What's Blockchain in a plain English?

Blockchain is just a type of database which continuously maintains the list of records called blocks. Each block has a timestamp and a link to the previous block. The data in a blockchain cannot be modified, they are resistant to update. With the use of end to end network and distributed server, a database is managed automatically by itself. This is an open ledger which can record transaction between two parties effectively and verifiable way. 

These is a database which is secured by design. This is an example of fault tolerance with distributed computing system. This kind of functionality helps blockchain to record any kind of critical records like bank transactions, amount, medical data, etc.

The blockchain was first of all introduced by Satoshi Nakamoto. In the year 2008 and implemented 2008 as a main component of digital currency bitcon.

In blockchain there is not any main copy in which all the data is stored. If a person wants to change his data copy, it will be shown as reflected until the file is verified by verifier. Because of this the chances of security breaches or hacking reduce. Blockchain uses public and private keys to encrypt. Here the private key is only with the receiver or owner, it is not shared anywhere. At the time of transaction only public key is shared. Here public key is also in encrypted format.


There are some disadvantages of blockchain:
The transaction part takes too much time. As transaction verification process is too long it takes more time compared to other technologies.
It consumes more storage.
It is costly in comparison with other technologies.  

There are some custom blockchain software development done by custom software development company in india.

Road to AI and its Applications

Since the creation of computers or equipment, their capability to complete various tasks has grown exponentially. Man have advanced the power of computer systems in various diverse in work domains with the custom software by the softwaredevelopment companies, in terms of increased speed, and reduced size with respect to time.

A division of Computer Science named Artificial Intelligence follows creating the computers or equipment as intelligent and smart as human beings.

Artificial Intelligence

According to [ CITATION RCC16 \l 1033 ], Artificial Intelligence is defined as “The science and engineering of creating intelligent machines, particularly intelligent computer programs”.

Artificial Intelligence is a technique of making a computer, a computer-controlled robot, or a software that think logically, in a similar manner the human mind work and think.

Artificial Intelligence is accomplished by reading and studying how human brain thinks, and how humans absorb and learn, take decision, and work while attempting to solve a problem, and later using the results of the study as a base to develop intelligent software and systems.

Thus, the development of Artificial Intelligence started with the purpose of replicating human intelligence completely.

Goals of Artificial Intelligence

AI gives possible goals to pursue system that can think like humans and act rationally. Artificial Intelligence aims at creating expert systems which exhibit intelligent behavior, to learn, demonstrate and advice users for the help they are seeking for. Thus, software development companies should build software system with the Artificial Intelligence that tries to implement Human Intelligence in equipment and machines that can understand, learn and behave as human beings.

Artificial Intelligence Technique

Artificial Intelligence Technique is a way to consolidate, well organize and effective usage of the knowledge in such a way that can be perceived by the people who offer it. The knowledge should be useful in every situation even though it is incomplete or inaccurate.

AI techniques raise execution speed of the complex programs by correcting the errors.

Applications of AI

Artificial Intelligence has been prevailing in various fields and domains such as

• Gaming 

Games are interactive computer program, basically a developing area in which the areas of human level artificial intelligence are pursued. Artificial Intelligence plays vital role in strategic and calculated games such as chess, checkers, etc., where machine can consider numerous possible locations based on heuristic knowledge. It also take into consideration the time-limits of the each phase of game.

• Expert Systems 

Artificial Intelligence enables a system to identify and diagnose situations without the presence of any human expert. There exists few applications which integrate equipment, software, and important information to deliver reasoning and advising. The Expert Systems rely on knowledge of human experts for example Planning, scheduling, taking Financial Decision, Diagnosis, troubleshooting and taking corrective decisions.

• Computer Vision Systems

It is a combination of techniques and ideas from Computer Graphics, digital image processing. Computer Vision systems understand, and follow visual inputs on the computer for example, photographs taken by spying aeroplane for the purpose of gathering information for geographical maps. In medical terms, diagnosing the patients using clinical expert system used by doctors. For the investigation purpose like police using the computer software for face recognition of the criminals portrait made by forensics artist.

