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Tag: Data Analytics Training

What Are The Ways Big Data is Being Used To Create The Next Generation of Mobile Apps?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Big data tops the charts when it comes to providing a considerably better user experience by increasing app engagement and optimizing resources correctly. While it not only makes content for users more relevant but also personalized content and when analyzed from a business point of view, it improves conversion rates. To put it in simpler words, the future of big data is the gold mine that app developers need in providing information and creating apps that users want.

Here is a breakdown of the various ways in which big data is being used to create the next generation of mobile apps:

Seamless and easy to use UX
Big data is incredible in providing insights that help tract every movement of a user by crunching numbers to improve overall user experience. Additionally, it also helps in signaling developers when apps do not meet either the design standards or the UX. Studies suggest that most app users stick to or delete an app based on its user-friendly quotient, which is the ease of use. This kind of information helps big data constantly make improvements for user interface and reduce friction.

Machine learning and artificial intelligence

With the help of machine learning and usage of artificial intelligence, big data can recognize failure patterns if any and suggest improvements.  Also, this helps understand any glitches that might be acting as slowdowns, including loading time for a website or a page.

Predictive analytics and customization
Big data helps customize the user experience and deliver content based on previous usage patterns. This is where predictive analytics come into play by suggesting what you should buy or what you should watch. This gets increasingly better as you consistently use a particular service.

Widely used by companies like Netflix and Amazon, predictive analytics shows up an image or shows pricing options based on user data buying patterns and more. Basics of predictive analytics are taught during a Data Analytics Course.

Increase app engagement

Users often get more engaged with a particular app and keep returning to it frequently. A term referred to as- app stickiness, this actively engages customers more than its competitors and factors like duration session, the flow of content on the screen and churn tracking help in contributing to stickiness.

Real-time analytics

Real-time analytics help an app developer to analyze data related to that app and make dynamic changes based on the present situation. The mobile app market in itself is a pretty dynamic one, where things significantly change every minute. Organizations are using real-time analytics o predict patterns that include flying for airlines when visibility is good, avoiding certain roads to get rid of traffic, avoiding extreme weather conditions, sharing driver and customer live locations, estimates fares at a given point in the day and more.

Evolve marketing strategies

Big data can help make better marketing strategies, by capturing user data that helps app developers understand the kind of people their users are. Existing strategies are reworked on to reach out to new users and rearrange older users. Study of user demographics, buying patterns social behavior of apps, posts liked, websites visited, all of which can be used to build individual user personas which are then used to strategize marketing strategies.

Considerable cost reduction

Lastly, big data helps understand and predict app development costs, since building a standard app might often be time-consuming and quite expensive. This not only includes the app development process costs but also calculates the number of developers, designers, testers and more will be needed to have an app up and running. Additionally, the longer time it takes to build the app, the higher the cost graph goes.

Top Reasons Why Big Data Analytics Is One of the Best Career Moves!

Posted on by Imarticus Learning
Reading Time: 3 minutes

What is Big Data?

We are living in an era where we consume data more than anything, be it food or water or even electricity. If you use technology to help yourself in your day to day chores or if surfing the internet to find information for your purpose is an on-going thing, you are one of the beneficiaries of the big data.

So what exactly is the big data? In the most basic sense, big data can be understood as the collection of data that is extremely large in size and is accumulated on a continuous basis.

Big Data AnalyticsIf we go by the general definition of it, the big data is an industry that deals with systematic extraction and storage of information in the form of data from various data points; it also helps to perform complex analysis of extremely large data sets.

The data stored is used to perform analysis to identify patterns, trends or establish association and relationship among different variables especially relating to human behavior and interactions. Examples of some big data sets include data generated by the stock exchanges, social media sites, etc.

Many big corporations are using big data today to gain customer insights and design their policies and price their products accordingly to gain maximum output from their investment on customer acquisition and other areas.

