Webinar on Software Development Life Cycle (SDLC) - Overview

Imarticus Learning conducted a session on the Software Development Life Cycle (SDLC) and the role of a Business Analyst on July 8, 2016. SDLC is often used in systems engineering, information systems and software engineering to describe a process for planning, creating, testing, and deploying an information system. SDLC is also often referred to as (i) Systems Development Life Cycle or (ii) Application Development Life Cycle. Mr. Mukhraj Saberwal, Solution Architect at Accenture, conducted this online webinar session where over 75 users from the world of academia and industry were present.

Mukhraj went on to illustrate the five key stages in SDLC, which include (i) Requirement Analysis, (ii) Design, (iii) Implementation, (iv) Testing and (v) Evaluation, with special focus on the role of a Business Analyst. He broke the process down further into the following 10 stages of software development.

1. Initiation – This process begins when the sponsor identifies a need or an opportunity and creates a concept proposal.  
2. System Concept Development – In this stage, the scope or boundary of the concepts are defined. This includes (i) System Boundary Document, (ii) Cost Benefit Analysis, (iii) Risk Management and (iv) Feasibility Study. 
3. Planning – Next, a Project Management Plan provides the basis for acquiring the resources needed to achieve a solution. 
4. Requirement Analysis – Analyses user needs and develops user requirements with detailed functional requirement documentation. 
5. Design – In this stage, the detailed plans are transformed into complete, detailed systems. The design document focuses on how to deliver the required functionality.
6. Development – At this stage, the design is converted to a complete information system that includes (i) installing systems environment, (ii) preparing test cases, (iii) coding and (iv) refining programs. 
7. Integration and Test – This stage is undertaken by Quality Assurance staff and demonstrates that the developed system conforms to the requirements as mentioned in the Functional Requirements Document.
8. Implementation – This includes the implementation of the system into a production environment and resolution of problems identified in the earlier Integration and Test phase. 
9. Operations and Maintenance – Once the application has been implemented, it is important to prepare a document that describes the tasks to operate and maintain the information system in a production environment, including in-process reviews. 
10. Disposition – This stage describes the end-of-system activities and emphasis is given to proper preparation of data.

Now that the SDLC life cycle was clear, Mukhraj went on to explain the key types of SLDC methods, including Waterfall, Agile, Incremental and Iterative, with special focus on Waterfall and Agile.

The Waterfall Model is a sequential phase-driven approach that focuses on completing one step before moving on to the next. A formal handover is given from one function to the next and the entire project is delivered in one single big bang. The advantages and disadvantages of this methodology were discussed in detail.

Next, we moved on to the Agile or Adaptive Model, where the entire system is not built at once, but rather develops incrementally.  Unlike the waterfall model, less time is invested upfront to document requirements, since the development is done sequentially. The key characteristic of agile software development is to collect customer feedback, which happens simultaneously during the process of development and implementation.

Towards the end, Mukhraj spent a lot of time on the role of a Business Analyst in each of these SDLC methods. Right from planning to design, development and implementation – the Business Analyst plays a key role in the development of any software or application. Once his session was over, he answered over a dozen questions from our curious and enthusiastic BA aspirants.

At Imarticus Learning, we have 2 programs that focus on how you can become a Business Analyst and play an integral role in the SDLC. Business Analysis Certified Professional (BACP) is our 75-hour classroom program that can be undertaken at our centers in Mumbai, Bangalore, Chennai and Delhi. The other offering is the Certified Business Analyst (CBA) program, which is a 33-hour online course. To know more about either of these programs, send us an email at info@imarticus.org

What is Big Data?

We live in a world of data. This data is generated by transactions, feedback, and real-time interaction with customers, partners, suppliers, and employees.

Here are the 5 V’s of big data:

• Volume: This refers to the large amount of data generated every moment. Think of all the emails, Twitter messages, photos, video clips and sensor data that is generated and shared every second. Data is not just in terabytes, but zettabytes or brontobytes of data is generated. On Facebook alone people send 10 billion messages per day, click the like button 4.5 billion times and upload 350 million pictures each and every day. Taking all the data generated in the world between the beginning of time and the year 2000, it is the same amount that is now generated every minute. This keeps making data sets too voluminous to store and analyze using legacy and old database systems. With big data technology one can now store and analyse these data sets with the help of distributed systems, where parts of the data is stored at different places, connected by networks and brought together by Big Data software. 

