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Category: Analytics

How Business Analysts Can Identify and Reduce Sales Gaps?

Posted on by Imarticus Learning

 

Sales are worked for and don’t happen by chance. The marketing and sales departments need the sales funnel, the pipelines and effective strategy to translate them into currency. Your Business Analyst can help you grow your sales as these great marketers show you the way.

Why sales persons need a BA?

CEO Warren Kurzrock, of Porter Henry a sales-training consultancy says that salespersons don’t have enough time to track their activities, leads, and efforts. The solution lies in the statement of Aventri’s market strategy director John Kearney that you will be able to spot patterns and figure out why your leads turn into a loss, win or no go when you look through the data analysis lens and analyze performance. It helps you measure performance in terms of a lose-win ratio during the whole cycle of sales.

Motivation through effective reviews:

In the words of CEO Matthew Cook of SalesHub, it is the sales team’s motivation that drives your bottom-line, productivity, and company culture. If this is true then BA can drive the levels of sales-adrenaline up through effective reviews to help conversions. Who needs better motivation?

The gap-analysis puzzle:

Consultant Catherine Yochum at ClearPoint stresses the need to extrapolate the present state to the future goal to understand the process and resources to scale them to the future. The first step to improve your sales process through analysis is to conduct a gap analysis. This is the process of reviewing your resources and processes and predicting how they will scale into the future. 

Chief Editor Aaron Orendorff of Shopify Plus advises a gap analysis of potential Vs performance could outstrip expectations. They turn businesses to finding the right factors of success rather than dealing with solutions to problems. That for you is a solution that can be scaled and extrapolated.

Solve the cold-leads issue:

B2B marketer Amanda Nielsen of New Breed Marketing specifies the solution lies in attaching weights to qualify leads potential into levels like 

  • Marketing-Qualified ones worth the investment
  • Sales-Qualified ones ready-to-reach
  • Worth-talking-to opportunities

Opportunities mean clients. And clients mean business and brand-ambassadors.

Copy-what-works strategy:

The whole purpose of BA is to find the winning strategy which reduces leads loss, generates better strategies and provides you with insights and training on why errors occur and how not to replicate them. A satisfied customer is akin to asset acquisition.

Empower your staff:

Motivation runs high when you can forecast efficiently and juggle the leads to improve the sales funnel through an effective strategy. BA provides your sales and marketing team with the tools to succeed. It shows them how to achieve and equips them with the required timely knowledge and strategy.

In conclusion, BA sets the pace, helps you with timely strategy, reviews, causes for failures and helps literally set the timetable for work. That’s as easy as it can make your job. One must, however, being in sales convert those leads with people-skills that only the human touch can add value to.

NLP in Insurance Trends and Current Application

Posted on by Imarticus Learning

Today the insurance industry is on the disrupt cusp having embraced NLP, text analysis and AI just like the customer-service and legal industries. The large volumes of data generated by insurance companies with their various products, a large number of marketing channels, a massive customer database, and a spread of market over diverse geographies is astounding. This data has of recent been leveraged to provide meaningful trends, data and insights that are transforming, simplifying and improving business in areas like claims, customer-service, product planning and management, marketing, pricing and everything in between.
Trends detected:
The Everest Company reports that analytics tools from third-party vendors are anticipated to grow four-fold by 2020. The value of the NLP market globally will be a whopping 16 billion$ by 2021 and tech titans like Salesforce, Google, Intel, Yahoo, and Apple already a large part of the investors.
Benefits of NLP to the Insurance industry:
Some of the accruing benefits are

  • Meaningful data streamlining to the proper agent or department immaterial of geographical location is now a snap.
  • Decisioning in various departments and by the agents is enabled by ensuring timely accurate and meaningful data helps them plan better while improving the C-Sat scores and user experiences.
  • SLA delivery and response times are reduced improving customer services and their experiences.
  • Fraudulent, multiple claims and account-activity can be effectively monitored and detected at the earliest.

The following segments of the Insurance chain benefit greatly

  • Policy underwriting, maintaining and actuarial
  • Relationship management of channels, clients, claims, finance, and HR.
  • Security, fraud and corporate management.

