5 Qualities to Look for When Hiring a Data Scientist

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What makes a good data scientist? Most employers and recruiters prioritize skills testing when searching for the perfect candidate. After all, hiring someone who lacks technical skills can be a costly mistake. However, successful data scientists also have qualities that a skill test alone cannot identify. They have a range of skills and qualities that you can’t learn from a book. So, what are they and how do you identify them?

Such is the pressure to make the right hire that firms and recruiters are increasingly turning to artificial intelligence (AI) and machine learning (ML) based solutions. According to The Guardian, an ML-powered app Headstart is being used by a number of top companies including BP, Expedia and Vodafone to help them find the best candidates. Using a series of predictive and contextual algorithms, Headstart screens candidates and matches them with suitable roles. In this post, we will look at a range of skills that all successful data scientists have. Here is our list of the five essential qualities to look for when making your next hire.

1. Data Intuition

Just because a candidate has a data science degree or a data science certification doesn’t mean they have good data intuition. Data scientists with this quality are excellent at identifying patterns within sets of structured and unstructured data. The role of data scientists is constantly evolving and they must now understand the needs of customers as well as the needs of their organization. A good interview question that will help you uncover a candidate’s ability to identify data patterns is to ask them to create a quick data visualization. Let them choose whichever programming language they feel comfortable with such as Python or R, and ask them to demonstrate their ability to pick out a key pattern in a small dataset.

2. Iterative Design

Data scientists need to be able to work as part of a much larger team in order to deliver results. In the world of big data, it’s the data scientists who ask the questions and the data analysts who provide answers. Data scientists then take these results and draw conclusions or insights before deciding upon the next step. This iterative design process is crucial to the success of any IT department, yet not all candidates will have the ability to work in this way. While it’s essential to choose a candidate with a data science certification from a reputable data science course in India or elsewhere, you also need someone who enjoys the iterative development process. During the phone screening interview or the technical interview, ask candidates to explain the last project they worked on in detail. How did they address obstacles along the way? How did they work to make improvements? The answers to these questions will help reveal whether they are able to improve products through the process of iterative design.

3. Statistical Thinking

A skills-based technical interview will tell you whether a candidate has a solid background in data science and big data analytics and should indicate whether they are good at statistical thinking. However, it’s up to the recruiter to check this during the interview stage. While a candidate’s resume may tell you that they have completed a data science course in Hyderabad or Bangalore, it may not give you a good idea of their communication skills. During the interview, ask your candidates how they would resolve a question using statistics. For instance, does the description to every YouTube video contain the word ‘and’? How would they test that? What script would they create? This question will help highlight any candidates statistical thinking ability.

4. Hacker’s Spirit

The latest research shows that over 90 percent of data scientists have a Master’s Degree so you can be fairly confident that any candidate who makes it through the screening phase and into the technical interview has a baseline competency in common programming languages such as Python and R. However, while a skills-based assessment will show you candidate’s proficiency in bash/command line, SQL and Java but it won’t tell you how they react to working with new or unfamiliar coding languages. This is known as a ‘hacker’s spirit’: can someone work with unfamiliar codes or formats or even create their own tools when they can’t find a solution?

The best data scientists have this ‘hacker’s spirit’ and a life-long love of learning. They constantly re-train and learn new coding skills on the job. A good interview task to determine whether a candidate has the willingness to learn new skills is to challenge them to explain or write in plain English how an algorithm or query would work in a coding language that is unfamiliar to them. This task gives you an insight into their ability to think, problem-solve and react to new challenges, just as they might expect to face when they work for you. They might not arrive at the correct answer but you can tell whether they have a ‘hacker’s spirit’ and are ready to face the constantly evolving challenges in your workplace.

5. Creativity

Creativity is an essential quality for any data science candidate. They may have completed a data science and big data analytics course at a prestigious university but are they able to use their knowledge to solve real-life problems? Data scientists routinely execute database runs and queries but in order to be successful, they also need to be able to design new ways of architecting queries. After all, if their results simply answer questions that have previously been asked, what new insights will your organization gain? This is where creativity comes in; can a candidate solve a real-life issue?

