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IQLECT: Aims to Grow in the Real-Time Data Analytics Sector from $4 Billion in 2016 to $20 Billion in 2021

While working on few products in companies like Amazon and Microsoft, where we had to deal with high volume and high speed data in almost real time manner, we as team struggled and even after huge collective efforts we were not able to handle things on expected lines and not to mention could not process all data in quasi real time as desired.

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IQLECT: Aims to Grow in the Real-Time Data Analytics Sector from $4 Billion in 2016 to $20 Billion in 2021
IQLECT: Aims to Grow in the Real-Time Data Analytics Sector from $4 Billion in 2016 to $20 Billion in 2021

BWDisrupt interacted with Sachin Sinha, founder of IQLECT which was launched on 1st Nov 2012.

Traditionally data has been analyzed in offline manner for majority of the scenarios. Data warehouse and Hadoop are examples of such offline analysis and how we have been dealing with data for long time. From last few years we have been witnessing that more and more use cases and businesses are demanding data to be analyzed when the data is still in the wire. The offline data analysis gives us insights after the event has occurred. Therefore we can only see reports or might do further deeper analysis to find some patterns and try and learn from them. On the other hand, real time data analysis gives us the ability to find interesting patterns to potentially alter the events while they are still happening. The data capture, data analysis and decision making, all happens when the event which is creating data is still going on. This allows businesses to react to the events by integrating the data insights with the business operations in continuous and real time manner for much better results.

Trend clearly indicates that businesses have already started adopting real time data analysis in large scale manner. Financial services needs to identify frauds when they're still happening to take corrective actions and check the losses. Same is true with the Telcos for detecting and stopping call frauds. Location based services needs to respond in realtime based on correlating and analyzing different kinds of data to offer better customer experiences and doing 1-on-1 marketing. E-commerce needs to do lot more personalization and customization for each individual visitor or customer. The real time pricing, offers etc needs data to be analyzed in real time fashion. The IOT can only be enabled if we collect, process and analyze data in real time. The safety and security of machines or premises need to collect data from various sensors and slice n dice them for taking preventive actions to avoid any failure or untoward incidents. The need for real time data analysis has never been greater or more emphasized.

To see this from high level, real time data analysis could protect the bottom line by reducing the losses due to fraud etc. It could further grow the bottom line by improving efficiencies through process automation or optimization in dynamic and real time manner. Further it could also affect top line by delivering better customer experiences using customization or recommendations. Overall real time data analysis has the potential to impact the business and operation in holistic and definitive manner. While the offline analysis are mostly data science where measuring the ROI could be a difficult process in itself, the real time data analysis on the other hand affects both the top and the bottom line, thereby making the impact of data analysis on business lot more measurable.The realtime big data market is huge. Globally the real time analytics is overtaking the traditional offline analysis. The current market size is close to $4 Billion with projected value of $20 Billion by 2021. IOT and the Industrial internet has the potential to push it further.

IQLECT offers fully baked up, ready to use real time big data analysis platform on cloud as software-as-a-service. IQLECT also offers all-in-a-box fully loaded server for on-premise deployments. IQLECT has created the whole stack from ground up designed specifically for real time data analysis. It has converged all necessary software into a single box for enhanced ease of use where data ingestion, data analysis, complex event processing, action/notification all can be performed without writing any code by just using the dashboard. The platform comes loaded with many default apps but users can extend it based on their own need to any level.


Simple – start within hours

• Software convergence – no silos
• All in a box model - off-the-shelf
• Analytic as part of the platform
• Doesn't require expert or consultant

Scales – convergence simplifies scaling
• Unit of compute,single entity to scale
• Single distributed layer
• Throw machine to scale model
• Admin less, autonomous

Self serve – ready to use, for humans
• SaaS - fully baked up, on cloud
• Manage all using dashboard
• No coding for adding new analysis
• Box model for private cloud

Cost effective – cloud promise

• Start small, pay as you grow
• Box based on commodity hardware
• Doesn't require experts or geeks
• Faster time to market


Tell us the story behind genesis of this startup?

While working on few products in companies like Amazon and Microsoft, where we had to deal with high volume and high speed data in almost real time manner, we as team struggled and even after huge collective efforts we were not able to handle things on expected lines and not to mention could not process all data in quasi real time as desired.