• Speech Recognition

A procedure of converting a speech signal to the arrangement of words. The typical usage includes Voice Dialing, Call routing. It can also be used for data entry.

Various intelligent systems are proficient at hearing and grasping the language in terms of verdicts while a human communicates to it. It handles different accents, Background Noise, etc.

• Intelligent Robots

Robots are able to perform and accomplish the human tasks. They are built with sensors to detect physical data from the real world such as temperature, movement, sound, heat and pressure. They have competent processors, various sensors and huge memory, to exhibit intelligence. In addition, Intelligent Robots are proficient at learning from mistakes committed and adapt changes as per the new environment.

Conclusion: [ CITATION RCC16 \l 1033 ]
The custom software development companies can develop and use various systems which have in-built Artificial Intelligence. The capabilities of the system with Artificial intelligence will increase the effectiveness and speed of the work along with the time consumption.

How SaaS software outsourcing startups can ensure success

It’s now been 10-15 years since the SaaS software outsourcing industry’s birth but when we look closer, we see that almost 50% of SaaS startups have received the funds, which indicate significant amount of investment capital being channeled into this category. (Gartner, 2016) Forecasts the SaaS market will grow at 20% in the present year i.e. 2016, almost 3 times as quick as growth in the software industry, and there is a lot of opportunity for more global penetration as time progresses.

Salesforce is a perfect example for these facts, consistently growing at more than 30%.

These are some of the principles the SaaS startups can follow to ensure success. These were the principles followed by Salesforce.

Articulating your value should be simple

 

Address these points to make sure that you have a feasible business in hand:

• The product you make, satisfies the need and consumers are willing to pay.
• Large number of potential customers to ensure growth of your company.
• Keep your solution complex so that entrants face high barriers for competition.
• Market and selling your product well.

Team should be revived using a common cause

 

Not all SaaS software outsourcing startups can provide motivation to their employees; companies that do will assemble a team inspired by purpose rather than incomes.

Salesforce had a truly ambitious goal: to change the way of delivery of enterprise software. Marc Benioff, founder of Salesforce was the one who recognized in 1999, that Internet could be used to deliver SaaS. This notion was unbelievable at the time. Companies were unwilling to store their data on 3rd party servers. CIOs had lot of concerns about SaaS. They doubted a web-based application could compete with packaged software with respect to factors like:

• Security
• Performance
• Functionality
• Control

Every entrepreneur wants a team enthusiastic about the company mission. Truly great companies are those that impart desire and dedication among their employees.

Keep a strong relationship with your customer

 

In SaaS software outsourcing empire, a “sale” is just the beginning of a good and a durable relationship with the customer. SaaS providers should be in agreement with the needs of their customers; if their service doesn’t provide CLV, the customer won’t renew the lease.

SaaS providers are landowners, leasing access to functionality and data storage on their servers. Being a successful “landowner” (SaaS company) means having happy “occupants” (customers).

A SaaS support team defines a new role in the company: Customer Success Manager (CSM). Access to a CSM would be available to customers willing to invest in a higher-priced plan; in exchange, the CSM would analyze customers’ use of software and proactively suggest best practices. CSMs would frequently chat with customers and provide periodic scorecards to highlight features or add-ons for the service.

Platform building

 

Build a platform that provides value for the majority of your customers. Listen to their requests and use them to guide, but not command, your product roadmap.

Trust as a Value

 

For a flourishing SaaS business, you want to maintain customers for eternity. Your user base should grow over time, ideally using more features of your service at higher price points as they become more sophisticated.

Convincing one and all

 

You need to convince clients that SaaS is the best approach. You need to convince industry experts and media that your startup is a disruptive force. You need to convince clients that your solution is as reliable as established offerings. And you need to convince everyone that your company is introducing a sea change right now, and they don’t want to miss the boat.

Risk seeking approach

 

Startups have their own advantages against other companies. They are agiler, creative and are able to leverage newer technologies. They solve day-to-day problems in newer ways. Entrepreneurship is about risk taking, and making bold statements about “what’s next.”

‘Everyone overestimates what can be achieved in a year, but underestimates what can be achieved in a decade.’

