The quality of faculty at GL is unmatched compared to other institutes – Venkatesh Radhakrishnan, Sr. Research Analyst at RRD

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From an amateur in Data Science to a Hackathon winner, Venkatesh has come a long way in his career. He was a newbie in this field since he’s from a commerce background, but he still didn’t lose confidence or direction throughout the course. Here’s how he did it: 

What is your professional background?

I completed my graduation with B.Com in Informations System Management from Ramakrishnan Mission Vivekananda College, Chennai by 2015. Then, I started as a Research Associate in RRD – Global outsourcing solutions, APAC. In 2017, I took a BABI course with GL. Currently, I am working as a senior research analyst at RRD.

How did you develop an interest in this course? Why did you choose Great Learning?

As soon as I started working in RRD as a research associate, I got aware of the Analytics field in terms of information & intelligence value. As a market researcher, I developed an immediate interest in this field and started looking for institutes that provided courses in this stream. I came across GL and it’s brand value. I got in touch with the team, got interviewed for the course and cracked admissions in BABI course at GL. 

How did you transition from a Research to a Data Science role?

During my time at GL, I spoke to my organization about my course and requested them to let me explore my skills in order to check applicable areas. After their approval, we started developing a proof of concept for clients; which turned pretty well. In the past 2 years, as a company, we have come a long way in terms of our practice and implementation of analytical tools. I feel privileged to be a part of an organization where through the help and support of management, I could transition to the Data Science field.

What did you think was the best thing about this program?

The best thing about this course is it provides flexibility in terms of learning things at one’s own pace. The course and curriculum are designed to promote learning and not spoon-feeding. There is an ample amount of time to learn, understand, practice and implement the concepts. The quality of faculty at GL is unmatched compared to other institutes.

What would be your advice to the future aspirants?

The advice would be to develop key skills in identifying areas where analytical tools can be applied to make things easy and profitable. One needs to study and research properly to understand the deployment of tools in favour of Business.

Upskill with Great Learning’s PG program in Business Analytics and Business Intelligence and unlock your dream career.

Your essential weekly guide to Data and Business Analytics 

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By 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, according to Gartner. The march of analytics into the collective consciousness of businesses around the world is unstoppable now, and the implications are far-reaching. From a glut of new skills that employees need to learn, to the shiny new applications of Analytics that’s changing the way humans live, there’s quite a lot of activity going on here. We try to make sense of all that news in our digest that encapsulates the Analytics landscape.

Here are some articles that will take you through recent advancements in the data and analytics domains. 

Businesses Face Three Biggest Challenges While Leveraging Big Data

According to a report from Dun & Bradstreet, the three biggest challenges businesses still face when it comes to leveraging big data are protecting data privacy, having accurate data, and Analysing/processing data. The global big data market was estimated at $23.56 billion in 2015 and now is expected to reach $118.52 billion by 2022.

Big Data & Business Analytics Market to Rear Excessive Growth During 2015 to 2021

Due to the tremendous increase in organizational data the adoption of big data and business analytics has been increased within organizations to better understand their customer and drive efficiencies. Read more to know about Drivers and Challenges of Big Data and Business analytics market. 

‘Jeopardy!’ Winner Used Analytics to ‘Beat the Game’

An aggressive strategy, mathematical finesse, a sharp mind, and a willingness to take risks were some of the factors that spurred ‘Jeopardy!’ game-show contestant James Holzhauer to win 32 consecutive games and rake in more than $2.4 million. Read more to know how this happened. 

The Age of Analytics: Sequencing’s New Frontier is Clinical Interpretation

Today, genomic data is being generated faster than ever before. And those on the frontier of this field are trying to make sure that data is as useful as possible. While the surge in sequencing has benefited many patients, the genomic data avalanche has caused its own problems. Read more about the challenges and proposed solutions to manage and analyze the volumes of genomic data. 

Times Techies: Upskilling is Key to Meeting Demand For Analytics

An exhaustive Nasscom-Zinnov report released last year flags a huge talent demand-supply gap in the artificial intelligence (AI) and big data analytics (BDA) family of jobs. By 2021, the total AI and BDA job openings in India is estimated to go up by 2,30,000. But the fresh employable talent or university talent available will be just 90,000, leaving a huge gap of 1,40,000. 

Happy Reading!

