Big Data Visualization means taking the help of various elements like maps, pie charts, and graphs to graphically represent any fact or information. The use of big data visualization tools simplifies complicated information. It also facilitates better statistical analysis to make an informed decision. 

The primary purpose of data visualization is to help readers understand any issue more closely and without any ambiguity. In other words, it simplifies difficult statistics and algorithms and helps the reader easily comprehend the same. 

Especially for large and medium scale businesses, big data professionals closely scrutinize each aspect of data visualization to help stakeholders take appropriate business decisions. Data visualization and big data visualization denote the same thing. Both terms have been used interchangeably throughout this article.

What are the big data visualisation challenges?

Big Data is a complex dataset with a high volume. Because traditional data visualisation techniques have several drawbacks, such data cannot be seen using them.

Perceptual Scalability: From a significant amount of input, the human eye is unable to fully extract all of the pertinent information. If the dataset is vast, even desktop screens can have their constraints. It’s not always possible to fit too many visualisations on a single screen.

Real-time scalability: Real-time information is always desired, but it is rarely feasible due to the time required to process the dataset.

Interactive scalability: Interactive data visualisation aids in understanding the contents of datasets, however because large data volume grows exponentially, it takes a while to see the data.

But the problem is that occasionally, when attempting to visualise the datasets, the system may freeze or crash.

Merits And Advantages of Big Data Visualisation

• According to a research report by MIT (Massachusetts Institute of Technology), brains receive visual information better than text. 
• Data Visualisation facilitates better comparative study and analysis.
• Colors and patterns in the data presentation help readers understand the nitty-gritty of any issue better.
• Data Visualisation is a more attractive and unique way of presenting facts without looking monotonous. 
• Data Visualisation helps you grasp the underlying trends and gist of the information without getting confused. 
• Sometimes, presenting data on a spreadsheet takes lots of time and effort. A simple bar graph or pie chart is enough to represent the same data easily and quickly. 
• Good data visualization presents all significant facts in a detailed manner without looking into text and spreadsheet columns.  

How can massive data visualisation blunders be avoided?

Giving business users insights is big data visualization’s primary goal. It may seem more difficult than it is to select the best visualisation tool from the many available choices (Microsoft Power BI, Tableau, QlikView, and Sisense are just a few product names) and use the proper strategies to produce clear and understandable dashboards. If you believe that you require any help with this matter, you may enlist the aid of big data consultants to assist you in selecting and/or customising the best visualisation solution.

[Read more: Big Data in Healthcare – Everything You Need to Know]

Big Data Visualisation: Types And Techniques

The conventional bar graphs and pie charts still form the core elements of data visualization Services. However, in the last few years, several new techniques and types of big data analysis have emerged and started getting used for both academic and business purposes. 

• Maps
• Flowchart
• Infographics
• Tables
• Dashboards
• Graphs
• Bar chart
• Pie chart
• Bullet graph
• Cartogram
• Word cloud and many more.

There is no end to this list. Some new elements are added to this list every month and find their use in data visualization purposes. 

Uses of Big Data Visualisation

• Big Data visualization has an overwhelming use while writing academic papers or research reports. 
• Data Visualisation is a key in government surveys before they proceed with any development work. 
• It is crucial to helping entrepreneurs take the right decision at the right time.
• Data visualization is useful for sales personnel to extract meaningful data for sales conversion.  
• Corporate tycoons and thought leaders often refer to data visualization tools and elements before announcing new plans or implementing any decision.
• All companies and organizations hire data scientists and statisticians these days. The primary task of data professionals is to extract meaningful information from raw data to help the companies multiply their profits and ROI. 

[Read more: What is Big Data Security? Top Challenges and Solutions]

The Conclusion

We are now living in a digital era. According to Kissmetrics, high-quality infographics and other forms of data visualizations are likely to be read 30-times more than plain text. 

However, you can’t use all forms of data visualizations at the same time. You have to strike a perfect balance between the types of elements that you should use in your research report or survey article and the length of the text to be used in the same. This is how you can create a perfect balance between data, elements, and text while enhancing its readability. 

FAQs (Frequently Asked Questions)

Which are some of the most popular techniques of Big Data Visualisations?

 Line Charts-These are used quite prominently for analyzing sales, ROI, and profits of the company. Firms can also use line charts to analyze their weekly feedback reported by customers. 