Focus less on freemium model

 

Focus on the basics first, learn how to sell and deliver the product proficiently, and then dive into freemium model. Only then will the greatest rewards be most tangible. Focus on transforming freemuim users into paying customers quickly and then only open the floodgates when your funnel is mature enough to handle it.

Conclusion:As you build your startup, focus on your foundational principles. Take one step at a time while keeping your audience and client needs nearby heart, that will create an organization that just doesn’t get the job done but is also loved by people.

Bibliography

Gartner. (2016). Worldwide Public Cloud Services Forecast. Stanford: Gartner.

How are analytic benefits delivered by Big Data as a Service

As companies work to make big data globally available in the form of easily usable analytics, they should consider outsourcing functions to the cloud. By choosing Big Data as a Service solution that handles the resource-consuming and time-consuming operational aspects of big data technologies such as Hadoop, Spark, Hive and more, software development companies can focus on the benefits of big data and less on the grunt work.

In order to include big data in their fundamental enterprise data architecture, adaptation of and investment in Big Data as a Service technologies are necessary. A new data architecture suited for today’s demands should be comprised of the following components:

Extraordinary performance, analytic-ready data store on Hadoop. 
How can big data be swift and analysis-ready? A best practice for building an analysis-usable big data environment is to create an analytic data store that piles up the most frequently used datasets from the Hadoop data lake and structures them into multi-dimensional models. With Hadoop having an analytic-ready store on the top of it, organizations can get the quickest response to queries. These models are understood by the business users easily, and they facilitate the consideration of how business contexts change as years pass by.
This analytic data store should support reporting for the known-use cases, but also empirical analysis for accidental scenarios. The process should be comprehensive for the user, eliminating the need to know whether to query the Hadoop directly or use the analytic data store.

Semantic layer that facilitates “business language” data analysis. 
How do a lot of business users access big data? To hide the complications of raw data and to uncover data to business users in easily understood business terms, a semantic overlap is required. This semantic layer is a logical representation of data, where business rules’ application comes in the picture.
For example, a semantic layer can describe “high-value customers” as “those customers who are with the company for more than 3 years and continue making new or renewal purchases on a regular basis.” The data for “high-value customers” might have been obtained from different tables and gone through multiple levels of calculation and transformation, before coming to the semantic layer, all obscure to the business user who queries for “high-value customer.”
Formerly, business users would have to query Hadoop directly, which is unrealistic, or request information from IT, which means waiting in a row of reporting requests. A semantic layer assists business users to analyze and explore data using acquainted business terms — without the need to wait for IT to prioritize requests. It also allows reusing of data, reports and analysis across different users, maintaining orientation and consistency and saving IT the struggle of responding to every individual request on a case-by-case basis.

A multi-tenant big data environment. 
How can big data be accessed throughout the organization regardless of where people sit? With well-known demand for analytics, software development companies need to embrace a hybrid centralized and decentralized approach to data. This allows different teams to include local data sets and semantic definitions at the same time accessing the enterprise data resources that IT constructs.
This hybrid approach can be attained with a multi-tenant data architecture. In this architecture, IT collects and cleans data into a shared Hadoop data lake and prepares a core semantic layer and analytic data store from that data.
IT then creates virtual copies of the centralized data environment for different business functions, such as personnel, finance, sales, marketing and customer support. In this manner, IT keeps the authority in data governance and semantic rules, while business functions and departments can observe the impact of their daily business activities against historic or company data stored in Hadoop.

User-friendly ways of consuming analytics. 
How can the big data analysis experience be made user friendly? An absolute consideration for the end-user delivery of big data is the form in which data will be symbolized. These data interfaces should meet the unique and customized needs of all users. This requirement includes providing extremely interactive and responsive dashboards for business users, instinctive visual discovery for analysts and minutely detailed, scheduled reports for information consumers.
While each style is distinctive, the best practice is to ensure that each interface is not a separate tool, so that creating, collaborating and publishing information is done with reliability and precision. This is only achievable through a semantic layer that ensures data values remain steady, while data presentations might differ from one user interface to another.

