The pandemic-caused lockdown has given rise to the second wave of technology reforms for organizations and has proved the need for automation. Most companies do major of their operations through digitized means and automation today. Artificial intelligence, digital aids, IT are important for performance. This shift of platform in the business world has resulted in a demand for individuals with strong technical skills. The work personalities that have digital skills in both existing, and upcoming technologies. Professionals who are proficient in the tools of demand are required to operate the digital face of organizations. DevOps is one such strategic concept that is used to improve the use of IT that has an impact on the services provided by an organization.

In traditional practices the SDLC process was too slow, then DevOps came in between and showed how a faster and continuous production and delivery of software provides a good profit to the organization and became today’s demand in the market. According to studies, organizations that have adopted DevOps, have over 60% improvement in their software development cycles. The DevOps market has already proven to grow faster than any other field in the software development sector. That’s why DevOps is a better carrier right now and in upcoming years.

Basically, DevOps helps to improve the overall efficiency of any program. It provides developers with more control over the production of any product. Most tech companies use it because it provides a far better production infrastructure than other methods. This is exactly why a future in DevOps would mean a career that will be challenging, but equally rewarding.

The DevOps market size keeps increasing day by day. As organizations try to transform their digital presence, they are seeing for professionals to take charge of digitization. As in result, more DevOps engineers are being hired.

Individuals trained in DevOps have many career options such as:

1. DevOps leader
2. Automation Architect
3. Code release manager
4. Software developer/tester
5. Security Engineer

DevOps is a growing technology. Therefore, in the upcoming times, it will only rise. As many stages of software development will depend on it, professionals will begin to move towards it. It is best to start your journey as a DevOps professional with a course that teaches you the basics of DevOps. The best way to rise in this industry is to keep learning and adapting to new technologies.

Yes! DevOps is the best career option. In 2021 and the upcoming year. DevOps will be the best choice for IT professionals as it is in demand and so many companies are adopting the DevOps culture. In India, so many companies have adopted DevOps culture and they can deduct the cost and enhance the software quality and software delivery has been improved. Continuous integration and continuous deployment/ delivery help the Company to reduce the cost, improve software quality, and help to deliver software faster in the market.

DevOps is all about collaboration and communication. The best benefit of working in DevOps is the organizational culture to be working in. It promotes better collaboration between the teams and helps you to develop and sharpen your networking skills to advance your future career paths.

DevOps is a recent skill. This means several professionals who are working in this domain will most likely have upskilled themselves. This will increase your value in the IT industry. Your career is defined by how much you are keen to learn and invest in your career. If you don’t upskill, you will soon be kicked out of demand, as IT technologies and methods are being developed every day. 

DevOps helps you apply a comprehensive approach to building applications. It helps you in Security skills as well in your profile. DevOps skills help bridge the gaps in your profile, which helps you make the value of your’s in the IT domain.

As a DevOps Engineer, there are opportunities for you to work in any company in the IT sector. Start-ups, consultancies, multinational companies, etc., are looking to integrate a DevOps model into their business and create new DevOps Engineer roles to strengthen their current tech teams.

As DevOps continues to transform modern business technology, more specialist senior-level positions are expected to be formed in the next five years, with wide-scale adoption of DevOps anticipated across the industry. It means, more than enough opportunity for internal progression is there as businesses invest in a DevOps function.      

As per the report in 2017, 78% of CIOs and CTOs considered applying DevOps into their organization (a 4% increase in 2016). An additional increase is being expected this year as multiple tech businesses realize the benefits of DevOps Engineers that can work over functions. This is the time for a sustainable carrier. The market has expanded up to 40 to 45 percent over the last five years, increasing the DevOps demand.

Learning DevOps will enable you to learn many more tools and technologies. Companies favor skilled and experienced people with multiple skill sets to improve company costs. If you are a DevOps person, you will be a valuable asset to your company. With DevOps, you will know how to utilize different tools and implement solutions faster and efficiently.

DevOps is a standard career choice now. According to research from Forbes magazine, a DevOps specialist with a basic degree in high school also can earn a middle wage of almost $106,734. Nevertheless, your salary will depend on your role, the average pay for the different roles isn’t completely different. For example, a DevOps Release Manager earns approximately $92K, a Site Reliability Engineer $125K, and a DevOps Engineer $115K. Researches have analyzed, in the upcoming years, DevOps will be the major hiring criteria of the IT companies.