Big data analytics as a career option

In the contemporary world data is the fuel for exponential growth. Corporations today use data in conjunction with other progressive technologies like the machine learning and artificial intelligence to help process large piles of information and gain valuable insights from the same. The new technologies not only help to do too much in too little time but it also minimizes the chance for random human error, which is very likely when dealing with an extremely large amount of data set.

Since the big data is a fairly new field, very little is known about the career options and the work required to build a career in the domain. If we were to compare it with traditional occupations that are held in high regard, data analysts have a similar role to play as the doctors in our lives. Basically, they are the doctors for data, who help to analyze and scrutinize data and find any anomalies if they exist.

Big Data Analytics CareerThe most important factor why big data analytics is a good career option can be understood through the lens of general economics.

At present the demand for data analytics professionals in the industry is higher than the labor supply for the same, this means that it’s not only a growing field but also the one with higher perks and remuneration.

According to IBM reports the jobs in the data industry in the US alone will increase to 2720000 by 2020, higher than the current supply of professionals in the field. A career in big data is the most sought after especially in the developed economies.

According to sources, the current size of the analytics market is around one-tenth of the global IT market and is assumed to grow and become one-third of the global IT market in the coming years, another reason why it’s a good career move.

Finance and Analytics online coursesThe number of job posting on some of the reputed job portals has also shown a significant increase from the previous year indicating high growth in the field.

Big data analytics adoption is growing with the minute, another important indicator that favors the career move in the data industry is the increase in companies that are adopting big data analytics to improve their day to day functioning and cut their costs on futile marketing and advertisement.

The use of big data helps to make things more contextual for the customers and optimize the output and price for the brand in the process. The big data course is designed to help develop a comprehensive understanding of the subject and is beneficial for those who are eying for a career in this field.

How Analytics Can Help You Prevent Customer Problems Before They Arise?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How Analytics Can Help You Prevent Customer Problems Before They Arise?

It has been proven time and again that establishing customer loyalty is a necessity for companies these days for the simple reason that it needs one-seventh of the resources needed to acquire a new customer. Although the agenda is clear, it’s not that easy these days. With an abundance of choices among consumers in today’s markets, it has become really hard to stop them from switching to a competitor and maintaining loyalty to your company.
Foreseeing a difficult situation before it arises is imperative for any business to get successful and with such an abundance of data in the modern age, organizations can work to address problems and minimize customer problems even before most of them arise. Data analytics, more importantly, Predictive Data Analytics helps companies do that.
A major industry where this can be seen in abundance is the mobile retail industry where the majority of those who come for mobile replacement claim the device is not working properly, while mostly they don’t always know how to use it properly.
Preparing for the unseen
Many people say that you can’t count the number of chicks before hatching, well Predictive Data Analytics can do it for you.
Predictive Data Analytics can be used to predict customer behaviour by utilizing the purchasing and visiting patterns. With modern technology so advanced businesses can use the data prediction tools to get an advanced idea of buyer demographics and plan out their orders and strategies accordingly. A major industrial example of the same is Price Optimizer Software. Companies these days use price optimizer software to determine how consumers will respond to a particular price for their product.
Three of the data predicting models have been mentioned here, these are:-
Data from Visiting
Collecting data on parameters such as the number of visits on the sites, duration of these visits, locations visited and duration since the last visit, businesses can accurately predict the purchasing patterns of a customer concerning the future visits and purchases. This important data gives these businesses an edge which allows them to classify and plan accordingly so that they can ensure regular visits as well as the loyalty of customers.
Predicting the Particulars
Predictive Analytics can also make available the location as well as online as well as offline data to the retail marketers and armed with knowing who the buyers are and by analyzing their website and shop visits retail marketers can successfully predict the buyer’s visit with accuracy up to a really small timeframe.
Certainty Establishment
Retailers can use Predictive Data Analytics to personalize the services provided to an individual or group. A business must have proper marketing techniques and strategies which cover every group and individual alike. With regards to the purchasing decisions, there are many traits to a customer’s overall behavior and when these traits are separated and smartly implemented by companies and businesses, they’ll be becoming better in identifying and segmenting different customers according to the wants and needs.
Conclusion
Several companies these days are trying to leverage the power of Predictive Data Analytics to outgrow their bottom lines and grow their businesses while effectively combating problems before they even arise. With excellent data analysts at their disposal, these companies can effectively predict customer behaviour and get better in predicting customer problems.