Bar charts: It helps firms and business houses understand the intricate aspect of sales like which products were sold in maximum quantity, how much traffic was generated on a particular web page, and many other details. 

How can you use Pie Charts to show data?

Pie charts have become a frequently used element for big data analysis. Under a pie chart, the entire component is assigned 100%, and other key statistical figures are traced out as percentages to the whole component.

How can you perfectly visualize big data?

It depends upon the tools that you use for such visualization. You can use Tableau, QlikView, graphs, Infographics, and various other tools to make a stunning big data presentation using these tools. You must have a good command of these tools before using them in your big data presentation.

If you are new to big data getting familiar with key concepts of big data elements and visualization tools can help you a lot.

The post What Is Big Data Visualization? What Are Its Techniques? appeared first on WTM - Technology, Business, Finance, Digital Marketing.

Data engineers make sure that all data are thoroughly checked, inspected and verified before entering into official records for further analysis, while data architect looks closely into various aspects to facilitate the smooth process of data extracting and structuring them accordingly.

Their primary responsibility is to inspect data warehouses and relational databases to scrutinize the accuracy of data. The ultimate aim of both data architects and data engineers is to extract quality data for analytical purposes.

Data Professionals—Key Statistics

According to BLS (Bureau of Labour Standard, US), data professionals are in great demand these days. The growth in data-related jobs is likely to grow around 9% from 2021-2028.

Currently, more than 50% of data professionals are residing in the United States. There’ll be a massive demand for both data architects and data engineers across the other part of the world.

Why Are Data Professionals In Demand?

Over the last few years, the demand for data professionals has considerably increased. Data professionals are the ones who can conceptualize and process raw data into meaningful information. It helps businesses earn quality leads which can be converted into sales later. By 2025, IDC (International Data Corporation) stated that the present data capacity of servers will increase ten times. Only experienced data professionals can manage such huge data and extract meaningful information from the same. Both data engineers and data architects have to work together to collaborate for the fulfillment of data management goals.

Which of the two roles—Data Architect or Data Engineer—is best for you?

The data architecture function demands years of expertise in a previous data-related role; both roles necessitate a thorough knowledge of database systems, data processing tools, and prior experience working with big data. Keep this in mind while choosing a profession or recruiting. You must first comprehend the distinctions between the jobs in order to assemble a team for data management that works well.

If a candidate is being interviewed for a position as a data engineer, ask about their expertise creating software and APIs as well as their knowledge of various databases, data wrangling methods, and data processing strategies.

Conversely, when interviewing data architects, make sure to acquire a feel of their “data mindset” by asking about the data projects they have previously managed. Observe this

Your data management team will be led by a data architect, to whom you should feel comfortable handing power.

[Read more: An Overview of Big Data Architecture]

Job Profile of a Data Engineer

A data engineer can be hired to monitor any aspect of a data project. Be it strategizing data warehouses or verifying data to frame analytical programs based on data-driven information, a data engineer is a multi-tasking person with several responsibilities that rests on his shoulder.

It is the duty of a data engineer to process a large volume of data and draw inferences from the same for business analytics. Data engineers need to have strong computational and analytical skills.

Job Profile of a Data Architect

Data architects are responsible for the overall maintenance of the data warehouse and storing relevant data on the servers. They have to also ensure that data is properly stored and out of reach of people outside the organization.

Data architects look beyond data modeling and data mining. Data architects have to work in tandem with database administrators, developers, and engineers to create a robust data-driven app, which can help the company achieve its goals.

[ Read more: How Can Small Businesses Leverage Big Data?]

Data Engineers and Data Architects: Top Skills

SQL/databases

Python/R/Java

Data warehousing

Machine learning

Data visualization

Data modeling

Data architecture

Extract, Transform, Load (ETL)

Data Engineer: Other Important Skills

A critical thinker

Analytical abilities

Leadership qualities

Immaculate communication skills

Meticulous in operational skills

A team player

Ability to work under intense pressure

Data Architect: Other Important Skills

Advisory skills

Strong problem-solving skills

Analytical abilities

Negotiation skills

Good listening skills

Good Presentation skills

Complete domain knowledge

Certification Process for Data Engineers and Data Architects

Anyone desirous of handling either of the profiles needs to have proper certifications. Without sufficient skills and professionals qualifications, It’s impossible to become a data professional.

