Startup India

Start-up India campaign aims at promoting bank financing for start-up ventures to boost entrepr

Startup India campaign aims at promoting bank financing for start-up ventures to boost entrepreneurship and encourage all start-ups including startups like small software companies in India with jobs creation. The Prime Minister of India, Mr. Narendra Modi had announced the “Start-up India” initiative in his Independence Day speech this year. This initiative aims at boosting entrepreneurship and promoting new ideas by creating an ecosystem that is encouraging for growth of Start-ups. The objective is that India must become a nation of job creators instead of being a nation of job seekers.


A start-up is an entity, private, partnership or limited liability partnership (LLP) firm whose headquarter is in India, which was opened less than five years ago and have a turnover less than Rs. 25 crore annually. To be eligible for considering as start-up, the entity should not be formed by splitting up from any established company  and its turnover should not have crossed Rs25 crore during its existence.

(, 2016) Benefits given to Startups by Startup India:

  • All startups include software development companies startup will be exempted from paying income tax on their income for the first 3 years.
  • 80% rebate on filing a patent application.
  • Exemption of tax on capital gain.
  • Faster patent registration and protection for Intellectual Property Rights (IPRs) is provided under the Scheme.
  • The government launched a mobile app on 1 April 2016 recently and a web portal that will allow all companies to register in a day.
  • Compliance regime based on self-certification
  • No inspection for 3 years of start-up businesses in respect of labour, environment law compliance post self-certification
  • Easier norms for start-ups to exit within 90 days. Bill will be introduced in the parliament.

Government's role in boosting start-up:

The Ministry of Human Resource Development (HRD) and the Department of Science and Technology have agreed to partner in an initiative to set up over 75 start-up support hubs in the National Institutes of Technology (NITs), the Indian Institutes of Information Technology (IIITs), the Indian Institutes of Science Education and Research (IISERs) and National Institutes of Pharmaceutical Education and Research (NIPERs).

(, Startup India Event 2016, 2016) Special Benefits:

  • Start-ups in the manufacturing sector are exempted from the criteria of prior experience/ turnover without any relaxation in quality standards or technical parameters in public procurement.
  • Income Tax exemption is available for first three years. However, for example, software development startup will be eligible for tax benefits only after obtaining certificate from the Inter-Ministerial Board.
  • Rs10,000-crore fund for new enterprises, equal opportunity in government procurement, a Rs500-crore credit guarantee scheme and easier exit norms.
  • Japanese Softbank, which had already invested $2 billion in Indian startups, has pledged total investments of $10 billion.  

Eligibility for start-up:

  • To become eligible as a start-up and get a green signal from the Inter-Ministerial Board, the entity should be the one which aims to develop and commercialise, a new product or service or process or a significantly improved existing product or service or process that will create or add value for customers or workflow.
  • To be considered as eligible as start-up the entity, should be supported by an incubator, which is funded from Government of India as part of any specified scheme to promote innovation.


Though startups face difficulties in terms of compliance, taxation regulation with Digital India, as far as the Department of IT is concerned, there is a significant number of VC, funding, and mentorship to support all startups including software outsourcing companies in India.


Why is SaaS so popular among software development companies in India?

Software-as-a-Service (SaaS) has paved its place as the business service delivery model of everyone&

Software-as-a-Service (SaaS) has paved its place as the business service delivery model of everyone’s choice for custom software development companies - whether be it association or analytics. Yearly global survey of CIOs by (Gartner, 2014), found that 72% of responses indicated that respondents are already using SaaS, yet these results validated there is still high potential for greater adoption of SaaS. In another study (KPMG, 2014) specified that 50% of the executives surveyed identified cloud-delivered SaaS as their most likely investment area.

Core business functions, office and instant messaging software, project management software, design software and games, financial accounting software and financial payroll software are delivered by SaaS model.

SaaS is Popular for the Enterprise:

The popularity of SaaS is constantly rising because it makes deployment simple and assists in reduction of customer acquisition costs. With SaaS, service providers can support multiple customers with a single version of a product. This is called the multitenancy approach and it allows companies to scale as quick and as much the amount needed without replacing costly infrastructure or adding IT staff or assistance from software companies India.