Imarticus Learning is adamant about providing the data science enthusiasts with an opportunity to grow their skills in data analytics and benefit from the ever-increasing need of ever-increasing data analysts. The Data Analytics PG course available at Imarticus Learning can be useful for both companies as well as employees to get their data science skills up to date and benefit.

What Are The Tips To Prepare For a Hadoop Interview?

Posted on by Team Imarticus
Reading Time: 2 minutes

The popularity of big data has been growing at an immense rate opening the doorway to a spectrum of jobs that require skilled professionals. Noteworthy among these is the job of a Hadoop developer; challenging, technical and well paid, Hadoop is known to be one of the best segmentation of big data and analysis and a developing platform for candidates interested in a career in data science.
Learn Hadoop to pursue a career as a Hadoop analyst, Hadoop developer, or a Hadoop Architect, Hadoop tester among other job roles on the Hadoop platform. If you are looking for a career in this domain, it is highly essential to understand that a Hadoop developer not just created codes in programming but is also expected to have an expertise of multitasking while as his job, which includes programming in Java, writing scripts, reviewing log files, scheduling jobs across clusters on Hadoop amongst others.
Basic skill set for a Hadoop interview
Hadoop works with a number of other software like Ambari, HBase, Hive, Pig and more, therefore, knowledge of technologies is essential. While it is important to also have an idea about other visualization and ETL tools, SQL, gateway and edge nodes, basic cloud computing, some of the must-have skills an interviewee needs to possess during Hadoop training include JAVA, Hadoop Framework, Pig, HDFS, MapReduce, and Floop.
Tips to prepare for a Hadoop interview
Cracking a successful Hadoop interview does not essentially mean having specified skillsets but also ensuring that all of the interviewee’s questions are addressed. While Hadoop in big data is a relatively new concept, here are a couple of tips to help you prepare better for an upcoming Hadoop interview.
Knowledge of Programming Languages
Java experience is as important as it can since Hadoop is a software-based on Java. If your career path monitors progress from C++ to Java, nothing like it. Knowledge of other programming languages like OOAD, JS, Node.js, and HDFS only add to your skillset and make your resume stand out from the rest of the candidates.
Big Data experience
If you have experience working with big data, a Hadoop interview would be fairly easy to crack, since Hadoop is mostly built for the working of big data.
Technical Expertise
To crack a Hadoop interview, you not just need hard skills for Hadoop but also various other technologies that include Flume, Sqoop, Hive, Pig and more. These technologies often seem smaller, however, they make data processing easier on Hadoop.
Interview domains that are essential to prepare for
Along with a good grasp of relevant skill sets, listed below a couple of interview domains every interviewee needs to prepare for-
Practical experience
Theoretical knowledge is important, however, most interviewees are tested on practical knowledge. Expertise in the practical field subjects candidates to various degrees of exposure otherwise impossible by merely learning theories.
Communication Skills
Hadoop experts have to communicate with people in various other job roles, that often include engineers, analysts or even architects. In cases like these, good communication goes a long way.
Knowledge of domain
The interviewee is expected to know the A-Z of Hadoop along with its basic functionalities. You may be expected to back your interview answers with sufficient theoretical or analytical examples.
Conclusion
Big data is growing at an immense rate and more professionals are getting enthusiastic to work in the field. An extensive Hadoop training can go a long way in helping a big data enthusiast to master the best skills in the market and make it big as a professional.
For more such information, feel free to visit – Imarticus Learning

How Big Data Fits In With The E-Commerce Customer Engagement?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Briefing the Big Data

If you are reading this wearing the glasses you ordered online, chances are that big data has some role to play in your purchase. If you are unfamiliar with the concept of big data reading the following piece of content here is going to add a whole lot to your knowledge base.