• Velocity: This refers to the speed at which data is generated and the frequency at which data moves around. Think of social media messages going viral in minutes, Frequency at which credit card transactions are checked for fraudulent activities or the milliseconds taken by trading systems to analyze social media networks to interpret signals that trigger hints to buy or sell shares. Big data tools allows to analyze the data while it is being generated without the need of first putting it into databases systems and then analyzing it. 

• Variety: This means the different types of data we can use now. In the past our major focus was on structured data that properly fits into tables or relational databases such as financial data (for example, sales by product or region). 80 percent of the world’s data is unstructured format and therefore can’t easily be put into tables or relational databases—for example photos, video sequences or social media updates. With big data tools we can now harness differed types of data like messages, social media conversations, photos, sensor data, video or voice recordings and bring and analyze them together with more traditional, structured data.

• Veracity: This means the messiness or trustworthiness of the data. With many forms and types of big data, quality and accuracy are less controllable, for example Twitter posts with hashtags, typos and colloquial speech, abbreviations etc. Big data and analytics tools allows to work with these types of data. The volume often causes for the lack of quality or accuracy, but entire volume of fast moving data of different variety and veracity have to be turned into value. This is the reason why value is the one V of big data which matters the most. 
• Value: This means our ability to turn our data into value. It is really necessary that businesses make a case for any attempt to collect and leverage big data. It is easy to fall into the buzz trap and start embarking on big data initiatives without a clear knowledge of the business value it will bring.

 There are 3 reasons why we are generating data faster than ever:

• Processes are increasingly automated
• Systems are increasingly interconnected
• People are social and continuously generate data exhausts by interacting online

Data, in general, falls into 3 categories-

·         Business application data (e.g., SAP or Oracle ERP)

·         Human generated data (e.g., social media) and

·         Machine data (e.g., RFID, Log Files etc.).

In addition to this data, click and mobile business app based transactions, Human generated data — explosive growth of blogs/reviews/messages/emails/pictures.  The Twitter alone generates more than 7 terabytes — 10s of millions of tweets per day and is growing rapidly. Facebook is estimated to generate more than 10 terabytes a day. Social graphs such as product recommendations based on circle of friends, jobs you may like (linked in), the products you have looked at, people who are your contacts etc.

Big Data Analytics Use Cases

Big Data Analytics is used in almost every domain. Here are few examples

• Predictive Maintenance using sensors  — High-end cars use telemetry to know that an engine part is likely to break down before it actually does, based on the vibration or temperature patterns, a technique known as predictive maintenance. The idea is that a part does not fail all at once. Instead, it deteriorates over time until it eventually breaks. By monitoring the part real-time, you can spot problems before they become obvious. 
• Energy Management — Many firms are using big data for energy management, including energy optimization, smart-grid management, building automation and energy distribution in utility companies. The use case is centered around monitoring and controlling network devices, manage service outages, and dispatch crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network. 
• E-tailing – E-Commerce – Online Retailing Use Cases - E-tailers like Amazon.com are constantly creating target offers to boost customer lifetime value (CLV); deliver consistent cross-channel customer experiences; harvest customer leads from sales, marketing, and other sources; and continuously optimize back-end process orchestrations. 
• Recommendation engines — increase average order size by recommending complementary products based on predictive analysis for cross-selling.
•  Cross-channel analytics — sales attribution, average order value, lifetime value (e.g., how many in-store purchases resulted from a particular recommendation, advertisement or promotion). 
• Event analytics — what series of steps led to a desired outcome (e.g., purchase, registration) Right offer at the right time 
• Next best offer  – deploying predictive models in combination with recommendation engines that drive automated next best offers and tailored interactions across multiple interaction channels. 