SaaS is Popular with IT:

A recent survey of 1000 IT professionals by (Forrester, 2014) found that they are turning to SaaS products which are hosted, as a way to decrease the load of management of non-mission-critical applications such as HR and CRM. Plus, the subscription-based SaaS pricing model can help in maintaining the IT budget costs consistently or lower than packaged or homegrown software.

The reason why SaaS is popular are its benefits. These are some of the advantages of using SaaS model:

1. Time to benefit is reduced

In SaaS the software (application) is installed and configured beforehand. This is what, it makes it different from the traditional model. The user has the advantage of keeping a provision for the server for an instance on cloud and in few hours the application is ready for usage. This eases the time spent in installation and configuration for a software development company, and can lessen the problems that can get in the way of the software deployment.

2. Costs incurred are less

SaaS has a differential charging facility since it usually resides in a shared or multitenant environment which incurs lower hardware and software licensing costs, compared to traditional model.

Another advantage is that the customer reach in the market can be increased since it allows SMB to use a software that otherwise they would not use due to the high licensing cost.

Maintenance costs are reduced as well, since the SaaS vendor owns the environment for using the software and it is split among all customers that use that solution.

3. Integration with scalability

Usually, SaaS solutions are located in scalable cloud environments and have integration with other SaaS offerings. While in traditional model, a new purchase of software or a server is must. They only need to enable a new SaaS offering and, in terms of server capacity planning, the SaaS provider will own that.

4. Beta releases & upgrades

SaaS vendors provide the facility of upgrading the solution and then it becomes globally available i.e. for all their customers. The amount of costs incurred and the amount of effort put in, for upgrades and new releases are lower than the traditional model that usually compulsives the user to buy an upgrade package and install it, or the user pays for the specialized services to get the environment upgraded.

5. Easy to use and perform POC

SaaS offerings are easily usable since they come with best practices for software development companies and samples inside it beforehand. Users can do POC and assess the software functionality or a new release feature in advance. Also, they can have more than one instance with different versions and do a fluent migration from one version to another. Even for large environments, users can use SaaS offerings to assess and check the software before buy it.


In view of software companies in India, SaaS will remain the leading cloud model of the future. Smart businesses that are focus more on time and energy on their core objectives rather than delving in the IT’s unwanted services should evaluate whether the above benefits are aligned with their own corporate goals.


Forrester. (2014). The Public Cloud Market Is Now In Hypergrowth. Cambridge: Forrester Research.
Gartner. (2016). Worldwide Public Cloud Services Forecast. Stanford: Gartner.
KPMG. (2014). Elevating business in the cloud. Amstelveen: KPMG.


What are different types of Big Data as a Service (BDaaS)

The fame of Big Data lies within its wide-ranging definition of employing high volume, velocity, and

The fame of Big Data lies within its wide-ranging definition of employing high volume, velocity, and different data sets that are difficult to manage and excerpt value from. Clearly, most software development companies can identify themselves as facing Big Data challenges and chances today or in future. This, therefore is not a new issue yet it has a new quality as it has been aggravated in recent years. Cheaper storage and omnipresent data collection and availability of third party data has overtook the capabilities of traditional data warehouses and processing solutions. Businesses investigating Big Data frequently recognize that they lack the capacity to process and store it sufficiently. This shows either an inability to employ existing big data sets to the fullest or inability to expand their current data strategy with additional data.

Three strata of cloud computing as a service

Big Data as a Service is in the business of countless as-a-Service offerings. The most noteworthy ones that allow us to classify any subsequent services are threefold. Infrastructure as a Service (IaaS), e.g. virtual machines, networks, storage devices, or servers, is the most basic building block and includes everything (real or virtual) you would expect inside a data center. Above this level, exists the Platform as a Service (PaaS) which includes frequently employed software like web and database servers, or Hadoop and its ecosystem. At the next level is Software as a Service (SaaS) which are still nonspecific but more user interactive services like Email, CMS or CRM. Finally, past SaaS are usually domain or business specific applications.

