Top 6 ways you can grow your business using data science

• Evidence-based decision-making leads to better business outcomes

Traditional ways of growing a business has been a game of grit and high stakes risk. But with data science at your disposal, you have nearly endless streams of data at your disposal with the capacity to analyze for highly actionable insights that can drive predictable outcomes. Thanks to the help of intelligent modeling by data scientists, an organization can use its existing data to simulate outcomes from a variety of potential course of actions and determine the one that can lead them to the highest probability of desired business outcomes. This means data accumulation, sorting, filtering and more needs to be at the core of every company’s decision-making process. However, this often proves challenging with 80% or more of data remaining unstructured and requiring predictive analytic tools to turn the random streams into actionable business insights. This process is reiterative which means your company can refine both the modeling strategies and decision making to continuously fine-tune and becoming more efficient at making better and more intuitive decisions based on trends.

• Managing Businesses Efficiently

With data science, businesses can potentially unlock the ‘unreadable’ silos of behaviors of customers and markets to unearth actionable insights from their data streams and grow the business along predictable paths. Data Science is the metaphorical magic wand that can discover trends and patterns sitting embedded deep within the data to help businesses make meaningful analysis and at least, have a considerable inkling or prediction of events to come. With the help of data science, businesses can render processes, workflows, and even intangible behaviors more transparent and thus, more manageable. Data scientists can enable you to assess the health of your business and even be able to anticipate the success rate of their various business strategies. Most importantly, they will render every aspect of the business quantifiable by devising key metrics and analyzing relevant data to track business performance. Beyond management, data science can also help companies assess the talents and suitability of potential candidates and help hone leaders by tracking performance, success rate, and relevant factors. This can also potentially usher in a new era of transparency and accountability in employee management across the organization.

• Directing Actions Based on Data Trends and Evidence

The job of a data scientist I to plumb the depths of an organization’s data. This can help them understand the current state of affairs, thanks to insights drawn from existing data through which they can study and analyze trends. This, in turn, will help them to devise plan of action and recommend course correction strategies for better performance, increase in conversion and retention rates, improved efficiency, and profitability. Consider reaching out to an 24/7 IT Support provider for guidance on implementing data analytics in your business today.

• Increased innovation for better business offerings

Companies are starting to increasingly employ AI-enabled analytics tools to assess and improve their offerings and even create entirely new lines of products. A survey from MIT Sloan Management Review showed 54% of businesses using AI to improve time-to-market. Data analytics allows businesses to put together sets of different sets of data strands, such as, buying behavior, consumer sentiment, demographics and more to help understand customer sentiment at a deeper level than ever before. This can help businesses brainstorm to solve pre-existing customer pain points and evolving needs with time. This can help businesses keep their offerings fresh over time. For larger organizations, AI and ML-enabled tools can help sort through potentially hundreds of test scenarios to determine the best possible course of action for maximum efficiency, performance or, productivity benefits. Data analytics also has the ability to discover unknown variables that is beyond the perception or capability of humans to discern on their own due to the sheer enormity of the data. would have never identified on their own. For example, IBM’s Watson was able to sift through digital records to identify six new cancer suppressors within two months. That amount of work would have taken researchers years.

• More efficient talent management for the organization

No need to plod through resumes anymore with big data-enabled head hunting. Now it’s easy for data scientists to sort through the entire compendium of information available on potential candidates including social media, corporate databases, and job search websites to find the most relevant fit for the organization. They are able to use various analytical algorithms like clustering and classification to sift through vast amounts of data and arrive at the right conclusion. The right IT Consulting Company can help you implement data science-driven talent management quickly and efficiently.

• Assessing Business Decisions

With data analytics, companies are empowered to take decisions based on predicted outcomes. The next logical step, of course, is to assess them through several hypothesis-testing tools. After decisions are taken and implemented, companies need to understand exactly how they will impact their performance and growth. It is possible for decisions to throw up unperceived negative outcomes, in which case they need to mitigated or managed swiftly. Businesses stand to save and even make a great deal of money just by not having to go through the ‘trial and error’ phase of non-evidence based decisions and being able to take the most optimal path to growth and profitability.