Challenge areas and improvements seen:
Text analysis and NLP is the new buzzword with virtual assistants, chat-bots and such are replacing the personal touch and face-to-face interaction. This has helped the market grow as it reaches out to the masses and improves response times of queries, policy issue times, generation of reports and receipts and more that mean better customer-service and experiences.
Enterprise data access across geographies is a click away with adaption to NLP. Health data, customer profiling, cashless treatment facilities, smart recommendations of policies and such are examples of the betterments seen in the insurance sector brought about by data analytics, NLP and conversational interfaces like Google, and smart use of data to grow the business.
Channel management is another area where proper allocation and tracking of the various channels have improved by digitization, use of text analysis, and NLP across agents, digital channels, direct sales and brokers involved. Better products based on customer preferences, insights into improving marketing channels, training of agents, workforce allocation, policy servicing, and many more areas have changed and benefitted.
Customer retention was a huge challenge that has improved considerably with technological adaptations. Faster claim analysis and use of captured data for verification have been a contributive factor. Quicker underwriting, informed actuarial practices, better policy management, elimination of large workforces and insurance jargon, reduced labour costs, usage of data in daily transactions and better tracking have been some of the huge payouts the insurance industry benefits from through such embracing of technology.
Fraud detection and multiple claimants went undetected for long and are almost 10% of the European claims according to Insurance Europe. That has changed dramatically now with technology to prevent frauds and cyber security using the latest blockchain technology with AI, NLP and text analysis.
In parting data which is effectively democratized, analyzed and used can actually improve business value and customer retention through better experiences. The NLP and technology of text analysis are responsible for the disrupt that is present in the insurance sector.

Future of Big Data Hadoop Developer in India

Posted on by Imarticus Learning

In this era of electronic and digital devices, most people are using Big Data, ML, AI and such without really understanding what goes on to provide those services. Data is at the very center of any application and the sheer volumes of data generated, the variety of sources and formats, the need to manage, clean, prepare and draw inferences for business purposes and making decisions is being used extremely widely. And this spawning of data, means the projects involve Big Data and that technology has to evolve and changes to manage it. This also indirectly implies the need for Hadoop developers. The relationships are symbiotic and spur growth in each other’s needs.

Why Choose Big Data Hadoop As a Career

• Since data is an asset people trained on handling the large amounts of data performing analytics on it and providing the right gainful assets for business decisions are also fast being considered invaluable assets.
• Those employees who do not re-skill to include managing Big Data face the risks of getting laid off. For example, TCS, Infosys, and many other data giants laid off nearly 56,000 people in just one year.
• 77% of the companies and verticals across industries are adapting to use Big Data. Thus many are recruiting data analysts and scientists. Even the non-IT sector!
• The payouts are second to none in the category and a large number of aspirants are taking up formal Hadoop careers, both newbies and those changing careers mid-way.
• Data is growing and will continue to be used even in the smallest of devices and applications creating a demand of personnel to handle Big Data.

The Hadoop Career Choice

Pros:
• Big data applications and demand for trained personnel shows tremendous growth.
• Job scope is unending since data continues to grow exponentially and is used by most devices today.
• Among the best technology for managing Big Data sets Hadoop scores as the most popular suite.
• The salaries and payouts globally are better than for other jobs.
• Most verticals and industries, a whopping 77%, are switching tracks to use Big Data.
• Hadoop is excellent at handling petabytes of Big Data.
Cons:
• Your skills need to be of practical nature and constantly updated to keep pace with evolving technology.
• You need a combination of skills that may require formal training and is hard to assimilate on your own before you land the job.

