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Category: Management

FAQs during Business Analytics Interviews

Posted on by Imarticus Learning

[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid”][vc_column_text]FAQs during Business Analytics Interviews

1. List some of the frequently used documents a Business Analyst always has at his disposal.
A business analyst associated with an organisation needs to have the following documents at his
disposal.
● Requirement Management Plan
● User stories
● Project vision Document
● Business Requirement Document
● Use cases
● Functional Specification Document (FSD) or Functional requirement specification (FRS)
● Requirement traceability matrix (RTM)
● Test case
● System Requirement Document (SRD) or System requirement specification (SRS)

2. What is the difference between a BRD and SRS?
A BRD or Business Requirement Specification explains the functional specifications of software
at a very high level. It is formal in nature and describes the client’s requirements.
An SRS or System Requirement Specification explains the technical and functional
speciations of software and is created by an in-house system architect instead of a business
analyst.

3. What is Gap analysis?
The process of evaluating the differences n performance between software applications and
business information systems to understand if required business needs are being met or not is
called Gap Analysis. The word Gap, here, refers to the present state of the organisation and the
targeted state.

4. How do you explain the importance of non-functional requirements?
Non-functional requirements are important in describing how a system needs to behave ad
what might be the possibility of constraints in functionality. Non-functional requirements make
the system easy to use and also prevent the system from being abandoned.

5. What do you mean by Personas?
Personas can be termed as detailed fictional profiles that follow the methodology of following a
User-Centred Design. A persona represents a certain segment of users that fall under a given
demographic and helps analysts within an organisation comprehend and study behavioural
patterns and attitudes of users.

6. Name the most important elements of an activity diagram.
The most important elements of an activity diagram are listed as follows:

● Action node
● Initial Node
● Activity Final Node
● Decision
● Activity Edge
● Join
● Fork
● Merge

7. Explain UML modelling.
UML or Unified Modeling Language is a modelling language that consists of a set of diagrams
that are integrated and developed in a manner that can help both system and software
developers specify, visualise, construct and document software systems and their artefacts. UML
has also been used for business modelling as well as modelling for other non-software systems, regardless
of whether the system is regular or larger and more complex. The advantage of using a UML is that
business analysts can help their internal project teams communicate among themselves
seamlessly.

8. List 5 common problems you may face in business analytics.
The most common problems in business analytics may include:
● Improper or a complete lack of communication during the process of implementation
● Lack of extensive support from the software provider which may hamper analysis results
● Organisation members may be unreceptive to provided or suggested solution
● Lack of industry knowledge on the part of software provider
● Less possibility of user adoption when the solution provided is noot mobile

9. Describe Business Model Analysis.
A Business Model Analysis helps to understand the working of an organisation, whether the
the venture is delivering and meetings its goals, how viable it is or may turn out to be and how
valuable it is.

10. Name 5 important tools used in Business Analytics.
Five of the most important tools in Business Analytics are:
R
SAS
Excel
QlikView
RapidMiner

11. What is risk management?
Every decision in an organisation, be it big or small, involves a certain amount of risk that comes
with it. Businesses try and manage these risks in a way so that losses that follow are minimised

and do not have an impact o the overall revenue or taint the company’s reputation. The concept
of controlling or effectively managing the risk taken is called risk management.[/vc_column_text][/vc_column][/vc_row]

Essential Tips to help you nail the Job Interview like a pro

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Essential Tips to help you nail the Job Interview like a pro

Job interviews! Dislike them or fear them as much as you want to, but there’s no having your way
around a job interview if you want to land that dream job. The current market is cut-throat in terms of
competition and you won’t stand a chance unless you are well-prepared to tackle any interview that
comes your way. Luckily, there are multiple ways in which you can up your game and ace that interview.
Read on for the best tips that will help you make a worthy impression on your HR Manager.

Know your route

You should know the venue of your job interview like the back of your hand because the last thing you
want to be is, late, out of breath and disoriented. Plan your route beforehand, check out the map, get an
idea of the possible traffic conditions that might prevail during the time your interview is scheduled, and
figure out what your mode of commute is going to be. Try and plan your schedule in a way s that you
arrive atleast 20 minutes early to your designated time, this way you’ll be able to hang out near the
premises, explore the building or even grab a coffee.