About Nora:

Nora Erspamer is the Director of Digital Marketing at New Charter Technologies, a group of companies specializing in 24/7 IT support services. She is an experienced marketer and sales strategist with a demonstrated history of working in various technology industries. Skilled in strategic campaign development, lead generation, and marketing automation software. Her blog can be found at https://newchartertech.com/blog/.

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Importance of Big Data Analytics

Organizations can use big data analytics systems and software to make data-driven decisions that can enhance business-related results. More efficient marketing, new sales opportunities, customer personalization, and increased operating performance can benefit. These advantages may provide competitive advantages in a successful approach over rivals. 

How does Big Data Analytics work?

Data scientists, data analysts, statisticians, predictive modelers, and other analytics professionals collect, process, clean, and analyze growing volumes of structured transaction data as well as other forms of data not used by conventional BI and analytics programs.  Increasing volumes of organized transaction data not used by traditional BI and analytics systems are collected, cleaned, and analyzed by analytics professionals.

An overview of the four steps of the process of data preparation is given here: 

Data professionals gather knowledge from several sources: Often, it’s a mixture of semi-structured and unstructured details. Although each organization will use various data streams, some familiar sources include internet clickstream data, web server logs, cloud applications, mobile applications, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the IoT. 

• Data is processed: After data is gathered and stored in a data warehouse, data professionals must arrange, configure and partition the data for analytical queries. Thorough data analysis from analytical queries allows for better efficiency. 
• Data is cleansed for quality: Data professionals scrub the data using scripting tools or business applications. They look for any mistakes like duplications or errors in formatting, and arrange the details and clean it up. 

With analytics tools, the stored, processed, and cleaned data is analyzed. It includes data mining tools, predictive analytics, machine learning, deep learning, text mining, statistical analysis software, artificial intelligence (AI), data visualization tools, and mainstream business intelligence software.

Uses and Examples of Big Data Analytics 

Here are some examples of how it is possible to use big data analytics to benefit organizations:

• Customer Acquisition and Retention: Market data can assist businesses’ marketing campaigns, responding to trends to improve customer satisfaction. For instance, personalization engines for Amazon, Netflix, and Spotify can enhance user interactions and develop customer loyalty.
• Targeted Ads: Personalization data from sources may help create effective targeted ad campaigns on a person and a broader scale. 
• Product Development: Big data analytics can provide insights into product feasibility, development choices, measurement of progress, and guide changes in best outputs for the company.
• Price Optimization: Retailers can choose pricing models that use data from various data sources to optimize sales and model them.
• Supply Chain and Channel Analytics: Predictive analytical models can assist with preemptive replenishment, B2B manufacturers networks, inventory management, optimization of routes, and notification of imminent delivery delays.
• Risk Management: Big data analytics can detect unknown risks for efficient risk management strategies from data trends.
• Improved Decision-Making: Extracting information from business users’ applicable data will help companies make faster and wiser decisions.

Advantages of Big Data Analytics 

• Quickly processing vast volumes of data, in several different formats and forms, from various sources.
• Rapidly making better-informed strategic decision-making decisions can benefit from and optimize the supply chain, activities, and other strategic decision-making areas.
• Cost savings, which can benefit from efficiencies and optimizations of new business processes.
• A deeper understanding of customer needs, attitudes, and sentiments can contribute to better marketing insights and provide product development knowledge.
• Improved, better-informed strategies for risk management that rely on large sample data sizes.

Challenges of Big Data Analytics 

Despite the wide-reaching advantages that come with the use of big data analytics, there are also barriers to its use: 

• Data Accessibility: Storage and retrieval are becoming more complex with more significant volumes of data. Big data should be adequately processed and managed to ensure that less skilled data scientists and analysts can access it. 
• Maintenance for Data Quality: Data quality control for big data takes considerable time, effort, and money to maintain properly. High quantities of data come in from several sources and in various formats. 
• Security of Data: The complexities of big data systems pose specific security problems. It can be a complex undertaking to correctly resolve security issues within such a complex Big Data ecosystem. 
• Choosing the Proper Tools: It can be confusing to choose from the vast array of big data analytics tools and platforms available on the market. So, companies need to know how to choose the right solution that aligns with users’ needs and infrastructure. 
• With a potential shortage of internal analytics expertise and the high recruiting cost, some companies are finding it hard to fill the holes.