The best way to determine this is to give candidates a coding challenge and ask them to speak aloud as they solve it. This will give you chance to see where they have gone wrong and you’ll be able to course-correct them in real time. This gives you an insight into the quality of their thinking and their ability to develop new solutions to existing problems. Competent data scientists constantly have to design new strategies to work with structured and unstructured data.


Whether you are an employer or a recruiter, these five qualities of successful data scientists will give you a head start in seeking out the best candidates. When making your next hire, make sure you look for candidates with a good blend of data intuitive, statistical thinking skills, a ‘hacker’s spirit’ and a healthy dose of creativity. Data scientists with these qualities are guaranteed to help your company thrive and prosper.

Cloud Computing and Its Types

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Could you imagine a device or software developed in the 1960s that may be affecting your life right now? Could you imagine it may have evolved so much through the decades that you don’t even realize you are actually using it as you are reading this article?

Enter the world of Cloud Computing.

The terminology of cloud computing may be around from the early 2000s, but you have to go back until the 60s to find its first use. In fact, at that time computer bureaus allowed companies and firms to rent time for some of their projects, instead of buying a computer facility for their needs.

Move on to the era of PC and buying computers became quite affordable. These “renting” services became obsolete, and yet the idea for a company to pay when needed an access to computer power came back under different forms, such as service providers, utility computing, and grid computing of the late 1990s and early 2000s.

It’s around this time that cloud computing rose as the best solutions as a service for business needs.

By definition, cloud computing refers to providing on-demand computing services through an internet connection, and it’s offered as a pay as you go service.

The next question you may have is: how does it work?

Companies may not have the need to own a computing storage or data centers, so they pay for a service to rent their computing infrastructure and they have access to their applications or stored data from the cloud service provider.

Is it really a good thing to trust an outside company with your data? It may sound strange, but the answer is yes. Companies and firms don’t have to put up with the upfront costs of building and maintaining their own IT infrastructure. They just pay the computing space they need, when they need it. For a cloud computing service, this becomes a profitable business, as they only have to sell the same product (cloud storage) to more and more customers.

So, if you think about it, somewhere in every corner of the world there must be huge buildings that contain these massive computer storages, but why then are they called cloud computing (services)? An underlying concept about cloud computing is that the location of the storage and all its features (such as hardware or operating system) doesn’t concern the end user at all. This is why the people in charge call it the cloud computing. They took the metaphor of the cloud from old telecoms network schematics, in which the public telephone network (and later the internet) was often represented as a cloud to denote that the underlying technologies were irrelevant.

Ok, we’ve seen so far how cloud computing has become an integral part of many businesses’ existence, let’s talk now how important it actually is.

Would you like to know how much the IT industry can count on money spent for cloud computing services? Would you like to know how much this number grew in 2017? Well, here are the facts.

Thanks to a research by IDC, more than a third of all the money spent on IT goes to building the infrastructure to host cloud computing. And at the same time, the expense foreseen for in-house IT capabilities will keep going down as more and more companies prefer to opt for the cloud, no matter if the cloud computing service is a public service (which means the company rely on a cloud service outside of its direct control) or a private service. Another research conducted by 451 Research says that in 2017 companies spent on IT hosting and cloud as much as one-third of their entire IT expense “indicating a growing reliance on external sources of infrastructure, application, management and security services”.

Also, analyst Gartner says that the firms that are now using the cloud globally will opt for this service in full by 2021. According to Gartner, global spending on cloud services reached $260bn in 2017 up from $219.6bn. The analysts also highlighted the fact that cloud computing services are increasing in size at a faster pace than they imagined. Although data can’t say yet if this growth is caused by the demand of the market or if cloud services are accountable because of the different products they are putting in front of the companies. Surely cloud subscriptions are a more profitable solution than one-off licenses.

You have now a clear image of what cloud computing is and how it is going to affect companies worldwide. It’s time to dive deep into the different options a cloud service may present itself to satisfy a client’s need.

First of all, cloud computing can be identified as Infrastructure as a Service, or IaaS. This type of cloud service is aimed to provide enterprises the access to vital web architecture, such as storage space, servers, and connections, but at the same time, the companies don’t have to buy and manage by themselves this internet infrastructure. Iaas is a totally scalable service, and this helps to save money both for the clients and the companies offering this solution. Iaas lets a business to develop and grow on demand. It is also the foundational ground for the other types of cloud computing services, as it provides also the infrastructure that runs the service.