As a data professional, you’ll handle the company’s data, which are highly sensitive in nature. So companies won’t take any chance. They will hire you as a data executive only when you have adequate academic qualifications to manage it.

The Conclusion

It’s clear that the job profile of both data engineers and data architects is not competitive, but complementary in nature. One can’t work without the other. That is why all leading MNCs (Multinational Companies) and big enterprises hire both data engineers and data architects at their firms.

Just as you can’t run a website effectively without the help of both web developers and content writers, similarly, you can’t smoothly run your business enterprise without the help of data architects and data engineers.

FAQs (Frequently Asked Questions) 

What Should I study to become a data engineer or data architect?

Complete a Certified Data Management Professional Course from Institute for Certified Computing Professionals (ICCP), Chicago. Visit its website ICCP.ORG to know more about these short-term and long-term courses and also their recognition.

Can I also study these courses online?

Yes. You’ll find several similar certification courses online offered by various companies like Google, IBM, and others. You have to personally scrutinize these online data science-related courses and their recognition before making payment (if any) or enrolling in any such courses.

Which is the most well-known course in Data Engineering?

Microsoft Certified Solutions Expert Certification course in data management and analytics is quite popular among the youth. It imbibes the right technical skills and attitudes in candidates to help them cope with the intricacies of data management tools and techniques.

The post Data Architect Vs. Data Engineer: What’s the Difference? Which Is Better? appeared first on WTM - Technology, Business, Finance, Digital Marketing.

Many small and big businesses are using this system nowadays. Moreover, we will more and more industries leveraging the benefits in the future. If you are confused about these applications in real life, we are here to help you out. By reading this article, you will find out how big data application is working in real life. So, let’s get started.

Best Big Data Applications in Real Life

As mentioned above, many industries are leveraging the benefits of using big data applications. In the following, we are covering the ten best industries that are using these applications to give everyone a better service:

1. Healthcare Industry

From previous records to patient details, the healthcare industry has to manage a large number of data. However, it could be hard to manage big data when a healthcare sector is only relying on electronic data systems. This is why many hospitals are collecting data from cell phone apps and making use of Big data in Healthcare. Google Maps are also helpful in this case.

2. Education Industry

The education industry has a huge amount of data related to faculty, courses, results, career predictions, and students. Most institutions are learning the use of various analytical tools and big data applications. Big data in the education industry can add value for both students and teachers. You can also track a teacher’s performance by using these applications.

3. Banking and Securities

It’s not unknown that banking is one of the pioneer industries for using big data systems. It’s essential for the banking and securities industry as they are dealing with crucial public data. Moreover, banking sectors have to deal with illegal trading activities in the financial market. On the other hand, this industry also relies on big data to ensure security.

[Read more: An Overview of Big Data Architecture]

4. Insurance

Just like banking, insurance sectors are also using big data applications. However, this industry is still facing some problems managing a huge amount of data. Many companies are using these applications for big data for giving customers personalized services and pricing. Moreover, it has become easier to access everything for real-time monitoring.

5. Government

To improve public relations, most government sectors are using big data tools. It’s more essential as the staff are dealing with a huge amount of data regularly. Moreover, they have to keep track of various things. This is why these staffs need proper studies and suitable analytics tools for big data to stay up to date.

6. Marketing

Marketing is another crucial sector that needs to use big data applications. Most industries are upgrading from traditional marketing strategies. With the evolution of technology, most companies are changing their marketing strategies. For creating online campaigns and other marketing strategies, they need to manage a huge amount of data. So, it can improve the customer relationship.

[ Read more: How Can Small Businesses Leverage Big Data?]

7. Media and Entertainment

You see, the growth of digital gadgets is increasing every year. The media industry also deals with a huge amount of data. Besides digital platforms, social media platforms are also essential in this industry. Moreover, the amount of data in this media and entertainment industry will significantly increase in the future. Many big media sectors use Hadoop for analyzing data.

8. Space Sector

Space is another essential sector where staff is using various big data applications for collecting and analyzing data. From outer space information to satellites information, it’s essential to use some top-notch tools. On the other hand, it’s essential to study data before launching a rocket or space ship. That’s why NASA collects data from different satellites before launching a rocket.