Conclusion
Big data is important to the enterprise and is a fundamental part of the enterprise data architecture. To utilize big data's full potential, software development companies need to quicken the investments made in technologies that proficiently and successfully perform analysis and assist in storage of data. Cloud solutions for big data and analytics make this possible. With them, enterprises can achieve future data growth, and in turn, excel in the ever evolving big data environment.

How to establish a successful BYOD policy for a software development company

The success of a BYOD program lies in cautious creation of a bring-your-own-device policies, but many companies are negligent to write them. BYOD trend is becoming the leading strategy for provisioning subscriber devices in most software development companies in India over the next few years. There are a lot of interesting strategies, products and services that make BYOD effective and easy for the companies. BYOD isn't a free-for-all, do-what-you-want condition. Cautious planning and end-to-end strategizing are required before a company purchases any systems for managing BYOD.

Here are some of the key factors to consider for establishing a successful BYOD policy:

The permissible devices

The devices used during the decade of Blackberry services were pretty clear (i.e. only the Blackberry phones were used for work). Now in the era of iPhone and android, this decision is not that easy. Make sure you specify the devices that are permitted in your corporate network. The version and model number of these devices should also be taken into consideration while selecting the devices. The device choices can be any of the following:

• Android phones
• iPhones
• Android tablets
• iPads
• Laptops
• Phablets (i.e. Phone + Tablets)
• Notebooks

Security of data and devices

There is a lot of confidential information in the mobile devices connected, and accessing the corporate network of your software development company. There is a need of strong password attached to devices of employees at all times. Many employees don’t even have a password or screen locks on their personal devices, because they see it as an interference to quick access, so this needs to be addressed to prevent security breaches. Other security factors include the use of antivirus apps, other security softwares and proper configuration of firewall in your BYOD policy.

Services for selected devices

It's important for employees to understand the helpdesk boundaries when questions or problems creep up with personal devices. To set these boundaries, you'll have to provide a solution for the following questions:

• How much support for initial connections by personally owned devices, to the corporate network will be available?
• If a device breaks, what support is assured from IT representatives of the software development company?
• Is there a provision of application support on devices owned by the employees?
• Will you limit Helpdesk to tickets addressing email, calendar and other personal information management-type applications only?
• Is your support basically a remove and reconfigure operation?
• Will you provide other devices on a temporary basis to employees while their phone or tablet is being serviced?

Permissible applications

You need to make a decision on what apps will be allowed or banned which is commonly referred to as whitelisting or blacklisting. A BYOD policy should explain that IT has the authority to prohibit the use of certain applications that might threaten the security or integrity of the data used in corporation. This applies to any device that will connect to your network, whether corporate or personal. The concern is whether users can download, install and use an app that presents security or legal risk on BYOD devices that access sensitive corporate information of a software development company. What if a poorly written instant chat messenger steals your organization's address book?

Alignment with acceptable use policy

Allowing personally owned devices to potentially connect to your VPN introduces concern regarding what activities may and may not be permitted. Some of the points that require discussion are:

• If you set up a VPN tunnel on a mobile device and then your employees post to social networking, is this a violation?
• What if your employees browse objectionable websites while on their device's VPN?
• What if they transmit, either purposely or not, inappropriate material over your network, even though they're using a personally owned device? What authorizations are there for such activity?
• What monitoring approaches and tools are available for enforcement of such policies?

If you already have an acceptable use policy in place, integrate BYOD policy with it.

Employee Exit Plan

What happens when employees with devices on your BYOD platform leave the organization? How do you enforce the removal of access tokens, e-mail access, data and other proprietary applications and information? The consideration of how will the back up of user’s personal photos, apps, video, etc. will be performed before the mobile device is wiped, is of prime importance. Make a clear plan, document it and share this with the employees.

BYOD agreement

A written and properly implemented agreement between authorized users and the organization is essential. Companies should run any proposed policies by their legal advisories before drafting any agreements and putting them into practice. Laws vary significantly from authority to authority and from nation to nation.

Conclusion:  If you have not embraced BYOD yet, get ready, because its propagation will only continue to accelerate. With a strong BYOD policy, IT can sleep better at night knowing that they have governed their BYOD environment.