A DevOps career permits you to deploy a broad set of skills that are constantly refined due to the new challenges you solve every day. It’s a constantly fascinating career, encompassing a broad variety of work. Being part of the DevOps process from build to release, you are continuously increasing your understanding of the framework, facing new technologies, and obtaining new knowledge to enable the faster deployment of better quality software.

Having involvement in every element of the DevOps ecosystem provides you as well a broad business understanding as you work to maintain and monitor an effective relationship between operations and development.

DevOps is in high demand, and many companies are incorporating the DevOps process to make their development processes even much faster. There is a high demand for DevOps professionals over various IT domains. In India, a DevOps Engineer earns approximately a salary of INR 7 lakhs per year, according to Glassdoor.com. This statistic changes with experience, skill sets, and the company, you work for.

Training place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, and SRE courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post 15 Solid Reasons Learning DevOps Is Good For Your Career Advancement. appeared first on Artificial Intelligence.

Software development Life cycle (SDLC) is a process of producing high-quality software at the lowest cost and in possibly less time. Generally, SDLC has well-tested and ready-to-use phases which provide an organization to help in creating high-quality software. ISO/IEC 12207 is an international standard of software life cycle process. This standard defines all the tasks which need to develop and maintain software. SDLC targets to produce high-quality software by meeting the expectations of clients within the time limit and in budget. It is made up of a plan which describes how to develop, maintain, alter, and improve the software.

Why we need SDLC

Basically, SDLC is a method with the process, which helps in creating high-quality software. By this you can understand the whole criteria of producing effective software, that’s why SDLC is important. Without SDLC you can’t create a standard software because it gives a standard way to produce an effective and efficient software that will run in the market with client expectations which will help him in managing his part of work. With time we update the software as per customer feedbacks to get a better result which is also a part of SDLC.

Benefits of the Software Development Lifecycle

• Forms the base for project planning.
• Helps to estimate cost and time.
• It gives the clarity of the project and the development process.
• Enhance the speed and accuracy of development progress.
• Minimizes the risks and maintenance during the project.
• Its given standard improves client relations.
• SDLC implement checks to ensure that the software is well tested before being installed in greater source code
• Developers can’t move to the next step until the prior one is completed by SDLC.

What are the SDLC phases

Software Development Life Cycle Models

Waterfall Model:-

A little too old and harsh model is the Waterfall model. It is one of the old-fashioned SDLC models that is not much preferred in the modern software development ecosystem.

The reason it is not favored much is that it runs on a very inflexible structure conditioning that the entire set of requirements should be laid down from the very beginning of a project. This limits the freedom and flexibility of the actual design and development of software.

After completing the development, the product goes through the test for meeting its initial requirements. If it is not good enough, it is to be restructured, which is a lot of work.

Usually, software development companies resist dealing with Waterfall though it still seems to be an effective model for the handful of projects.

RAD Model:-

The rapid Application Development (RAD) process is an adoption of the waterfall model. It aims to developing software in a short period. The RAD model is based on prototyping and iterative development with no specific planning involved. The process of writing the software itself involves the required planning for developing the product. The RAD model is based on the concept that a better system can be developed in less time by using focus groups to collect system requirements

• Business Modeling
• Data Modeling
• Process Modeling
• Application Generation
• Testing and Turnover

 Spiral Model:-

The spiral model is a risk-based process model. This SDLC model helps the group to adopt elements of one or more process models like waterfall, incremental, etc. The spiral technique is a combination of fast prototyping and concurrency in design and development activities. The following will explain the typical uses of a Spiral Model –

• When there is a budget compellable and risk evaluation is important.
• For intermediate to high-risk projects.
• Long-term project commitment because of probable changes to economic priorities as the requirements change with time.
• Customer is not sure of their requirements which is ordinarily the case.
• Requirements are complicated and need evaluation to get clarity.
• Some changes are expected in the product during the development cycle.

V-Model :-

In this model execution of processes happens in a sequential method in a ‘V-shape’. It is also known as ‘Verification and Validation model’. The V-Model is an Expansion of the waterfall model and is based on the association of a testing phase for each related development stage. That means for every single phase in the development cycle, there is a directly associated testing stage. This is a disciplined model and the next phase starts only after completion of the previous phase.