After IaaS, you can find Platform as a Service, or PaaS. This particular service is often created inside IaaS clouds by specialized engineers that render the scalability and deployment of any application trivial and help make your expenses scalable and predictable. Very low budget companies can start out their application on a PaaS and focus on its development. You can also scale and design your product since it is based on cloud computing. A downside to PaaS is that there may be some restrictions that could affect your line of work.

Last but not least, Software as a Service (SaaS) is a well-known cloud service and its existence dates way back cloud computing. This application will allow you to work on the cloud and will help you to make your project scalable since you are using the software architecture of the cloud. It will also sensibly decrease the problems given by maintenance, support, and operations by having the application run on computers belonging to the vendor.

These are the cloud computing services you can find to help your company with storage issues or application issues. Look out for the best solution and you will be on your way to leverage cloud computing.


Data Analytics is Making Waves in These 5 Fields

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The amount of computing power available today is truly remarkable as compared to the scenario just 10 years back. Constant innovation in technology is driving markets upside down more often than sustainable levels.

Trends are changing fast and the industry has not been ready to cope up with them. Jobs are being put to obsolescence through new technologies like Artificial Intelligence, Automation, Cyber Security, Data Science, and Robotics. Let’s look at 5 fields that Analytics is creating waves in to understand the changing business landscape:

  1. Real-Time Analytics
    This is set to be one of the most disruptive business forces today, so much so that 91% of data scientists are interested to work with real time data. Immediate insights are naturally bound to be more useful than analyzing a pool of collected data. Thus, this is helping companies to embrace new opportunities which would have otherwise proven impossible to spot.
  2. Changing Hiring Patterns
    Talent Acquisition is now based on better-informed decisions while recruiting, thanks to analytics. Big data helps identify red flags during the hiring process. The cost of acquiring new employees and training them is going down. Research suggests that a staggering 69% of talent acquisition professionals make use of age-old operational methods such as spreadsheets and other ad-hoc tools to maintain databases and analyzing key business metrics, hence the vast scope on using analytics.
  3. Improved Marketing Efficiency
    What makes analytics even hotter for you to learn about? The fact that it is also being extensively used to derive what the customers are actually interested in buying, especially in the e-commerce space. If you’re a decision maker, such insights will help you in defining market blueprints for the company. Every step of a customer’s buying journey – Awareness, Consideration, and Decision, is optimized via data pools to streamline all aspects of the marketing funnel.
  4. Healthcare Ingression
    With new apps and wearable techs enabling users to keep a track of calorie count, work as pedometers, and even measure heartbeats, a mind-boggling amount of data is being created and stored. And soon you would be able to share such data and actionable insights with your doctor who will use it as a part of his diagnostic inputs. An example of the scenario is the partnership between Apple and IBM where healthcare data collected from iPhone and Apple Watch users will be shared with IBM’s Watson Health cloud healthcare analytics service.
  5. Cyber Security
    Big data comes with threats, thus making it mandatory for all to leverage data analytics in order to mitigate cyber security risks. Employees, especially in research and development domains, are expected to know all about the technological aspects of handling cyber-attacks. For example, IBM has quickly capitalized on the potential of analytics and cyber security to introduce hundreds of security products.

Thus, such prolific impact of Data Analytics proves how the market is quickly responding to the advantages of technology and shifting gears accordingly. This is bound to have a much worse impact on the job prospects of outdated business methods and technologies, hence calling for an immediate need to upskill for job seekers as well as current employees.

4 Ways Big Data Analytics Is Transforming the Manufacturing Industry

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For a long time, the manufacturing industry was associated with a slew of problems – health risks, worker unions, poor optimization methods, and what not. However, technology and Big Data Analytics (BDA) to be precise, emerged as a game changer and is now taking the factories and production units to the next level.