 Source: https://imarticuslearningblog.wordpress.com/2016/06/21/information-about-what-is-big-data/ 

What is Big Data?

We live in a world of data. This data is generated by transactions, feedback, and real-time interaction with customers, partners, suppliers, and employees.

Here are the 5 V’s of big data:

• Volume: This refers to the large amount of data generated every moment. Think of all the emails, Twitter messages, photos, video clips and sensor data that is generated and shared every second. Data is not just in terabytes, but zettabytes or brontobytes of data is generated. On Facebook alone people send 10 billion messages per day, click the like button 4.5 billion times and upload 350 million pictures each and every day. Taking all the data generated in the world between the beginning of time and the year 2000, it is the same amount that is now generated every minute. This keeps making data sets too voluminous to store and analyze using legacy and old database systems. With big data technology one can now store and analyse these data sets with the help of distributed systems, where parts of the data is stored at different places, connected by networks and brought together by Big Data software. 

• Velocity: This refers to the speed at which data is generated and the frequency at which data moves around. Think of social media messages going viral in minutes, Frequency at which credit card transactions are checked for fraudulent activities or the milliseconds taken by trading systems to analyze social media networks to interpret signals that trigger hints to buy or sell shares. Big data tools allows to analyze the data while it is being generated without the need of first putting it into databases systems and then analyzing it. 

• Variety: This means the different types of data we can use now. In the past our major focus was on structured data that properly fits into tables or relational databases such as financial data (for example, sales by product or region). 80 percent of the world’s data is unstructured format and therefore can’t easily be put into tables or relational databases—for example photos, video sequences or social media updates. With big data tools we can now harness differed types of data like messages, social media conversations, photos, sensor data, video or voice recordings and bring and analyze them together with more traditional, structured data.

• Veracity: This means the messiness or trustworthiness of the data. With many forms and types of big data, quality and accuracy are less controllable, for example Twitter posts with hashtags, typos and colloquial speech, abbreviations etc. Big data and analytics tools allows to work with these types of data. The volume often causes for the lack of quality or accuracy, but entire volume of fast moving data of different variety and veracity have to be turned into value. This is the reason why value is the one V of big data which matters the most.

·         Value: This means our ability to turn our data into value. It is really necessary that businesses make a case for any attempt to collect and leverage big data. It is easy to fall into the buzz trap and start embarking on big data initiatives without a clear knowledge of the business value it will bring.

 There are 3 reasons why we are generating data faster than ever:

• Processes are increasingly automated
• Systems are increasingly interconnected
• People are social and continuously generate data exhausts by interacting online

Data, in general, falls into 3 categories-

·         Business application data (e.g., SAP or Oracle ERP)

·         Human generated data (e.g., social media) and

·         Machine data (e.g., RFID, Log Files etc.).

In addition to this data, click and mobile business app based transactions, Human generated data — explosive growth of blogs/reviews/messages/emails/pictures.  The Twitter alone generates more than 7 terabytes — 10s of millions of tweets per day and is growing rapidly. Facebook is estimated to generate more than 10 terabytes a day. Social graphs such as product recommendations based on circle of friends, jobs you may like (linked in), the products you have looked at, people who are your contacts etc.

Big Data Analytics Use Cases

Big Data Analytics is used in almost every domain. Here are few examples

·         Predictive Maintenance using sensors  — High-end cars use telemetry to know that an engine part is likely to break down before it actually does, based on the vibration or temperature patterns, a technique known as predictive maintenance. The idea is that a part does not fail all at once. Instead, it deteriorates over time until it eventually breaks. By monitoring the part real-time, you can spot problems before they become obvious.

·         Energy Management — Many firms are using big data for energy management, including energy optimization, smart-grid management, building automation and energy distribution in utility companies. The use case is centered around monitoring and controlling network devices, manage service outages, and dispatch crews. It gives utilities the ability to integrate millions of data points on network performance and lets engineers use analytics to monitor the network.

·         E-tailing – E-Commerce – Online Retailing Use Cases:

E-tailers like Amazon.com are constantly creating target offers to boost customer lifetime value (CLV); deliver consistent cross-channel customer experiences; harvest customer leads from sales, marketing, and other sources; and continuously optimize back-end process orchestrations.