Incremental Model :-

The incremental model is not a distinct model. It is radically a series of waterfall cycles. The requirements are divided into groups at the initial stage of the project. For each group, the SDLC model is adhered to develop software. The SDLC process repeats with each release adding more functionality till all requirements are met.

use of  Incremental Model:-

• When the requirements are much superior.
• A project has a lengthy development program.
• When Software team are not well skilful or trained.
• When the customer demands an immediate release of the product.
• You can develop precedence requirements first.

Agile Model :-  

The agile model is a model which promotes continuous interaction of development and testing during the SDLC process of any project. The agile model is a combination of iterative and incremental process models with aims on process and customer satisfaction by continuous delivery of working software products. Agile Methods have divided the product into small incremental builds. These builds are issued in iterations. Each iteration lasts from typically one to three weeks. Every iteration involves cross-functional teams working together on various areas like

• Planning
• Requirements Analysis
• Design
• Coding
• Unit Testing and
• Acceptance Testing.

At the end of the iteration, a functional product is displayed to the customer.

Iterative Model :-

In the iterative model, the iterative process starts with the implementation of a small set of software requirements, makes  enhancements in the evolving versions till the complete system is implemented and ready to deploy on the market. In this model development of the life cycle doesn’t start with full requirements, instead, it begins with the implementation of just a part of the software, which will be reviewed to identify further requirements later. This process is repeated till the new version of the software is produced at the end.

Big bang model :- 

The big bang model comprises focusing all types of possible resources in software development and coding with little bit or no planning. This model works best for small projects with the smaller size development team who works together. It is useful in academic software projects as well. It is also an ideal model where requirements are either unknown or a final release date is not provided.

Advantages of the Big Bang Model

• This is very easy to use model
• Little bit or no planning required
• Easy to handle
• Very few resources are needed
• provides flexibility to developers

Disadvantages of the Big Bang Model

• Very High risk & uncertainty.
• Not a good model for difficult and object-oriented projects.
• Poor model for long-term and ongoing projects.
• Can become very expensive if requirements are not properly understood.

Prototype Model :-

The prototype model starts with the gathering of required information to start the development process. In this the developer meets the client, understand the purpose of software and identify the actual requirement. Then a quick design is created, focused on each aspect of the software which will be visible to the user. Then it goes ahead with the development of prototype, customer checks and try to identify if any modification needs to be done. In this step, looping occurs and better versions of prototype are created. It continuously happens being in touch with client to show him if any further requirements needs to be done.  This process remains continue till the user is satisfied. Once the user is satisfied, the prototype is converted into the actual system to deploy in market.

DevOps:-  Let’s understand. In the agile Model, both Development and testing activities were concurrent, unlike the waterfall model. It was lost on practices that didn’t come up to speed with agile practices. Due to lack of collaborations between developers ad the operations team, slow down the development process and releases. Then software companies started realizing the need for better collaboration between teams and faster delivery of software. It gave birth to the DevOps approach. DevOps enabled fast software delivery with minimum problems to fix and faster resolution of problems. The term DevOps is deprived of two words development and operations. DevOps is a practice that allows a single team to manage the whole application development life cycle, i.e. development, testing, deployment, etc. The aim of DevOps is to shorten the development life cycle. DevOps is a software development approach that helps in producing high-quality software with reliability and in less time. DevOps is a software development method that aims at communication, integration, and collaboration between IT professionals to enable continuous deployment of products.

Which SDLC Model is Best

As far I have understood, DevOps is the best model in today’s software ecosystem which provides a better development life cycle with high effectiveness and efficiency in work progress.  But it doesn’t mean rest models are not useful, they are also useful and still getting used by some organizations who feel that model is best in their work. DevOps is a practice of bringing development and operation teams together whereas, Agile refers to the continuous iterative approach, which aims at collaboration, customer feedback, small, and continuous releases. DevOps’ purpose is to manage end-to-end engineering processes. It helps in increasing an organization’s speed to deliver applications and services.  The agile purpose is to manage difficult projects. The agile development process divides the product into smaller pieces and integrates them for final testing. It can be implemented in many ways, including Scrum, XP, etc.

The post Software Development Lifecycle (SDLC) Beginners Guide appeared first on Artificial Intelligence.