9. Transportation

The transportation industry is quite new to deal with big data applications. However, the use will increase shortly. Many transportation sectors are using these tools for route planning, traffic control, mapping, and more things. Using real-time processing has become a crucial activity. Some sectors are also using mobile apps for collecting data from customers.

10. Weather Patterns

Using big data tools has improved the weather forecasting sectors. These organizations are collecting information from local areas and satellites. The staff can predict more accurately after analyzing data. On the other hand, using these tools is also essential for weather studies, natural disaster prediction, and other related things. Hadoop is a good tool for weather pattern studies.

Conclusion

Finally, you know about the top big data applications in real life. Besides these industries, we will see more small and big companies are using these applications. It’s necessary if your business has a large number of data. For more information, you can start your research or contact an expert.

FAQs

Q: Any big data applications that have an effect on your daily life come to mind?

As big data is used in industries like music, television, and film, it is altering how people live their lives. Medical and Health Services. Marketing and shopping.

Q: What practical applications of big data are there?

It has brought in a “culture of analytics” at many firms, where even non-technical staff members may input data and have access to data-driven insights. Most importantly, almost every industry uses big data to make predictions about consumer behaviour in order to plan for the future.

Q: What types of applications have been created using the big data concept?

Big Data is used in a wide variety of public services, including fraud detection, health-related research, financial market analysis, and environmental protection.

The post 10 Top Big Data Applications in Real Life appeared first on WTM - Technology, Business, Finance, Digital Marketing.

The data revolution has changed our digital world. The growth has started in the early 2000s as data scientists are trying to generate more data in a smaller space. This is why they designed DBMS (database management system). It has become cost and time-efficient.

In this article, we are covering what is big data architecture, layers, best practices, and how to build one. Hence, you will get a complete overview by reading this article. Let’s get started.

What is Big Data Architecture?

In simple words, big data architecture works as the foundation of big data analytics. Data scientists use this overarching system to manage a large number of data. It can provide an environment where people can store vital business information. The architecture includes four big data layers.

Big Data Architecture’s advantages

1. Using parallel processing to increase performance

Big data architectures use parallel computing, in which multiple multiprocessor servers carry out calculations simultaneously, to process enormous data volumes quickly. Large problems are divided into smaller components that can be tackled at the same time.

2. Flexibility in scaling

Big Data architectures allow the environment to be scaled horizontally to accommodate the magnitude of each application. Big Data solutions are typically operated in the cloud, where you only pay for the computing and storage resources that you really utilise.

3. Individual discretion

For use in Big Data architectures, the industry offers a variety of platforms and solutions, including Azure managed services, MongoDB Atlas, and Apache technologies. Combining solutions will yield the the most appropriate choice for your diverse workloads, installed systems, and IT skill sets.

4. Compatibility with associated systems

By utilising Big Data architectural components for IoT processing, BI, and analytics processes, you may construct integrated platforms across many types of workloads.

[Read more: Big Data in Healthcare – Everything You Need to Know]

Big Data Architecture Layers

As we mentioned above, big data architecture has four logical layers. Let’s find out how these layers work in the following:

Sources Layer

This environment manages both real-time and batch processing of big data sources. It can process relational database management systems, IoT devices, and data warehouses.

Management & Storage Layer

This layer receives data from various sources. Moreover, it converts data to make it compatible with analytics tools. The management and storage layer also store the data as per its format.

Consumption Layer

The consumption layer can receive results from the analytics tools. Plus, it will present the result to the pertinent output layer.

Analysis Layer

This layer is also essential for extracting business intelligence from the storage layer.

[ Read more: How Can Small Businesses Leverage Big Data?]

Big Data Architecture Best Practices

Before you design database architecture, you need to understand the value of this system. Moreover, you also need to understand how to use the data for your business. This is why you need to implement the following big data architecture principles:

Preliminary Step

Your company’s big data project should understand the value and vision of your business. On the other hand, it needs to understand architecture principles, framework, and work requirements. In some cases, big data reference architecture should have a good understanding of the business landscapes.

Big Data API

Checking data service API is also essential to practice before choosing a database solution. Make sure you are checking if the database solution has standard query language. Also, understand how to connect the database, the scalability, and security mechanisms.

User Interface Service

An ideal big data architecture should be customizable. This means the database should be accessible for the cloud and people can use the dashboards. So, before you are choosing a database, make sure you are checking the user interface service.