Always dress the part

You should plan what you are going to wear well in advance to your interview. Business formals work
the best for any interview, commonly know as corporate classics. In case you know anybody who
already works at the place you’re going for an interview, ask them for tips and inputs regarding the
dressing culture prevalent in office. However, it is always safe to go for formals for any interview.

Do a thorough research

Dig deep when you are doing your research about the company and your job role. Take the opportunity
to gather as much knowledge as you can about the job description, company values, work culture and
ethics and aims and objectives of the company. This is to prove how enthusiastic and eager you are to
take on the new role and how effective an asset you want to become for the organisation. Additionally,
if you have questions for your employer, this would be a good way to prep for it.

Print your resume

It is always a good idea to carry a few extra copies of your resume even though you’ve sent it via email
and your employer has it right in front of them. This works to your benefit in showing how organised
you re as a person; additionally, this might help if these are more than one HR managers interviewing
you.

Be enthusiastic and confident

One of the biggest skills you bring to the table is confidence. Whether it is a dazzling smile to greet your
interviewer or a firm handshake while introducing yourself, confidence goes a long way in making that
first impression.
Listen intently
Always listen intently to what your interviewer is saying. Sometimes the answer is hidden in between
the lines and sometimes everything is not meant in the same way as it has been spoken. Listening
intently goes a long way in proving your communication skills as well.

Give to the point answers

Make sure you answer what has been asked directly. Often candidates make the mistake of not
understanding and answering the specific question that has been asked. If you have failed to understand
your employer or HR manager’s question, please clarify before you answer.

Follow up

Do not hesitate in following up with your interviewer post the interview. That reminds your employer of
your enthusiasm and your interest in the company as well.
Go walk that interview like cakewalk![/vc_column_text][/vc_column][/vc_row]

Using Machine Learning to Conduct Sales Forecasting

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Using Machine Learning to Conduct Sales Forecasting

As the future of machine learning slowly transforms the present, models and algorithms are increasingly becoming more powerful, flexible and scalable. They’re also perfectly capable of being adapted into any industry, regardless of how they’re used and to what end.

An example of that is the use of machine learning in sales forecasting. When product catalogs expand in volume and variables become more complex, machine learning algorithms make the process of sales prediction easier and more real-time in the following ways:

Analyzing Sales Variables

Machine learning algorithms can be used to sift through data dumps of prices and stocks as well as to conduct analyses of traffic to certain products and pages or identifying trending products. Thanks to such analyses, retail and e-retail firms can identify which products are likely to perform well and what measures to take to ensure the success of other offerings now and in the future. Some of the variables that directly affect sales are:

  • Price of products
  • Supply of products
  • Market trends
  • Demand for products
  • Marketing tactics

Revising Compensation

Sales compensation is a powerful driving force in motivating sales employees to achieve targets. However, what’s often seen in companies is that sales targets can be unachievable or based on incorrect metrics that can hamper top employees’ performances, even cause them to leave. Machine learning systems can help to identify Key Performance Indicators (KPIs) based not only on past performance but also on the overall performance of the company and external influential factors. Here are some ways in which machine learning training can set better sales goals:

  • Setting achievable targets
  • Adapting the right frequency for revision of metrics
  • Identifying the ideal incentive
  • Implementing compensation and revisions

Identifying and Maintaining Benchmarks

Benchmarks are ideal scenarios that firms use as a target to meet or emulate. Machine learning algorithms can be leveraged to identify these benchmarks using the aforementioned sales indicators as well as past data dumps of employee performance and business targets. Benchmarks are just that– they needn’t be used if it’s business as usual. But to stay ahead of competitors and identify winning strategies, it’s essential that a firm has a goal to work towards and an ideal situation to use as a comparison. Upon failing to meet benchmarks, companies can turn the lens inwards to identify loopholes in the sales cycle, demotivation in employees or products or services that have fallen out of favor.

Maximizing Sales

A data dump is the most important asset to a machine learning algorithm, an arsenal of sorts. This arsenal can be used to predict prime prices that are attractive to customers yet profitable for the company. They can also be used to upsell, cross-sell and recommend stock-ups to avoid going out of stock and losing out on potential sales. Future sales can also be predicted; this, in turn, can be used to drive investments into departments or services and advise marketing strategies to maximize the bang for their marketing buck.

Carrying out A/B Testing

A/B testing is crucial for firms who do not know what marketing strategy will work or what products will do well in the market despite initial research, however thorough. Machine learning can conduct such tests without running too much risk to the business or demanding human resources and attention.