Big data can be understood as a big pile of the database that is amassed over time from various sources called data points, the big data helps in the identification of patterns, trends, and aid in providing useful insights for businesses and other parties in possession of the data. It is generally valued by big corporations to target their customers with ease.

The Virtual Customer

Big data is the norm today no matter what industry are you in, Big Data Training is the ultimate solution in the age of digital data and global consumers. Sales is a smooth sail in the digital ocean, courtesy of the big data!

The digital world has a new way of marketing things, different from traditional means of marketing the online and digital marketing expenditure has different components to it in terms of cost for content and creatives to lure in the targeted customer.

So how do you quantify and asses the performance of your marketing expenditure in the digital world? Well to address this query we have to dive deep in the concept of customer engagement in the dot com world. So customer engagement here unlike the traditional methods of marketing is counted through likes and comments and online traffics on the website.

Among the countless boons of technology, the one counted among the giants is enhanced globalization and making the concept of the world as a global village come to life. This has helped a lot of organizations to increase its customer base globally.

Talking about the businesses whose sole existence is credited to the merits of technology, e-commerce has a lot to be thankful for. Anyone who can connect to the World Wide Web could be a prospective customer.

To determine if you can target the one visiting the world wide web to come to visit your store needs a lot of insight about the visitor’s taste and preference his past track record of purchasing a product and the general trend of things, this is where the big data fits into the picture.

Big data boosting Ecommerce

What has been observed today is that customers are missing the human touch which can be understood in a sense as a personal attention deficit that the traditional brick and motor stores provide to a great extent. In the cutthroat landscape of eCommerce, you can’t just go with one size fits all approach, you have to style your market in a customized fashion making it more personalized for people visiting your E-stores.

Today the customer’s willingness to pay is not just limited to providing quality products, in the contemporary the game has changed altogether, people see everything as an experience, the better you polish that aspect of the business the more probability there is to gain a fair share in the market and retrain the customers.

Big data has amassed the data about all the variables that could possibly affect a buying decision. From personalized gifts to customized loyalty programs it delivers upon all the aspects of personalization that will generate new customers and will help retain the existing ones into the customer list.

Research has also shown that those who are using big data tools for their e-commerce business are likely to be more productive and efficient in their functioning. Big data not only delivers for the sellers on e-commerce but it’s also a magnificent time-saving tool for the customers as it helps them in a lot of aspects, from finding the right product to choosing the best fit.

Selling online is a different story, the big data pumps your marketing by only targeting those who matter based on the past records of purchase and preferences, and this naturally increases the conversion rates and the return on the marketing expenditure.

Conclusion

Big data is a game-changer for the e-commerce business segment given the presence and deep penetration of the digital world. Personalization of products and other customized services is enhanced to a huge extent with the application of big data.

How To Improve Your Supply Chain With Data Visualization Techniques?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Simplifying Supply Chain Management

If you have ever ordered a tangible good and got it within the stipulated time it’s because of a good supply chain management process in place by the company you ordered from. Supply chain management entails how a product is transferred from point A to point B, where point A is the place of storage and point B is the customer’s delivery address.

Supply chain management starts from the grassroots level, from sourcing raw materials needed for production to delivering it to the end consumer it covers every aspect of the product movement. The people working to manage the supply chain process have to carefully monitor the entire flow of goods and have to devise a strategy to help propel efficiency and productivity in the process.

The process has in its purview various sub-segments like sourcing, demand planning, inventory management, and logistics, the aim is to make the process smoother by individually focusing and renovating outdated methods of sourcing and distributing products to the end consumer.

The benefits of supply chain management range from increasing efficiency, cost minimization, and profit maximization to better demand planning.

All your e-commerce shopping expeditions are made possible because there is a supply chain process working 24 * 7 to facilitate your shopping spree.

What are data Visualization techniques?