  1. Making Factories “Smarter”

    Industrie 4.0 is a quintessential example of how modern factories will look like. It’s a German government initiative – a high-tech strategy to promote computerization of manufacturing that has laid the foundation, the roadmap of smart factories covering every process, from product idea to development and from recycling to maintenance.

    Industrie 4.0 comprises of:

  • Interoperability:

    Machines and sensors are connected to a network and work in sync.

  • Automation: 

    Physical devices are capable of making decisions on their own and thus, are automated.
    While experts believe that India is one of the ideal countries to benefit from the Industrie 4.0 model, Cincinnati, Ohio has already declared itself an “Industry 4.0 demonstration city” and is investing a significant amount of money for innovation and development in this area.

Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Optimizing Quality Checks

    Intel has been one of the biggest companies to actively incorporate BDA into its manufacturing processes. Since quality assurance is an important part of its chip-manufacturing process, as is with most manufacturers, it has to run about 19,000 tests on each individual chip. However, harnessing the power of BDA it was able to drastically reduce these steps. For instance, Intel’s analytics system can now go through historical data collected during the manufacturing process at the wafer level and identify only those chips that actually need testing. The chipmaker saved about $3 million in manufacturing costs way back in 2012 using the predictive analytics process implemented on its line of Intel Core processors.

  1. Improving Accuracy and Quantity of Production

    McKinsey gave the perfect example of how BDA can improve manufacturing practices to a great extent. A bio pharmaceuticals manufacturer that produces a certain category of pharma products involving blood components, hormones, and vaccines has to monitor more than 200 variables to ensure purity. However, surprisingly, the yields of two separate batches of the same product produced using the exact same process can vary by as much as 50% to 100%. Given how expensive health care products can be, even a 10% yield difference can cost a lot. Fortunately, there is an easy solution. By dividing the entire production process into smaller segments and applying data analytics on each, the project team can process the inter dependencies and identify the parameters directly responsible for the yield difference. So, modifying these parameters accordingly the team can improve the production quantum by as much as 50% easily, thus saving annual costs by as much as $10 million.

    Read Also: How To Solve The Biggest Industry Problems With Big Data Analytics

  1. Bettering Collaboration to Promote 3D Printer Factories and MaaS

    3D printers are trending as much BDA. A 3D printer factory can work naturally and most efficiently on a foundation set by BDA. Moreover, we can have a new type of service – Manufacturing-as-a-Service (MaaS) just like Software-as-a-Service we have today.
    3D printer manufacturers such as Materialise and Shapeways are already working on MaaS. With a production of about 200,000 items a month, the latter is doing an astounding level of business with the help of automated software and 3D printers that run 24/7. With BDA, these factories are able to work in a highly collaborative environment where the flow of data and information through engineering, machine operators, quality control, etc. is seamless. The result is remarkable efficiency and quick feedback implementation.

To conclude, Big data analytics (BDA) is the future of manufacturing. It’s providing us the tools and the technology to help create the world where there are automated factories that produce at their highest efficiency and cause minimum wastage of time and resources. Also, the top players are already aware of it and so have taken the lead.

5 Free Resources for Every Business Analyst

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The past few years have witnessed a steady democratization of data. The sheer volume of data generated by present day businesses puts the role of a Business Analyst in particular focus. Gartner holds out 2017 as ‘The year that Data and Analytics go mainstream’. This creates a strong demand pull for capable and skill ready Business Analysts, and the Internet provides ready access to a treasure trove of free resources that ought to be a part of every Business Analyst’s Toolkit.

Follow Leading Analytics Blogs

The best things in life often come free. So do these blogs which are insightful, informative and allow the reader the comfort of reading and upskilling at one’s own pace. Some of the most notable mentions are:-

  1. Forrester
  2. IBM Big Data Hub
  3. Oreilly
  4. Oracle
  5. ZDNet
  6. Business Analyst Learnings
  7. Bob The BA
  8. BA Times
  9. Modern Analyst
  10. The Analyst Coach

Reading these blogs will serve two key purposes. On one hand, they will render an industry overview of the current best practices to the reader. On the other hand, they will provide pointers on which direction the individual should proceed in order to further develop him/ herself.

