Thursday, 20 April 2017

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.



Friday, 10 March 2017

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.

Monday, 9 January 2017

How are analytic benefits delivered by Big Data as a Service

software development companies

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.