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6 Ways Big Data and Analytics Are Driving Profitability for the Telecom Industry

Today’s world of communications is driven by data produced by connected devices, social media networks, customer behaviours, call data records, government portals, and billing information. Telecom operators are finding it very difficult to deal with this avalanche of data. However, big data and analytics solutions can transform this challenge into an opportunity efficiently. By leveraging big data and analytics solutions, telecom operators can integrate new types of data in larger volumes and in real-time. Also, data analytics can help telecom operators in optimizing service, improving customer satisfaction, boosting revenues, and enhancing overall business value. In this article, our team of analytics experts has highlighted a few important ways in which big data and analytics can impact the bottom line and drive profitability for the telecom industry.

Quantzig offers customized analytics solutions to telecom companies to improve returns from CAPEX and OPEX. Request a FREE proposal to know more about our portfolio of services.

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Benefits of Big Data and Analytics in Telecom

Big Data and Analytics Benefit #1: Improved Customer Insights

By leveraging big data and analytics solutions, telecom operators can turn structured and unstructured data into actionable customer insights. Big data and analytics solutions enable telecom operators to establish customer-centric KPIs, develop better customer profiles, and provide more targeted offers. Now, with the help of big data solutions, operators can retain that data longer and join different datasets together to gain better insights. Following are the components of customer data that telecom operators can consider reaching meaningful customer insights:

  • Customer information data
  • Device data
  • Usage data
  • Location data

Telecom operators serve a huge number of customers. By analyzing such customers’ data correctly through customer churn analysis, sentiment analysis and clickstream analysis, telecom operators can segment their customers into similar groups. Furthermore, this can help them to personalize their approach to meet the needs of each customer group and identify valuable customers, which is very crucial for strategic decision-making.

Big Data and Analytics Benefit #2: Accuracy in Churn Prediction

One of the most critical challenges that telecom operators face is customer retention. For any company, acquiring a new customer is more expensive than retaining the old ones. Therefore, churn prediction is very crucial for telecom operators. Big data analytics solutions include techniques such as data mining that helps to determine unseen patterns and decision trees, which enable early detection of customer’s value loss and long-term forecasting. Also, it enables telecom operators to identify factors responsible for customer churn and influence customer decisions.

Our customized analytics dashboard helps telecom companies realize and implement real-time analytics to derive insights on reducing fraud risks and losses. Request a FREE demo right away to gain better insights.

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Big Data and Analytics Benefit #3: Enhanced Customer Experience

Big data analytics offer insights that help in improving the customer experience at every touch point through fast feedback, high-performance services, and personalized offerings. Big data applications allow telecom operators to unlock new insights in real-time that help them to proactively offer products or services to their customers at the right time. Additionally, this aids telecom operators to up-sell and cross-sell their offerings and improve revenues.

Also, with the help of predictive analytics, telecom operators can accurately predict and identify scenarios that lead to bad customer experience. But with the help of big data and analytics solutions, telecom operators can turn all the potential bad experiences into good ones by providing them user-specific solutions.

Big Data and Analytics Benefit #4: Increased Revenue

Big data and analytics solutions can bring considerable value to decision-making and provide more meaningful insights. This can further help to build competitive advantages and a more efficient cost structure. Driven by insights into big data trends, telecom operators can launch new business models and venture towards new niche segments.

Big data and analytics solutions provide telecom operators capabilities for business optimization, that can help them to boost revenue through better-targeted marketing strategies and minimize costs by identifying revenue leakages. By leveraging big data and analytics solutions, telecom companies can analyze the effectiveness of their marketing investments and optimize their marketing spends across channels to drive maximum ROI.

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Big Data and Analytics Benefit #5: Improved Quality Of Service

Big data and analytics solutions can help telecom operators to gain actionable insights into their networks in order to make them optimized, endurable, and scalable. This, in turn, helps in improving service quality in many cases. Also, big data and analytics solutions enable operators to optimize call routing by analyzing their network traffic with real-time call drop rate analysis.

Telecom companies can manage their network better through the measurement of cellular network performance and data traffic in real-time. Big data and analytics solutions can also efficiently help in adjusting maintenance schedules and proactive care by comparing real-time information with historical data.