How to Land that Dream job

Today it would be exceptional if a company does not use Hadoop and data analytics in one form or the other. Among the ones that you can easily recollect are New York Times, Amazon, Facebook, eBay, Google, IBM, LinkedIn, Spotify, Yahoo!, Twitter and many more. Big Data, Data Analytics, and Deep Learning are widely applied to build neural networks in almost all data-intensive industries. However, not all are blessed with being able to learn, update knowledge and be practically adept with the Hadoop platform which requires a comprehensive ML knowledge, AI deep learning, data handling, statistical modeling and visualization techniques among other skills.
One can do separate modules or certificate Big-Data Hadoop training courses with Imarticus Learning who provide such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
Doing a formal Hadoop training course with certification from a reputed institute like Imarticus Learning helps because: 
• Their certifications are widely recognized and accepted by employers.
• They provide comprehensive learning experiences including the latest best practices, an updated curriculum, and the latest training platforms.
• Employers use the credential to measure your practical skills attained and assess you are job-prepared.
• It adds to your resume and opens the doors to the new career.
• Knowledge in Big Data is best imbibed through hands-on practice in real-world situations and rote knowledge gained of concepts may not be entirely useful.
The best courses for Big data Hadoop and Advanced Analytics are available at the IIMs at Lucknow, Calcutta, and Bangalore at the IITs of Delhi and Bombay. This is an apt course for people with lower experience levels since their curriculum covers a gamut of relevant topics in-depth with sufficient time to enable you to assimilate the concepts.
The Big data training courses run by software training institutes like Imarticus are also excellent programs which cost more but focus on training you, with the latest software and inculcating practical expertise. Face-to-face lab sessions, mandatory project work, use of role-plays, interactive tutoring and access to the best resources are also very advantageous to you when making the switch.
Job Scope and Salary Offered:
Persons with up to 4 years experience can expect salaries in the range of 10-12 lakhs pa at the MNCs according to the Analytics India Magazine. Yes, the demand for jobs in this sector will never die down and is presently facing an acute shortage.
Hadoop Course Learning:
You can use online resources and do it yourself using top10online courses.com. However, formal training has many advantages and is recommended. Join the Hadoop course at a reputed institute like Imarticus Learning.
Hadoop has a vast array of subsystems which are hard to learn for the beginner without formal training. The course helps you assimilate the ecosystem and apply these systems to solving real-world industry-related problems in real-time through assignments, quizzes, practical classes and of course do some small projects to show off your newly acquired skills. The best part is that you have certified trainers leading convenient modes and batches to help you along even if you are already working.
The steps that follow are the Hadoop progressive tutorial in brief.
• Hadoop for desktop installation using the Ambari UI and HortonWorks.
• Choose a cluster to manage with MapReduce and HDFS.
• Use Spark, Pig etc to write simple data analysis programs.
• Work on querying your database with programs like Hive, Sqoop, Presto, MySQL, Cassandra, HBase, MongoDB, and Phoenix.
• Work the ecosystem of Hadoop for designing applications that are industry-relevant.
• Use Hue, Mesos, Oozie, YARN, Zookeeper, and Zeppelin to manage your cluster.
• Practice data streaming with real-time applications in Storm, Kafka, Spark, Flume, and Flink.
• Start building your project portfolio and get on GitHub.
Conclusion:
In parting, India and the bigger cities like Bangalore, Hyderabad, and Mumbai are seeing massive growth in the need for Hadoop developers. You will also benefit from a Hadoop training course in Data Analytics and it is worth it when your certification helps you land the dream career you want. So don’t wait. Take that leap into Hadoop today!

10 Interesting Facts About Artificial Intelligence!

Posted on by Imarticus Learning

Artificial Intelligence has received a lot of focus and attention in the last couple of years. There has been a boom in the innovations that have artificial intelligence at its base. Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.

Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms that mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails. You can also see the use of machine learning in e-commerce websites that use it to personalize the search or use of the web experience of their customers.

It is interesting to comprehend the capabilities of machines. Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning, or performing a task as intelligent systems.

There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth. Imagine the impact it can have on medical procedures, the continued support it could lend to the disabled, increasing their life expectancy.

Artificial intelligence is a technology that can improve the world for the better, however, it also comes along with some challenges such as machine accountability, security, displacement of human workers, etc.


But right now before the possible alarming impact of artificial intelligence, we could in the today, the now, enjoy learning about some interesting facts.

 

Interesting Facts About Artificial Intelligence

  1. It is interesting to note that research on artificial intelligence is not only a few years ago, but the inception of AI also goes back to the 1950s. Alan Turning is coined as the father of AI, back in the day he invested a test based on natural language conversation with a machine.
  2. Did you know that a lot of video games that engage humans over time are based on a technique of artificial intelligence and is called Expert System? This technique is knowledge-based and can imitate areas of intelligent behavior, with a goal to mimic the human ability of senses, perception, and reasoning.
  3. Autonomous vehicles are no longer a thing of the far future. The knight rider might actually become a reality in as close as the next 2-3years or less. These cars are based on artificial intelligence to recognize the driving conditions and adapt the behavior. These cars are in the test phase, already developed and almost ready to hit the road.
  4. There is a race that is warming up between social media corporations over perfecting the use of artificial intelligence to enhance the customer experience. Facebook and Twitter are two companies essentially applying AI to match relevant content to the people. Leading this race is Google, coming across as one of the most preferred and reliable search engines.
  5. IBM has created a supercomputer based on AI, called Watson. One of the major challenges of creating Watson was the programming that needed to be done so that it could understand questions in most of the common languages and the ability to attend to those questions in real-time. The development is such that currently Watson is not only applied in various industries but was recently successful in teaching people how to cook.
  6. Sony created a robotic dog called Aibo, one of its first toys that could be bought and played with. It could express emotions and could also recognize its owner. This was the first of its kind, however, today you will find more expensive and evolved versions of the same.
  7. At the rate at which Artificial Intelligence is being adopted in various areas of our lives, it is predicted that it will replace 16% of our jobs over the next decade.
  1. Artificial Intelligence Training CoursesIt is a fact that with increased intelligence and ability to perform tasks with accuracy, over the next few years it is predicted that close to three million workers will be reporting to or will be supervised by “Robot-bosses”.