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Some of the crucial predictions for this field in the years to come are-

• Data Privacy

The privacy/safety of data has always been a concern. With the exponential increase in the amount of data seen worldwide, it has become even more crucial to secure it. Likely, businesses will now recognize that data privacy and governance can’t be accomplished with separate standalone tools. Therefore, it will most likely be essential to implementing this as an integral part of the analytics infrastructure to serve the objective. 

• Data Scientists

All this while, we have seen data scientists participating in tasks related to the stage of pre-production development. Code translators can draw the best possible conclusions when they are passed on to the next step. But it is predicted that there will be more in the years ahead than data scientists will be entitled to do. They will themselves be able to handle enormous data independently, thereby reducing the number of required code translators. The code translators that will be involved also have the advantage that they do not deal with too much work.

• Emotional Analytics

Customers play a pivotal role, without any doubt, for a company to flourish. Simply put, they’re no less than a company treasure. Thus, understanding customer behavior helps to achieve better outcomes as it helps to work according to their needs and demands. In the coming years, companies will likely begin to prioritize “emotional analytics” as never before. Predictive models and AI would be required to analyze the choice of words, voice tones, facial expressions, and much more to understand human behavior. It will thus pave the way for customer profile tailored products and services. 

• Machine Learning

Machine learning, needless to say, has seen a wide range of applications. So much so that there is barely any sector that has not seen the practicality of machine learning. However, what has been observed over the years is that the building of its machine learning platforms has been of great importance. But the future will likely show us a completely different picture. It is a potential scenario in which we can see business backing out from coming up with their own machine learning platforms. Perhaps now, they realize that more value is acquired by applying Machine Learning to business issues. Investing in Machine Learning is a better option than investing resources on their own in building and maintaining the tools.

• Cost-cutting

2020 has jolted the world to the extent that it might take several years for the world to recover. With that being said, every company will now look for alternatives to reduce costs in the coming years. As far as analytics is concerned, companies may consider partnering with those already established. Because by partnering with new/emerging companies, they would not want to get into any risk. 

Conclusion

There is no limit to how many predictions can be made about anything. The same is the case for analytics. It is not all shocking to keep adding to the list as this sector doesn’t want to get old with practically loads of predictions.

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The data helps improve diagnosis and can help analyze numerous issues, including symptoms, pharmaceuticals, and dosage. Without this data, it will be difficult for medical professionals to come to the right conclusions.

Some of the Advantages of Big Data in Healthcare are mentioned below:

• Enhanced Performance for Activities
• Patient Advance Care and Treatment
• The Right Treatment to Discover Diseases
• Personalized and Inclusive Communication
• Reinforced Access to Key Information

The hurdles to big data analytics in healthcare lie beyond the opportunities. Healthcare Big Data has its features, including heterogeneity, insufficiency, promptness and durability, anonymity, and management. These features introduce several challenges to data storage, mining, and sharing to facilitate health-related science. 

Some of the Complexities of Big Data in Healthcare are:

• Due to the lack of efficient data governance procedures, data collection is one of the biggest obstacles for healthcare organizations. It needs to be clean, accurate, and correctly formatted to use data more efficiently to be used across different healthcare systems. 
• These days, most patient records are kept in a centralized database for quick and easy access, but the real issue is when this data needs to be shared with outside healthcare professionals. 
• Data security is one of the top obstacles with constant hacking and security breaches for most healthcare providers that need to be handled regularly. 
• The healthcare industry must be very cautious when dealing with susceptible data and even patient data, which is significant. Not only can data leakage prove costly to healthcare companies, it is also unethical to disclose it without prior authorization.

Conclusion

Although data analysis brings a lot to the table, healthcare organizations need to make sure that their data is used correctly. Key points to remember are providing appropriate employees with the resources to access the data to enable them to make data-driven choices independently and ensure that the data they obtain is as close as possible in real-time. Big data and data analytics are compelling. It just requires individuals with the knowledge of how to use it behind the wheel of control.