Big Data and Analytics Benefit #6: Enhanced Security

Telecom companies can use big data and analytics solutions to investigate and identify fraudulent activities simultaneously. Data analytics solutions can help companies monitor large volumes of data gathered from customer demographics, customer usage patterns, sentimental data, geographical usage trends, and behaviour data. Furthermore, this can help telecom operators to predict the likelihood of unexpected behaviour through analytically driven surveillance.

Big data and analytics solutions can also be used for identifying fraudulent claims and unauthorized devices, customer data protection and tracking payment processing. This enhances security and helps telecom companies to manage risks efficiently.

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Top 3 Benefits of IoT Analytics That Businesses Can’t Ignore

From consumers in a digitally rich era to enterprises harnessing new technologies, everyone and everything is connected in an internet of things world. IoT analytics has made it possible for businesses to gain actionable insights that can help them improve decision making effectively. Almost all businesses today are looking for real-time data to decide their next step that delivers a competitive advantage. IoT analytics is an analytical approach that involves analyzing data generated by sensors. The potential of these sensors can be deciphered from their ability to collect data about the physical environment. These data can be then combined and analyzed with various other forms of data to identify valuable patterns that can further help businesses in different aspects such as understanding customers behavior, customer sentiment analysis, detecting fraud, and ensuring security.

At Quantzig, we understand the importance of applying analytics to the incoming streams of data and the impact that it can create for businesses when the volume of data is growing continuously. And to help companies understand and leverage IoT analytics for their business operations, our team of experts have highlighted a few important benefits of IoT analytics.

Want to know how IoT analytics can help you improve your business operations? Get in touch with us.

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Benefits of IoT Analytics

Benefit #1: Helps in identifying inefficiencies

Implementing IoT analytics can enable businesses to turn the big data that they gather into potential revenue. For example, If IoT analytics is used in mining businesses to monitor their processes of production, it can verify accurately whether automated mining systems are performing well or not. If performance is not as expected, it can further help in identifying the part of the process line that fails to yield the expected results.

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Benefit #2: Assist in predicting future trends

IoT analytics solutions are leveraged by many industries today to identify future trends. The energy industry is perhaps one of the major industries that is taking advantage of IoT analytics today. Internet of Things is being used in several ways to improve this industry. One such application revolves around tracking the usage of energy to create reliable estimates of demands in the future. Also, analytics in IoT can help in tracking the performance of different business functions. This is a huge benefit of IoT analytics for the manufacturing industry.

Request a free demo to learn how IoT devices and analytics can help your organizations to leverage targeted marketing and promotions.

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Benefit #3: Improves customer analysis and engagement

IoT analytics can help in identifying the trends of customers buying behavior and patterns. By combining Big Data with IoT analytics solutions businesses can track their customers’ buying patterns and their preferences. Also, it can help in sentiment analysis and provide customers the services they are looking for. Additionally, it also enables businesses to promote targeted advertisements to better serve their customers by offering a personalized experience.

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Weekly Round-Up: A Storyboard on the Benefits of Big Data Visualization

LONDON: Quantzig, a leading analytics services provider, has announced the release of their latest storyboard on the benefits of big data visualization and how it can help businesses churn the large chunks of big data and harness the information lying beneath it. 

By utilizing big data visualization tools, companies can anticipate the available data to trace outliers, trends, patterns and interact with it to make informed decisions. The need for big data visualization tools can be inferred from the fact that the global business Intelligence and analytics software market is expected to grow significantly by 2019.

Quantzig’s analytics solutions have helped more than 55 Fortune 500 companies to make data-driven decisions and boost their revenue. Below, we have rounded up some of Quantzig’s recent success stories for this week and have also highlighted ways in which big data visualization have helped companies to speed up the process of comprehending large and complex data.

Leverage Quantzig’s optimized data analytics solutions that are designed to address your analytics requirements, reduce costs, and deliver outstanding performance. Get in touch with us today!

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#1: Big data visualization facilitated robust risk management strategies: Making informed decisions, improving competitive position in the market are challenging tasks for businesses. Are you finding it difficult too? Not anymore. Big data visualization holds the solution to this. By utilizing big data visualization tools companies can manage storage and integration of big data. Furthermore, this can offer actionable insights into competitors’ strategy and help in devising robust risk management strategies.