Conclusion

Machine learning has permeated every industry today, so much so that every good machine learning course explores the benefits of technology across fields. By using machine learning algorithms and persevering through trials, businesses can transition into higher performances, better sales, and more impressive profit margins.

Is Big Data The Key To Curing The NHS?

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Is Big Data The Key To Curing The NHS?

The essential and key aspect of every developed nation-state is access to better healthcare facilities. In a dwindling mass of third-world countries, we often find that poor healthcare affects the economic resources that remain untapped for a long. The National Healthcare system developed in the United Kingdom in the aftermath of World War 2 was the most progressive decision undertaken by the state and sovereignty for its citizens and that protects them till today.

This healthcare system can also be accessed by international citizens who stay in these places for a short period of time owing to various reasons. Since its establishment in the early 1950s, it has facilitated an increase in the life expectancy of people. However, handling such large amounts of patient records can be extremely gruesome and challenging especially with the late detection of many diseases the NHS has of late been suffering from a series of major losses. It can, however, be avoided with the emerging technological renovations happening all over the space, especially with the emergence of Big Data.

Big data training helps in involving and combining unstructured databases with a structured database and helps in providing the best solutions to the data barriers with its system of integrating, transforming and empowering the services.

The benefits of big data are clear, and it has become much easier for organizations to collect and store this level of data from their customers and stakeholders. The challenge is to convert that data into information that can help improve operations. For the NHS, its test run operations in Scotland have helped in not just collecting data but also implementing the analysis techniques to understand the warning signs of various new diseases. This targeted intervention can help the NHS from not just run into deficits but also save many more lives.

However, this intervention has to be systematically curated and the needs of the organization addressed effectively to overcome the barriers that exist in the implementation of data analytics. These businesses provide solutions in the market that can cater to almost all niche business operations and ensure that the products and services provided by them are catered effectively.

The predictive data analytics helps in providing a potential light on the patient flow and hospital demands and allows the NHS to make informed decision-making. It helps in allocating the NHS appropriate resources and improving its time efficiencies.

But there also exists a barrier to the implementation process. The big data analysis is seamless but requires huge investment, especially in cases of NHS where a large amount of information has to be provided and the IT infrastructure and data have to be organized to ensure the flow within the business.

Therefore, utilizing this big data across the organization needs to be balanced with an effective training process for the staff to work with these technological assessments. This data also has to be regulated and protected to avoid any mishappenings. It will require initially huge financial investments and operational changes and trained staff to handle the situation at times of crisis.

Therefore, what we have to look at now is whether this system is effective and can it really change the dynamics of healthcare in its absoluteness. Arguably we could say that the investment process is too difficult considering the present scenario of the market systems and the long-term potential to drive down costs across the NHS. However, in today’s world, technological means have the potential to save a company from going into bad daylight and bring about a revolution in the system process and ensure that the healthcare system can become really effective in the long run.

Industry Updates – Analytics (June)

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The world of Analytics has never been more interesting and in demand, and the future looks very exciting. In this newsletter, we have included blogs and videos that will help you understand how the Analytics market looks like, what skills you need to get into this domain, how SAS and Tableau are bringing revolution in specific sectors, and how Machine Learning is changing the world around you. You will also get a glimpse of the life of a data scientist at Flipkart, and catch up on the top stories you might have missed earlier.

Industry Updates – Analytics (November)

Posted on by Imarticus Learning

In the last few months, data privacy has been the most discussed topic on online channels. After Cambridge Analytica episode, we had Facebook’s rough period of scrutiny, reassessment of significant data seeking applications, and new policies & recommendations shared by global/local compliance bodies. However, away from this noise, other analytics companies are making quick progress in enhancing predictive analytics capability to help business solve their complex problems. We have brought some exciting blogs and videos for your knowledge appetite. We have also included an inspiring story for all analytics leaders and enthusiasts. Hope you enjoy this edition.

Industry Updates – Analytics: April

Posted on by Imarticus Learning

In the last few months, data privacy has been the most discussed topic on online channels. After Cambridge Analytica episode, we had Facebook’s rough period of scrutiny, reassessment of significant data seeking applications, and new policies & recommendations shared by global/local compliance bodies. However, away from this noise, other analytics companies are making quick progress in enhancing predictive analytics capability to help business solve their complex problems. We have brought some exciting blogs and videos for your knowledge appetite. We have also included an inspiring story for all analytics leaders and enthusiasts. Hope you enjoy this edition.