The output of health data is expected to increase in the years ahead. In reality, healthcare reimbursement models are changing; meaningful use and pay for success are emerging as significant new factors in today’s healthcare environment. Profit is not and must not be a prime concern. Healthcare organizations must acquire the resources, infrastructure, and techniques available to leverage big data effectively. 

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Why People Make Use of Big Data

Big data analytics allows organizations to gain better insight, understand client processes, make important organizational choices in compliance with research and qualitative trends, and prepare strategic business steps. Big data helps to make decisions that affect retention and create more significant gains for the company when it comes to human resources and HR organizations. Besides, when used carefully, the data can make a substantial difference. However, not all HR departments are willing to do this, with many still trying to find out how they can process and analyze this massive amount of data. 

Metrics of HR: How to Define Which Metrics are Useful

Knowing what drives an organization is essential for deciding what strategic decisions to lead. This experience can often be seen in their current staff during training courses, onboarding, and performance evaluations in people-centered organizations. Trends will emerge that can drive HR into practice if this information is collected. 

Standard HR metrics include recruitment, compensation, and benefits, training, workforce, organization effectiveness, retention, performance, or career management. 

Three steps are necessary for converting this data into useful information.

• Predictive Analytics: The attempt to predict what might happen in the future based on the past. 
• Study and Surveillance: Collection of information on why and what is happening today. 
• Reporting: To explain explicitly what has happened that can be used for potential reference. 

Use Analytics with People

All this information can then be shown as intelligence, which is actionable. It can also be used to provide potential prediction forecasts to predict a similar incident better before it happens. HR analytics is an on-going method of collecting and evaluating. In order to optimize and make the data more usable, active payroll systems or HRIS must go through the above steps. 

Leveraging People Analytics

Once the metrics for the organization and its processes have been developed, these five effective ways to use big data are used. 

The Achievement of the Best Talent from the Beginning Promotes Success:

HR professionals have direct access to information on what the company needs to be effective. Some of these factors may include resilience, effort, and ingenuity in complex situations. Such values are known as core competencies in some organizations. To understand what behavioral qualities the company best suits, HR managers should look beyond traditional recruiting metrics, for example, years of experience. Use the data and search for a candidate that best reflects the characteristics of the organization. Therefore, it can help to identify qualities to recruit new employees before they are engaged effectively. Collect data about the sources of employment, any increases in salaries, promotion time, and overall efficiency throughout employment. All this data can be used in the future to identify the most suitable candidates. 

The Key to Longevity is to Learn When Employees Leave:

The role of HR managers is not limited to whom to hire, who to encourage, and who the principal performers are to retain your workers. They should understand how the employees go to learn how to keep their jobs. When HR and management can foresee workers’ success and have the chance to implement methods for early recruitment, the issue of devastation becomes less. The ability to predict that top performers already have a foot outside the door in advance, and why, helps key players to stay there before they are tempted to travel elsewhere. 

Knowing the Culture of Business Drives Progress in HR Policy:                     

HR experts affect many of the necessary changes in corporate culture. The challenge is to have sufficient information to decide the use of changing tools. Training programs will help employees show the habits that a company’s culture wants most. Specific incentives to step into roles that can lead by example can be given to culturally acceptable. In the sense of additional compensation and financial rewards to encourage conduct representing company values and having a positive effect on corporate culture. The company itself determines the type of employees it attracts. In addition to the right skills and resources, the experts will always search out an applicant and determine if the candidate is a good cultural match for the company.

Self-Serve Employees Enhances the Quality of Data:                                 

Quality ERP systems now come with a wide range of tools to help people with payrolls and payroll departments to collect information much more efficiently. The self-service applications handle timesheets, expenses, and absences. Efficient ERP software makes it easier for any team to track time, track costs, and book the leave. Duplicated documents from employees to management to HR are not difficult to handle. All workable tasks can be managed remotely by centralizing all these activities on a single portal. Therefore, all information is gathered in one place to make ongoing reporting simpler. The design of communication software and resources for project management means that staffs are up to date and can solve potential problems quickly.                                                                                                                                                                              Implementing Improvements Allows Dynamic Decision-Making:               

Big data analysis is to every company both a profit and a disadvantage. Every organization should collect data and execute strategies; the difference is more successful than the competition. The appropriate use of valuable data from a company must be a priority. Staying ahead requires flexible versatility. Predictive analytics with ongoing research optimize the business, and when needed, a company will be better prepared to act quickly. 