Quantzig’s Big Data Analytics Helps A Pharmaceutical Industry Client Devise Robust Risk Management Strategies

#2: How will virtual reality transform big data visualization?  Although there are layers of technological capabilities that companies have been using to seek meaningful outcomes from the available data, the use of virtual reality in transforming big data visualization is a relatively new concept. The large amounts of data generated by organizations can be understood only through a series of data analytics steps, and effective data visualization plays a vital role in enhancing this strategy.

How Will Virtual Reality Transform Big Data Visualization?

#3: Big data visualization improved customer engagements significantly: Are you wondering how to address customer challenges and demands? Data visualization of heterogeneous data can help as it helped one of Quantzig’s clients who was facing the same issue. The client approached Quantzig to leverage its expertise in providing big data visualization solution. Quantzig’s visual analytics solution offered unparalleled visibility into heterogeneous data and enhanced customer engagement by innovating their products and analyzing their journey.

Big Data Analytics Helps a Leading Gaming Industry Client to Improve Customer Engagements

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#4: Big data visualization boosted operational efficiency and on-time delivery: Analyzing real-time information in every step of the production process is not an easy task for businesses. Don’t you agree? This is where big data visualization comes to the rescue. By using big data visualization tools companies can gather actionable insights from various sources, which include social media, business transactions, and information from machine-to-machine or sensor data. Additionally, it can help in aligning business strategies and objectives and identifying industry trends and consumer buying patterns.

#5: Big data visualization enhanced product launch planning and decision making: Creating real-time data repository and reporting system is an essential component for any business for data-driven decision making. This is where big data visualization comes into the picture. It has the potential to assist in analyzing various data sources and setting up data mapping and normalization methodologies which results in better decision making and effective business planning.

Global pharma company improves product launch planning through real-time big data analytics reporting

#6: Big data visualization and master data management reduced the cost of ownership and skyrocketed profit margins: This is one of the most significant success stories of Quantzig, where the client managed to gain strategic insights into the entire manufacturing operations through real-time cost analysis dashboard. This helped them in reducing the cost of ownership and improving profit margins by 10%. Moreover, visual analytics also helped the company to seek instant feedback and pro-actively solve problems before they arise.

Global pharma company improves product launch planning through real-time big data analytics reporting

With the help of Quantzig’s predictive analytics solutions, a lot of businesses are taking improved decisions for better and profitable orders. Are you interested in our data analytics solutions? Request a FREE proposal now!

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#7: Realizing savings of $12 million in 12 months with big data visualization: Do you really think consolidating large volume of data and obtaining real-time insights is a humongous task? If yes, then you must go through our latest success story where Quantzig’s big data visualization tools and predictive analytics solution helped the client to save $12 million in 12 months. With big data visualization tools, it becomes easy to gain meaningful insights for process automation and optimization. Furthermore, this helps in enhancing process coordination, determining operation requirements, planning procurement, production operations, and labor utilization which results in significant savings.

Big data analytics saves USD 12 million for industrial equipment manufacturer

#8: Big data visualization revamped decision making by predicting future network scenarios: Are your data analytics models hindering informed decision making? If yes, then you must be going wrong somewhere in assessing a plethora of data. This is clearly evident in Quantzig’s latest success story where the client was dealing with the same issue. But by utilizing big data visualization tools and predictive analytics solutions, the client was able to realize a great improvement in its decision-making process. Furthermore, these solutions paved the way for quick verification of the results of the analysis with different input parameters and make accurate predictions.

Big Data Analytics Reduces Transport Network Congestion

#9: How to create a synergy between big data visualization and human intuition? The rise in machine learning and computational power has driven the growth of big data visualization. As a result, the trend of adopting big data visualization among organizations for decision making is on the rise. However, still, many companies rely on human intuition and experience for better and effective decision making. But businesses need to realize that combining the power of big data with human intuition can unlock immense possibilities.