In terms of educational background, PayPal looks for candidates with programming, statistics, economics, and mathematics background with a focus on logical reasoning, data interpretation and a programming mindset.

PayPal, one of the largest online payment processing firm globally, has several data science teams working as Center of Excellence (CoE). For instance, Customer Support Intelligence focuses on research and development to solve NLP issues at scale. On the other hand, Global Data Science, Risk Analytics, Workforce Planning and Forecasting, Merchant & Consumer Analytics are focused on enabling Business/Risk/Compliance and work in a global construct.

We got in touch with V. Chandramouliswaran, Sr. Director, Global Financial Crimes and Customer Protection & Chennai Centre Head, PayPal India, to understand the hiring process for data scientists at the company. 

Skills Required

PayPal mainly focuses on two critical areas while hiring data scientists — functional skills and business. On the functional side, preference is given to candidates with machine learning skills, OpenCV and deep learning. “We also recruit many Business Analytics individuals for whom the data science functional experience requirement is less onerous,” said Chandramouliswaran.

On the business side, experience in payments, banking, risk, customer management, marketing experience is a huge plus.

In terms of educational background, PayPal looks for candidates with programming, statistics, economics, and mathematics background with a focus on logical reasoning, data interpretation and a programming mindset. “However, we have never focused on hiring only from top-tier institutes and are happy to provide opportunities to top talent with the right skill set and passion for solving large scale problems and driving global impact.”

“For us at PayPal, skills and educational background are not as important as experience and exposure,” added Chandramouliswaran. Candidates who have worked with big data, built data science models, solved pressing business problems are preferred.” We look to hire talent that constantly challenges us and pushes us to innovate every day.”

Interview Process

The interview process at PayPal consists of two significant steps:

Screening by talent acquisition and business teams: The first step is screening by the talent acquisition team, who spend about 20-45 minutes explaining the role and understanding the fitment. The business team then spends about 30-45 minutes explaining the roadmap, challenges and cultural fitment. 

Interview process: Post the screening, the candidate goes through an average of five rounds of interviews, each lasting 45-60 minutes covering the fundamentals of data science, ability to program and work with data, ML Libraries (for engineering DS roles) and also address a business case on analytics or model development.

Chandramouliswaran said one of the critical challenges in recruiting is the limited availability of talent for niche skill sets such as NLP and deep learning. Other challenges include a lack of understanding of the digital payments/fintech ecosystem’s nuances and strong business acumen. “Data scientists, in general, go around with a hammer in their hand looking for a nail, and that is the biggest challenge,” he said. 

Chandramouliswaran highlighted the typical mistakes in data scientists’ hiring process:

• Not doing due diligence and completing all key fundamentals of the hiring process. Late hire is always better than a wrong hire.
• Not following a consensus-driven approach.
• Not exposing the candidates to real-time business problems and consumer impacting decisions.

PayPal addresses such pitfalls by evaluating candidates based on their approach to solve real-time problems and implementing the solutions using a small dataset, and exposing the candidate to PayPal’s values, people, work and work culture. 

PayPal primarily relies on traditional ways to recruit data scientists, such as outbound efforts via job boards, campaigns, and referrals. Some of the non-traditional ways include connecting with potential talent during events, meet-ups and networking platforms.

PayPal posts most of the data science roles on its website. 

Interview Questions At PayPal 

The questions depend on the nature of the role. If the role has a heavy data science tilt, then the candidate would be expected to demonstrate a sound understanding of cutting-edge algorithms and will have to engage in a dialogue on ‘what’ works and ‘why’, explains Chandramouliswaran. 

For the Business Analytics role, guesstimate-type questions are asked. “Outside of these, there will be questions on the data skills that a person possesses, an ability to pull, scrub and analyse etc. Needless to say, the candidate needs to be comfortable with a programming language of his or her choice,” he said.

Being A Data Scientist At PayPal 

PayPal believes in inclusive hiring. The skill sets PayPal look for include: 

• Real-world data science problems solving skills
• Question the ‘why’ behind the problem and rejecting a one size fits all approach
• Strive to understand the business
• Always put the team ahead of themselves
• Identify themselves with PayPal’s values

A data scientist at PayPal is expected to carry responsibilities such as problem structuring, data preparation, model & strategy building, model & strategy validation, benefits measurement, and more. “A huge expectation is also in being comfortable with ambiguity. There have been many instances in which problems will be poorly defined, and having the requisite business background to engage in a dialogue to get to a well-defined and impactful problem statement collectively will be a differentiator,” he said.