Your essential weekly guide to Artificial Intelligence – July 24

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Artificial Intelligence has spanned its wings to the vast majority of industries, but its impact on the Healthcare industry has many life-altering implications. Here are a few articles that showcase how Artificial Intelligence is being harnessed to enhance medical procedures and treatments. 

Elon Musk’s Neuralink Says It’s Ready for Brain Surgery

The startup, Neauralink just unveiled its plan to implant paralyzed patients with electrodes that’ll let them work computers with their minds. The company will seek U.S. Food and Drug Administration approval to start clinical trials on humans as early as next year, according to President Max Hodak.

Could Artificial Intelligence be The Future of Cancer Diagnosis?

Doctors have access to high-quality imaging, and skilled radiologists can spot the telltale signs of abnormal growth. Once identified, the next step is for doctors to ascertain whether the growth is benign or malignant. Some scientists are investigating the potential of artificial intelligence (AI). In a recent study, scientists trained an algorithm with encouraging results.

My Robot Surgeon: The Past, Present and Future of Surgical Robots

As robots used for surgeries become increasingly common, we trace the journey of surgical robots in India. 

 

Apart from Healthcare, these are some interesting applications of Artificial Intelligence in other sectors: 

Expressway Operators to Use AI to Forecast Traffic Jams

Plans are afoot to enlist artificial intelligence in anticipating traffic snarls in the year-end and New Year holidays. Central Nippon Expressway Co. said it will start trials of an AI-assisted forecasting system to predict congestion on the Tomei, Chuo and other expressways in Japan during the Bon holiday season next month.

Kitchen Disruption: Better Food Through Artificial Intelligence

Players in the food industry are embracing artificial intelligence to better understand the dynamics of flavour, aroma and other factors that go into making a food product a success.

Earlier this year, IBM became a surprise entrant to the food sector, announcing a partnership with McCormick to “explore flavour territories more quickly and efficiently using AI to learn and predict new flavour combinations”.

This AI is Helping Scientists Develop Invisibility Cloaks

The general idea behind an invisibility cloak is that it gives the wearer the ability to move through the world undetected. The first step is to engineering a material that can do that. A team of South Korean researchers has developed an AI capable of designing new metamaterials with specific optical properties.

Happy Reading!

What are the best Data Science and Business Analytics Courses for working professionals in India?

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The term analytics and data science have garnered a lofty prominence in the past decade mostly used interchangeably. As it stands strong today, business analytics is finding applications across functions ranging from marketing, customer relationship management, financial management, supply chain management, pricing and sales, and human resource management among others. It has also made a place for itself across industries, spanning its wings even to the most traditional ones such as Manufacturing and Pharma. 

A bachelor’s degree with a minimum of 2-3 years of work experience is mandatory to enrol for almost all business analytics or data science programs out there. It holds a great career scope for graduates in the field of engineering, business management, marketing, computer science and information technology, finance, economics, and statistics among others. 

All things said and done, there are certain challenges that professionals might face while looking out for the perfect business analytics or data science course to steer their career on the growth path:

– Lack of time and issues with balancing work and course schedules

– Financial Barriers

– Inflexibility in the course structure

– Obsolete curriculum or irrelevant modules

– Inaccessibility to the course

An institution or a course that focuses on combating these challenges and provide a comfortable, valuable, and manageable learning experience is the ideal course for professionals. At Great Learning, we strive to focus on these issues and design courses to suit the needs and resources of the aspirants.

Here is a comparative study of various Data Science and Business analytics program with Great Learning’s PG program in Business Analytics and Business Intelligence:

What are the best Data Science and Business Analytics Courses for working professionals in India?

Great Learning has been changing the lives of professionals across domains for over 5 years now. Having imparted more than 5 million hours of learning, we have touched professionals in 17 different countries, and are working towards reaching more geographies to transform careers of professionals across the globe. 

The post-graduate program in Business Analytics and Business Intelligence was the first program to be launched by Great Learning in the year 2014. Since then, there have been more than 50 batches with 5000+ professionals enrolled and successfully completed the course. The program has been ranked #1 Analytics program in India for 4 years in a row by Analytics India Magazine and has involved 300+ Industry Experts and 25+ India’s Best Data Science Faculty to impart quality skills and practical learning. Having propelled more than 2,500 career transitions, the success of the program can also be gauged by the fact that 90% of our alumni refer the course to other professionals. 