Creating a Synergy Between Big Data Analytics and Human Intuition

#10: Big data visualization transformed the business ecosystem: Business forecast might sound complex, but you need not worry if you have proper big data visualization tools in place. A leading financial services firm was dealing with similar predicament but experienced a significant change in their business outcome by leveraging Quantzig’s data visualization solutions. These solutions helped them to discern patterns in the extracted datasets. This enhanced transparency of the risk management process. Additionally, it helped in analyzing assets and a better understanding of idiosyncratic risk factors.

Data Visualization Best Practices | A Success Story on How it Turned Out to be a Wise Investment for a Financial Services Firm

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Weekly Round-Up: A Storyboard on The Benefits of Big Data Solutions

LONDON: Quantzig, a leading analytics services provider, has announced the release of their latest storyboard on the benefits of big data solutions and how it can help businesses derive actionable insights from huge and complex data sets.  With the availability of an enormous volume of data, it has become a daunting task for businesses to quickly identify, fairly evaluate, and continuously drive market excellence.

By leveraging big data solutions, companies can improve decision making and gain the ability to direct, optimize, and automate decisions, on demand, to achieve desired business goals. By applying big data technologies, businesses can make informed business decisions, while ensuring information privacy and security.

Quantzig’s analytics solutions have helped more than 55 Fortune 500 companies to make data-driven decisions and augment growth. Below, we have rounded up some of Quantzig’s recent success stories for this week and have also highlighted ways in which big data solutions have helped companies to drive business growth by delivering data integration and data management.

Are you finding it difficult to curate, process, and analyze the huge data sets available? Get in touch with our experts to check out how our big data solutions can help you.

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#1: Identified and combated fraudulent activities by leveraging big data solutions: The unexpected surge in datasets, though a game changer, has become a major roadblock for many big players. Are you facing the same problem? Don’t worry, our big data solutions can help you just like how it helped one of our clients facing the same issue. By leveraging big data solutions, companies can capture massive datasets prudently and can analyze them to derive actionable insights. Also, big data solutions can help companies to develop a robust big data management system and can prevent fraudulent activities, which in turn, can lead to the growth of ROI.

How Quantzig’s Big Data Analytics Services Help Combat Fraud in the Telecom Sector

#2: How are big data solutions revolutionizing supply chain efficiency? Are you tired of supply chain waste and inefficiency? No more, as big data solutions are here to help you out. Big data technologies have the potential to help companies gain better insights into the ability to track and monitor supply chain activities in real time. Also, big data applications can help in analyzing market trends and suggest optimum inventory levels at various locations. Furthermore, big data analytics has made the collection, tracking, and timely delivery all possible through data.

5 Ways Big Data and Analytics is Revolutionizing the Food Industry

#3: Big data solutions improved decision making and offered actionable insights into day to day business activities: Adapting to evolving consumer expectations and increasing demand for transparency is one of the biggest challenges businesses facing today. Don’t you agree? This is where big data solutions come to rescue. By using big data applications, companies can sharpen their analytic skills and familiarize themselves with the complexities associated with the distribution networks and massive volume of structured and unstructured data. Big data analytics can be linked directly to the decision-making process to deliver the value for improved business performance.

How a Food Industry Player Benefitted from Infusing Big Data Insights into their Decision Making Process

#4: What are the common mistakes businesses must avoid while developing a big data management strategy? A proper, rigid, and reliable analysis enables you to make fact-based and rational decisions, but if mistaken, big data analytics can lead you astray and you might end up suffering a huge loss. So, you must avoid making some common mistakes such as going too rigid in processes and products, relying on the same KPIs always and many more. Therefore, data scientists need to utilize big data solutions properly as these solutions act as a pair of glasses that helps them see the actual reality of a business’ performance.

3 Common Mistakes to Avoid in Big Data Analytics

#5: Big data solutions enhanced the performance of business campaigns: Do you really think tracking demographics and consumption patterns of the end-users is a difficult task? If yes, then you must go through our latest success story where Quantzig’s big data solutions simplified huge volume of campaign data and translated the extracted data into intuitive reports and real-time dashboards that quickly conveyed the status of the ongoing campaigns. Furthermore, this improved the client’s business outcome and customer satisfaction.

Improving Campaign Performance with the Help of Big Data Analytics – A Case Study on the Media Industry

Empower Your Business with Analytics

For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Our analytics solutions can empower your business and real-time decision making. Request a free proposal now!