Any organization pursuing real results in response to its talent strategies should evaluate the available data and then act. Such data already exists in the company, and ERP systems will give precisely what is needed to function most efficiently to match the data needs. Transform the data into practice and then, over time, automate this action. The use of human analytics is a constant and non-exclusive method. Break it down into morsel processes, and HR managers will be able to transform the company. 

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Visualization of Big Data is among the most critical components of working with Big Data analytics solutions. When images reflect the flow of raw data, decision making becomes much more straightforward. The Big Data visualization tools should include a specific collection of features to reach and surpass the expectations of the customer:

• Capability to handle different forms of incoming information
• Ability to add different filters to change the outcomes
• Flexibility during the study to communicate with the data sets
• Capability to connect to or provide feedback for other applications to receive incoming data
• Potential to provide users with collaboration options

Listed below are some of the best big data visualization tools:

1. FusionCharts Suite XT

FusionCharts is part of InfoSoft Global, a supplier of data visualization software products. About 80% of Fortune 500 firms use it. In 2001, the concept of FusionCharts came to a 16-year-old Pallav Nadhani. He found himself dissatisfied with Microsoft Excel charting capabilities while completing his school assignment. The charts come with default modes, and it says that it is possible to build the first chart within 15 minutes. Extensive Docs, Ready to use dashboards, and customized Tech Support are included in it. It provides more than 90 charts and graphs, from the simplest to the most advanced, like Funnel and Heat.

2. QlikView

QlikView is a product of Qlik, a software company based in Radnor, Pennsylvania, United States. QlikView is one of the fastest-growing software that is simple to work with for business intelligence and data visualization. It offers Associative Search that uncomplicated decision-making. The Associative Experience lets you concentrate, whenever and wherever you like, on the essential info. It enables real-time collaboration with co-workers and partners and comparative data analysis. It allows you to merge the relevant data into a single app. It also ensures that the right people in the company have access to the information by its secure security features.

3. Tibco Spotfire

Tibco Spotfire is a platform for analytics and business intelligence that gives you fast insight into your data. It is available in Editions for Desktop, Cloud, and Devices. It has a recommendation engine powered by AI that significantly shortens the time of data discovery. The Data Wrangling function allows you to identify data outliers, inconsistencies, and deficiencies quickly. FIFA used the app during the 2010 World Cup to offer viewers analytics on previous results by country teams. Procter and Gamble, Cisco, NetApp, and Shell are Spotfire power users.

4. Watson Analytics

Watson Analytics is a cloud-based analytics tool from IBM that lets you find insights into your data quickly. When you upload your data to Watson Analytics, it shows you the questions that can help you answer and then give you immediately based data visualizations. Through natural language processing, you can also explore your data. Automated predictive analytics, one-click analysis, smart data discovery, streamlined analysis, open advanced analytics, and dashboards for self-service are other prominent features. Watson analytics also allows for cognitive computing, which in turn provides data with more insightful information.

5. Tableau

By integrating data from different sources with just a few clicks, Tableau lets you see and understand your data. By using custom filters and their drag and drop features, you can create interactive and versatile dashboards. Tableau claims to work with the way you think naturally-Ask questions, alter viewpoints, and expose the meaning. It can be found in laptops, the web, and utility models. Tableau Online and Tableau Server allow you to share your data and dashboards easily. It also has an abundance of video tutorials online that make it easy to use, especially for non-techies.

6. Datawrapper

Datawrapper is a convenient tool to build visualizations such as infographics, maps, data tables, and responsive charts. Its use among publishers and journalists is widespread. Popular users include The Washington Post, The Guardian, The Wall Street Journal, and Buzzfeed. It is effortless to use and does not need to be used by a coder.