Best business analytics and business intelligence course for professionals

Our alumni have been placed with some of the top Analytics firms and reputed MNCs such as IBM, Accenture, HSBC, KPMG, LatentView, Myntra, Rakuten, RBS, Shell, Tiger Analytics, UST Global, and many more, with an average salary hike of 48%. This alone speaks a lot about the value and industry relevance of the program. Know more about Great Learning’s PG program in Business Analytics and Business Intelligence here. 

Here are a few testimonials by our PGP BABI Alumni. Read-Along:

GL puts a lot of effort to make the curriculum up to date matching world-standards Sowmya Vivek, Independent Consultant – Data Science, ML, NLP

The best part of GL is its experienced faculty – Sriram Ramanathan, Associate Director for Data Products at Scientific Games

The best takeaway is the approach with which I now perceive business problems – Pratik Anjay, Data Scientist at Walmart

The course aided my old desire to pursue finance as a career – Sahil Mattoo, Data Scientist, DXC Technologies

The guidance from the GL faculty is an important driver of my success – Priyadarshini, Analyst at LatentView

 

Book a call with us at +91 84480 92400 and our learning consultants will guide you through the program details and the specific queries that you might have.

10 Most Common Business Analyst Interview Questions

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Preparing for a Business Analyst Job Interview? Here are a few tips and the most useful and common business analyst interview questions that you might face. 

Before attending an interview for a business analyst position, one should be through about their previous experience in the projects handled and results achieved. The types of questions asked generally revolve around situational and behavioural acumen. The interviewer would judge both knowledge and listening skills from the answers one presents. 

The most common business analyst interview questions are:

 

1. How do you categorize a requirement to be a good requirement?

A good requirement is the one that clears the SMART criteria, i.e., 

Specific – A perfect description of the requirement, specific enough to be easily understandable

Measurable – The requirement’s success is measurable using a set of parameters

Attainable – Resources are present to achieve requirement success

Relevant – States the results that are realistic and achievable

Timely – The requirement should be revealed in time 

business analyst interview questions

 

2. List out the documents used by a Business Analyst in a project?

The various documents used by a Business Analyst are:

a. FSD – Functional Specification Document

b. Technical Specification Document

c. Business Requirement Document 

d. Use Case Diagram

e. Requirement Traceability Matrix, etc.

 

3. What is the difference between BRD and SRS?

SRS (Software Requirements Specifications) – is an exhaustive description of a system that needs to be developed and describes the software – user interactions. While a BRD (Business Requirements Document) is a formal agreement for a product between the organization and the client. 

The difference between the two are:

business analyst interview questions

 

4. Name and briefly explain the various diagrams used by a Business Analyst.

Activity Diagram – It is a flow diagram representing the transition from one activity to another. Here activity is referred to the specific operation of the system.

Data Flow Diagram – It is a graphical representation of the data flowing in and out of the system. The diagram depicts how data is shared between organizations

Use Case Diagram – Also known as Behavioural diagram, the use case diagram depicts the set of actions performed by the system with one or more actors (users).

Class Diagram – This diagram depicts the structure of the system by highlighting classes, objects, methods, operations, attributes, etc. It is the building block for detailed modelling used for programming the software.

Entity Relationship Diagram – It is a data modelling technique and a graphical representation of the entities and their relationships. 

Sequence Diagram – It describes the interaction between the objects. 

Collaboration Diagram – It represents the communication flow between objects by displaying the message flow among them.

 

5. Name different actors in a use case diagram?

Broadly, there are two types of actors in a use-case:

a. Primary Actors – Start the process

b. Secondary Actors – assist the primary actor

They can further be categorized as:

i. Human

ii. System

iii. Hardware

iv. Timer

 

6. Describe ‘INVEST’.

The full form of INVEST is Independent, Negotiable, Valuable, Estimable, Sized Appropriately, Testable. With this process, the technical teams and project managers to deliver quality products or services.

 

7. What is Pareto Analysis

Also known as the 80/20 rule, Pareto Analysis is an effective decision-making technique for quality control. As per this analysis, it is inferred that 80% effects in a system are a result of 20% causes, hence the name 80/20 rule.