Data Sources

Before a database is designed, you need to consider the data sources. It’s essential because the database can normalize the data to a common format. Moreover, you need to consider this practice as it can take care of both structured and unstructured data.

[Read more: What is Big Data Security? Top Challenges and Solutions]

How to Build a Big Data Architecture

To design big data reference architecture, you need to follow some crucial steps. Let’s find out how to design an architecture:

1. Analyze

The foremost thing you need to do is analyze the problem. A business can have various big data problems such as data velocity, variety, and challenges. Moreover, your business can face other problems with the current system including data warehouse modernization, data lake implementation, data archival, and unstructured data processing.

2. Select a Vendor

The next step of building a big data architecture is very crucial. If you are a beginner, it would be good if you are choosing Hadoop. This is one of the best big data reference architecture tools. So, you can easily manage your company’s big data. Hadoop manages data of some popular companies such as Hortonworks, Microsoft, Amazon Web Services, and Mapr.

3. Capacity Planning

Capacity planning is another crucial step when you are designing architecture. Before designing, make sure you are considering data volume and daily ingestion volume. Moreover, you need to consider multi-data center deployment and data retention period as well. Plus, make sure you are also considering the time period.

4. Disaster Recovery Planning

Last but not least, disaster recovery planning is essential for every database. Your company needs a backup plan for critical data storage. On the other hand, you need to consider some other things such as multi-datacenter deployment, backup interval, and more.

For more information, you can start your research at voltrondata.com.

Conclusion

Finally, you know about how to build a big data architecture, layers, and best practices. You see, this architecture could be very helpful, especially for business information. If you want this system for your company, make sure you are contacting top data scientists. For more information, you can start your research.

FAQ’S

What three categories of big data exist?

Structured data, unstructured data, and semi-structured data are the three categories into which big data is separated.

What are big data’s four characteristics?

Volume, velocity, diversity, and veracity are the four traits that most commonly characterise big data today.

What is a big data example?

Big data is derived from a variety of sources, including customer databases, transaction processing systems, documents, emails, medical records, clickstream logs on the internet, mobile apps, and social networks.

The post An Overview of Big Data Architecture appeared first on WTM - Technology, Business, Finance, Digital Marketing.

In this article, we will cover what is big data, the types, and the benefits of using it in the healthcare sector. By reading this, you will find out how big data can improve the healthcare industry. Let’s get started.

What is Big Data?

Before we start with the types of big data in healthcare, we need to understand the basics. In simple words, big data is a set of complex data. This type of data is mostly used in big industries and sectors. Moreover, big data includes both structured and unstructured data.

When you are using this system for healthcare sectors, it can give predictive, analytics, and prescriptive service. Most data scientists use big data in healthcare to gather patients’ records, boost work productivity, and do other things.

Well, the best thing is there are various types of big data in healthcare sectors. In the following section, you will find out about it.

[Read more: What is Big Data Security? Top Challenges and Solutions]

Types of Big Data in Healthcare

As we mentioned above, you need to know about the types of big data. Let’s find out in the following:

1. Electronic Health Records

Electronic Health Records or EHRs are very important in the healthcare sector. By using this system, a healthcare center or hospital can store all the information of a patient including previous treatment results, illness, and more related things by going from paper to electronic medical records. Most doctors and healthcare staff keep these records up to date. So, they can help patients heal fast.

2. Administrative Data

Administrative data is another popular type in the healthcare industry. When a medical clinic provides various services, they collect all information from patients at the initial stage. Moreover, this type of big data in healthcare collects data from individual patients. Using this service can be profitable for the medical clinic as well. Plus, medical staff can save a lot of time too. Also know Volunteer Abroad.

3. Clinical Data

This is another common type of data. In simple words, this data is used by various institutes or medical clinics. It helps these organizations to allocate resources between each other.

Besides these types of big data, insurance claims and patient survey types are also getting popular nowadays. So, we will see growth in the near future.

[ Read more: How Can Small Businesses Leverage Big Data?]