7. Microsoft Power BI

Microsoft Power BI is one of the big data visualization tools that make it easy for business people to analyze their visual information. They can also build plans based on it. It offers on-premise and in-cloud data access. It has two pricing schemes, one of which can be accessed for free. With a 1GB storage cap, the free one allows you to build, create, and post dashboards and reports. All power BI features are present in the Power BI Pro. It can consume live data, exchange data queries through the Data Catalog, and much more with full interactivity.

8. Infogram

To build infographics, Infogram lets you choose from more than 1 million photos. It makes it easy to access data by allowing you to edit the details in the editor and connecting to your preferred cloud service. Deloitte, Nielsen, Skyscanner, and MSN are some of its users. Steps that allow educators, journalists, and business professionals to visualize their data easily are easy to use. It has produced over 4.8 million infographics, which are viewed every month by over 500 people.

Conclusion

Each bit of data brings a story with it. And these big data visualization tools are the key to the story it tries to tell us. It allows us to consider the latest statistics and the market’s future developments. This is just a scratch in a plethora of web and standalone Big Data visualization solutions and tools. Each business should find the tool that works best for them and transform the raw data input.

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Listed below are types of Big Data Technologies

• Big Data Technologies in Operations- Give guidance on segments such as online transactions or any other data generated by a particular organization to the generated data daily. Using it for Big Data Technology software analysis. The data extracted is used to feed the Big Data analytical technology. In multinational corporations, internet shopping, and online ticket booking for movies, flights, and railways, operational big data technology is in usage.
• Big Data Technologies in Analytics-According to Mezmo, in analytics, big data technologies are more complicated than in operations. Big data analysis falls into this, allowing essential business decisions. This includes inventory marketing, weather forecasting, and time-series search.

Impact of Big Data in the Industry and Information Technology Market

• Artificial Intelligence- Artificial intelligence is referred to as computer science concerned with the creation of smart machines. It is utilized to complete a multitude of tasks that involve intervention in human intelligence. For example, AI in self-driving vehicles, or the iPhone Siri, with augmented machine learning and deep learning. It has provided a groundbreaking paradigm shift to the technology field. Imperious recognition has been achieved by AI’s significant contribution to formulating decisions to achieve an aim. In medicinal treatment, patient care, and surgical procedures, this technology is in implementation.
• DataBase (NoSQL)– NoSQL is a non-relational database delivery system for accumulation and data retrieval. It has implementations in real-time web applications and Big Data analytics. It is used to build and design modern applications. The technology traverses the architecture, horizontal scaling of data in a hassle-free process, and streamlined control over opportunities. Massive data storage occurs in NoSQL. The data structures used in NoSQL are quicker to calculate, unlike relational databases. NoSQL serves businesses such as Facebook, Google, and Twitter that deal with a vast amount of data every day.
• R Programming– R is a free software used for mathematical computation, data visualization, communication with Eclipse and Visual Studio support. Programmers state that data miners and statisticians use it as a popular language in the world. The implementation of R Programming has focused heavily on software and data analytics.
• Data Lakes– Stockpiling all data formats: structured and unstructured data of any size, data accumulation, not saving it by converting it into any structured form. It introduces multiple dashboard and data visualization data analytics, transforming big data, real-time data analytics, and machine learning to boost businesses. Organizations use Data Lake to evaluate machine learning data in log files, social media data, and IoT devices for click-streams. To make quick improvements in customer engagement, development, and decisions, companies interpret knowledge in a better way.
• Predictive Analytics– It is a branch of Big Data analytics used to use current data to assess potential actions. Machine learning tools, data processing, statistical modeling, and mathematical models are in utilization for Predictive Analytics. Predictive Analytics reliably gives the predicted conclusions. Predictive analytics tools offer organizations the value of extracting knowledge and behavior that can take place at a specific time. One such example is consumer’s buying patterns.
• Apache Spark– Apache Spark is the fastest and most widely used data extractor to transform big data. Apache Spark offers support for Python, R, Scala, and Java. It has built-in streaming technology, SQL, machine learning, and graph processing support. Hadoop uses Apache Spark for data storage and processing.
• Prescriptive Analysis– Through this analysis, enterprises get to know how’ within the timeline to produce a specific outcome. When a market transition occurs, prescriptive analytics helps to find multiple factors responsible for good performance. It finds the best approach for customer loyalty and satisfaction, company income, and organizational effectiveness.
• In-Memory Database– This is a computer’s main memory (RAM) controlled by an in-memory database management system. On disc drives, traditional databases are in storage. The traditional database management system, when considered, configures disk-controlled databases. In-Memory databases help to achieve fast operations without disc accession involvement.
• Blockchain– It is a proven Bitcoin digital currency database technology that provides data security. The software removes any deletions or modifications. Using this technology, companies using big data, like companies in the banking, finance, and retail segments, are firmly secured with their data.