 

8. Describe the Gap Analysis.

It is utilized to analyze gaps between the existing system and its functionalities against the targeted system. The gap is inferred to the number of changes and tasks that need to be brought in to attain the targeted system. It compares performance between the present and the targeted functionalities.

 

9. Name different types of gaps that could be encountered while Gap Analysis

There are mainly four types of gaps:

a. Performance Gap – Gap between expected and actual performance

b. Product/ Market Gap – Gap between budgeted and actual sales numbers

c. Profit Gap – Variance between targeted and actual profit

d. Manpower Gap – Gap between required and actual strength and quality of the workforce in the organization

 

10. What are the various techniques used in requirement prioritization?

Requirement prioritization, as the name suggests, is a process of assigning priorities to the requirements based on business urgency in different schedules, phases, and cost among others.

The techniques for requirement prioritization are:

a. Requirements Ranking Method

b. Kano Analysis

c. 100 Dollar Method

d. MoSCoW Technique

e. Five Whys

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job as a Business Analyst, upskill with Great Learning’s PG program in Business Analytics and Business Intelligence, and learn all about Business Analytics along with great career support.

How will Cybernetics And Artificial Intelligence build our future?

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We live in a world where what was considered science fiction mere decades ago has become a reality. Global, wireless internet coverage, 3D printed technologies, the Internet of Things powered by AI-based assistants, and, of course, cyborgs, are all part of the reality we live in.

Cyborgs? Yes, those are real. Look at Dr Kevin Warwick. The man can operate lights, switches, and even computers with the power of his mind thanks to a handy chip implant. Neil Harbisson has overcome achromatopsia thanks to an implant that allows the artist to process colours in real-time on a level unachievable by anyone else on the planet. 

If you were to do some research, you’d find out that these pioneers are merely the tip of a cybernetically enhanced iceberg bringing up the real question: if we’ve already come so far, what awaits us in the future?

Cybernetics

Some of the most prominent projects diving into the exploration of cybernetics feel like they were taken from a cyberpunk novel. And yet they are real. More on the matter, they mark what is potentially the future of humankind as a species. 

Full-spectrum vision. Typically, humans believe that the way we “see” the world is the only possible way. Cybernetics engineers would beg to disagree. A simple injection of nanoantenna has proven to give lab mice the superpower of night vision. The experiment taking place in the University of Massachusetts has only recently moved towards practical studies of the effects the antenna have on rodents, but it has already proven itself to be among the first stepping stones towards cybernetically enhanced eyesight. Additional breakthroughs in the field have shown promising results in turning eyes into video cameras, or even development of artificial retinas capable of returning sight to the blind. 

Artificial brain cells. Modern advancements in the niche of cybernetics have already grown neurons – the basic components of a human brain – in laboratory conditions. These cells, artificially raised on an array of electrodes are proving themselves as a superior replacement to the hardware and software controllers we have today. 

More on the matter, scientists are already using brain-computer interfaces in medicine. Most are designed for therapeutic purposes such as the Deep Brain Stimulation designed to aid patients with Parkinson’s disease.

We will be able to use said technology to create connections that operate via the remaining neural signals allowing amputee patients to feel and move their limbs with the power of their mind. In some cases, as it was with Nigel Ackland, some might even go as far as to use the word enhancement when talking about top tier prosthetics.

Enhanced mobility. Stronger, faster, more durable – those are the goals of military-grade exoskeletons for soldiers that are already branching out into the medical niche and serve as prosthetics for amputee victims.  The combination of hardware and AI-based software eliminate the boundaries of human capabilities while minoring the vitals of the wearer in real-time. 

Technopsychics. The University of Minnesota is working on a computer to brain interface capable of remotely piloting drones. The machines can detect the electrical signals transmitted by the brain to control functioning machines in real-time. If you can navigate a quadcopter through an obstacle course using only the power of your mind today, imagine what we’ll be piloting remotely tomorrow. 

Nanorobots. Self-repair, growth, and immunity to diseases will soon be true thanks to a simple infusion of nanobots into your bloodstream. Modern researches explore the idea of developing your blood cell’s little helpers that can be controlled in the cloud from your smartphone!

Artificial Intelligence

As you may have deduced from the examples above, the advancements in the cybernetics niche are directly related to the progress we make with Artificial Intelligence or Machine Learning technologies. 