Paypal provides ample opportunities for data scientists to grow through:

• Using real-time data to solve end-user problems
• Building landscapes that make technology more accessible for users who are not digital natives
• Working with large datasets that support critical decision-making
• Building consumer-friendly products and solutions

“With several data science functions located in India, careers can grow both laterally and vertically,” he said.

Pro Tips

For an analytics professional who wishes to carve out a career in the analytics industry, Chandramouliswaran advises them to be comfortable with data and numbers, and have an innate curiosity and problem-solving skills. “Building the right skill set through courses and competitions along with solving real-world problems will set the stage for people interested in analytics well on their way,” he said. 

The post Data Science Hiring Process At PayPal appeared first on Artificial Intelligence.

Data scientists at Bigbasket have to juggle multiple responsibilities since they are part of a small team supporting a growing and dynamic startup business.

The analytics and data science at Bigbasket is centrally located. Set up in 2013, the 10-member team works with various partners to focus on key business deliverables: analytics work products (reports, dashboards, deep-dives, etc. used by business teams directly) and data science work products (ML models, and OR models that power product features that benefit customers). The team also manages the end-to-end analytics ecosystem, including analytics infrastructure, analytics data pipelines, and deployment of analytical solutions and data science models. 

To understand more about the data science team and the hiring process, we got in touch with Subramanian MS, head of category marketing and analytics at Bigbasket. 

Required Skills

Subramanian said deep expertise and problem-solving skills are a must in an analytics professional. The expertise includes a solid understanding of algorithms, operation research and machine learning skills. “Some of the overarching traits we look for in all recruits include attention to detail, ownership and proactiveness etc. We believe these are key traits to succeed in the fast-paced, startup environment of Bigbasket,” he added. 

In terms of educational background, Bigbasket looks for engineering graduates with or without experience. As Subramanian shares, many of their team members have pursued and completed advanced machine learning and artificial intelligence programs before joining Bigbasket or, in some cases, while at Bigbasket.

Subramanian said some of the vital skills are problem-solving, analytical thinking, communication skills, attention to detail, ownership and proactiveness. Educational background is further used as a filter to shortlist candidates.

Interview Process 

Subramanian detailed the interview process for candidates, both experienced and fresher. 

The interview process for experienced candidates:

• SQL test since it is a critical skill used every day by analytics and data science professionals
• Setting up interviews with other team members to understand the cultural and experiential fit

The interview process for fresh hires:

• An assessment focused on understanding a candidate’s analytical, reasoning and verbal skills 
• Setting up interviews to help the candidate understand more about the opportunity and the interviewers to assess if the candidate will be a good fit

The traditional methods Bigbasket relies on recruiting are campus hiring, sourcing candidates through hiring partners and referral from Bigbasket employees. The non-traditional methods include a partnership with entities that offer online programs in analytics and data science, and social media, including LinkedIn.

The post Data Science Hiring Process At Bigbasket appeared first on Artificial Intelligence.

Insurance works with large amounts of data, about many individuals, many instances requiring insurance, and many factors involved in solving the claims. To add to the complexity, not all insurance is alike. Life insurance and automobile insurance are not (as far as I know) the same thing. There are many similar processes, but data and numerous flows can be different. Machine learning (ML) is being applied to multiple aspects of insurance practice.

Insurance is about risk. The insurance industry sets rates based on expected payouts so that, hopefully, they end up with positive revenue. Setting rates and understanding payout in order to maintain profitability is complex, and the industry hope is that ML can help in achieving that goal. Note, here, I’m focusing more on ML than artificial intelligence (AI), because many of the complex statistical tools that are now considered ML can more efficiently accomplish some of the tasks than would neural networks, expert systems, or other purely AI tools.

There are multiple ways machine learning can help in the insurance industry. Let us take a look at three.

Insurance Underwriting

Health and life insurance are complex. There are multiple factors that go into understanding an individual’s risk factors for disease, illness, and mortality. Insurance underwriters have historically used a core set of factors such as male/female, age, and smoker/non-smoker. When other factors have been used, such as zip code, the problem of red-lining has appeared in insurance as well as the more well-known area of financial red-lining. Therefore, there are regulations about how some demographic information must be used.

