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The shift of businesses to big data and the interconnectedness of devices via the Internet of Things (IoT) has provided hackers with a more extensive range of devices to attack. This has resulted in companies stepping up cybersecurity to secure broader parts of their network. Nevertheless, all it’d take for a hacker to facilitate a security breach is to discover a new malware signature undetected by firewalls and antiviruses. Although the association of big data and IoT places businesses at an even increased risk of getting their data stolen, it can also be used to boost cybersecurity. Interestingly, firms are starting to acknowledge cybersecurity’s importance. They are now implementing various techniques. There have been increased Google search queries on cybersecurity like ‘

How Big Data Fosters the Cybersecurity Sector

When tech-savvy individuals work with big data, the first thing that comes to mind is how to protect this mass of data. Hence, the idea that big data could be leveraged to improve cybersecurity doesn’t occur instantly. However, the concept of using big data to better cybersecurity is not just sensible but efficient. Research conducted by Bowie University has revealed that around 9 in 10 businesses that utilize big data fend off internet threats better. The key to this improved performance is big data analytics. Big data analytics involves analyzing massive volumes of data using

Historical Data Analysis for Cyber-Attack Visualizations

By leveraging big data, firms can

Conclusion

Hackers have always been attracted to large amounts of data. That’s why IoT and big data have increased the risk of experiencing a cyber-attack. Nevertheless, even though firms are looking to protect the sensitive data they hold, it could be used as a weapon against cyber criminals.

The shift of businesses to big data and the interconnectedness of devices via the Internet of Things (IoT) has provided hackers with a more extensive range of devices to attack. This has resulted in companies stepping up cybersecurity to secure broader parts of their network. Nevertheless, all it’d take for a hacker to facilitate a security breach is to discover a new malware signature undetected by firewalls and antiviruses. Although the association of big data and IoT places businesses at an even increased risk of getting their data stolen, it can also be used to boost cybersecurity. Interestingly, firms are starting to acknowledge cybersecurity’s importance. They are now implementing various techniques. There have been increased Google search queries on cybersecurity like ‘ how to get a Turkish IP address ’ and ‘best antiviruses for PCs.’ The demand for cybersecurity tools like VPNs and malware protection software has increased because of the high levels of cybercrime. This article will reveal how big data helps the cybersecurity field become chúng tôi tech-savvy individuals work with big data, the first thing that comes to mind is how to protect this mass of data. Hence, the idea that big data could be leveraged to improve cybersecurity doesn’t occur instantly. However, the concept of using big data to better cybersecurity is not just sensible but efficient. Research conducted by Bowie University has revealed that around 9 in 10 businesses that utilize big data fend off internet threats better. The key to this improved performance is big data analytics. Big data analytics involves analyzing massive volumes of data using specialized tools like Excel and Python to gain business insights. This data could be relational in the form of tables or non-relational in the form of plain text with keys and values. Data could also be unstructured in the case of images, videos, and audio. Big data analytics aims to help businesses make intelligent decisions, understand market trends better, and get a feel of what customers prefer to enhance efficiency and customer satisfaction. However, the field has morphed into one that facilitates the retrieval of crucial information from large amounts of data which helps in various sectors, including cybersecurity. Big data analytics for cybersecurity works when past data is analyzed to predict future trends. When machine learning is merged with big data analytics, firms can better analyze historical data to determine what constitutes normal behavior. The outliers are considered abnormal behavior and an alert for a potential cyber-attack pop-up. For instance, if the usual work period for an organization is 9 to 5, machine learning will recognize that pattern to establish average behavior. If there’s a login made to the business network at an unusual time, the system would be alerted to prevent that potential digital security attack. Nevertheless, the applications of machines and deep learning go beyond this chúng tôi leveraging big data, firms can forecast cyberattacks and develop competent measures to avert them. For instance, an organization that has previously fallen victim to an internet attack can perform an analysis of the hacker’s activity. By leveraging machine learning, the patterns can be formed from the hacker’s actions to model a possible security breach attempt. Nevertheless, analyzing historical data to predict cyber-attacks isn’t restricted to companies that have experienced them. A firm without any prior digital security breaches can explore the vast amounts of industry data to spot the patterns made by hackers. The penultimate result would be a visualization of the events leading up to the attack. Subsequently, machine learning engineers can implement preventive measures to stop possible online attacks. This way, even though hackers are targeting companies that collect large amounts of data for attacks, these firms can, in turn, use the weapon provided by big data to frustrate cybercriminals.Hackers have always been attracted to large amounts of data. That’s why IoT and big data have increased the risk of experiencing a cyber-attack. Nevertheless, even though firms are looking to protect the sensitive data they hold, it could be used as a weapon against cyber criminals. Analyzing historical data and leveraging machine learning techniques can draw patterns from typical hacking behavior. Machine learning engineers can then create models to predict and prevent a future attack.