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#6: Big data solutions increased periodical margins and visibility into manufacturing data: Understanding the best performing products, product families and geographic regions is not an easy task for businesses. But big data solutions can help in dealing with such issues efficiently. This is clearly evident in our latest success story where the client was facing the same challenge and approached Quantzig to leverage its expertise in providing big data services. Quantzig’s big data solutions offered unparalleled visibility into manufacturing data which in turn improved periodical margins significantly.

Quantzig’s Big Data Analytics Helps a Leading Car Battery Manufacturer Blend Data with Inventory and Order Backlog Data-Sets to Increase Periodical Margins

#7: What are the top big data use cases for modern businesses to zoom ahead? Organizations today have access to huge volumes of datasets, but it wouldn’t help them unless they decipher the method to draw insights from them. . Analyzing the benefits of big data is not as simple as putting all the data in a single place. Therefore, organizations should essentially take time to identify and crystallize the true benefits of big data for their business such as the use of big data for personalization of services, detecting fraud, log analytics and much more.

4 Huge Big Data Use Cases for Modern Businesses to Zoom Ahead

#8: Big data solutions helped to identify and explore the new area of investments: Are you facing difficulty in identifying new areas of investments for your business? If yes, then you must be going wrong somewhere in analyzing the unstructured data and curtailing wastage of your resources. Quantzig solved one of its client’s similar issue by offering its big data solutions that proved beneficial in effectively analyzing the unstructured data and uncovering the lucrative areas of investment in the marketplace. Furthermore, this paved the way for establishing optimal utilization management (UM) process.

Big Data Analytics Helps a Leading Health Insurance Company to Establish an Optimal UM Process and Identify New Investment Areas

#9: Big data solutions boosted customer satisfaction, retention rate, and reduced churn rates: Customer satisfaction and their loyalty can be a cause of concern for businesses, but you need not to fear if you have proper big data solutions in place. A leading probiotics food manufacturer was dealing with the same issue but experienced a great change in their overall customer experience by leveraging Quantzig’s big data services.  These solutions helped them to anticipate the future behavior of their customers and segment them accordingly in order of their importance. Additionally, it assisted in offering personalized product and services to the customers, which further aided them to efficiently calculate risks.

Big Data Analytics for a Leading Probiotic Foods Manufacturer: A Quantzig Case Study

#10: Why big data solutions have become a pre-requisite for every business? Today technology has made data one of the most crucial assets for companies. It is one of the important keys that can improve the competitive advantage of one business significantly. In fact, if you are searching for ways to empower the productivity of your business, then maximizing the amount of data being analyzed is one of the most important success strategies.  Therefore, it has become a prerequisite for businesses to leverage big data solutions in order to meet customer expectations and effectively manage the influx of data.

Top 3 Reasons Why Every Business Needs a Big Data Analytics Strategy

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How Advanced Analytics is Changing the Business Landscape in 2019?

What is Advanced Analytics?

Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.

Are you finding it difficult to make informed business decisions, while ensuring information privacy and security? Get in touch with our analysts who can complement you with a shared need to discover and build those capabilities and drive continuous market excellence.

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What are the Different Advanced Analytics Techniques?

In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:

Descriptive analysis

The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”

Predictive analysis

The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.

Simulation and optimization

Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.

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Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.

 

Why Advanced Analytics Is Becoming So Important for Businesses? 

In this era of digitalization  new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:

  • New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
  • Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
  • Advanced analytics software facilitates better analysis of collected data, relationships, and future events.

Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.

Key Benefits of Advanced Analytics

Detailed analysis helps in personalized marketing

Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.

Better performing advanced analytics techniques improve accuracy

Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.

Identify the level of risk in the implementation of the decision

By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.

One of our recent case studies highlighted how advanced analytics helped a consumer products manufacturer to develop an aggressive growth-centric data strategy. Read the full story here.

Final Thoughts

Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.

We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.

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The Mind-Boggling 4 Vs of Big Data

Have a look at the devices you own. In totality, there must be over a terabyte of media, files, and documents over all the devices. Then, there are millions and millions of such devices. If such a volume of data was not enough, then there are supercomputers, data centers, and huge servers all across the world. The overall data currently produced in the world each day is so large that it may require a supercomputer on its own to process it. Such characteristics of big data which is critical for insight generation is dictated by the four V’s.