Data is the driving force behind the fast growth of the information technology industry. Big data technology is widening the horizon into emerging innovations, making it easier to operate.

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• Banking: Banks make use of big data to keep massive financial information secure. This big data is monitored for spending expenditure trends, credit card transactions to identify and deter fraud before it happens. You can get a call/mail from your bank to make sure the transaction is legitimate if you swipe your card for a high-value purchase. In addition, most banks are using this big data to detect identity breaches. For example, if a salaried person makes small-value grocery transactions at the beginning of the month, but suddenly the bank sees an increase in gas stations and convenience stores all over the city, the bank knows something is up. They may contact their customer and ask about the recent transactions and decide whether the customer’s card is stolen and needs to be frozen or not.
• Online Shopping: Big data in the retail sector has brought about a dramatic shift in the industry. From the moment a customer starts shopping, retailers exploit big data. Targeted ads for your mail delivery, big data seems to be everywhere. For customized shopping experiences, the web pages you visit track your cookies and history, giving retailers a fraction of the information to improve their offerings.
• Vital Monitoring: Wearing a device like a fitness watch tracks routine activity and sleep. A smart way to keep fitness and safety under control. Big data combines with technology to transform our behaviors, helping us monitor our own immunity and ensure we maintain safe habits and combat the vital issues.
• Energy Consumption: Big data, combined with smart IoT devices, make it possible for smart meters to self-regulate energy usage to allow efficient use of energy. These smart meters are mounted in communities to collect sensor data across urban space. They decide where energy recedes and flows highest at any given time, to be redistributed uniformly throughout the entire grid, mainly where the most needed to ensure efficient energy delivery across the given network is required.
• Logistics: Big data streamlines logistics under strict schedules to operate smoothly. This is commonly used in transportation for scheduling flights, forecasting seat demand based on seasonal variations, performing market analysis based on the current social patterns or events, and predicting any flight delays based on weather data. Besides, big data is implemented to forecast accurately the number of planes expected in the future based on current use and deployments of the fleet.
• Digital Advertising: Data science and Big data are widely used in the digital marketing field. You need to see digital billboards, display banners at airports, and apply data science algorithms on different websites to help marketers attract potential customers. Targeted digital ads based on the user’s history and their digital footprints ensure a higher CTR (Click-Through-Rate) relative to conventional ads.
• Healthcare: Healthcare is another sector that produces vast quantities of data each day. Big data lowers the cost of treatment as there are fewer chances of unnecessary diagnosis. This helps predict infectious outbreaks and determine what preventive steps should be taken by identifying them in the early stages to stop preventable diseases. Patients can receive evidence-based treatment, which is recognized and administered after reviewing the same medication’s previous effects.
• Entertainment and Music: OTT and on-demand music services, such as Netflix and Amazon, use big data to devise predictive machine learning algorithms that evaluate the user’s music viewing tastes to recommend new shows and songs that the user thinks would enjoy. You may have noticed that, for example, if you start watching science shows on OTT channels, pretty soon, all your suggestions will be for new shows focused on science and technology since the algorithm knows you like technology.
• Home Security: Big data also plays a critical role in helping law enforcement officials know where the next crime will occur and allowing them to redeploy their resources. When fed into predictive algorithms, vast quantities of big data can help which station may need extra officers to help deter crime before it ever occurs. Big data helps to keep your home secure too. A home security device installed in the house connects to an operator database that automatically identifies any hazards the device senses to alert the user of any suspicious activity. Some security systems also double up as smart power managers by handling home lighting, TV, and other electrical equipment while not in operation.
• Education: The education industry is a storehouse of big data about students, faculty, courses, results, etc. A proper study and analysis of this data is useful in providing valuable insights that can be used to improve the functioning and operational efficiency of educational institutes. A detailed review of each student’s records can help understand the progress, desires, strengths, and limitations of each student in preparing a customized study program in alignment with their career goals.