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Enhancing Cybersecurity Measures For Data Protection

It is impossible to escape from data breaches today, so why not strengthen cybersecurity through simple action plans? Data is an important asset to any organization, which is why it is an important area of concern for every C-suite leader and a primary target of cybersecurity attack by criminals. In recent years, both large and small organizations have been affected by data breaches. With predictions that

Start Simple is the Key

Some of the general ways to protect data include: • Data Encryption — converting the data into a code that cannot be easily read without a key that unlocks it. One can also opt for multifactor authentication measures. • Masking certain areas of data so personnel without the required authorization cannot look at it. • Creating data backups so that it can be retrieved in case of massive data loss. • Conducting regular audits to review the data storage systems, data strategy and loopholes, suggest improvements to guard the system and mitigate and prevent potential threats. C-suite leaders should also make sure to conduct enhanced recruitment checks and credit and criminal record checks on people with access to data. • Educating and training employees about remote work cybersecurity, social engineering scams like phishing and sophisticated cybersecurity attacks like ransomware and other malware designed to steal intellectual property or personal data. Companies can enrolled them for • Implementing strong end-point security, and continuous monitoring of activities and events on endpoints to detect and block threats that get past initial defenses. • Install strong firewall to control internet traffic coming into and flowing out of the business. • Limiting data accessibility by determining what an employee needs access to and ensure they have access to only what they need. Such limitations will help in efficient data management and ensure it is being safeguarded from theft or loss.

It is impossible to escape from data breaches today, so why not strengthen cybersecurity through simple action plans? Data is an important asset to any organization, which is why it is an important area of concern for every C-suite leader and a primary target of cybersecurity attack by criminals. In recent years, both large and small organizations have been affected by data breaches. With predictions that ransomware attacks , cloud data breaches, attacks on endpoint devices and the Internet of Things (IoT), to continue to rise in 2023 and beyond, protecting data has been a chief priority for enterprises. Data breaches happen on an everyday basis, endangering email addresses, passwords, credit card numbers, social security numbers and other highly sensitive data. Today, we live in an age where one cannot afford to ignore the implications of leaving data unguarded. As per CSO, about 3.5 billion people saw their personal data stolen in the top two of 15 biggest breaches of this century alone. Last year, European budget airline EasyJet suffered a major breach that began in January.   In this data breach, credit and debit card details of over 2,000 customers were exposed via emails and travel information. In May, Russian delivery company, CDEC Express, suffered a major breach when it was discovered that the records of 9 million customers were for sale on the dark web. In June, a breach at the PostBank in South Africa affected between 8 and 10 million beneficiaries who receive social grants every month, as an unencrypted master key was stolen by employees – thus granting complete access to the bank’s systems and the ability to change information on any of the bank’s 12 million cards. Even social media platform Twitter saw a security breach where hackers targeted about 130 accounts, tweeted from 45, accessed the inboxes of 36, and were able to download Twitter data from seven. These instances prove that securing data is not only an IT problem, nor is it limited to large firms, in reality anyone can be victim to these data based cyber threats. According to Cybersecurity Ventures in 2023, the annual costs of these attacks is expected to reach an incredible US$6 trillion. As data breaches and other cybersecurity risks will increase with expanding computer networks, companies are investing heavily to deploy the best of cyber defense capabilities, to protect their critical assets. Meanwhile, C-suite leaders should identify where their most important data and sensitive business information lies, which can enable proactive monitoring and allocation of more resources to safeguard the most vital assets. A successful cybersecurity approach should consist of multiple layers of protection that spread across all the networks, computers, programs, and data that one intends to shield against data based cyber chúng tôi of the general ways to protect data include:Data Encryption — converting the data into a code that cannot be easily read without a key that unlocks it. One can also opt for multifactor authentication measures.Masking certain areas of data so personnel without the required authorization cannot look at it.Creating data backups so that it can be retrieved in case of massive data loss.Conducting regular audits to review the data storage systems, data strategy and loopholes, suggest improvements to guard the system and mitigate and prevent potential threats. C-suite leaders should also make sure to conduct enhanced recruitment checks and credit and criminal record checks on people with access to data.Educating and training employees about remote work cybersecurity, social engineering scams like phishing and sophisticated cybersecurity attacks like ransomware and other malware designed to steal intellectual property or personal data. Companies can enrolled them for cyber range training Implementing strong end-point security, and continuous monitoring of activities and events on endpoints to detect and block threats that get past initial defenses.Install strong firewall to control internet traffic coming into and flowing out of the business.Limiting data accessibility by determining what an employee needs access to and ensure they have access to only what they need. Such limitations will help in efficient data management and ensure it is being safeguarded from theft or loss. While installing antivirus and anti-malware software are important arsenals of data protection, there are a wide range of other options C-suite leaders must explore: enterprise data protection, cloud data security tools, all in one security softwares for mobiles, web browsers, emails and more.