Shadow Analytics Influencers

The analytics space is clustered with data scientists and veterans who have championed the field. Many of them regularly share interesting knowledge nuggets that are worth absorbing and pondering over. A few of these influencers are:

  1. Jeremy Waite: Jeremy heads Digital Strategy for Salesforce’s EMEA Marketing Cloud and can be followed here.
  2. Ben Lorica: Ben is O’Reilly Media’s Chief Data Scientist and Director of Content Strategy and an authority on Big Data. He can be followed on Twitter here. He blogs here.
  3. Bernard Marr: An astute analytics practitioner and author, Bernard can be followed here.
  4. Kirk Borne: Renowned in the data community especially as a data scientist, he posts here.
  5. James Kobielus: Spearheading IBM’s thought leadership projects in analytics, his posts are available here.
  6. Avinash Kaushik: An Indian focusing his data knowledge around web analytics and customer satisfaction, Avinash’s blogs are a delight to read.
  7. Paul Shapiro: Being a digital marketer behind data, Paul takes a slightly different approach than other influencers and focuses on the analytics behind marketing.

The follower can make a weekly list of all fact based tidbits that are shared and also use this list for further online research and study.

Attend Webinars

Webinars are seminars conducted over the web. In spirit, they are the closest thing to classroom coaching with the added convenience of the viewer’s home. Webinars provide participants some great opportunities to pick up new concepts. Here are some effective webinar sources to follow –

  1. Gartner: This is an expansive repository to learn anything about Analytics. From how to measure customer experience to building a data-centric organization – everything can be found here.
  2. Dataversity: Slightly more advanced, the content here relates to the details of data governance, data modeling and how to monetize.
  3. Kissmetrics: The sessions here are built around embedding analytics in eCommerce and SaaS businesses.

All of the above will help arm an individual with the know-how of what is needed for analytics assignments and how to go about them.

Start Playing with Data

It is one thing to learn theories and quite another to put them into practice. Having picked up the concepts, the individual should start living with and loving data. Several online portals such as Analytics Vidhya provide various data sets and challenges for users to solve. The more time the user spends solving such challenges, the better he/she gets. There are also data hackathons and online challenges all of which can be good communities to connect, share and grow. Some of the better-known resources are:-

  1. Springboard
  2. Event Brite
  3. Tessella
  4. Venturesity
  5. Datazar
  6. Data World
  7. KD Nuggets

Visualize, Visualize, Visualize

Finally, analysis is only half the battle, while presentation is the other. Every Business Analyst worth his/her grain of salt knows how important a well-designed dashboard or report is. A user must learn the tips and tricks to build a visually appealing dashboard that is intuitive and instantly connects with a business expert.

Tableau is a leader in this respect finding place in Gartner’s 2017 Magic Quadrant. It allows for one year’s free installation and uses for learning purposes. There is a steady community of help and support around this. Blogs like Analytics Vidhya, provide a step by step guide on how to get better at the visualization game.

How to Get the Non-Technical People in Your Office Excited About Analytics

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Almost every modern business today is looking to tap the power of analytics—some to gain competitive advantage, others to gain market leadership, and some others to simply avoid being obsolete. Consider these findings from a report by Salesforce, which says that as many as 90% of high performing companies feel that analytics is critical for driving the company’s business strategy and improving operational outcomes.

Fostering an Analytics-Driven Culture
More and more organizations are now concentrating their efforts on building an analytics-driven culture by putting analytics in the hands of every employee in the organization. While organizations have embarked on the journey to bring about a culture of analytics, a long road awaits them ahead. Driving a culture change is hard and requires commitment from every business function.

A culture change strategy has to be carefully planned and executed else it can fall flat. For instance, if it impacts employee productivity or adds a new set of responsibilities, then it is bound to see a lot of resistance. Then, of course, there are non-technical employees in every company who fail to see the value in data. Getting these non-technical employees excited about analytics is a different ball game altogether.