Hadoop or a substitute distributed compute and storage technology at the platform level naturally builds the core of a BDaaS. Subsequently, any BDaaS solution includes the PaaS layer and may be SaaS and/or IaaS. This leaves us with four possible groupings for BDaaS:

  • PaaS only – focuses on Hadoop
  • IaaS and PaaS – focuses on Hadoop and optimizes infrastructure for performance
  • PaaS and SaaS – focuses on Hadoop and productivity & exchangeable infrastructure features
  • IaaS and PaaS and SaaS – focuses on total vertical integration for features and performance

Big Data as a Service Models


Big Data as a Service (BDaaS) offerings in the cloud can be categorized into one of four types:

Core BDaas

The core BDaaS implements the minimal platform, e.g. YARN and HDFS having Hadoop and a few popular services like Hive.  Amazon Web Service’s Elastic MapReduce (EMR) is the most noticeable core BDaaS and represents this model. EMR is one of myriad services in Amazon’s offering and EMR assimilates well with many of the other services just as NoSQL store DynamoDB or S3 storage. Users can combine them to build something like data pipelines to a comprehensive full company infrastructures around the EMR service. However, composability of its services being Amazon’s strength, also signifies that the core BDaaS offering is meant to stay nonspecific to interact with the other services.

Performance BDaaS

One way of vertical integration for BDaaS is in the downward direction to include an optimized infrastructure. This allows to get away with some overheads of virtualization and specifically build hardware servers and networks keeping in mind Hadoop’s performance needs.

Businesses are served, understanding and working with Hadoop that are rising, but are held back by scale and complexity. The software development company can outsource their infrastructure and platform needs and management around Hadoop to an infrastructure service provider. Business can then emphasize on putting Hadoop to work and the stack from SaaS upwards. A package pricing strategy based on storage and compute usage aims to remove common problems of choosing between performance and cost optimization, and give anticipated, fixed costs.

Feature BDaaS

The other way of vertical integration for BDaaS is in upward direction to include features past the common Hadoop ecosystem offerings. The feature driven BDaaS emphases on efficiency and abstraction to get users started with Big Data quickly. The feature BDaaS company’s services includes web and programming interfaces and database adapters pushing technologies like Hadoop into the background and their service reaches into the SaaS layer. In fact, Hadoop clusters are initiated, scaled and even stopped transparently according to the load requirement.

The feature method uses IaaS to provide computing and storage with a noteworthy difference. Being independent from a cloud provider allows a feature BDaaS to leverage computing and storage as a fully scalable and more importantly interchangeable commodity. Amusingly, the compute and storage from IaaS are pass through pay as you go and thus ideal for very variable, volatile, or exploratory workloads.

Integrated BDaaS

Finally, another possibility is a fully vertically integrated BDaaS that syndicates the performance and feature benefits of the previous two BDaaS. This is an interesting approach since it could result in the perfect BDaaS, which is productive and supports business users and experts, and provides supreme performance. Both feature and performance BDaaS are at initial stages and the integrated BDaaS could in practice turn out to be a good solution to this difficult problem.


As Big Data is growing as a topic, business and service models are evolving and we can see the similarities and differences between the three competing types of Big Data as a Service. The core BDaaS has been around for quite a time and is in use by many software development companies especially as part of a bigger architecture or for uneven workloads. It has settled as a model supporting the provider’s broader service architecture.

The feature BDaaS needs a proof to be competitive on a performance level, though the commoditization and service level generalization means that at the end of the day, winning of this model isn’t dependent on squeezing the most performance from comparable hardware, but on a dollar to dollar basis. The performance BDaaS, will face business demands from companies that diminishingly are willing to take on the complex challenges of building their own data architecture and linked SaaS layer, and progressively want to focus on their value adding domain specific processes.


Trends in software outsourcing this year, 2016

We have seen software outsourcing companies welcoming increased standardization and cloud computing

We have seen software outsourcing companies welcoming increased standardization and cloud computing options of all flavors, use their influence to renegotiate or rebid their deals, and settle into a best-of-breed approach to offshore outsourcing.