We need the software capable of driving the hardware to its limits if we are to dive deeper into cyborg technology. Artificial Intelligence is supposed to become the bridge between the man and the machine according to prominent research such as Shimon Whiteson and Yaky Matsuka. These scientists are exploring new ways AI can help amputee patients to operate their robotic prosthetics. 

Furthermore, AI is expected to take control of machines doing sensitive work in hazardous areas. According to BBC, we already have smart bots capable of defusing bombs and mines yet they still require a human controlling them. In the future, these drones (and many more, responsible for such challenging tasks as toxic waste disposal, deep-sea exploration, and volcanic activity studies, etc.) will be powered purely by algorithms. 

Lastly, machines are expected to analyze and understand colossal volumes of data. According to Stuart Russell, The combination of AI-powered algorithms and free access to Big Data can identify new, unexpected patterns we’ll be able to use to mathematically predict future events or solve global challenges like climate change. 

What a time to be alive! 

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.

 

15 Most Common Data Science Interview Questions

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Data Science is a comparatively new concept in the tech world, and it could be overwhelming for professionals to seek career and interview advice while applying for jobs in this domain. Also, there is a need to acquire a vast range of skills before setting out to prepare for data science interview. Interviewers seek practical knowledge on the data science basics and its industry-applications along with a good knowledge of tools and processes. Here is a list of 15 most common data science interview questions that might be asked during a job interview. Read along.

 

1. How is Data Science different from Big Data and Data Analytics?

Ans. Data Science utilizes algorithms and tools to draw meaningful and commercially useful insights from raw data. It involves tasks like data modelling, data cleansing, analysis, pre-processing etc. 

Big Data is the enormous set of structured, semi-structured, and unstructured data in its raw form generated through various channels.

And finally, Data Analytics provides operational insights into complex business scenarios. It also helps in predicting upcoming opportunities and threats for an organization to exploit.

data science vs big data vs data analytics
How is Data Science different from Big Data and Data Analytics?

2. What is the use of Statistics in Data Science?

Ans. Statistics provides tools and methods to identify patterns and structures in data to provide a deeper insight into it. Serves a great role in data acquisition, exploration, analysis, and validation. It plays a really powerful role in Data Science.

 

3. What is the importance of Data Cleansing?

Ans. As the name suggests, data cleansing is a process of removing or updating the information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data and hence the accuracy and productivity of the processes and organization as a whole.

 

4. What is a Linear Regression?

The linear regression equation is a one-degree equation of the form Y = mX + C and is used when the response variable is continuous in nature for example height, weight, and the number of hours. It can be a simple linear regression if it involves continuous dependent variable with one independent variable and a multiple linear regression if it has multiple independent variables. 

 

5. What is logistic regression?

Ans. When it comes to logistic regression, the outcome, also called the dependent variable has a limited number of possible values and is categorical in nature. For example, yes/no or true/false etc. The equation for this method is of the form Y = eX + e – X

 

6. Explain Normal Distribution

Ans. Normal Distribution is also called the Gaussian Distribution. It has the following characteristics:

a. The mean, median, and mode of the distribution coincide

b. The distribution has a bell-shaped curve

c. The total area under the curve is 1

d. Exactly half of the values are to the right of the centre, and the other half to the left of the centre

 

7. Mention some drawbacks of the linear model

Ans. Here a few drawbacks of the linear model:

a. The assumption regarding the linearity of the errors

b. It is not usable for binary outcomes or count outcome

c. It can’t solve certain overfitting problems

 

8. Which one would you choose for text analysis, R or Python?

Ans. Python would be a better choice for text analysis as it has the Pandas library to facilitate easy to use data structures and high-performance data analysis tools. However, depending on the complexity of data one could use either which suits best.

 

9. What steps do you follow while making a decision tree?

Ans. The steps involved in making a decision tree are:

a. Pick up the complete data set as input

b. Identify a split that would maximize the separation of the classes

c. Apply this split to input data

d. Re-apply steps ‘a’ and ‘b’ to the data that has been divided 

e. Stop when a stopping criterion is met

f. Clean up the tree by pruning

Decision tree
Steps involved in making a Decision Tree

10. What is Cross-Validation? 

Ans. It is a model validation technique to asses how the outcomes of a statistical analysis will infer to an independent data set. It is majorly used where prediction is the goal and one needs to estimate the performance accuracy of a predictive model in practice.