Mining For Big Value In Big Data

Nate Silver knows a thing or two about the value of Big Data. He famously predicted who would win 49 out of 50 states in the 2008 Presidential election and the following year was named by Time one of The World’s 100 Most Influential People. Silver’s Five Thirty Eight site focuses on “data journalism.”

But Silver warned in a recent keynote at the Rich Data Summit that Big Data can be misunderstood and oversold. A popular notion, Silver said, is that “you get your data, you press a button and all of a sudden you have extremely valuable output. This idea is very wrong and dangerous.”

In fact, the work data scientists do is far more complex. “Data scientists aren’t interested in data for data’s sake, we’re interested in relationships,” he said.

Jenny Dearborn, author of the book Data Driven: How Performance Analytics Delivers Extraordinary Sales Results, says we are a tipping point in our ability to collect, manipulate, analyze and act on big data.

“We finally have the ability to manipulate all this data we’ve been collecting and understand what to do with it,” says Dearborn, Chief Learning Officer at SAP. “We certainly had the information before, but it was hard to access and compile and get a big picture view of it; it was very much nose to the tree. Now we can see the forest and patterns and trends and ‘what does all this mean?’

“With all this information we can for the first time really answer some very big strategic questions: ‘What is the business problem we’re trying to solve?’  ‘What are the big trends here’?’” says Dearborn.

But she agrees with Silver there is no magic button to realizing the benefits of Big Data. 

“It’s challenging, because it’s not just having the data, but knowing what to do with it,” says Dearborn. “Knowing what questions to ask, what business problems you are trying to solve and how do you apply analytics to the data you have to answer those big questions. There’s a lot of big strategic thinking that needs to happen in front of looking at your stacks of data or all your reports, and sometimes companies don’t take the time to ask those big questions. “

Discovering New Flavors

Some Big Data insights are relatively straightforward. Coca Cola has leveraged results from its network of Freestyle drink dispensers to create a new flavor. Freestyle, a kind of drink factory in a touchscreen box, lets customer mix and match over 170 brands of beverages at fast-food outlets, movie theaters and elsewhere. The soft drink giant is able to collect and analyze all those choices. When it saw a pattern of customers mixing Cherry and Vanilla Coke flavors, voila, it created a new, instantly popular flavor, Coke Cherry Vanilla.

Analyst Doug Henschen at Constellation Research points to manufacturing companies like GE and John Deere who are using Big Data to anticipate when parts are going to need to be fixed, resulting in savings on inventory and maintenance costs.

Analyst Bob O’Donnell agrees and adds that even with the right structure and investment Big Data may fall short of expectations.

O’Donnell also says some companies aren’t prepared to leverage Big Data results. A Big Data analysis may help Company X find, for example, that its product doesn’t appeal to single women over 40, but there may be no support internally at the company to change the product or strategy to address that market.