We look at a three-point approach that you can adopt to make analytics interesting to non-technical people:

  1. Showing Results: Just telling employees the numerous benefits of analytics is not going to do the trick. The key lies in showing concrete results regarding how data analytics can drive better business decisions. For instance, you can get the marketing team to jump on the analytics bandwagon by showing them how insights derived from analytics can help them deliver individualized messages and product offerings. On similar lines, sales team needs to know exactly how leveraging analytics can help increase revenue share; product development team needs to understand how they can design better products using analytics, and so on. Once they start looking at the advantages, it will only be a matter of time before they realize the game-changing potential of analytics in business decision making.pgp business analytics (pgp-babi) great learning
  2. Making Analytics a Natural Part of the Workflow: If employees have to undergo training to learn a new tool or are required to adapt to a new interface to leverage analytics, then they will resist getting on-board. This is where embedded analytics, wherein an analytics tool is integrated into a business application instead of a separate platform, can prove instrumental. Through embedded analytics, features specific to an analytics platform are made available on a business application. In other words, analytics becomes a natural part of the workflow. This significantly improves analytics adoption, especially amongst non-technical people, as they don’t have to leave their business applications and at the same time get information in context. A report from Logi Analytics states that users adopt embedded analytics two times faster than traditional analytics tools.
  3. Think Big, Start Small: While your end goal could be to bring in an analytics culture throughout the organization, it’s easier said than done. You need to understand that the shift will be gradual. Adopt a step-wise approach and target the departments where benefits will be most visible. Regularly share successful case studies across the organization via emails, news updates or town halls. Conduct training sessions on how similar results can be achieved for every function and department. In essence, devise a long-term strategy, starting with key departments and moving on to transform the entire company culture.

The journey to be an analytics-driven organization and in course taking even non-technical onboard is far from fast and easy. The key to transformation lies in being patient—remembering that getting everyone used to analytics and trying to transform an entire company culture cannot happen overnight.

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6 Reasons Why the Healthcare Industry is Investing in Analytics

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New age technologies are changing the world as we know it. And there is no better example of this than the impact of big data and analytics on the Healthcare Industry. Apart from helping hospitals and companies to cut down on costs and increasing profits, analytics has been instrumental in improving the quality of life by helping to diagnose diseases, determining the most effective course of treatments, and decreasing the overall mortality rate.

As Charles Doarn, director of the Telemedicine and e-Health Program at the University of Cincinnati puts it, “Our healthcare system is in desperate need of reform, and technology is one of the tools that can help. It can be a paradigm shift in how we practice medicine.”

  1. Prevention of Diseases – Prevention is better than cure. The latest wave of wearable tech gadgets and healthcare apps flooding the market is reminiscent of the paradigm shift on the consumer side, with an increasing amount of investments being made to maintain body fitness rather than repairing the damages of an inactive lifestyle. In a poll carried out by Accenture, 85% of the physicians say that the use of wearable tech helps to increase patient engagement by enabling them to keep a track of their daily regime. Furthermore, 73% of organizations say that they have seen a considerable positive ROI through Big Data Analytics. To expect further returns, more healthcare companies need to tweak their business plans to include big data and analytics in their long-term growth strategies by leveraging apps and wearable tech markets.pgp business analytics (pgp-babi) great learning
  2. Aids Diagnosis – Analytics provides a mechanism to analyze the volumes of healthcare data created every day and derive important insights about the ailments which would otherwise prove impossible to predict. At a micro level, doctors will soon be making use of analytics to know the complete history of their patients, thus administering better treatments. At a macro scale, access to a pool of nationwide healthcare data will help governments to spot epidemics and outbreaks, hence developing coping strategies in advance.
  3. Competitive Necessity – Research suggests that better integration of analytics and big data into the healthcare industry can help save as much as $300 Billion per year. And this is just in the US alone. Thus, the cost of inaction can prove costlier than the cost of action, since competitors in this domain are bound to integrate analytics sooner or later. The global healthcare data analytics market has been growing at a CAGR of 25.04% over the period 2013-2018, which has put a lot of pressure on small and big companies alike. Hence, basic Game Theory dictates that everyone has to jump in eventually to stay in the game. Implementing in-house analytics solutions such as dashboards for clinicians with necessary tools to analyze incoming data can go a long way.
  4. Reduces Fraud and Abuse – Costs in the Healthcare Industry are often increased due to cases of fraud and abuse which exist in the market. Such frauds can be in the form of multiple prescriptions being issued in the same person’s name, patients receiving treatment from multiple hospitals simultaneously, etc. Analytics helps to safeguard them from such abuse by using predictive analysis to detect exceptions and patterns. For example, the Center for Medicare and Medicaid Services prevented more than $200 Million in a single year using this practice.pgp business analytics (pgp-babi) great learning
  5. Real-time Monitoring – The actual power of data analytics can be realized through the analysis of real-time events to better monitor the health of patients. Care providers are notified of any sudden fluctuations in the patient’s conditions to help them respond to emergencies and take crucial life-saving decisions on the way. On the other hand, advanced wearable tech can detect any future hospitalization requirements even before the doctors realize the need for the same. For example, the weight of the patients suffering from obstructive heart disease can be tracked to detect fluid retention and identify the need to visit the hospital for check-ups.
  6. Increases Confidence – Since the decision-making process is backed up by extensive insights about the patient’s health, the management can take better decisions and be confident about the expected outcomes. This drives up efficiency and makes sure that the business environment is on the course of continuous development. Moreover, quicker business and administrative decisions can be taken which further optimize operations. The Texas Children’s Hospital was able to generate savings of $74 Million through operational improvements via Analytics over a course of just 18 months!

