE world, Cigniti has something called Testlets.

These Testlets are pointed solutions. Testlets talk about consumer partitioning, social indexing, and data mutation. When an organisation is implementing Big data, Cigniti can partner with them as an independent testing partner.

CIOL: What is the scenario with regards to Big Data testing in India?

Raj Neravati: A lot of organisations are looking at Big Data because they want to become smart in their service offerings.

Airline industry is using Big Data analytics to study consumer behaviour pattern to know what is making the latter to buy or become loyal to a particular brand. The scope of Big Data in retail industry is also very large. Similarly, banks are also trying to utilize and implement Big Data.

Data has become very critical in decision making. We can help organisations to understand whether they are looking at the right data sets or not. Along with the three Vs that the industry associates with Big Data, Cigniti also introduces the fourth V that is ‘value'.

We understand that organizations want to use the volume to test the data, they need diversified data and they need the velocity at which computing can happen. It is very important to note whether the patterns you are analyzing are providing the right value or not.

Testing in production (TIP) is a new concept that we have embraced. Today none, not even the Facebook or LinkedIn, from the social media industry has investment so that they can define all scenarios in QA and then enable production. Instead, they started doing testing in production. They make sure that they leave some meaningful code in production which gains all the intelligence and analytics and understands the consumer behaviour.

They do not prepare large test programs to send a clean code into production. Rather, they create a theme with rapid deployment, until they find a data pattern that connects users in production. Then they find quick ways to extract the pattern. Flickr has ten bills into production everyday and FB probably has it to do it in every half an hour. What these companies are actually doing is; instead of investing in large data sets they are trying to do TIP.

Cigniti has solutions around testing in production. Then we have performance testing testlets and security testlets. In this way, we have point solutions in each type of functionality that can be implemented across the focus areas in an enterprise.

CIOL: What is the limitation of present day analytics tools that enterprises have been using in data warehouse for analytics purposes? Where do they fall short when it comes to Big Data?

Raj Neravati: Earlier, the entire analytics was based on business intelligence (BI), and those analytics were implemented using structured data. When you come to Big Data, with its capability and computing power, you can work with unstructured data also. Unstructured data can be a simple text, or number, picture, audio or video file, or analytics itself.

The sources of unstructured data are social networking platforms. Big Data comes into place when you have to analyse all these unstructured data in real time and derive meaningful analytics

CIOL: Most of the CIOs, however, do not seem to have any plan for Big Data as of now because they are not very clear about the technology?

Raj Neravati: This is true to an extent. Not all are adopting Big Data. Social media platforms are using it big time. The other industries, who are also looking at it are insurance, retail, and banking. Banking is still in the nascent stage of adoption.

Big Data is not yet perceived as a commodity yet. It will definitely take time. Everybody is today talking about cloud, but how many organisations are implementing it? Probably not more than 30 per cent of enterprises are implementing cloud. If you look at it, the SMBs implement cloud much faster, enterprises take time. However, in Big Data, enterprises are coming forward where their revenue generation is associated with analytics.

CIOL: Which are the sectors that you are targeting at? What are the challenges that you see in the market?

Raj Neravati: We are looking at social media platforms, retail industries, and insurance industry. Like any new technology, when you are entering you have a certain set of assumptions. If you do not define the outcome then the journey becomes poor and immature. So that is where the challenges come in.

CIOL: Can you suggest a few best practices for big data implementation?

Raj Neravati: As of now it is domain specific. Let's take a different approach to it. If you look at the entire journey of Big Data, it has different phases. First from the different data sources you can derive a descriptive analysis. A descriptive analysis is where you get the description of the data.

Once the organisations learn it, the second stage is called as diagnostic analysis where you analyze the basic descriptions and start slicing and dicing this data set to understand what kind of diagnostics you want to create. The third stage is the predictive analysis; from being descriptive and after diagnosing the data, now you should be able to predict the consumer behaviour. The next stage is prescriptive analysis, where you see and shape the future. As of today, Amazon, Google, Bloomberg, Facebook and LinkedIn are some of the major companies using Big Data.