    With Machine learning and language recognition, it is no surprise that 85% of telephonic customer service jobs will be performed by computers and will not need human interaction.  By the dawn of 2020, it will be possible for all customer digital assistant to recognize people by face and voice.

Organizations and private sectors have recognized the opportunity that AI investments can have on the future of their businesses. Hence have set up major investments in the development of the same.

Finally, one must remember the anticipated impact of AI is on calculated assumptions and predictions.
However, one thing is clear, that AI in the future will impact the internet, its citizens, and economies.

Read More 
The Promises of Artificial Intelligence: Introduction

What is Machine Learning and Does It Matter?

Posted on by Imarticus Learning

Learning can be explained as a process which improves performance from experience, an extension to this definition would be, the process of Machine Learning (ML) which can be explained or defined, as a method through which computer programs, that habitually or spontaneously improve their performance through experience.

This would basically translate into machine’s learning to improve their performance based on limited programming interventions. ML can be considered as an extension to Artificial Intelligence, which believes that Machines should be able to adapt and learn through experience.
ML is not a new innovation and has been around for years, however with new computing technologies, ML has evolved, most of the algorithms have been around, however, the ability to apply complex algorithms to big data, in a loop and more rapidly, is a recent development.

ML is quite integrated in our everyday lives, so much that we might not be consciously aware how frequently we are using the application. For example, there is great excitement over Google’s Self Drive Car, a product of ML. Spam emails being diligently dumped away, or frequent recommendations while shopping online, or offers from particular brands of your interest being brought to your notice, are all direct outcomes of Machine Learning.

Besides the basic applications, more recent complex uses of ML would be early fraud detection in banking, a lot of businesses are able to have a consolidated look at what their customers feel about them, emotional and sentimental analysis is possible through data mining techniques, again a direct product of Machine Learning.

In current times Machine Learning matters, for the possibilities and advantages it offers. There are growing volumes of data available to us easily, computational processing is cost effective, and we have better data storage opportunities, all this indicates that we are right in the center of exciting times, where, we will be able to analyse bigger and complex data faster and more accurately.

Which directly means that organisations will have a better vantage point to make informed decisions, leading to better profits and avoiding risks.

The Advantages of Machine Learning

With a good investment of time in creating training data for machines, learning can then be expedited through experience and learning through algorithms. Implementation and automation then become easy for machines, upon learning, a machine can process several images without any fatigue as opposed to a human brain, which might deliver data with errors.

With good training data input and intelligent processing, with an accurate algorithm, the output can be phenomenal. Hence it is believed that big data and Machine Learning is a great combination, opening doors to various opportunities.

Application of Algorithms in building models may expose links which can help an entity make better decisions with minimal human interventions, keeping biases away.

Most organisations in recent times have understood the importance, benefits and value of Machine Learning technology, as most insights from the available data can be received in real time, hence giving companies an edge over their competitors, and assisting them in better aligning their needs with those of their customers.

Due to these paybacks, application of Machine Learning can be seen in Financial services, Healthcare, Marketing and Sales, Transportation and logistics, Government agencies like Utilities and Public Safety.

So while machine learning has many advantages, it has a few challenges, however, the benefits of the application outweigh the limitations. The ability to decipher big data, with minimal programming, faster and accurate results in real time, will see Machine Learning be applied in various aspects of our daily lives.