The post Machine Learning Can Help The Insurance Industry Throughout The Process Lifecycle appeared first on Artificial Intelligence.

We are living in a defining moment in history when businesses need a reformed approach—with emerging technology maturing and consumers expecting a faster pace of delivery—teams are overworked, and agility has become a mandatory requirement. As a result, the role of finance and accounting is evolving to support these tremendous changes.

In 2019, the IBM Institute for Business Value published the report “The enterprise guide to closing the skills gap” in which it indicated a staggering “120 million workers in the world’s 12 largest economies might need to be retrained/reskilled in the next three years as a result of intelligent/AI-enabled automation.”

As CFOs implement plans to prepare their teams for the future, finance and accounting professionals are under pressure to enhance their value offering and reduce costs while acquiring new skills.

Emerging digital technologies provide the finance and accounting function with a path to fulfill these objectives while meeting business demand for advanced analytics, efficient operations, and strategic decision support.

Robotic process automation

Robotic process automation (RPA) presents a clear and sustainable avenue to transforming the finance function.

Although several digital tools can be leveraged to automate finance and accounting processes, RPA is currently recognised as one of the few emerging technologies capable of automating a significant amount of finance and accounting end-to-end processes.

The importance

In a recent RPA webinar hosted by IMA, attended by nearly 1,500 finance and accounting professionals from all around the world, 34 percent of participants acknowledged RPA would be the emerging technology with the most significant impact on the profession in the next three years.

In India, RPA is bound to create new sets of job opportunities for people.

According to a recent report, the RPA market in India will grow at a CAGR of above 20 percent during the forecast period of 2019-2025.

The report says the RPA market in the country is driven by the increasing demand for automated accounting and process management.

Further, to ensure automated transaction processing improves over time, RPA vendors are also focusing on developing best-in-class intelligent process automation bots that learn as they work.

Businesses that have incorporated finance and accounting professionals into their RPA program have reaped the benefits of more robust automation solutions, less costly implementations, and improved employee satisfaction.

Unlike what some might think, RPA at scale—or fully-leveraged—could be a perfect solution for a small or mid-sized business with overworked finance and accounting teams needing relief and leaders seeking to elevate their limited resources’ offering.

By implementing RPA, start-ups can reassign their teams to more pressing matters once their schedules have been cleared of repetitive work. It could equally serve as a monumentally transformational initiative in larger enterprises where opportunities in other parts of the organisation may be brought to light.

Specific to finance and accounting departments, team members who learn of this technology, proactively train staff on RPA, and/or lead RPA programs, tend to gain more benefits, both professionally and organizationally, than those on the receiving end of automation solutions.

Organizations with finance and accounting functions that are equipped with business professionals who are cross-functionally trained find themselves far ahead of their peers with more time to focus on higher value-added tasks.

The historical nature of the finance and accounting function’s role dictates that many of its processes are repetitive and rule-based—two of the most critical criteria in identifying good RPA candidates. Therefore, it is not surprising that most RPA implementations begin in the finance and accounting department.

The Impact

As RPA is an emerging technology with one of the lowest barriers to entry, the impact of RPA on the finance and accounting function is two-fold:
Finance and accounting processes will be automated with RPA
Finance and accounting professionals can upskill with RPA
Misconceptions about RPA technology cross several extremes—from “It will automate all of our jobs” and “Only IT can implement it” to “RPA couldn’t possibly do what I do” and “RPA has no applicability to finance and accounting processes.”

Each of these misconceptions can be dispelled through knowledge of what RPA is and the actual capability of the technology.

Most RPA software are made up of three primary components: the bots, a bot manager, and a workflow design module.

The bots perform processes, the bot manager enables scheduling and allocation of developed processes, and the workflow design module is where processes are developed.

Although it is tempting to say—and is widely said—during an RPA implementation, people do not create bots. The truth is, they develop the processes that bots will perform.

In organizations that have made progress along the RPA journey, they operate in an environment where finance and accounting professionals work alongside human and digital co-workers. They receive data from bots and supply inputs to them for processing. This is a different world—the technology to make this a reality already exists and is currently in place in many enterprises.