Big Data Rules of the Road

Andreas Weigend, the former Chief Scientist at chúng tôi who now runs Social Data Lab, shared some rules of the road at Rich Data Summit when it comes to starting a Big Data project.

1) Start with the problem, not with the data. If you start with data it grows exponentially and it will be a hose you can’t clean fast enough. Be clear about what question or problem you are trying to solve.

2) Be wary of consultants who say ‘Give us all your data and we’ll give you insights.’ Focus on decisions and actions you can take yourself.   

3) Use metrics that matter to your customers. If you’re in a business that ships products to consumers, it may seem great to find out they’re arriving a day ahead of schedule. But actually that’s a hassle for the customer who planned to be home a day later to receive the package and finds an ’undeliverable’ note on their door.

4) Let people do what people are good at, and computers do what computers are good at.

5) Don’t blame technology for problems that you have in your institution. Weigand uses the example of not being allowed to use third party software when he was teaching at Stanford. “I got a note for using LinkedIn in one of my courses,” he recalled. “You wonder what planet this person is living on.”

Big Data and the Cloud

Analyst Charles King at Pund-IT says the growth of open source frameworks for handling Big Data sets like Hadoop and Apache Spark have led to more companies experimenting with and embracing Big Data.

“You can put together a Hadoop system relatively cheaper, though there’s a lot of assembly and technical expertise required,” says King. “Or you can have a third party like HortonWorks do it.”

He notes that operating a Big Data platform typically requires trained data scientists, who are in relatively short supply.  King also expects to see more cloud-based Big Data projects that require a minimum of on-premise infrastructure. “Certain types of one-off projects could run only 1-6 months,” he said. “As Big Data matures, I think in the short term we’re going to see a growing number of companies offer Big Data as a service with the cloud as the backend.”

Big Data is also entering new areas such as physical store locations. A company called RetailNext helps big retailers do Big Data analysis in part by analyzing video feeds of how customers act in retail locations, e.g. what displays they gravitate to or ignore.

“If you look at chúng tôi chúng tôi or any ecommerce site, they have so much data and they use analytics to constantly improve the way they run their websites,” Alexei Agratchev, CEO of RetailNext, recently told The San Jose Mercury News. “Then you walk through Nordstrom or Victoria’s Secret and nobody has any idea what happens.”

Weigand says whatever the Big Data project you embark on, keep an eye on how it’s going.

 “Does your product or service get better or worse with a Big Data project over time? I think we all know examples from both.”

Photo courtesy of Shutterstock.

Data Warehouse: Key Tool For Big Data

Also see: Top 15 Data Warehouse Tools

Just as a warehouse is a large building for the storage of goods, a data warehouses is a repository where large amounts of data can be collected – it’s an important tool for Big Data.

Data warehouses and data warehouse tools have been with us for some time. The father of the data warehouse, Bill Inmon, coined the term more than a quarter of a century ago. He defined the data warehouse as a collection of data to support decision making.

Data warehouses are often associated with large amounts of data.  For some, they are measured in 100s of TB, PBs or even Exabytes in some cases. But for others, they can be as small as a TB or less. Data warehouses, then, are not just about size.

According to Greg Schulz, an analyst with Storage and Server IO Group, data warehouses are large repositories for storing and accessing large amounts of data in support of various reporting, business intelligence (BI), analytics, decision support (DSS), research, data mining and other related activities. Data warehouses are optimized to retain and process large amounts of data feed to them via online transactional processing (OLTP) and other systems. This data can then be used for reporting, search and analysis.

Databases deal with structured data. Their data is well-defined and well organized. It is organized strictly with each piece adhering to very specific fields. Typically, traditional databases harness OLTP and can process huge volumes of transactions rapidly. A data warehouse, on the other hand, utilizes online analytical processing (OLAP). It typically sits on top of one or more OLTP databases.

A data warehouse, then, is a central repository for an organization’s business information. It can incorporate disparate databases in addition to systems and processes. This facilitates the presentation of a unified and integrated approach to organizing data for better access and easy interpretation. Data warehouse tools make it possible to manage data more efficiently. This includes being able to more easily find, access, visualize and analyze data in order to achieve better business results.