7 Skills That Data Scientists Need To Know Via Big Data Analytics Courses

Posted on by Imarticus Learning

Data analytics is one of the most sought-after careers of today. Being a good data scientist involves developing a lot of skills essential to the job.
Here are a few skills you need to have on your resume if you want to become a good data scientist:

1.  Being capable of handling unstructured data

Unstructured data refers to any data that cannot be made to fit into any database tables. This data can include customer reviews, audio clips, blogs, posts, or even videos. Arranging such data into specific categories can be quite the daunting task. As a data scientist, you must be able to work with a lot of unstructured data. Some software that you need to know how to use for this purpose are NoSQL, Microsoft HDI insight, Polybase, Apache Hadoop, Presto etc.

2.  Good knowledge of Mathematics and Statistics

A good understanding of statistics is essential for anyone looking to become a data scientist. You must be familiar with all kinds of statistical concepts such as distributions and tests. Also, making predictions requires that you are familiar with the basic operation of calculus and linear algebra.

3.  Using data to tell a story

It is always easier for clients to understand data analytics if it is presented in a visual format using graphs, charts etc. Therefore you must have the capability to visualize raw data in a form that the layman can understand.

4.  Programming Skills

As a data scientist, you will be working with a lot of software that will require you to enter the code manually. As such, you must have a good knowledge of programming languages such as R and Python, which are normally used in data analytics. You must be able to write, understand and correct any code no matter the circumstances.

5.  A Competitive Spirit

As a data scientist, you will have to work on your toes more often than not. Therefore, it is essential for you to have a competitive spirit that will help you thrive. Hackerearth and NMIMS are two of the platforms that conduct hackathons, seminars and other competitions where you can gain more knowledge and understand all the latest trends in data analytics.

6.  Working on Projects

You must take up some live projects so that you get some hands-on experience in the field. This is important since most companies are looking for data scientists who are experienced in the field.

7.  Academic Qualifications

Most companies prefer their data scientists to have done their master’s degrees in the fields of computer sciences, mathematics, statistics and physical science. If you’re interested in working with research companies, then it will be advantageous to have a PhD in the same subjects.

How To Cope When The Project You Are Leading Fails?

Posted on by Imarticus Learning

 
Everyone fails at some time or the other. Failures are meant to be dealt with and learned from. Yet, there isn’t a single training course that deals with fears of failure, coping with failing projects or handling failures at work. And this, despite the fact that project failures cost money, maybe losing a client and leaves you despondent and feeling completely at sea. So read on to discover the simple path.

Realization and acceptance of failure:   

Admit the failure and accept the fact. Don’t run away from it and blame others. After all, as a Project Manager, you will realize that there are many factors leading to the failure of projects. Face it that you are not alone and solely responsible. Failures can happen to all of us. How you walk-on is more important than how you celebrate project success.

Know when to let go:

Never get stuck with the sinking boat. There is only the drowning way out if you don’t bail out in time. The tell-tale signs of reduction in buy-ins, missed timelines, apathy from the senior management, costs incurred so far, and last but not the least your ego and knowing when to let go.

Review and review constructively:

The project has failed and it is now your failure. So get to the task of a constructive passionless review of where, why and when the train got off the rails. Take responsibility for your team and the failed project. In part, it is your failure!
Were deadlines missed, were resources competent and timely, were there any tell-tale signs of off-roading, what exactly have you failed at? Well, seek and you will find. Recognize your mistakes and be sure that they will ensure you don’t go the path of failure again.

Bias will color your sights:

Reviews often seek reasons for project failure. Seek them without bias. Have an outsider audit the process, don’t look for reasons that confirm the easy way out, and avoid blaming others when things don’t go, as you planned. If you knew along it was doomed, why did you not act? Optimism is positive when in moderation as is bias and playing the blame game.

Don’t play the blame-game:

The PM has to bear the brutal brunt of a failed project. It’s the job, not the person that is being blamed. Check to see what lessons you can learn, what were the signs you did not see, how can you prevent the same one happening again, how can you get your team up and running, what really was your role in the error. Now that it has happened, move on. Do what you can as the PM and do it best, is a great policy to follow, now more than ever!

Plan to better implement projects rather than produce better products:

Let your focus be on doing things differently the next time around. As leader of the team, concentrate on implementation practices rather than a high-velocity release of products. Your team will produce results when you lead from the front and help implement projects better on time and within the budget each time, every time. There will always be so much beyond your control to complain about another day.
In parting, let’s remember failure is a great teacher. Great lessons are learned and success implies always trying to gain control even beyond what you can actually control.