With the advent of big data and the demand to present it to probable beneficiaries, the importance of presentation and formatting of data is utmost. It helps to weed out the unrequired information and highlight the ones that help the end-user to gain better insights.

The end goal of providing any data to the user is to allow them to gain valuable insights and take action using the same. Data visualization training deals with how to present the data in an attractive format to grab the user’s attention in a jiffy and help them to implement changes through the knowledge obtained.

Data digestion is important for the end-user to act upon the information, with so much information available on the World Wide Web, obtaining useful data can be tiresome and lethargic. The challenge for the data developers is to make it catchy for the eyeballs.

According to psychologist data visualization techniques help the user to learn better, photographic memories tend to last longer than plain texts.

Externalities like bad weather or accidents have a major impact on the timings of delivery and transportation of goods. To take into account such external factors the data visualization techniques help in mapping the geography and its indigenous externalities based on past records that might influence the transportation and shipment process.

Data visualization techniques for SCM improvement

Data visualization techniques help to improve and renovate the supply chain dynamics on a macro scale of things. Supply chain management has various components, from demand planning to doorstep delivery, everything can be perfected with data insights.

Let’s understand how data visualization improves supply chain management. So if we trace back to the core of the supply chain it all breaks down to identifying the perfect routes. Data visualization techniques can help to identify and demonstrate the details of the route and the alternatives present transport a product from point A to point B.

Plotting the complex relationship between various data points related to supply chain management through network charts and flow charts the pictorial representations enriches the content of the data.

Now the data regarding the routes might include the distance covered, time taken, traffic congestions, road construct, etc. So for identifying the perfect route, you have to factor in for all these elements and then reach a conclusion. How tiresome will be to dive deep into the subject matter? There come to visualization techniques that will help to put out the information in a precise manner catering to the needs of the user.

According to various research related to data visualization, it was found that companies are spending more on the use of immersive visual ecosystems to better their data visualization for the supply chain management. The benefits of the same have been reaped in the form of better inventory management and improved performance.

Conclusion

The implications of data visualization techniques in the field of supply chain management are enormous. From reducing time to analyze complex data to making more informative changes in a complex variable scenario the data visualization technique has changed the supply chain management landscape drastically. Improving the duration of transportation and delivery of products based on data insights factoring in various elements that might affect the situation.

What Are The Analytics Apps To Reduce Operational Costs At Production Facilities?

Posted on by Imarticus Learning
Reading Time: 3 minutes

What Are The Analytics Apps To Reduce Operational Costs At Production Facilities?

Introduction

Digitization has taken over the world with a storm. With numerous benefits, it has proved to be a boon for all sectors in which data is involved. Data analytics is shaping the world in which businesses are evolving. It makes data much more than stored figures. It brings out various trends and patterns of different segments of society. Data analytics courses can help businesses increase their revenues in multiples, optimize costs, Improve efficiencies, make cumbersome business processes much more simple and also using data in ways one could have never imagined.

Applications serving the purpose

Mobile applications have increased the use of data analytics. People use their mobile phones all the time thus making it easy for businesses to acquire a lot of individual-specific data daily. The app-space is huge and is coming up with innovations every second. Not a single area has remained untouched with this huge growth. From Real Estate to food, everything has analytics involved. This innovation has not left production facilities untouched. Analytics is making work at production facilities a cakewalk. Some of the major applications which have their hand in making work easy at production facilities are listed below.

  1. Dozuki

Dozuki is a standard work software that has changed the way the production space operates. Dozuki is dominating the visual learning platform by analyzing various trends and making videos on work instructions like repairing a car, opening a refrigerator, operating a piece of particular equipment, etc. This app makes use of images and video clips that are interactive and translates them into work steps and procedures. It is a very convenient application with a simple user interface. It is packed with everything you are looking for in a production-based application. It removes the complexities of a classroom training program where one can easily forget the basics of a particular procedure. The feedback option in the application is quite effective and the users can give their inputs on how they want their procedure videos.