An obvious benefit of a data warehouse is that it can host a very large amount of data. High-performance databases don’t need to be cluttered up by an ever-growing volume of stored data, much of it historical. One way to keep them running well and freed up for immediate organizational needs is to offload some data into a data warehouse. In large organizations, multiple databases can feed one large database.

But perhaps the greatest benefit of the data warehouse is the ability to translate raw data into information and insight. The data warehouse offers an effective way to support queries, analytics, reporting, and modeling, as well as forecasting and trending against larger amounts of data and time.

Data warehouses are optimized to deal with large volumes of data. They are typically housed on mainframes, enterprise-class servers and more recently, in the cloud. Data from OLTP applications and other sources is selectively extracted for use by analytical applications and user queries. Different data warehouses receive and process different types of data. Data volume, frequency, retention periods and other factors determine the specifics of construction.

Even before technology selection and data warehouse design, however, a primary step is to determine business goals and objectives. Based on sound planning, it is important to conduct a data management program and begin collecting, normalizing and cleansing data. This is a vital ingredient if analysis, querying and reporting is to achieve any kind of accuracy.

Design and data cleansing must be supported by the right storage. Generally, data warehouses rely on large storage capacities that have durability, lower cost, and high performance. Older systems might be composed entirely of large collections of Hard Disk Drives (HDDs). But that is changing and data warehouses are appearing as be a hybrid mix of HDDs and solid state drives (SSD). Others are appearing that harness all flash arrays for the highest possible performance.

Additionally, a specific database technology might be selected based on familiarity, cost or time to value. Providers include SAS, Oracle and Teradata.

Another factor to consider is access by users. Some data warehouses are so large that they can become cumbersome for those seeking to use them for analysis, queries and reporting. In such cases, smaller data marts may be split apart for ease of utilization. Data marts can also be used to provide subsets of the data to different user groups. Alternatively, data marts are sometimes established first and then consolidated into a larger data warehouse.

In the top-down approach, data is extracted from disparate systems, cleansed, normalized, summarized and distributed to data marts where users can gain access. In the bottom-up method, the goal is to deliver value as quickly as possible by focusing on the data marts.

There is also a hybrid approach, which tries to blend both methods. It combines the speed of the bottom-up approach without compromising on the data integration benefits of the top-down approach.

More than yet another tool, the data warehouse is a central element in any Big Data infrastructure.

Another challenge is that some data warehouses have not kept up with the disruptively low cost of storing data. Newer tools and technologies have evolved such as Hadoop that act as repositories for the ever-growing volume of unstructured data. Similarly, data lakes are evolving which can consolidate multiple stores of unstructured and semi-structured data.

Cost can also be a consideration as some proprietary data warehouse tools can be expensive. But Schulz said that cloud-based and open source data warehouse platforms are becoming available that can accommodate structured and some unstructured data sources. Some have come on the market, for example, with hooks for working with semi-structured or non-structured data. This means that as well as databases, data streams such as video, audio, images and logs may be incorporated in some cases.

The dream of the data warehouse was to create a single source of truth in the enterprise. However, this goal remained elusive, said Anil Inamdar, Principal Corporate Consultant, Dell EMC Services. He explained that the data being fed into the warehouse came from other systems such as Enterprise Resource Planning (ERP). Additionally, mergers and acquisitions meant that companies would inherit multiple data warehouses that proved difficult to easily consolidate.

“It takes a considerable amount of time to create data warehouses and it has been a challenge to keep in sync with changes to multiple data sources as well as the introduction of newer sources,” said Inamdar.

Many technologies get labeled as legacy and are thereafter considered outdated. This label was applied, for example, to mainframes which caused them to largely fall out of favor in the nineties. Yet the technology remains relevant and continues to be a mission-critical element inside most large financial institutions.

The same thing could be said about data warehouses. All the hype, these days, is around data lakes and there is much confusion between data lakes and data warehouses. Both are used for data storage, but they take different approaches. Data warehouses adhere to a definite structure whereas data lakes are more fluid. Data lakes hold raw data in its native format. They encompass structured, semi-structured, and unstructured data but without rigid data structure requirements. Inamdar considers data warehouses to now be a subset of a larger data lake ecosystem.