Big Data and Analytics is truly affecting every aspect of healthcare, giving rise to a better practice of evidence-based medicine as compared to conventional medicine. It provides an extensive 360-degree view into the health background of patients, thus facilitating pathbreaking healthcare management techniques which have never been seen before. Hence, there is an increasing need in the industry to invest in Analytics for a healthier future.

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Decoding Internet of Things (IoT)

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It’s a Monday morning and you wake up with your regular 7.00 am alarm. Reminding yourself of an important meeting at 10.00 am, you get out of bed. As soon as you reach the dining room, your coffee maker starts brewing. The coffee is just the way you prefer it, dark with little sugar. You quickly finish your mug and head for a shower. The water is exactly as hot as you like.

Dressed up, you step out of the house, which locks down on its own, switching off the ACs and lights while drawing close all the curtains. Your car drives up to you, the air conditioner is already on and your current favorite track plays on as you settle yourself. The car takes the shortest route to work and on the way reminds you that it is due for regular servicing, while your fridge also sends an alert to visit the supermarket on your way back as you are out of bread and milk.

No, this is not the moment when you wake up from your dream. Although it does seem like a scene from a sci-fi movie, this will be commonplace in the world ruled by the Internet of Things (IoT).

Sensors Everywhere

IoT signifies a concept, wherein all the everyday devices from a refrigerator to a chair, a car to a toothbrush become smart objects. Embedded with built-in sensors, these devices gather data and take action on that data via networks and cloud-based software platforms. In simple terms, these devices become interconnected and have the ability to “talk” to each other. So, when you dismiss your alarm, your coffee maker gets the signal to keep your coffee ready, which further alerts your shower, and so on.

From the business world to transportation systems, retail, manufacturing, and our everyday lives, IoT will soon be used everywhere for making services smarter, better and more efficient. And IoT transforming our world in a big bang way will happen sooner than you think. Sample these findings:

  • According to Gartner, businesses are rapidly exploring IoT for facilitating new business models, improving efficiency, and generating new forms of revenue and are on pace to employ 3.1 billion connected things in 2017. 25 billion connected ‘things’ will be in use worldwide by 2020!
  • Business Insider states that by 2019, IoT will emerge as the largest device market in the world, more than double the size of the smartphone, PC, tablet, connected car, and the wearable market combined. It is expected to result in $1.7 trillion in value added to the global economy.

IoT meets Big Data

With IoT expected to touch every aspect of our lives in the near future, we cannot ignore the other side of the coin: Big Data. Billions of sensors capturing a massive amount of new, unstructured real-time data would mean the universe of Big Data will get bigger. Enterprises will have a whole new world of data to gather and analyze to be able to deliver new insights and establish new patterns and trends. Consider these examples:

  • A manufacturing unit could use insights from IoT for predictive equipment maintenance. Data from machine embedded with sensors could be monitored and matched with its predefined parameters. Any deviation can be easily identified and allows for timely action, preventing a major failure down the line.
  • Similarly, retailers can use IoT to personalize customer experience. In a smart store, retailers can analyze store traffic and send a sales personnel to help a particular customer in case the dwell time in an area is high. Retailers can also use location data to target their high-value, loyal customers in real time by offering customized in-store best price offers.