 Let’s look at some trends in outsourcing industry in this year:

Security becomes the epicenter

Security is on the mind from the boardroom to the break room, and it will impact outsourcing strategy in 2016. Certainly, security risk is poised to increase as telematics and the IoT becomes more prevalent in consumer and commercial products. Growing numbers of threat actors will use more and more creative ways to exploit weaknesses, often with devastating effect. Regulators will exact gradually large fines for poor security. Service providers have often been the weakest link in a company’s security and will need to find improved ways to address that concern.

The threat profile changes every day and with every additional protection comes a new weakness, not to mention it is becoming harder and harder to tie products together to deliver a robust security solution. This year, we expect to see the upsurge of the Chief Security Officer and more companies opting for specialized security vendors with Security-as-a-Service capabilities that can protect data no matter where it exists.

Offshore captives strike back

Companies will influence the experience they have gained in process maturity as a consequence of working with outsourced offshore teams and set up their own shops, predicts the objective of this tactic will be cost cutting by taking away the provider’s margin, as well as increase flexibility by removing contractual constraints. Rather than insourcing as a knee-jerk reaction to a bad outsourcing relationship and repeating previous mistakes, clients will benefit from lessons learnt and be smarter about what and how they exile.

Production workloads – hit the cloud

There’s no denial that Amazon’s first mover advantage with the public cloud. And IT shops who reached for the cloud first did so with trivial systems. But in this year, we’ll see more production workloads move to the cloud—and not just AWS. No CIO (Chief Information Officer) wants to tie up with just one cloud provider. IT pros recognize that the future of their data centers will symbolize many platforms, so we’ll start to see more CIOs experiment with other key public cloud options, such as Microsoft Azure and Google Cloud Platform.

The potential to move outsourcing from the ‘lift and shift’ of trivial processes to something more substantial is entirely doable in the cloud. The as-a-service outsourcing model makes it likely to combine infrastructure, software, and business process to create a platform that is much more segmental, scalable and intellectual. This platform can handle higher-level processes, creating results that increase revenues, improve profit margins, enhance customer service, and move the business ahead instead of running in place.

VMOs go conventional

Multi-sourcing has amplified the vendor management workload. As clients look for ways to address the challenges of managing ever more complex multi-vendor service delivery models, the VMO will establish itself as a way to provide a high-level, organization-wide view while at the same time managing day-to-day operational details and multiple touch points between different providers in the service delivery chain.

Integration challenges the flow

Customers adopting a great number of evolving digital technologies will face an ever-larger integration challenge. Many of the most powerful cloud technologies will require integration struggles comparable to those required to install ERP systems. As most software outsourcing companies in India do not have employees who are able to manage multiple emerging technology platforms, they’ll have to outsource service integration, change management and incident management. So we expect increasing partnerships among providers.

The service providers universe develops

Customers will buy from a growing list of technology providers. Customers will continue to turn to ITO, BPO, KPO, software outsourcing companies in India and cloud service providers who have radiated a digital trail for assisting in becoming digital businesses. They will source services from an ever-expanding list of evolving and digital technology providers. We’ll see more product-driven managed services as more product-oriented vendors, such as Cisco and others, move ahead than just selling their products to also delivering services around their products. We are already seeing this on a small scale, but expect it to rise this year as very large clients are growing their managed services capabilities.

Automation will reinvent relationships

Having shattered the opportunities to move work to lower-cost people, IT Outsourcing companies and BPO companies put their emphasis now on moving it to machines. Buyers with agreements designed to purchase people will need to settle their contracts to this new world. Both customers and providers will have to reconsider their deals as they integrate more RPA into IT service delivery.

Both parties will have to redefine roles and skills necessities for human jobs, as well as manage the touch points between automation functions and jobs performed by humans. This will present a substantial challenge for outsourcing relationships as agreements will need to be flexible to house these highly dynamic environments.

Agile sourcing emerges

With technology itself seeming to advance gradually, outsourcing decision making will have to speed up. Software outsourcing companies India who decide on a digital strategy will execute quickly in this year to avoid seeing a technology shift or an opponent jumping ahead. We see growing numbers of clients deploying substantial negotiating teams working on an agile basis to close smart deals quickly.