Industry Updates – Machine Learning (March – 19)

Posted on by Imarticus Learning

This issue of the AI newsletter discusses how a smart AI strategy ensures a transformative customer service in times where the customer is spoilt for choice. It also throws insight into how firms can use AI as a weapon to offer uniquely differentiated products. Our webinar gives you a sneak peak into AI is used in google maps and we also have short video by Rav Ahuja, Global Lead at IBM DBG, where he talks about the trans-formative effect of AI & ML across industries like Healthcare, manufacturing, and eCommerce.

Industry Updates – Machine Learning

Posted on by Imarticus Learning

This issue of the AI newsletter discusses how a smart AI strategy ensures a transformative customer service in times where the customer is spoilt for choice. It also throws insight into how firms can use AI as a weapon to offer uniquely differentiated products. Our webinar gives you a sneak peak into AI is used in google maps and we also have short video by Rav Ahuja, Global Lead at IBM DBG, where he talks about the trans-formative effect of AI & ML across industries like Healthcare, manufacturing, and eCommerce.

How To Use Data Science For Predictive Maintenance?

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How To Use Data Science For Predictive Maintenance?

Most businesses constantly face an issue while analyzing whether their critical manufacturing systems are performing to their full capacity while ensuring a consistent reduction in the cost of maintenance. Causes of potential concerns need to be identified early to help organizations come up with more cost-effective plans.

This is where predictive analysis fits the bill. Predictive analysis is used to predict if an in-house machine will malfunction or work correctly. Predictive analysis also helps to plan maintenance in advance, predict failures, classify failure types and recommend necessary actions to be taken after a system fails. The scope of data science is vast and predictive analysis only helps in proving that further.

Factors influencing the success of predictive maintenance

There are three factors that influence if a predictive model is going to be successful or not:

Having the right data

One of the most crucial factors influencing predictive maintenance is having enough data that helps analyze factors that may lead to failure. Additional system features like operating conditions, technical properties also need to be taken into consideration. Additionally, it is also important to make an inventory that will help note the kinds of failure that can occur, and which are the ones that can be predicted.

If at all there is a failure, what the failure process might look like. Having the right data for predictive maintenance also helps understand which parts of the system may have failed and how improvement in terms of performance can be brought about. A system has a vast life span of over a couple of years, which means data collection needs to be done over a couple of years to ensure correct statistics are taken into consideration. A basic data science course will teach you everything about data collection methods.

Framing a predictive maintenance model

The next step is to decide the best modeling strategy for the collected data and how it can lead to the desired output. While there are always multiple modeling strategies to choose from, a predictive maintenance framing strategy should keep a couple of things in mind:

Desired output for the model

Quantity of data collected

Measurements required to predict is a system will succeed or fail

Advance time to predict before a system fails

Setting performance targets for the model such as accuracy, precision and more

Evaluating predictions in predictive maintenance model

A predictive maintenance model predicts whether a system will succeed or fail, what are the conditions under which it might fail and how to ensure that it runs smoothly amongst others. After a predictive model is built, it gets highly essential to analyze how accurate the predictions have been, under what circumstances has the model been able to predict a certain failure or success conditions, and what can be done to combat the same.

Usage of data science in predictive maintenance

Using data science in building predictive maintenance models goes a long way and has its own set of advantages. Here is a lowdown of the ways in which data science has proved to be beneficial for the same:

Minimizing the cost of maintenance

Data science helps understand when to repair a system or machine and prevents unnecessary expenditures by predicting how frequently maintenance should be done.

Root cause analysis

Data science digs deeper into the causes of high failures and understands why systems malfunction occasionally. It also helps suppliers deal with the potential supply of materials accordingly.

Reduce unnecessary downtime

Predictive maintenance is required to predict if an ad when systems might malfunction. A prior data science analysis only helps in lessening the risk of unforeseen disasters.

Efficient planning for smooth operations

Data science ensures that there is no time wasted in fixing systems that are not vital or replacing equipment that has no usage. This way it plans labor efficiently and also ensures that the operations of the business run smooth.

A course in predictive maintenance and building models is an interesting choice for professionals enthusiastic about pursuing a data science career.

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