·         Recommendation engines — increase average order size by recommending complementary products based on predictive analysis for cross-selling.

·         Cross-channel analytics — sales attribution, average order value, lifetime value (e.g., how many in-store purchases resulted from a particular recommendation, advertisement or promotion).

·         Event analytics — what series of steps led to a desired outcome (e.g., purchase, registration).

·         Right offer at the right time

·         Next best offer  – deploying predictive models in combination with recommendation engines that drive automated next best offers and tailored interactions across multiple interaction channels.

Source: https://imarticuslearningblog.wordpress.com/2016/06/21/information-about-what-is-big-data/ 

Business Analysis is Financially Rewarding

Business Analysis is the set of tasks and techniques used to work as a liaison amongst stakeholders in order to understand the structure, policies and operation of an organization, and to recommend solutions that enable the organization to achieve its goals (BABOK v2.0).

Every prospective business analyst wants to know if the career chosen is financially rewarding. Yes it is and we will come to that in a minute. What we first need to clarify is that there is no ‘One Business Analyst that Fits All’. The role of a business analyst can take many forms and this depends on the organization’s structure and its requirements. In fact, what is a ‘Business Analyst’ in one company could be a ‘Systems Analyst’ in another.

The median household income in America across all jobs was $52,000 per annum. In contrast the median salary of a Business Analyst in the US is around $70,000 per annum. The salary trend for a BA is more or less same in every major geography. For instance, the average salary in UK is about £26,500 per year whereas that of a Business Analyst is £39,000 per year, or 47% more than the national average pay.

As with most professions, the higher the experience, the higher the salary of a business analyst. Next, where you work is also a major factor in deciding your BA salary. The below table gives you a good idea of the salaries based on experience and geography.

Well, we have only discussed about the financial benefits of being a business analyst but the list doesn’t stop here. There are scores of other benefits of being an analyst which we will discuss in another article.

It is clear that there is a growing demand for Business Analysts the world over. One of the best ways to distinguish yourself from the competition is to get trained and certified in business analysis. 

Imarticus Learning offers short term certification courses in Business Analysis, that culminate with an industry endorsed Business Analysis certificate. The program also prepares you to clear the CBAP exam, which is the gold standard for experienced Business Analysts. To learn more, please click here.

So You Want to be a Business Analyst?

Pursuing a career in a fast paced, in-demand field, such as business analysis, may not seem like a challenge; however, competition for business analyst positions is pretty tough.

Even the most senior business analysts, the ones who qualify for the CBAP based on their years of experience across multiple knowledge areas, would probably qualify for less than the 50% of the business analyst job roles available in the job market today. They simply don’t have all the required skills.

If you are making a career transition, the stakes go even higher. Don’t expect to qualify for more than 20% of business analyst job roles.

At first this might sound hopeless. But let it sink in. Doesn’t that take the pressure off just a little? If one or two jobs stand out to you as, “I’m qualified to do that!”, then consider it your best working option in the short term.

Lets not lose sight of the fact that there are multiple paths leading to business analysis. Your professional experience counts in this career transition and the sooner you can move towards building relevant career experiences, the closer you will be to reaching your business analyst career goal.

If you are currently unemployed, watch out for roles that best match your current qualifications. In the short term, these will be easier to crack into. Since getting work experience is probably the biggest factor in successfully transitioning to a BA role, any job you take on gives you more opportunities than you have now.

If you are currently working, first consider whether or not you are already in a transitional role and can build on your BA experience and skill set by taking on relevant BA responsibilities.

If you are employed but lacking meaningful opportunities to practice BA tasks, then look at roles you are confident you could do, but that are also a bit of a stretch. These stretch roles will further expand your business analyst skills and give you more responsibilities, which you can then showcase to potential employers as relevant BA experience.

One of the best ways to differentiate yourself from the competition is to get trained and certified in business analysis. Imarticus Learning offers short term certification courses in Business Analysis, that culminate with an industry endorsed Business Analysis certificate. The program also prepares you to clear the CBAP exam, which is the gold standard for experienced Business Analysts.