  • Limble CMMS

The machine count in a production facility is huge. To manage all of the machines together is quite a difficult task. It is a maintenance software that a production facility needs. It monitors machines, equipment, spares and every other aspect which comes under such facilities. It keeps the machines running smoothly. Also, it optimizes the manpower cost which is involved in operating such giant facilities. This application is also rich in video content giving relevant information on manufacturing processes such as extraction, polishing, etc. It also has a simple and easy to use interface. It keeps a constant tap on the performance and productivity of machines. Such kind of applications also helps people to keep a tap on the physical maintenance of machines thus saving a huge cost which production facilities would have incurred on a sudden breakdown of any equipment.

  • Microsoft Power BI

This is a powerful analytics tool that can give insights on every aspect of a production facility. It can compile a huge amount of data on different processes and bring out relevant conclusions and process rectifications. The visuals are easy to understand and provides an in-depth understanding of various processes. It can automate the whole process by running various reports and publishing them in pre-decided time frames.

It removes the complexities of manual processes and makes production an error-free and smooth experience. This application has multi-fold benefits. It saves time and generates reports which can be easily deciphered by the people working in production facilities. Various metrics can be formed and tasks can be monitored with the help of automatic alerts. This helps people on maximizing the returns from the production function. In the end, everything is about optimizing costs and increasing revenues.

Conclusion

With every passing day, data is becoming a part of our lives. Analytics is shaping the whole world into a data-driven economy. With its increasing benefits, it is elbowing its way into all sectors and businesses. The production business is squeezing out everything it can and making the best use of data analytics in its day to day functioning and will eventually make itself a data-driven function that works on digitization and analytics.

How ISPs Are Using Analytics To Help Customers?

Posted on by Imarticus Learning
Reading Time: 3 minutes

The world is driven by big data

Big data is everywhere. Businesses of all tastes and flavours are getting their hands on this mega resource to make their businesses competitive and also to gain useful insights that can shoot their operations to the zenith of success. Big data is loaded with multiple benefits. It churns up the data to bring out conclusions hence, giving a huge helping hand in making vital business decisions.

What are ISPs?

ISPs are nothing but people you need for the smooth functioning of your daily necessity: The Internet. Internet service providers are companies that provide internet services across various geographical locations so that people can access and use the web. This market is dominated by both large and small local players. The big players have the internet lines managed independently by them. The big players of this market are companies like AT&T, Netcom, MCI, etc.  These days a new group known as the online service providers has also come into the picture. They manage all of their operations through online mode.

ISPs and data analytics:

Internet service providers like any other sector are utilizing the applications of big data and making the most of it. Big data has become a profit-driving point for these companies. With the help of big data, the geographical boundaries are analyzed and then the reach of the internet and strength of signals and network concerns are also brought into the picture.

Analysis of data is done based on these parameters and then the expansion of business is carried on after several tests on feasibility, accessibility, etc. These providers use online advertisements to advertise their services, hence collect information based on clicks on the ads, their page staying time and their page abandonment time.

ISPs are using big data to have access to a gold mine full of data. Using analytics, possible customers are tracked down and then presented with an ad based on their individual preferences and search histories. These ads are curated automatically with the application of analytics. This data also helps in providing better customer experiences.

These service providers with the help of big data overcome the bottlenecks of Internet services such as network speeds, connectivity concerns, etc. Also, big data helps inefficient capacity planning and brings out operational excellence. Such mechanisms help these providers to foresee any upcoming issues and glitches which can make the users compromise on their user experience hence, solving it beforehand and providing a buttery smooth surfing experience.

ISPs are providing a next-level user experience with the application of big data and analytics. ISPs are using big data to mitigate a lot of problems and extracting the best out of what is available. The area which is compromised on a little is data security. Though the services providers are extracting out all the goodness of big data and giving an upgraded the internet experience to its users the data entered by its users are on huge servers floating, ready to be pounded on.

Data security is one of the biggest concerns which is bothering individual users. Users regularly feed information of personal importance and relevance to their smartphones and laptops. The data thus entered has no specific, safe place to go. It wanders in an unrestricted environment which can be extracted by anyone.