The goal here is to define a data-set for testing a model in its training phase and limit overfitting and underfitting issues. The validation and the training set is to be drawn from the same distribution yo avoid making things worse.

 

11. Mention the types of biases that occur during sampling?

Ans. The three types of biases that occur during sampling are:

a. Self-Selection Bias

b. Under coverage bias

c. Survivorship Bias

 

12. Explain the Law of Large Numbers

Ans. The ‘Law of Large Numbers’ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.

 

13. What is the importance of A/B testing

Ans. The goal of A/B testing is to pick the best variant among two hypotheses, the use cases of this kind of testing could be a web page or application responsiveness, landing page redesign, banner testing, marketing campaign performance etc. 

The first step is to confirm a conversion goal, and then statistical analysis is used to understand which alternative performs better for the given conversion goal.

 

14. What are over-fitting and under-fitting?

Ans. In the case of over-fitting, a statistical model fails to depict the underlying relationship and describes the random error and or noise instead. It occurs when the model is extremely complex with too many parameters as compared to the number of observations. An overfit model will have poor predictive performance because it overreacts to minor fluctuations in the training data.

In the case of underfitting, the machine learning algorithm or the statistical model fails to capture the underlying trend in the data. It occurs when trying to fit a linear model to non-linear data. It also has poor predictive performance.

 

15. Explain Eigenvectors and Eigenvalues

Ans. Eigenvectors depict the direction in which a linear transformation moves and acts by compressing, flipping, or stretching. They are used to understand linear transformations and are generally calculated for a correlation or covariance matrix. 

The eigenvalue is the strength of the transformation in the direction of the eigenvector. 

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job in the field of Data Science, upskill with Great Learning’s PG program in Data Science Engineering, and learn all about Data Science along with great career support.

Is Design Thinking PepsiCo’s Secret to Market Dominance?

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Pepsico CEO Indra Nooyi took up the reins of the company when it was facing considerable drop in sales. As a way to address this, she revised her business strategy to make it more inclusive for consumers. She famously went after Mauro Porcini and sought his expert advice to redesign PepsiCo’s user experience. Eventually, her team resolved the problems by relying on an iterative process of understanding users and providing instinctive solutions.

Under the leadership of Indra Nooyi, PepsiCo prioritised user-specific solutions, designed products that were more human-centric and earned the company record-breaking revenues apart from accolades. User experience was not a part of PepsiCo’s business strategy until the early 2010s. Whether it was their product packaging, form or function, that human element was missing in the design. Once they focused on customer experience and made design a priority, customers responded by engaging with the brand more. From designing touch-screen fountain machines (Pepsi Spire) to launching a special line of women’s snacks, PepsiCo reconditioned the way consumers interact with products. Mauro Porcini successfully introduced a more consumer-centric PepsiCo to the world with design thinking being the key driver behind all these changes.

The company leveraged design to drive innovation and create relevant brand experience for their customers. Design thinking helped them change their brand’s visual identity and improve the product itself. Following an iterative prototyping process, Pepsico was able to align the company goals around the product, helping transform obscure ideas and overcome plausible blockers in production process.

What really helped PepsiCo’s journey towards success was a deep understanding of consumer needs – the idea that the product had to communicate with the consumer in a way in which was unheard of before. Getting a perception of what consumers wanted from each of the products- vending machines, fountains or consumables and crafting the experience accordingly helped the company reclaim the market. 

The Pepsi spire (a series of fountains and vending machines) is the most loved and the first in the design enhanced line of products. Pepsi Spire allows customers to customize their drinks by communicating with a highly responsive touch-screen fountain. Now, if you are wondering if design thinking is just about enhancing product packaging, it’s not quite so. Pepsi Spire is a classic example of how design thinking can impact all phases of product-customer experience. The spire is basically a futuristic machine that speaks to customers and invites them to interact with it. Its intelligent interface reminds customers of the order history and suggests new options based on the customer profile. They can also experience the infusion digitally by watching the whole process of adding their favourite elements in the drink on the screen in real time- right when they select it. This approach extends the enhanced customer experience to the post-product phase and makes it holistic. Pepsi Spire has now become iconic and inspired a series of intelligent vending machines. 