Revenue Streams for SaaS

SaaS on its own isn’t a profitable business model. It becomes profitable only when united with a strategic revenue model. Right pricing structure is the key in SaaS business space, involving a variety of themes and variations. Profitability is the one factor, the software outsourcing companies need to go after, right from the start.

Subscriptions

The biggest misconception today in SaaS business is that monthly subscription is the only source of revenue.

However, starting from subscriptions is not at all a bad idea. Subscriptions are the trendsetting SaaS pricing models. The benefits of subscriptions include:

• Continuously recurring revenue
• Better growth rate
• CLV

When analyzed thoroughly, the subscription model defeats the licensing model.

Upsells

“Upsell” is the generic term for improving both customer value and expenditure. Successful SaaS sales depends on upsells.

Attracting potential customers is a tougher job than upselling the existing customers for a software outsourcing company. It’s less profitable, too. It’s six to seven times more expensive to gain a potential customer than to retain an existing one. You get an extra layer of profit on the average profit, when you upsell an existing customer.

• Extra Storage, Speed, or DataOne of the commonly used ways to upsell customers, is by giving additional storage, speed, data, bandwidth, etc., along with pertinent costs. Depending on the product and customers, this could be a great way to provide users with the correct level of service at a suitable cost.

New Versions

Charging for new versions of your SaaS carries plenty of risks, but for some businesses, it is the most relevant way to charge. Here, the hardest part is persuading your customers that it’s the right thing to do.

Affiliate Sales

With a successfully executed affiliate program, the software outsourcing company can reduce marketing costs, effortlessly enter new markets, expand your business, and retain your existing customers. Customers who are successful with affiliate marketing will stick with you for longer duration, resulting in a reduced customer attrition.

But use this affiliate program with little cautiousness. If it isn’t properly checked or monitored, it can result in tacky marketing and uninvited exposure to adjacent markets.

APIs

It’s critical to focus as much on revenue streams outside the application as within the core product. One such beyond-the-application revenue stream is the API.

An API is a means of making the SaaS compatible with other software applications. Some developers will pay substantial API fees, eyeing the potential of a SaaS that is integrated and customized for their needs.

If an API is created, do an accurate cost forecasting. From a development perspective, APIs are expensive, and future support burdens may offset the potential cost.

White Label Licensing

If customers want to sell solution to their customers, it’s worth some consideration. Hence, charge should be levied accordingly and license agreement should be setup to avoid any future pitfalls.

Setup Fees

A setup fee is a nice way to validate clients during the onboarding process. A client who is willing and able to pay a setup fee is a client who can afford your service commits to use it.

There are always two sides of a coin. Some clients see it as a major turnoff if they are charged every time they turn around. Charge fees to customers only if the services generate true value to them.

Reporting

Some SaaS providers have successfully developed a revenue model by which they charge for reporting. Whether the reports are automatically generated or manually prepared, they can add a huge value to clients.

If a reporting model can be developed that gives value to the client, while at the same time proves the ROI of the SaaS, then charging the client is justified.

Advertising

It’s relatively easy and cost effective to build a SaaS with some space for advertising space and revenue. Ads within the SaaS is a hands-free way to earn enduring and recurring income. Companies like SaaSAds specialize in the integration of advertising within SaaS.

However, there are some risks involved. Some users may find these advertisements as annoying. Advertising work great, only for freemium models.

Customer Service

Customer service is not cheap. Hiring and retaining the right people is tough enough. Support personnel must be trained continually not only in the software itself, but in the best way of guiding customers to solve their issues themselves. If the software is complex and customer doesn’t have enough knowledge about it, it will cost a lot to have a fully staffed support team.

Customers needing additional support can be charged a monthly fee. For e.g. A retainer fee to get support when they need it.

Conclusion

SaaS business can be highly profitable, as long as the planning is for profits to happen. Discovering revenue streams other than monthly subscriptions is the key to maximize the profits for software outsourcing companies.