Winding-up

As RPA vendors strengthen their native offerings and progress with integrating technology partnerships, the complexity of the processes digital teammates can perform with intelligent RPA will undoubtedly increase. And even though widespread democratisation of RPAis the concept of a bot for every employee, may still be far off, digital teammates are already on the payroll and leaders are gladly assigning them finance and accounting tasks.

The post Robotic Process Automation: Transforming the world of finance appeared first on Artificial Intelligence.

Thriving and sustaining in today’s digital age requires a startup to focus on business values of both customers’ and employees’ along with their expectations. Large, successful companies already realize their core competencies and services, establishing themselves as the go-to-market place for a particular outcome. But the startup ecosystem across the world is in its infancy, requiring to be more agile and nimble in the modern competitive business landscape. With that being said, disruptive technologies can be a key enabler for companies seeking to drive innovation and efficiency.

Not every technology may revolutionize the business functions but some truly do have the potential to disrupt the status quo and pave the way for new revenue streams. Though more and more organizations look to deliver innovative products and services to their customers and thrive in the complex and changing environment, they must explore disruptive technologies that are continuously advancing at a rapid pace and set to transform life, business, as well as the global economy.

Reconceptualizing Your Business with Disruptive Technologies

It is no wonder that technology is rapidly becoming an inextricable part of human life and business alike. Indeed, reports show that technology will have a significant prominence over the next coming years, and this is where startups must embrace it to become innovative and productive. Let’s go through some disruptive technologies that are already impacting the business world and how startups can leverage it for their betterment.

Artificial Intelligence

AI has the potential to process troves of data and come up with actionable insights in order to assist in the decision-making process within an organization. It can enhance decision-makers’ forecasting ability to improve business operations. AI can also be used to transform marketing from the roots. Leveraging AI-driven tools to improve different aspects of marketing, manage digital campaigns and automate other business processes can be advantageous for startups. Business leaders can also use AI as a reliable alternative to traditional data security solutions, and identify distrustful behavior on their company websites.

Robotic Process Automation

Moving great ideas from blueprint to reality in today’s business arena can be a daunting task. But once prepared to drive that level of innovation, companies require much effort and a shift towards advanced technologies. That is where RPA can help. It simply automates menial tasks, without any need for human intervention. While businesses have to deal with a voluminous amount of data but don’t have the means to fully capitalize on it, RPA can help them through understanding all the data mining processes and provide meaningful insight to reap much value.

Big Data and Analytics

Large enterprises have long been taking advantage of data analytics. However, for startups and SMEs, it is a whole different story. Small companies need to whetstone in on data streams that will help them reach their goals and meet their business objectives. Already, big data and analytics has had a deep impact on organizations’ ability to better advance their decision making, identifying areas to cut costs and allowing for massive economic gains. Startups can use this technology to unearth concealed opportunities, recognize trends and patterns, problem areas and strengths.

The potential benefits of such and other technologies are incredible, but they also draw a set of challenges that businesses need to be prepared for their impact. If business leaders think and move strategically in the face of continually evolving technology landscape, they can get the most out of their technology implementations and respond effectively to uncertain consequences.

Technology Can Move Startups Beyond Conventional Tactics

Today, the rate at which businesses are adopting new technologies is tremendous. Moreover, the time it takes for a new technology to reach mainstream business is expediting exponentially. The time has changed now when one would have to quote oneself as an innovator. With the evolution of new-age technologies, companies just need to realize their potentials along with business objectives and move ahead by integrating them accordingly. They must determine which technology is best suited for their business requirements and help propel business excellence.

If implemented appropriately, disruptive technologies can introduce an entirely new business model and a new growth market. They can also bring new opportunities for startups and help address unrealized needs. Embracing such technologies can also help in bolstering businesses’ visibility, their reach to the target audience, and offering a highly personalized approach.

The post DRIVING INNOVATION IN STARTUPS WITH DISRUPTIVE TECHNOLOGIES appeared first on Artificial Intelligence.

Many businesses today are struggling to analyze and extract full value from the wealth of data being generated and gathered daily. The challenge that lies with business problem owners – whether this is a C-level executive, analyst or even operations manager – is how to effectively understand their data to drive further business value and optimize processes.