Benefits of Big Data in Healthcare

Now, you know what is big data in healthcare and the types. As the technology is getting upgraded, healthcare professionals can get several benefits. Here are the top 5 benefits of big data:

Provide High-Risk Patient Care

The best thing about big data is it can help healthcare professionals identify both high-risk and high-cost patients. As it has become easier to find high-risk patients, hospitals and healthcare centers can take quick action for starting the treatment. On the other hand, if a patient had some previous high-risk illness, the big data system can easily find the records.

Real-Time Alerts

Another good thing to use big data is its real-time alerts. By using this system, medical staff can get the warning of various cases. This way, they can prevent these diseases and start the treatment. Besides these benefits, real-time alerts can help doctors and healthcare staff with many things.

Reduces Overall Healthcare Cost

This is probably one of the best benefits of big data in healthcare sectors. You see, medical issues could be very costly. On the other hand, a healthcare center also needs to spend a lot of money on keeping records.

However, when they are using electronic health records, they can reduce the overall cost. On the other hand, it’s also beneficial for patients as they don’t need to pay for unnecessary services. Also know trt prescription online.

Improves Patient Healthcare

Another benefit of big data in healthcare is it improves patient health. When doctors have vast knowledge and information, they can apply the right treatment. It will improve patients’ healthcare. On the other hand, doctors can provide more clinical and accurate treatment. This way, the healthcare industry can grow easily and we can see more advanced treatment in the future.

Easy Patient Diagnostics

As we mentioned above, electronic health records can be very good for easy patient diagnostics. When a healthcare clinic is using this application, they can get medical records, previous illnesses, treatment plans, and more. With big data set, doctors can easily access each patient’s files and prescribe the best treatment.

Creating Innovative & New Therapies

The following example of healthcare analytics focuses on striving for a more promising and daring future for the medical sector. Big data analysis in healthcare has the potential to support the development of novel treatments and pharmaceuticals. Healthcare professionals can spot potential advantages and disadvantages in trials or procedures by using a combination of historical, real-time, and predicted measurements as well as a coordinated mix of data visualisation tools.

Big data analytics in healthcare can also be crucial in the creation of ground-breaking new medicines and cutting-edge therapies through data-driven genetic information analysis and patient-reactionary predictions. Healthcare data analytics can be used to optimise processes, innovate, offer security, and save lives. It is reassuring and clear, and it is the way forward.

Avoidance of human mistakes

You must have heard a number of times that a professional has accidentally administered the incorrect medication. The patient could suffer injury from this error, and it could even be fatal.

Since Big Data may be used to analyse client data and the recommended prescription, such errors can be reduced.

Doctors that see a lot of patients in a day can benefit from using this tool.

Conclusion

Finally, you know what is big data in healthcare and the benefits of it. We have covered everything about big data. However, there are a lot of things to understand. If you need more information, you can start your research.

The post Big Data in Healthcare – Everything You Need to Know appeared first on WTM - Technology, Business, Finance, Digital Marketing.

If there’s an attack, an unauthorized user can spread IP everywhere. As a result, you have to face reputational losses and fines. Moreover, an attack on your company’s big data storage could cause a huge financial loss.

By reading this article, you will find out about big data security, how to implement it, and its challenges. Let’s get started.

What is Big Data Security?

The goal of Big Data Security is clear; keeping out unauthorized intrusions with strong user authentication, firewalls, IDS, and IPS. Even it sounds like other network security strategies, big data allows an extra layer of protection. There are many other advantages of leveraging Big data for small businesses as well.

Its essential cause security tools need to operate three stages that are not present in the network. Let’s find out about these three stages:

Stage One: Data Sources

A company’s big data comes from various data sources. However, there are three primary sources of big data, social data, machine data, and transactional data. Social data comes from various social media activities such as likes, comments, tweets, video uploads, and more. Machine data comes from installing industrial equipment while transactional data comes from payment orders, delivery receipts, and invoices.

Stage Two: Stored Data

You need strong security toolsets such as planning, strong user authentication, and intrusion protection to protect stored data. Additionally, you need to run big data security toolsets with a distributed cluster platform. Plus, your company’s tools need to protect log files as well.

Stage Three: Output Data

The main goal of using a big data platform is running meaningful analytics across different types of data. This data analytics output gives the result to the dashboard, reports, and applications.

How Can You Implement Big Data Security?

There are several ways you can implement big data security to protect your company’s analytics tools. However, most organizations use Encryption for this process. Encryption is a simple yet effective tool for big data protection.