AI for IT Services Firms Backup Recovery And Cybersecurity

Posted on by Imarticus Learning

The coming of the Age of Artificial Intelligence is an apt way to describe how IT services like cyber-security, recovery of data and backup of data has been impacted by AI developments globally. Any event on cybersecurity throws up newer requirements in cyber-security and a bouquet of innovative solutions using artificial intelligence, cloud storage, and data recovery tools.
Virus detection is a challenge-area that about 29 percent of surveyed professionals look to AI for as per ESG research. Besides speedy discovery, 27 percent of the surveyed professionals in the security field look to AI to hasten the response time of reported incidents.AI is being touted as today’s technological marvel that can analyze huge volumes of code in very short time periods. This is rightly true and the mind-boggling speeds and analysis of data have made AI all pervasive and the panacea to almost all ills in the IT sector.
AI Vs ML:
The terms AI and ML are being used oft interchangeably. In reality, both are useful tools that differ in their thinking abilities. ML uses algorithms to detect breaches in security which restricts their use to think outside the set framework. On the other hand, AI does not need algorithms or any further data when it comes to terms with any issue. It arrives at an unassisted intelligent conclusion.Both AI and ML techniques are the focus areas of dealing with advances in cyber threats. These techniques when applied can transform the scenario from defence to early detection and quick response to cyber infections.
Areas, where ML makes a huge difference, are

  • In scouring huge data volumes across thousands on nodes looking for potential threats.
  • In firewall applications, gateways, and APIs where traffic patterns need to be analyzed.
  • In classifying data objects and governance of data
  • In access-control and authorization systems and practices using auto-generate policies, and analysis of the regulatory measure, rules etc
  • In detecting anomalies and setting baselines for user behavioural analysis and SIEM events of cybersecurity.

Hacker’s too can use ML and AI:

Alongside the new developments in AI, cyber-security, and ML there is a the all real possibility of hackers also using the same infection-detection technology and malware samples to advance the technology of cyber-threats. It is reasonable to predict that the very same techniques are used by hackers to create modified code-samples depending on the way AI detects infections. This then leads to a situation where the infections last longer and since the code is smaller it becomes near undetectable.

Storage challenges and Cybersecurity:

The feasible option for safe storage of data today is by backing data to a reliable disaster recovery cloud which enables rapid recovery of files while ensuring the data stays protected, safe and encrypted. The market has many technological options like Avast, CA Technologies secure, Keeper, etc that can help keep data copies out of reach by hackers and yet available on an easy-to-use platform.
In conclusion, the use of ML and AI can help resolve issues and challenges faced in cybersecurity, data recovery, and storage. The evolution of threats and detection techniques continue in tandem in a seemingly unending fashion where users and hackers are both looking to AL and ML for solutions.

How The IT Capital Is Perfect For Careers Beyond IT

Posted on by Imarticus Learning

The one advice most people get when they start out in their careers is to find a job that contributes to the top line of a company. To find something in the front row and not in a support function. Because when push comes to shove and the company is restructuring, support is often the first to go. Support functions also get the least amount of attention with regards to innovation and IT jobs often face the biggest brunt of all.
Some major IT support complaints include, “You don’t get the funding you want to do or the kind of work you want.” “No one takes you seriously.” “There is no opportunity for innovation, no need for groundbreaking technology and no requirement to be on the cutting edge of languages or coding.”
However, Investment Banking Technology is unique in this sense. In Investment Banking IT, it is critical to have a competitive advantage and being in IT starts to become less tedious because of the focus on keeping platforms relevant. And, they have the money!

The best part about IT in Investment Banking or any other data-heavy sector is that it’s a great diving board into Business Analysis.

Business Analysts are Change Managers. They try and understand the changing needs of an organization, gather data to validate that change and then understand the processes that will need to be implemented to meet that change and make sure that it’s implemented in a way that increases shareholder value. As a result, when working IT professionals from India’s silicon valley come to us for advice, we always recommend as a first option, that they look for Business Analysis Courses in Bangalore.

Business Analysts work across organizations and departments and can help in everything from Strategy – what needs to change, to implementation – let’s change it.