Source: http://imarticuslearningeducationinstitute.weebly.com/home/so-you-want-to-be-a-business-analyst-zenobia-sethna

Impact of technology on Investment Banking- Part one

Sorry mam, you will need to call back. Our systems are down.

Our servers are not working.

In January this year, HSBC Customers were unable to use online banking services for two days. An IT failure earlier in August led to 275,000 payments not being processed. That means late interest charges for you. A well-known firm lost 440 million Dollars in forty minutes because software updates sent erroneous orders into the market. Its no surprise then that news was out that banks will spend more on technology in 2016. Total bank IT spending across North America, Europe, and Asia-Pacific was expected to touch $196.7 billion in 2015, an increase of approximately 4.6% over 2014, according to forecasts by Celent.

The impact of Technology on InvestmentBanking can be summed up in money, money lost in systems going down, money lost when your technology is unable to mitigate risk efficiently, money lost due to customer loss on the back of slow responsiveness. The only way you are able to place a trade in the US to buy a Japanese stock is because Technology has enabled you to so.  It is technology that turns a trading strategy into a trading profit, enables new pricing models thereby creating new products that are relevant and conducive to the market. It is technology that adds value to the client in terms of shorter delivery times and better accuracy. The Investment Banking industry thrives on the flow, analysis and interpretation of information and technology holds the power to deliver that competitive advantage.

The current race between Investment Banks is not to create better cleverer products, because products can be imitated rather quickly, but to build unique streamlined technology platforms that can mitigate risk and improve efficiently thereby delivering seamless service.  Technology touches every aspect of Investment Banking, and underpins every deal that is transacted be it Securities Research, M&A or an IPO.  When a system is unavailable, millions can be lost. Thus robust systems and infrastructure is fundamental not only to profitability but also the evolving regulatory burden (Dodd-Frank, Basel III, Capital Requirements Directives 2 and 3 and OTC derivative clearing regulations) where technology also plays a big role.

The problem with technology however, is its obsolescence. As products become more complex the underlying technology must evolve as well. To secure computer systems, develop analytic capabilities and enhance customer-facing platforms, bank CIO’s expect to spending to jump 10 percent. 

http://www.americanbanker.com/news/bank-technology/banks-to-spend-more-on-tech-in-2016-especially-security-1077200-1.html. 

The Zen of Python

Dear Reader,

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.
• Readability counts.
• Special cases aren't special enough to break the rules.
• Although practicality beats purity.
• Errors should never pass silently.
• Unless explicitly silenced.
• In the face of ambiguity, refuse the temptation to guess.
• There should be one— and preferably only one —obvious way to do it.
• Although that way may not be obvious at first unless you're Dutch.
• Now is better than never.
• Although never is often better than right now.
• If the implementation is hard to explain, it's a bad idea.
• If the implementation is easy to explain, it may be a good idea
• Namespaces are one honking great idea -- let's do more of those!

Sound advice. You'd be forgiven to think this set of principles came from some dusty old philosophy manual. You'd be wrong. Believe it or not, this is an excerpt found on the FAQ page of the popular programming language, Python.

The Zen of Python is a collection of 20 software principles that influences the design of Python Programming Language. Long time Pythoneer Tim Peters succinctly channels the guiding principles for Python's design into 20 aphorisms, only 19 of which have been written down. The 20th is left to your imagination.

The whole story behind Python is rather playful and whimsical.  Guido van Rossum, the Dutch founder of the language, gave Python its name because he was reading the published scripts from Monty Python Flying Circus!  Van Rossum was looking for a name that was short, unique, and mysterious, so he decided to call the language Python. Oh, and he developed the programming language during the Christmas holidays because he had nothing better to do.

Python is based on the English language - focused on simplicity. If C, C++ or Java would take 20 lines to implement something, Python takes around 3 - 4 lines to achieve the same thing. No weird symbols for simple code or variables, no need for semi colons, and code is always nicely spaced. Python enforces clean, structured programming techniques and borrows freely from other languages.  It doesn't enforce a single model or approach to solving a problem (so Zen-like!).