They may have spreadsheets full of data and use simple data models to extract limited value, but how can they take this further? The answer lies with greater accessibility of machine learning through user-centric platforms. For the first time, this enables business problem owners—those with intimate knowledge of specific problems and their impact on operations—to connect advanced machine learning capabilities to business value.

The benefits are available to all
Machine learning has traditionally been viewed as requiring extensive resources, time and technical expertise, which often includes hiring data scientists – a highly specialized field where talent demand currently outstrips supply. Beyond this, data scientists are often too separated from a business problem to contextualize it and understand the full impact it has on operations.

Enter the citizen data scientists—employees not operating in dedicated data science or analytics roles, who can use a humanized machine learning platform to explore their data and easily deploy models to unlock the value it holds. Thanks to user-centric platforms, current employees can enjoy access to machine learning technology without the need for specialist training. This is a significant milestone in empowering data owners to quickly master their own data and complete operations at scale, without significant investment or expertise. At the company level, this puts advanced machine learning solutions into the hands of small and mid-sized organizations and their employees, who may be lacking data science expertise. But the increased accessibility of machine learning also generates fresh opportunities for data scientists, freeing up their time to get closer to business problems and focus their skill set on innovation for digital transformation projects.

New business capabilities  

A machine learning platform provides citizen data scientists with greater accessibility to the capabilities required to quickly prepare and visualize data, and subsequently build, deploy and manage a suitable model. Whether this involves suggesting actions to clean and correctly format data or recommending the most suitable model for a data set, a humanized platform is designed to guide users through the process from start to finish.

A core aspect of this approach is reducing the volume of mundane data preparation tasks. Think of business processes that are repetitive and involve analyzing data in a similar way on a routine basis, such as budget forecasting. Instead of tying up senior management resources for several weeks to finalize budgets based on expected business outcomes, managers can use an intuitive machine learning platform to quickly identify and set up a model capable of being reused to revise budgets annually – dramatically cutting the time investment in this process going forward.

Alternatively, take an advanced manufacturing company that develops and produces precision components. They may have machinery experts with decades of industry experience and a deep understanding of the data produced by equipment sensors – but they can’t identify patterns and areas for optimization without a dedicated data science team. With humanized machine learning platforms, these experts can input, cleanse and visualize data in minutes, then select an appropriate data model to uncover previously unseen insights.

Man meets machine: complementary capabilities

Machine learning platforms are intended to amplify existing employee skill sets. They remove a large amount of the time and resources traditionally invested into applying machine learning to business data, yet ownership and control of the process still lies with the user. This is key to successful use of machine learning technology.

Machine learning applications are excellent for risk assessment and management, and making data-driven judgement calls, but lack the intuition and creativity required to contextualize and problem-solve for human affairs. This is where humanized machine learning platforms draw the line between ‘human’ tasks and ‘computer’ tasks. They take on the labor-intensive, repetitive tasks such as data cleaning, data-driven model discovery, and model validation, and empower problem owners to focus their time and resources more directly on the business problem at hand.

Ultimately, the computer will always have to collaborate with a human when applying machine learning. To ensure project success, machine learning needs to form part of a human team, augmenting human skills, intelligence and capabilities. Humans have the unique capability to contextualize data and associated errors. Take a simple example where error codes are present in a large data set. A machine learning platform will struggle to contextualize this, but a human who is close to the business process can quickly provide an explanation, such as sensors being out of range.

Beyond the immediate benefits, machine learning platforms solve the issue of legacy once a citizen data scientist leaves the company. These employees can develop machine learning solutions to solve specific business problems, secure in the knowledge these accomplishments will still be operational, intuitive and reusable by colleagues once they have moved on.

Machine learning is now viable for every business

Machine learning is set to become increasingly common among businesses of all sizes as they push to optimize their daily operations. Don’t forget, business problem owners will always have a unique and intimate knowledge of a specific problem and its relevance to existing business priorities. For the first time, they can directly identify and enhance the value of their data by quickly harnessing machine intelligence at scale.

Applying machine learning to data no longer needs to be an arduous, resource-consuming project spanning several months. The rise of citizen data scientists is bringing significant opportunities for smaller and mid-sized businesses to quickly harness advanced machine learning capabilities to unlock greater insights and business value from their data.

The post Machine Learning Is No Longer An Arduous Process appeared first on Artificial Intelligence.