The definition of business analysis (according to the IIBA) is: “the set of tasks and techniques used to work as a liaison among stakeholders to understand the structure, policies, and operations of an organization, and recommend solutions that enable the organization to achieve its goals.”
Their focus areas cut across restructuring jobs by outsourcing, managing inventories, understanding how to spend on IT wisely and appreciating the growing role of technology in running corporations more efficiently.
A perfect case of Business Analysis is in outsourcing. A problem comes into the Business Analysts team – How do I cut costs in my Trading Department? The Business Analyst begins by gathering data about how the Trading Department works, what do they do, what can they do and how can you eliminate waste. They then try and understand the role of technology. Can the platform work better at eliminating waste? Where can this work better? Do I outsource this work? Would it be cheaper? What would the risks be? How do I transition a desk in Singapore to a desk in the Philippines as seamlessly as possible?
This is where IT professionals have the upper hand. Because of their comfort with dealing with large amounts of data and their ability to synthesize it, they evolve into perfect Analysts. The only difference being that they are doing it in a larger context.

The perfect Business Analyst will be the IT professional with excellent analytical abilities and single-minded focus on problem-solving combined with a strategic bent of mind that will not lose the forest for the trees.

This is what Business Analysis Courses in Bangalore such as BACP by Imarticus can help with. BACP or the Business Analysis Certified Professional is one of the few industry endorsed Business Analysis courses in Bangalore and one that is perfect for working IT professionals looking for careers beyond IT.

How to Become A Successful Data Analyst?

Posted on by Imarticus Learning

While the internet has overwhelming amounts of different options for learning how to become a Data Analyst, let us begin with the core understanding of what are the essential criterions for becoming a professional.

What is Data Analysis?

Let us first start with understanding what Data Analysis is. The term itself is self-explanatory. Data Analysis is the task of analysing different data from different sources and making it useful for different purposes.

What is the role of a Data Analyst?

So far you know what Data Analysis is, Let us understand the different tasks of a Data Analyst.
It involves a lot of numbers and algebraic functions. A professional Data Analyst assembles, processes, and implements statistical analysis of collected data. They make the accumulated data, useful. Data Analysts also help businesses and individuals in making profitable decisions. The analysts guide them in using the raw data and assist in making the raw data useful after applying specific algorithms and formulas.
Also Read: How to Differentiate Between Data Analysts and Data Scientists?

Skills required to become a Data Analyst

The primary, as well as significant skill requirements to become a successful Data Analyst, are extensive Technical Skills, Communication Skills and Attention to Detail. You also need to have a natural interest towards Mathematics and Statistics.

Technical skills to become a Data Analyst

  • Programming Skills: Statistical Language, Scripting Language and Querying Language
  • Data Visualization
  • Thorough knowledge of Databases
  • Understanding the applications of Data Mapping

You need to know some computer software. Learn statistical languages, such as SAS, R and Python; querying language, such as SQL, Hive and PIG and scripting language, such as MATLAB and Python.
To put it in a nutshell, you need to have the inclination of using data and making it ready for some purpose.

Academic qualification require

To begin with, if you are aspiring to become a Data Analyst, you need to have a degree in either one of the following disciplines: Computer Science, Mathematics, Statistics, Economics, Information Management or Business Information Systems.
Apart from the degree, your interest in the field also plays a very important role. Today, you may come across a wide array of courses that teach you how to become a professional Data Analyst. You may opt for MSc Business Analytics, MSc Data Science, MSc Data Science, MSc Big Data and Analytics. You can also opt for private institutes that offer short courses on Data Analysis. The future in this career also looks very promising.

Your responsibilities once you become a Data Analyst

It is crucial to know what you will be doing, once you have acquired your Data Analysis skills. If you choose to become a professional Data Analyst, the following are the responsibilities that you may have to partake.

  • It will be your role to analyze the company’s statistics, understand processes, drawback and the loopholes, and boost the efficiency of the process
  • You may have to manage the entire data of the company
  • You may have to initiate new process enhancements and ideas
  • It could be your responsibility to quality check the data and monitor it regularly
  • You may have to implement different tools to monitor different data, and evaluate them periodically
  • You may need to create survey data for different clientele

In the technically advanced age that we live in Data is surely the most crucial part in almost all industries. Therefore, the need for skilled and qualified Data Analysts is being demanded by all sectors. The future of a Data Analysts is very huge. Study and courses on data analysis are also being hugely in demand. From the financial and investment industry to the IT industry, the top companies are always on the lookout for competent Data Analysts. So, go on, prepare yourself for a brilliant future.
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