Looking closely at the trend we realize that 90% of data (since the dawn of civilization) getting created in just last two years, on the other hand we see that more and more use cases and businesses are demanding insights to be extracted from the data in real time vs. offline fashion.

Adding these two I realized that we need dedicated platform and solutions which gives us the ability to handle data out of the box in off-the-shelf manner where users can start upfront and grow or scale linearly. Which meant we need a platform which can ingest high volume of data efficiently, can process it while data is still on the move, and finally integrate the data insight with business operations to see the benefits from data in lot more concrete manner.

Initially I was more keen on solving problems rather than starting the business or venture hence I worked alone for several years to create the building blocks by writing whole stack from ground up. I was convinced that to handle data in real time we can’t create a homogenous solution by stitching many heterogeneous pieces. Therefore we needed to write all the relevant components towards same design goals.

While working alone I spent time in writing the initial software stack including BangDB, a nosqldb. I also offered BangDB under BSD license free of cost and saw many thousands of downloads for the same. People were writing to me about the high performance and ease of use for the DB. Many selected BangDB over some of the known popular Dbs in the market.

Slowly I was convinced that we could now take things to next level and in July 2015 I formally created a team and started working with full force towards the platform. During the same period I raised some money which helped us in going aggressively towards the goal. During the process many high calibre people joined the team, many known faces in software world got associated with us in different capacity and we saw vote of confidence coming from all the directions.

Some of the key features are as follows;

1. Stream processing – data agnostic
2. Clickstream analysis
3. Log analysis
4. Server or data center analysis
5. Integrated Dashboard - Ingestion, processing, store, notify all using dashboard
6. Pattern identification - Complex event processing
7. Auto kick of workflow for interesting events or patterns
8. Predictive analysis – train model, apply on stream
9. Hadoop connector - bi-directional

How is it different from the existing ones?
From the available options side in the market we had either a costly or a complex proposition. Costly proposition allowed user to cut down the time to market but only very few could afford. The complex proposition required dozens of highly technical people to create the platform by stitching various open source components, which invariably had long time to market, perpetual scaling issues and eventually becoming costly after some time. Therefore we needed a platform which can be affordable by anyone like SaaS and at the same time simple enough to allow people to start within hours as compared to many months.

QLECT brings cloud promise to the data analytics for real time insights. Here are the few key differentiators;


1. Time to market – cut the time to market from many months to hours
2. Simple hosted service on cloud – SaaS
3. Platform as a service for app building – PaaS
4. Converged box for Enterprise – DELL as partner for box (Small, Medium and Large)
5. AppStore for apps – ecosystem
6. Integrated Dashboard – Designed for humans

What is your current Funding Status?

 $3M
Monetization Model:
A. SaaS – Freemium ($0), Basic ($279) and Pro ($716)
B. PaaS – per API call
C. Converged Box : Small- $12500, Medium -$20000, Large - $30000

What are your traction details?
6 partners, 5 enterprise clients, 12+ cloud

What kind of future plans do you have in mind?
1. Add more horizontal and vertical apps
2. Open office in US – GTM for US
3. Partnership, Alliances
4. Next Fund in 1 – 2 Quarters

Please share some deatils about your Marketing Plans?
Decent spending in marketing for next year or so
1. Digital marketing
2. Events and close meetings
3. Hackathons for apps creating
4. Ecosystem building – Open source and apps
5. Thought leadership
6. Blog, webinar, podcast

What kind of Challenges did you face initially while launching this startup?
One of the major challenges in our line of business is to offer business solution or platform rather than technology solution or stack. To achieve simplicity which cuts across entire product is very hard. The very nature of our line of business is to deal with several complexities yet remain simple. Ease of use and intuitiveness is extremely important for wider adoption. It takes huge amount of effort to achieve simplicity which meets the enterprise grade requirements at the same time.

The other key challenge was to get the right set of people on board. Being a hard core tech company had its own share of challenges when it came to hiring but we have done well in this area so far

Real time data analytics market is really big and it's growing really fast. This is a huge opportunity for IQLECT $4 Billion in 2016 TO $20 Billion in 2021


Tags assigned to this article:
IQLECT Data Analysis real-time growth startup

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