Common myths and truths about BDaaS

It’s exciting to see Big Data-as-a-Service (BDaaS) starting to take off. Numerous media outlets have covered the effect of this emerging trend on software outsourcing companies. While there is not yet a precise definition of BDaaS, common subjects are emerging and diverging. Here is a take on some of the truths on BDaaS:

All of the BDaaS service providers have slightly different capabilities

A host of services now uses the BDaaS signature. These services have both commonalities and key differences:

Most vendors are cloud-based, but with different architectures such as single-tenant and multi-tenant. These BDaaS vendors are promoted as a managed service with varying levels of management. Some are Hadoop based or other open-source software based. A few vendors include access to external or third-party datasets. A few have their own innovative visualization capabilities, but most of them support industry-standard tools.

Most BDaaS vendors essentially offer Big Data Analytics Processing-as-a-Service. This distinctive issue will persist, requiring that buyers be assiduous in their evaluations.

Currently, as a consequence of the Big Data trend, enterprises can turn to Big Data as a Service (BDaaS) solutions to bring the storage and processing together. Interestingly, a definition and classification of BDaaS is missing today and various types of services compete in the space with varied business models and foci.

Big Data-as-a-Service improves enterprise dexterity and competencies

With big data’s well-documented capability, an outsider might wonder why most software outsourcing companies don’t have programs to implement BDaaS yet. There are a lot of complex new technologies, legacy investments, many other IT priorities and a shortage of skills that is delaying this implementation. BDaaS removes many of these technical and skill blockades to data processing, so enterprises can focus on using data efficiently and be more flexible and adjustable to change. BDaaS enables smoothness, agility and compatibility. For instance, enterprises find it's relatively faster and easier to adapting changes at lower cost and efforts. Smoothness is the extent to which your BDaaS can be quickly and cost-effectively repurposed and reconfigured in response to changes in the constantly moving world.

Security, privacy and integration are key issues for BDaaS

While many software outsourcing companies in India have developed skills and competencies to manage these worrying data challenges on-premises, the cloud is an altogether different environment. Organizations still find it difficult to understand this new paradigm, and integrate it with substantial legacy investments. BDaaS providers, many of which are digital groups and software companies, often have different approaches on issues like security and integration, requiring careful inspection.

Implementation challenges can arise from lack of preparation, crisis in expertise and compromised data security, all of which will have to be taken care of in the future management of BDaaS technology.

Here are some common myths on BDaaS:

Hadoop as a Service = Big Data as a Service

Big data is not only about Hadoop. While earlier definitions often closelycharacterized big data as newer formats like semi-structured data (sensor data,logs etc.), in common use, big data is often used for “all the kinds of data a company has to deal with.” (TechTarget, 2016)survey provides a meaningful insights on format of database organizations consider “big data”:

79% include structured i.e. organized data (e.g. customer records and transactions)
59% include semi-structured i.e. semi organized data (log, clickstream,sensor, social)
52% include unstructured i.e. unorganized data (videos, audio, images)

Add to this there are lot of analytic processing requirements for different data jobs in a company(real-time, batch and advanced), and clearly Hadoop is not the suitable engine for every big data job. That’s why visionary BDaaS providers offer multiple processing engines (Hadoop, MPP SQL and Spark) to address the full range of data and analytical needs.

Big Data-as-a-Service threatens Information Technology

BDaaS is poised to help IT leaders and enterprises, empowering them to quickly offer big data capabilities within the scope of existing data and governance programs. Instead of stickingto Hadoop for six months, IT can source BDaaS that meets companywide requirements and then reallocate crucial resources to help the business analyze data more effectively. IT can rapidly implement BDaaS in days or weeks, enabling their organization with extraordinary data access, infinite scale and new agile data analytics capabilities.

Conclusion

The term ‘Big Data as a Service’ may be rather inelegant and clumsy but the concept is not. As more and more software outsourcing companies in India realize the value of implementing Big Data strategies, more services will emerge to support them as well. Data analysis brings positive change to any organization that takes it earnestly, and this includes smaller or larger scale operations which neither have the expertise nor the budget nor time to develop that expertise to do it themselves.

Bibliography

TechTarget. (2016). TechTarget Big Data Analytics .