4 Vs of Big Data

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Why use Hadoop MapReduce Programming?

Its an age dominated by big data. Big data jobs are flourishing, and companies are paying over the odds for data scientists. All news revolves around data analytics, machine learning, and artificial intelligence. All such advanced technologies have to start somewhere. It all begins with the generation and processing of big data sets. Appropriate tools are required to handle such massive data sets which can then be used further for machine learning, AI systems, or generating business insights. Of multiple tools available in the market right now, Hadoop MapReduce is one of the most preferred data processing application, which is based on the Apache Hadoop framework. In short, Hadoop is an open-source software framework which stores data in a distributed file system, and its processing part is called MapReduce. The MapReduce framework is used by several players in the e-commerce industry including Amazon, Yahoo, and Zventus for high volume data processing. So why is MapReduce application so popular and what are theFree demo advantages of using it?

Simple coding model

Programmers using MapReduce framework needs to specify two program functions namely map function and reduce function. MapReduce uses a simple coding model as the programmer doesn’t have to implement parallelism, distributed data passing, or any other complexity. It not only simplifies the coding process but also reduces the amount of time taken to create analytical routines.

Scalability

The Hadoop architecture is highly scalable, as companies only need to add new nodes when they need to increase data storage and computational power. The structure allows it to distribute large datasets across plenty of inexpensive servers which can operate parallelly. Hadoop MapReduce programming enables companies to run applications from large sets of nodes which can use thousands of terabytes of data.

Cost-effective

Any sort of data mining process requires substantial computational power, which translates to higher power needs. Also, instead of buying dedicated servers and workstations, companies can just keep on adding new systems to the existing server to increase their computational power. Traditional relational database management systems incur high cost when scaled to levels of Hadoop MapReduce. As a result, businesses have to classify their data storage needs and downsize data to get rid of data which they think weren’t necessary. In such process, companies might end up deleting raw data to serve short-term priorities. Hadoop MapReduce allows the storage and processing of data in a very affordable manner. The storage cost per terabyte of data has reduced from thousands of dollars to few hundred dollars with this application.

Flexibility

Hadoop MapReduce programming allows businesses to have access to new sources of data and operate on various types of data. Since the programming enables enterprises to access both structured and unstructured data, significant value can be derived by gaining insights from multiple sources of data. Additionally, it also offers support for multiple languages and from sources ranging from social media, email to clickstream. Since MapReduce processes simple key-value pairs, it supports data type including images, meta-data, and large files. Consequently, programmers feel that MapReduce is easier to deal with than DBMS for irregular data set.

Speed

Since Hadoop used a distributed file system, data is stored in a cluster and is easier to map. Hadoop MapReduce programming can access data much faster wherever they are stored in the server. The speed is so impressive that it can skim through terabytes of unstructured data in a matter of minutes.

Security

Hadoop MapReduce programming used HDFS and HBase security platform which only allows access to approved users to operate on the data. Thereby, it protects unauthorized access to data and enhances the system security.

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How Data Analytics Can Find you ‘A Match Made in Heaven’?

The word online dating currently has quite a dodgy reputation. However, dating a complete stranger is not something new. It has been around in the form of meeting someone via friend’s circle, reference, blind dates, and arranged marriages. After all, we all were strangers to each other at some point in time. Nevertheless, the fact that online dating sites or apps match partners with similar interest so efficiently just amazes a lot of people. So one must wonder, how do online dating sites work? How can they so efficiently match partners and help people find ‘the one.’ This is because online dating sites don’t leave getting a perfect match to fate, they rely on data analytics and Free demoalgorithms to spread happiness across the world. So how can technology really build someone’s love life?

How do online dating sites work?

Using personality traits to match

One of the leading online dating site and app OkCupid learns whenever members answer questions that pertain to their personality and lifestyle. It determines how members would like their potential partner to respond and how significant the question is to them. For instance, the importance of race or religion may be crucial to some, but insignificant to others. With over 7 million active users in OkCupid, users have answered over 3000 questions, which assists predictive models to glean information from users’ profile and match them with their perfect mate. The data analytics tools that drive such online dating sites are so powerful that it can take 13 billion seeks relating to users profile in order to load a page of results.