This problem is being tackled by big data to a certain extent but is being worked on so that the improvements can be made at a really quick pace in the area of data security and privacy of the users. Only data can protect data. Thus, these gaps are being filled up gradually by exploring more and more avenues of big data in this dynamic sector.

Conclusion

Internet service providers and big data are moving hand in hand. Both of them together are capturing consumer behaviour and extracting information out of it at an all-new level. Though there are certain limitations the ISPs and data analytics courses are jointly brought into force to eliminate such hindrances and emerge out with more upgrades. ISPs are sailing quickly and analytics is the captain of the boat driving it towards the island of growth dancing along the waves.

How Big Data Can Help Boost Sales?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How Big Data Can Help Boost Sales?

Selling in general

Sales form the backbone of any organization. You need the revenue to soar up in order to quench up your profit maximization quest, that is what every capitalist aspires to do. No matter how good is the product or the services of a company, it will only grow the bottom line if it gets a good top line, i.e. sales.

Ever wondered how do salespeople close the deal and how do they manage to convert clients on the basis of leads? Well, the process is long and complex, it needs a lot of dedication and commitment to stay at the top of the game. If you ask a salesperson how did they manage to work wonders with a mere lead, the general response will be that they identified the needs of the customer and aligned it with the benefits provides by the products/ services they were selling.

A good salesperson knows the customer inside out and for that, you need to spend a considerable amount of time and money tracking the whereabouts of your customers and maintaining a personal relationship to some extent. You have to be a detail-oriented person who knows the nitty-gritty of things and is able to use the information to your benefit.

Sales in times of the big data

Sales in the times of the data revolution have changed drastically and more in favour of the salespeople easing their work with technology and insights. The above-mentioned approach to sales was commonplace until big data became the next big thing and technology evolved to complement this transition.

In the present scenario, whatever you need to know about your customer is available at your fingertips, thanks to big data. Name, number, email, address, etc. have become very generic and no efforts are required to gather the same. More advanced data about particular preferences, be it food habits or their personal hobbies everything and anything is available for those who use the World Wide Web and smartphones.

It doesn’t matter how insignificant a piece of information is, big data stores everything it can. Mostly the insignificant information about individuals combines to form a meaningful database that helps to obtain statistical inferences about a hypothesis.

For salespeople the big data is like a personal genie, they don’t need to wait long hours for meetings that might not even be relevant at the slightest in hope of conversion. Big data helps to boost sales by allowing salespeople to act upon the customer data available.

Big data Analytics Courses help salespeople by providing the required database to analyze past data and optimize their pricing given the demand and supply of the product or the services. It also aids in providing better visualization of the data to put in the right context. Other uses of big data in sales can be understood by the compelling case studies of the consumer.

What’s necessary for better conversion? Well, a good marketing stint of course. Marketing has evolved drastically over the past decade, with big data marketing that has eased the pain for salespeople through better targeting and advertising of the product and services offered.

From making predictions about what can sell well to designing the products that the existing customers need, big data helps a whole lot to make sales easier and more predictable than ever. Big data is also used to shape customer preferences.

The big data serves a special purpose for companies that are starting up and early in the process, it helps the companies to analyze data generated by the peers in the industry who already have been there for a significant amount of time and then helps to identify the loopholes and design their products and services better.

Conclusion

Sales are the backbone of every organization. You need revenue and profits to continue and grow your business empire, most of the expenditure related to operations is financed by the revenue generated through sales. In today’s scenario, big data plays a crucial role in increasing sales and in turn profitability of an organization. It helps the salespeople to target the right customer and design products more aligned with the needs of regular customers through its data insights.

How Predictive Analytics Help Troubleshoot Network Issues?

Posted on by Imarticus Learning
Reading Time: 4 minutes

 

Ten years ago, if a person had suggested a predictive model to prevent a network failure occurring due to a planned breach, people would have not believed him. Today, that has become a reality thanks to predictive analytic tools and different technologies including big data and statistical modeling.