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Listed below are the best big data tools with their key features

Hadoop

Apache Hadoop is a software platform used to manage big data and distributed file systems. It handles big data datasets using the programming model MapReduce. Hadoop is an open-source framework written in Java, which offers support across platforms. No wonder, this is the topmost best big data tools. More than half the Fortune 50 businesses are currently using Hadoop. Many big names include Web services from Amazon, Hortonworks, IBM, Intel, Microsoft, Facebook, etc.

Xplenty

Xplenty is a software platform for integrating, analyzing, and preparing data for analytics. This will put together all of the sources of your data. Its intuitive graphical interface can help you incorporate ETL, ELT, or a solution for replications. It is one of the best big data tools. Xplenty is a detailed toolkit for developing low-code and no-code data pipelines. It has marketing, sales, service, and developer solutions. Xplenty will help you make the most of your data without investing in the hardware, software, or related staff. Xplenty offers support via email, chat, phone, and an online meeting.

CDH (Cloudera Distribution for Hadoop)

CDH aims to deploy the technology to the business level. It is fully open-source and has a free distribution framework that includes Apache Hadoop, Apache Spark, Apache Impala, and many more. It lets users gather, process, manage, manage, discover, model, and distribute unlimited data.

Apache Cassandra

It is free of cost and open-source distributed NoSQL DBMS built to manage vast volumes of data across multiple commodity servers. It has high availability and is one of the best big data tools, and to communicate with the database, it employs CQL (Cassandra Structure Language). Cassandra’s high-profile clients include Accenture, American Express, Google, General Electric, Honeywell, Yahoo, and others.

KNIME (Konstanz Information Miner)

It is an open-source platform used for reporting, integration, analysis, data mining, CRM, data analytics, text mining, and business intelligence of enterprises. KNIME supports operating systems running Linux, OS X, and Windows. This can be considered a reasonable alternative to SAS. Among the top companies that use Knime are Comcast, Johnson & Johnson, Canadian Tire, etc.

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Big Data Analytics Rising Prominence

Big Data has risen by leaps and bounds, and a report estimated that the growth of Big Data at a Compound Annual Growth Rate (CAGR) of 10.9 % for the projected period 2019-2023.

• The most considerable growth will be seen in the telecommunications and IT industry, as it may raise US$ 63.9 billion in 2020 to US$ 105.2 billion in 2023.

• BFSI will rise from US$ 29.8bn in 2020 to US$ 51.7bn in 2023.

• Government and Defense will see an increase from US$ 13.0 billion to US$ 20.0 billion from 2020 to 2023, respectively.

Use Cases of Big Data Analytics

• Banking: Banks make use of big data to keep vast financial information secure. This big data is monitored for savings spending habits and credit card transactions to track and deter fraud.

• Energy Consumption: Big data, combined with smart IoT devices, make it possible for smart meters to self-regulate energy usage and allow efficient use of energy.

• Online Shopping: Big data in the retail sector has brought about a dramatic shift in the industry. From the moment a consumer begins shopping, retailers exploit big data. It is everywhere, from targeted ads to the distribution of your package.

• Education: The education sector is a storehouse of big data relating to students, teachers, classes, results, etc. A thorough review and analysis of this data are successful in providing useful insights that can be used to enhance the functioning and operational effectiveness of educational institutes.

• Healthcare: Healthcare is another sector that produces vast quantities of data each day. It helps predict epidemic outbreaks and determine what preventive steps should be taken by identifying them at the early stages to avoid preventable diseases. Patients can obtain evidence-based medicine, which is defined and administered after examining the same medication’s past results.

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