Other Companies taking Cue from PepsiCo

Using design thinking to drive business means designing solutions with customers in mind – not only will that lead to more customer satisfaction but also establish businesses as distinguishable brands. What company wouldn’t want that? Global leaders are already using design thinking to align their customer’s goals and step into the future. Let’s take a look at the top companies who have already benefited from this model. 

Apple:  Apple is undeniably a classic example of how reconstructing user experience through innovation can lead to revolutionary success. At its core, Apple remains a company that has always championed innovation and delivered unique customer-driven experiences – all thanks to design thinking. Apple products ranging from iPhone, MacBook to ios not just bring you exquisite usability but also optimised functionality. From providing a holistic user experience to predicting customer needs, Apple has successfully shown the rest of the world how it’s done.

Nike: Nike has been a pioneer in merging sports with fashion. A brand which primarily targeted athletes and helped them enhance performance has now become quite a fashion trailblazer. “Move forward” (their pet phrase) not only dictates their designs but also aptly captures their user imagination. All along, design thinking has been instrumental in shaping their advanced products and services.

Google: Needless to say, Google has been acing the game and how! Whether it is Google map or Google Pixel’s image software, Google products are glaring examples of enhanced designs. Google teams are constantly thinking ahead of time and designing products and services that answer futuristic customer needs. Google’s constant endeavour to design products with a focus on user experience has established the brand as a world leader in design thinking.

Design Thinking has been around for longer than we think and its focus towards building enhanced user experiences has made it a much coveted strategy for brand building today. To put it in Porcini’s own words,

“People don’t buy, actually, products anymore, they buy experiences that are meaningful to them, they buy solutions that are realistic, that transcend the product, that go beyond the product, and mostly they buy stories that need to be authentic.”

PepsiCo’s success has since then inspired many other companies to rethink their business strategy and hire design thinking experts. If you are an enthusiast, learn more about it here.

Your essential weekly guide to Artificial Intelligence – July 17

Reading Time: 2 minutes

Stay updated with the Artificial Intelligence breakthroughs, applications, and advancements across the globe.  

How La Liga is Using AI to Make Business Decisions 

In a very interesting interview, Sergi Torrents of LaLiga explains a calendar tool that the league uses to make decisions on what the kick-off time for matches should be. The calendar selector decides kick-off time with highest match audience. La Liga also uses sunlight planner to optimize the match schedule. Watch the video here!

Skilling Millennials in Disruptive Tech Will Take India to USD 5 Trillion Mark

To make India a $5 trillion economy by 2024-2025, the country urgently requires a renewed focus on skilling the youth in advanced technologies like robotics, Artificial Intelligence (AI), Internet of Things (IoT), 3D printing, data analytics and quantum computing. According to policy think-tank, Broadband India Forum (BIF), IoT and AI-based applications will create over 28 lakh jobs in rural India over 8 to 10 years with an annual value of Rs 60,000 crore.

This New Poker Bot Can Beat Multiple Pros – At Once

The 32-year-old Darren Elias is the only person to have won four World Poker Tour titles and has earned more than $7 million at tournaments. Despite his expertise, he learned something new this spring from an artificial intelligence bot. Poker has long been seen as a grand challenge for AI researchers, with properties similar to many real-world situations. Unlike in chess, poker players must choose actions without knowing what cards their opponents hold and Pluribus is doing a great job at beating elite poker professionals.

Now Artificial Intelligence Algorithm to Help Predict Storms, Cyclones

Using Artificial Intelligence (AI), researchers have developed an algorithm to detect cloud formations that lead to storms, hurricanes and cyclones. The study, published in the journal IEEE Transactions on Geoscience and Remote Sensing, shows a model that can help forecasters recognise potential severe storms more quickly and accurately.

Alberta Researchers Use AI to Detect Depression in Voices

PhD student Mashrura Tasnim and professor Eleni Stroulia developed a methodology that combines several machine-learning algorithms to more accurately detect depression using acoustic cues. A realistic scenario is to have people use an app that will collect voice samples as they speak naturally. The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time.

A Chinese AI Startup is Tracking Lost Dogs Using Their Nose Prints

Megvii, a Chinese AI startup that supplies facial recognition software for the Chinese government’s surveillance program, is expanding its technology beyond humans to recognize different faces of pets. Megvii says it has an accuracy rate of 95 per cent and has reunited 15,000 pets with their owners through the app. Read how they do it!

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