No wonder Python is snaking it's way into programmers hearts -- It is currently the second most popular programming language globally, after Java, and used by the likes of NYSE and Google.

Imarticus Learning offers online python certification. This is 100%  Career Assistance program, our team provides a rigorous industry mentoring process that is customised to your needs. Additionally, the team conducts interview preparation sessions, resume building workshops, 1-1 mock interviews while also providing you access to our extensive corporate network and recruitment teams.

By Zenobia - Imarticus Team

MDP on Leading Organizational Transformation: What To Expect

A gentle reminder that our forthcoming Management Development Program on Leading Sustainable Organizational Transformation, by renowned Organizational Development expert and Change Agent, Roland Sullivan, is only a few weeks away!

Our Management Development Programs are extremely practical and hands-on. They endorse the use of live cases studies and real-life business problems to deliver learning. As Roland himself likes to say, the Age of Training is over; instead, now is the Time for Learning, which ensures participants are equipped with the needed skillsets to apply in the real world and bring about transformations within their firm, department or their teams.

The workshop is on 9th and 10th June, 2016. We look forward to your nominations.

Register Now to avail early bird discounts!

Source: https://imarticuslearningblog.wordpress.com/2016/06/03/mdp-on-leading-organizational-transformation-what-to-expect/

Studying for the CFA? Here are some must read books

Many of our Imarticus Financial Analyst Program (IFAP) students go on to appear for Chartered Financial Analyst (CFA) exams, spurred on by what they have learnt in the IFAP program here at Imarticus. We often get asked for book recommendations from students who want to appear for the CFA exam. When you register for the CFA, you will already receive a lot of recommended readings and study notes to help you prepare. These are the materials you need to read on priority. Having said that, below are a few books we can recommend, should you be so inclined:

Kaplan CFA Basics: The Schweser Study Guide to Getting Started Bruce Kuhlman: As the title suggests, this book is aimed at starters that have not taken the CFA level 1 yet - it is ideal for students and other non-finance professionals who are getting into studying finance and accounting for the first time. This book is very easy to follow, and provides a very good overview of the main concepts of the CFA so that you do not get totally lost before starting your study. This book is rather expensive and if you have previously studied Finance or have experience working in Finance, we suggest you skip this book.

Schweser Studynotes for 2011 CFA Level 1 Exam (Volume 1-5 Plus Quick Sheet) Kaplan Schweser: These study notes are the most widely used and most effective to prepare you for the Level 1 exam; they also include notes for Levels 2 and 3. They are superb for reviews after you go through all the CFA study materials.

2010 Stalla CFA Level 1 Study System (Study Guides, Lecture Notes, Passmaster Cd, Multimedia Lecture Disc, Flashcards) Stalla Review for CFA Exams

Stalla is a CFA study provider that offers preparation courses for the CFA exam, and publishes quite good study guides to prep you for your CFA exam. The USP of the Stalla books is the software that comes with it. Some people may find reading easier, while others favor the use of more "interactive" ways of studying. If you are in the "other" category, these books are for you. They also have level 2 and level 3 books.

Equity Asset Valuation (CFA Institute Investment Series) Jerald E. Pinto, CFA:

This is a very important and rather popular book covering the four important features of equity valuation: DDM (dividend discount model), free cash flow models, price multiples, and residual income. It is not only good to help CFA study (level 2 in particular), but is also a reference book for valuation as it is done in the investment management industry. This book also stands out among its peers for having clear, worked-out examples to further help you understand.

Quantitative Investment Analysis (CFA Institute Investment Series) Richard A. DeFusco, CFA:

This book is worth mentioning because it does a brilliant job explaining and simplifying the statistics portion of the CFA exam. It also delves into the other CFA subjects such as statements analysis, time value of money, etc. but goes much deeper into the statistics area for those who are interested in this area in particular.

http://imarticuslearningeducationinstitute.weebly.com/home/studying-for-the-cfa-here-are-some-must-read-books