Encrypted data is not useful for hackers as they are not getting the key to access it. The best thing about the encryption tool is, that both input and output information is protected.

Alternatively, you can build a strong firewall to protect the security tools. A firewall can help by filtering traffic that enters and leaves your company’s servers. You can prevent attacks by checking a third party’s access.

[Read more: What is big data and why it is important?]

Big Data Security Challenges

You will face several challenges while securing big data. However, you have to understand that these challenges can pertain to the cloud as well. So, don’t take anything for granted when you are hosting a big data platform in the cloud.

• Advanced analytics tools and nonrelational databases are new technologies compared to other tools. It can be challenging for security software.
• Sometimes big data administrators mine data without giving notification. So, your security tool needs to alert you of suspicious access.
• Big data security tools can protect data storage and ingress. However, they are not that effective when it comes to data output from multiple tools.
• The size of data installation can be too big for routine security audits. So, this can introduce multiple vulnerabilities on several nodes and servers.

Big Data Security Issues

1. Fake Data Generation

Fake data generation is a serious threat to businesses because it consumes time that could otherwise be spent identifying and resolving other pressing issues. False flags for fake data can also lead to unnecessary actions that can potentially reduce production or other critical business processes. One way to avoid this is for businesses to be critical of the data they are working on to improve business processes. An ideal approach would be to validate the data sources on a regular basis and evaluate Machine Learning models with diverse test datasets to detect anomalies.

2. Employee Theft

Every employee now has access to a certain amount of crucial business information thanks to advancements in data culture. Although it promotes data democratization, there is a great likelihood that a worker may mistakenly or intentionally disclose critical information. Startups as well as established tech organizations frequently experience employee theft. Companies must create legal procedures and secure the network with a virtual private network to prevent employee theft. Additionally, businesses can do away with the capabilities of data kept on local discs by using a Desktop as a Service (DaaS).

3. Data Poisoning

There are several machine learning solutions available today, such as chatbots, that are educated on enormous amounts of data. Such systems have the benefit of continuously improving as people interact with them. However, this results in Data Poisoning, a method for attacking the training data for machine learning models. As the altered training data may impact the model’s capacity to make accurate predictions, it might be viewed as an integrity attack. Catastrophic outcomes include logic corruption, data manipulation, and data injection. The best method of combating the evasion is outlier detection, which allows the training pool’s injected pieces to be distinguished from the data distribution as it now exists.

Big Data Security Technologies

You need to use various tools to secure big data. Well, these tools are not new. They only can secure different types of data.

1. User Access Control

User access control is one of the most basic security tools, but many people prefer it for their companies. If you want to use a strong user access control, it requires a policy-based approach. So, it can automate role-based settings.

2. Encryption

Most companies use the Encryption tool for securing data-in-transit and at-rest. Well, these tools need to do this for a large volume of data. Moreover, Encryption tools need to manage different types of data such as machine-generated and user data. These tools also work on different toolsets.

3. Centralized Key Management

Centralized Key Management is one of the best practices for big data security. Just like other technologies, this one applies strongly in big data environments. The best thing about it is people are using this tool for years.

4. Physical Security

While ensuring virtual security, many companies forget about physical security. Make sure you are building it into your own data center. Most physical security systems deny the access of strangers. You can use security logs and video surveillance for physical security.

Closing Thought

Finally, you get to know what is big data security. It plays a crucial role to protect your organization’s big data analytics tools. There are several ways you can use for protecting your tools. However, make sure you are choosing them as per requirements.

Note: We’re looking for contributors or experts on Big data write for us, who are interested in contributing articles on big data-related topics for our blog.

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The popularity of big data technology is increasing day by day. Most industries are adopting big data technology for business information. Hence, many business leaders who are slow to adopt things are curious about this new technology.

So, how does big data work? To answer your question, we are explaining each detail in this article. You will also find out about the benefits of big data for small businesses. Let’s get started.

What is Big Data?

‘Big data is a term that is used to describe the volume of the data an industry creates. Usually, this term is used for business purposes where we see a large number of data. Most companies create it to operate basic procedures.

According to some sources, nearly 51% of small business owners believe that having big data is essential for growth. However, there are many benefits of big data, especially for small businesses.