In simple words, a predictive system looks for irregularities or patterns in data and identifies issues in a network or a server before they transform into bigger problems. This piece of information can then be used to troubleshoot it. An example would be a network outage due to failure in the power supply that can be predicted by looking at the irregularities in the flow of power supply in the days before the outage occurred. The possibilities are immense and that is why it looks so promising.

To make this clearer, let’s understand the basics first.

Analysis of Network Behavior

The basic premise of using predictive analytics to troubleshoot network issues is to let it analyze the behavior of the network. For example, analyzing the flow of data in a communications line can help it understand if any loopholes in it could create a possible entrance for a data breach.

This information can then be used as a preventive measure; a defensive mechanism can be laid out even before the breach is attempted, thereby safeguarding the data available in the line. This not only helps in the security but also in network management and policy setting. To know what is happening inside a server network and monitoring it is real convenience for network managers. It halves their daily maintenance work.

Additionally, analytics can also give out trends and insights to organizations. If a certain type of communication mechanism is known for overloading, companies can avoid creating similar structures and instead opt for better versions or entirely different infrastructures. This information can then be utilized during infrastructure development, especially when it comes to the development of server rooms for IT organizations where data breach and upper thresholds need to be monitored by the second.

Predictive Analytics in Action

Experts suggest that such technologies should be put to use in those sectors where issues can cause discomfort to a whole crowd of people. They are referring to healthcare and other emerging sectors like power distribution and aviation traffic management. Network management in these sectors will help increase safety and security and minimize issues/accidents.

Healthcare systems actually need this technology because it can help hospitals better care for their patients who require 24×7 technical support and are continuously connected to the hospital’s server.

Other than looking at the historical data provided by the network, other parameters like weather conditions are also taken into account. There can be a possibility that a thunderstorm could switch off a hospital’s network because of a power supply failure. If the effect of weather on the network can be predicted, then alternatives can be put in place just in time. Although such alternatives are already in place for emergencies, what such models will help in is better implementation and preparation.

A popular example is the use of predictive analytics in emergency services is how General Electric Power uses AI to manage its power grids in the US. According to this example, the predictive model helps the company get rid of the scope of manual errors in its system. It says that simple errors made at the service provider level can sometimes cause outages in the whole sector. This can be avoided if the data entry is taken online and passes through a filter that is connected to such a model.

Any mismatch in the data as compared to what is expected of it will trigger an alert and the response teams can quickly get in action. This is already being executed by GE Power even as it finds ways to make the entire grid system automated. This does not necessarily mean the absence of service engineers, but just the absence of potential errors that they are sometimes bound to make.

All of this is possible only because of the presence and availability of historical data. Without it, the predictive analytics models cannot compare the new tasks. This is one of the challenges that new sectors face as they do not have sufficient data to work on.

Some Challenges in this Field

Predictive analytics don’t fare well for environments that are rapidly changing. This means that environments where the relationship between two actions is quick, the model finds it difficult to grasp it and thereby ignores it and moves to the next action. This can pose a problem because it can lead to incorrect prediction, or worse, even dangerous predictions.

Availability of data, as noted above, is another hurdle but not something to be worried about. For sectors like healthcare, power, and retail manufacturing, there is abundant data. The challenge then is to source and save it properly which can be used to create the models.

Experts also point to the lack of implementation on the part of engineers. Scientists are continuously toiling to create predictive models that help in error detection but on-field engineers and workers are not supporting the system by providing or utilizing data. This can be a field engineer working on a local transformer for GE Power or a systems engineer at the grids network office not willing to listen to the model’s alerts. This shows that there is also a need for awareness among workers and engineers. This is definitely a radical change in how things work but embracing them is the only way to make it serve us better.

Predictive analytics, despite its active use in detecting and troubleshooting network issues, is still at a nascent stage in the global scale. While some countries and corporations are innovating in the field with ample help from scientific organizations, the practice will only strengthen when it comes to the mainstream. And that might take some more time.