Likeliness and popularity scores

The leading online dating app Tinder, uses likeliness and popularity score to show users the best match. Each profile or person will have a popularity score ranging from 1-10. The app thereby shows a profile that is rated eight other profiles that are similarly ranked. For instance, a new profile is shown to selected few users, if users who have higher likeliness score like the profile then their ratings increase. Otherwise, they are matched against people with a lower score to determine the actual rating.

Personal characteristics

In the world of dating and relationships, individual characteristics can matter a lot. For instance, for some people complexion, height, and age matters a lot, and thereby the matching algorithms show matches that comply with users’ preferred range. However, for others, race, religion, nationality, food preference, and work matters. So people lookout for partners in that specific category, and online dating sites and apps can easily present such matches before them.

Behavioral data

Similar to the movie recommendation engine of Netflix and product recommendation engine of Amazon, online dating sites know if you like a person, you might also like another that is similar. But of course, they must also like you back, so dating apps take the match from both sides before any communication can start. Dating companies are focusing on facial recognition as people are drawn to certain facial characteristics and features. This way they can better match people by learning what kind of facial feature users prefer and match them to people they would like.

Social media monitoring

A lot can be said about a particular person based on his Facebook profile and the kind of post they share. Online dating sites curate user data from different social sites and analyze profile pictures, page likes, and movie, books, and music preferences to make match predictions.

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Numbers and Data Judging Art – Predicting Movie Success with Big Data in the Film Industry

Movies and films are the primary source of entertainment for audiences across the world, so much so that they are almost synonymous with the word entertainment. This claim is only helped by the fact that the valuation of the global film industry stood at $38 billion as of 2016, and is expected to grow significantly in the coming years. The lucrative opportunities persistent in the film inDemodustry is urging production houses, both large and small, to produce and launch multiple movies within a short period of time. Consequently, numerous movie titles are released in a year, and people only recall few titles that are produced by reputed production houses. Another problem faced by the film industry is estimating the ROI, or in simple terms, whether the movie will be a success in the box office. Production houses allocate millions of dollar of the budget into the production of the films. As a result, it is essential for them to predict the success of the movie.

Data analytics evolution in the film industry

Historically, movies have always been about arts; the only data relating to a film would come from box office numbers and ticket sales. It was impossible to predict the success of the movie beforehand, and producers would usually have to rely on their judgment. However, that’s no more the case today as alternative distribution platforms have given players in the film industry a source to collect data. They have replaced traditional approaches of predicting success through data mining and data analytics. It is not surprising that the film industry is turning toward big data to increase the success rate of their movies.

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demand and capacity planning

Benefits of Big Data in Healthcare – A Revolution in the Making

The healthcare industry is posed with a different set of challenges each day, ranging from new disease outbreak to maintaining optimal operational efficiency. Added to that the increasing demand for patients to get better care at their convenience, there is only so much that advances in medical science can do. Tackling such challenges requires a different kind of approach. Big data and advanced analytics may just be the answer to the toughest of healthcareDemo challenges. With a plethora of data available to players in the healthcare industry including financial, clinical, R&D, administration, and operational data, big data in healthcare can generate meaningful insights to improve the overall efficiency in this industry. The significant growth in the healthcare data analytics sector also points towards the rapid adoption of big data in healthcare.

Big data in healthcare – Leading towards a better future 

Advanced patient care

A platform such as electronic health records (EHR) collects all related demographic and medical data including lab tests, clinical data, diagnoses, medical conditions, and allergy information. Having such data facilitates and supports healthcare practitioners to provide quality care. Healthcare analytics can assist physicians to make a better decision and also provide personalized care.

Improve operational efficiency

The importance of big data in healthcare is highlighted by the fact that healthcare companies use it as part of their business intelligence strategy. For instance, by examining historical patient admission rates and analyzing staff efficiency, healthcare facilities can optimally allocate healthcare personnel to a particular shift without having to overstaff or understaff. Predictive analytics is vital to achieving the goal of providing better care and cutting down on healthcare cost simultaneously. Additionally, it can also reduce medication errors, financial and administrative performance, and reduce readmissions.

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