Given the benefits, in the coming future, every industry sector will keenly evaluate ways to capitalize on the enormous amount of data generated with IoT. That said, deriving value from all this data is easier said than done. A gigantic amount of unstructured data will be pouring in every minute, and not all of this will be valuable. Finding actionable information will be the key in this case. This is where data scientists come into the picture. No wonder, data science is repeatedly termed as the sexiest job of the 21st century.

Already high on the demand index, the popularity of qualified data scientists with the ability to sift through this data and find real, meaningful insights will increase multifold in the coming decade. Industry watchers and analysts are emphasizing that the time is ripe for professionals to add an analytics skill set to their resume.

Key Trends in Business Analytics in 2017

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In the past few years, business analytics landscape has evolved immensely. Companies are no longer wondering how to harness the potential of their data. Instead, they are closely exploring data of all types, shapes, and sizes from all the possible disparate sources to get relevant business insights.

As we look ahead, the business analytics space shows no signs of slowing down and continues to witness the development of several new trends. Let’s take a look at the important ones that will define business analytics in 2017:

1. The Focus will Shift from Predictive to Prescriptive Analysis:

In the last few years, more and more industry segments have started tapping into predictive analytics to understand customer behavior, recognize opportunities and increase revenue share. For instance, e-Commerce companies today rely on predictive analytics to generate cross-sell opportunities and identify when to run a promotion or special discount day. Similarly, hotels predict expected number of guests on a day and adjust pricing accordingly.

While until now gaining predictive capabilities have been top of mind for organizations, scales will now start turning in the favor of prescriptive analytics. Essentially, prescriptive analytics offers insights into steps/decisions you need to take to achieve an intended goal. It helps you see how a particular decision will deliver in the future, empowering you to adjust that decision before it is actually made.

According to research firm Gartner, while currently, only 10% of organizations are using prescriptive analytics, by 2020 the number will grow to 35%. It forecasts the market to grow to $1.1 billion by 2019.

2. Analytics will Become Pervasive, Courtesy Embedded BI:

Increasingly businesses are realizing that best results from analytics can be derived when it is a natural part of the workflow. This is giving rise to the trend of embedded analytics, wherein an analytics tool is integrated into a business application instead of a separate platform. Embedded analytics makes features specific to an analytics platform available on a business app. Thus, you no longer need to install or learn a new tool or adapt to another interface and structure. In 2017, trend for embedded analytics will gain momentum with companies expecting analytics to enrich every business process.

3. Cloud Analytics Will Gain Prominence:

Companies of all sizes today are evaluating cloud analytics platforms as it gives them the ability to scale up and down as per the business requirement. In 2017, the trend is slated to gain more steam with cloud being used not only as an analytics delivery platform but also as a source of data.

Modern-day businesses are fast realizing the need to analyze data outside the existing data warehouse. Data from diverse web applications and cloud-based applications, such as Google Docs and Google Analytics need to be factored in to gain comprehensive insights. Given this, cloud-based and hybrid analytics tools (that incorporate legacy databases, yet allow organizations to take advantage of cloud) will continue to win the race.

4. Collaborative Analytics Come to the Fore:

With data becoming more accessible and sharing becoming easier thanks to the cloud, collaborative analytics will become the order of the day in 2017. Information sharing via PDFs will become a thing of the past. The new age of collaborative analytics will see professionals share live interactive workbooks to make business decisions. Collaborative analytics will help with functionalities like setting up intelligent alerts, sharing embedded dashboards and generating automated reports at a scheduled time to enhance the decision-making process. Leveraging collective intelligence of the organization to look at facts and insights provided by analytics will become the norm.

Clearly, interesting times are ahead for business analytics with all these trends further transforming the space and organizations continuing to invest in analytics. As per Gartner, analytics will remain the top focus for CIOs through 2017. With analytics continuing to be at the centre-stage, the demand for skilled data experts will intensify further in the coming years.