Emerging Technologies and Opportunities for Big Data Applications

Introduction Software development companies are trying to be up to date with the emerging trends for


Software development companies are trying to be up to date with the emerging trends for Big Data. Big data has got a well-recognized place by The Government as well. Government has recognized big data by categorizing it as one of the ‘Eight Great Technologies’ which will drive the world to future growth. The (COMMUNITY, 2014) reports on the increase in data being produced and the importance of new types of computing command in order to reap the economic value of the data.

Big Data

According to (COMMUNITY, 2014), the following is a working definition of Big Data:
“Big Data refers to huge volumes of data with high level of complexity as well as the analytical methods applied to them. This requires more cutting-edge techniques and technologies in order to develop meaningful information and understandings in tangible time”.

Analytics is considered to be the inherent part of new techniques and technologies for Big Data. The scope of analytics covers three roles:

a) Descriptive analytics - to understand what is happening in the world, using visualization techniques, some modeling and regression.

b) Predictive analytics - to predict what will happen, using forecasting.

c) Prescriptive analytics - to work out what we want, using simulation, optimization, scenario testing and Multi-Criteria Decision Analysis.

Trends in Big Data Analytics

Big data technologies and practices are moving quickly. Here’s what one should know, according to (Mitchell, 2013), to stay ahead of the game :

a) Big data analytics in the cloud

This allows users to access extremely scalable computing and storage resources through the Internet. It allows companies to get server capacity as needed and expand it rapidly to the enormous scale required to process big datasets and execute complicated mathematical models. Cloud computing reduces the price of data storage because the resources are shared among many users, who pay only for the capacity they actually utilize. Companies can access this capability much more quickly, without the expense and time needed to set up their personal systems, and they do not have to purchase enough capacity to accommodate highest usage.

b) Hadoop: The new enterprise data operating system

Hadoop is by far the most popular implementation of MapReduce. MapReduce is a completely open source platform which handles Big Data. As it is flexible, it works with multiple data sources. It either aggregates multiple sources of data in order to do large scale processing, or reads data from a database in order to run processor-intensive machine learning jobs. It has several diverse applications, but one of the top usages is for large volumes of constantly changing data. Changing data may be web-based or social media data, location-based data from weather or traffic sensors, or machine-to-machine transactional data.

c) Big data lakes

Traditional database theory dictates that you design the data set before entering any data. A data lake, also known as an enterprise data lake or enterprise data hub, turns that model on its head. It offers tools for people to analyze the data, along with a high-level definition of what data exists in the lake.

d) More predictive analytics

Predictive analytics is the branch of data mining concerned with the prediction of future prospects and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other unit to predict future behavior.

With big data, analysts have not only more data to work with, but also the processing power to handle great numbers of records with many attributes.

e) In-memory analytics

It works by increasing the speed, reliability and performance when querying data. Business Intelligence deployments are typically disk-based, that is the application queries data stored on physical disks. In contrast, with in-memory analytics, the queries and data exist in the server's random access memory (RAM).

The use of in-memory databases to speed up analytic processing is increasingly popular and highly valuable in the right setting. Many web application development companies are making use of In-memory analytics to attain more reliability and greater performance.

f) More, better NoSQL

Alternatives to traditional SQL-based relational databases, termed NoSQL (short for “Not Only SQL”) databases, are rapidly gaining importance as tools for use in specific kinds of analytic applications.

 According to (Wikipedia), the working definition of NoSQL is as follows:
“A NoSQL, formerly referred to as "non SQL" or "non-relational", database provides a mechanism for storage as well as retrieval of data which is modeled in means other than the tabular relations used in relational databases.”


While the subject of Big Data is broad and encompasses many trends and new technology developments, Software development companies in India are keeping pace with the global market. It becomes essential for organizations to cope with and also handle Big Data in a cost-effective way. The various technologies emerging for Big data applications are Hadoop, Column-oriented databases, MapReduce, Schema-less databases, or NoSQL databases to name a few.


Mitchell, R. L. (2013, Oct 23). 8 Big trends in big data analytics. Retrieved Apr 25, 2016, from 8 Big trends in big data analytics:
Rouse, M. (2012, June). Cloud ERP. Retrieved 04 20, 2016, from
Wikipedia. (n.d.). Retrieved from