[Read more: Why Big Data is Important and Applications of Big Data]

Benefits of Big Data for Small Business

As we noted above, big data for small businesses has so many benefits. If you are a beginner or want to get in-depth knowledge regarding this technology, make sure you are reading below.

1. Developing Better Services

The best thing about big data technology is that you can collect the data and understand your customers’ requirements. Big data will help you analyze the behavior of your customer on previous trends.

When you understand your customers’ requirements, you will be able to develop the services or products as per their requirements. Moreover, big data technology now allows companies to test product designs and find flaws.

2. Build a Brand Identity

When you are starting a small business, you need to build a brand identity. A brand can make or harm a company. So, it’s important to build a brand identity in this competitive market. The good thing is big data technology can help you with it.

When you have the data, you can understand your company’s strengths as well as the required improvements you need to make. The data can inform everything from logo design to brand partnership.

3. Cost-Effective Revenues

When we are talking about big data for small businesses, we should not forget about its cost-effective benefit. Well, when you are using big data technology, it will give an umbrella of opportunities without any costs.

For example, you can get data on social media engagement without paying a penny. On the other hand, many platforms provide genuine data without any costs. This way you can increase more revenue for the company.

[Read more: What is Big Data Security? Top Challenges and Solutions]

4. Personalized Promotion

 You may know that personalization is the future of marketing. If you are doing personalized promotion from now, your brand will grow faster. According to some reliable sources, a recent study is saying that around 50% of customers is spending time with those brands that are providing personalized web experience.

Each person has different requirements. So, you can use big data technology to understand it and personalize their experience.

5. Manage Your Team

Not just customers, big data will help you understand your employees. You see, employees are the pillar of a company. So, if you can’t understand their value, you can’t grow your brand.

When you have big data, you can understand which employee needs further training and which one is offering the most value. On the other hand, you will be able to help your team to be more productive.

6. Build Loyalty

Getting new customers is a sign that your brand is growing. However, your loyal customers will help it keep alive, even when you are not getting new customers. That is why building loyalty is one of the best benefits of big data for small businesses.

When you can understand a customer’s requirement and provide a personalized product, they will come back again. Hench, some loyal customers will also suggest your brand to their close circle.

7. Enhance Customer Service

 Customer service is a crucial factor, no matter what is the niche of your business. The good thing is you can improve customer support by analyzing big data. There is a huge amount of data when you are receiving customers’ phone calls, emails, and live chat.

By using big data technology, you will be able to manage each of them. Moreover, you can reach them to a personal level to provide a better service.

How Small Businesses Can Get Maximum Benefit From Big Data?

Follow the below steps to get the maximum benefit from Big data for your small business:

Step 1: Produce incisive reporting.

Once you have access to interesting data, you must find a means to visualize it. Clear reporting makes it simple to draw conclusions and communicate your findings to other team members and stakeholders.

Step 2: Sync up your info.

An outdated and inconsistent set of data won’t give you any insightful data, and any data solution is only as good as the data it uses. You must ensure that your data is integrated and synchronized.

To ensure that your client data is properly connected across your app stack, you may verify that your contact database is consistent and up-to-date by adopting an integration solution. Contact management can be streamlined via real-time contact syncing.

Step 3: Utilize your CRM wisely.

Most likely, you already utilise a CRM to manage your customer data, but you may not be making the most of the information contained therein.

Take the time to properly analyse the data you already have in your CRM before venturing into other analytics tools. Here are some important inquiries to make in the process:

[Read More: Omatic Connectors & Integrations Breakdown]

Conclusion

Starting a business sounds exciting but you need to be aware of everything. This means you need to know the basics of big data technology. It’ll help you understand what your customers need. For more information, you can start research.

Note: We’re looking for contributors or experts on Big data write for us, who are interested in contributing articles on big data-related topics for our blog

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Big data deals with the large volume of data from the day-to-day business activities and gives you insights that help you to make better decisions and strategic business moves.

You need to analyze both structured data like numeric information and unstructured data like emails, text documents, audio files and financial transactions. With the rapid growth in the Internet of things in households and businesses, there is a huge requirement to handle torrents of data streams. This is where Big Data comes in handy to deal with the data effectively in all types of formats.

Contribute articles to Web Tech Mantra on the Big Data topics that can add value to our audience. The contributing articles must be related to Data visualization, Social data and community engagement, Data Privacy and Security, Data Analytics tools and techniques.

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