Hadoop is a data storage option that has gained traction over the last several years. Hadoop, though, does not replace all previous data architectures. Rather than being a data warehouse or a database, it is a file system and a data framework. As such, Gartner research found that less than 5% of companies plan to replace their data warehouse with Hadoop.

The reasons are simple. Replacing a data warehouse from scratch is a massive undertaking. Technologically and culturally, it is not for the faint of heart. That said, Hadoop offers low-cost, high-speed data processing. It can be used to great effect, for example, as a layer on top of a data warehouse.

How Instagram Uses Big Data And Artificial Intelligence

Analyze how Instagram uses Big Data and Artificial Intelligence to make the app a cut above

Instagram marketing involves a lot of legwork – you have to first study your audience, then gather data and relevant information. Then, you have to use the findings to plan Instagram campaigns that will resonate with your target market.  

However, all of these would never be possible without Instagram’s big data and

Metaverse Artificial intelligence

.  Instagram itself uses big data as one of the most effective methods of establishing trends, deducing patterns, and ultimately uncovering actionable insights. 

Meanwhile, as a user, the more data points you have at your disposal, the greater the number of outcomes you can derive for your Instagram marketing efforts.

It’s a fact. Instagram is doing great in the AI and big data department, so much so that Instagram CEO Kevin Systrom boasts that they’re

“also going to be a big data company.”

So, in this guide, we will carefully analyze how Instagram uses Big Data and Artificial Intelligence to make the app a cut above.  

  Image by Fauxels @Pexels

How Instagram Uses Big Data and Artificial Intelligence

1. Creating Personalized Feed 

Instagram’s use of AI and Big Data has contributed immensely to creating a personalized feed for each user. It’s why the Instagram algorithm works in a way that allows every user to find contents they are interested in. 

Instagram uses AI to sort its feed so as to show posts that users tend to like or share. This is because the technology learns over time what content is valuable and relevant for each user. 

GIF by South Park @Giphy

2. Initiating Targeted Advertising 

On Instagram, Big Data and AI technologies are applied to derive analytical insights into user behaviors. This will then provide information about search preferences and user engagements. 

The data generated from using these technologies is very valuable for promoting any business. AI will reveal the interests of its target audience through likes and the accounts they follow. 

3. Blocking Offensive Contents 

Cyberbullying and sharing of offensive content are becoming rampant on social media apps. Statistics have shown that around 42% of young people experience cyberbullying on Instagram. 

As for offensive content, it’s becoming the order of the day on most social media platforms as well. So, Instagram has identified AI and Big Data as valuable tools for tackling the spread of these vices on their app.

With Big Data and AI, the Instagram algorithm can quickly identify inappropriate content. When this happens, an instant notification will be sent for Instagram to block or delete the content. 

This is an excellent measure for creating a safe space and

improving the user experience on IG

.   

4. Searching the Explore Page 

The Explore page is the holy grail of Instagram. This search tool is enabled by the integration of artificial intelligence. It allows users to keep up with the trending topics, especially by tracking tags.

With this search function, users can discover new accounts, content, and information. This explains why businesses scramble to feature on the explore page— featured contents are likely to

draw more visitors and followers

to their Instagram profile. 

5. Filtering and Blocking Spam 

With so much stuff being shared on the app every day,  it is likely that a few will be spam. The question is, what methods does Instagram use to identify spam messages?

For spam communications, Instagram uses the text analytics algorithm “DeepText” from Artificial Intelligence. Its spam filter recognizes spam communications in more than nine languages, including English, Arabic, and Chinese. These messages are automatically erased once they are recognized, thus

furthering cybersecurity

It doesn’t stop at that. The technology also enables the blocking of fake content and fake accounts. 

 Image by Andrea Piacquadio @Pexels

6. Studying Human Behavior

With Big Data, it is easy for an app like Instagram to understand human behavior through the content they share. Since there is a lot of content shared daily, there’s a need for the algorithm to interpret them. This is especially so because it’ll help the technology gather actionable insights.

Machine Learning is often used to review a large amount of data that the app gathers. And by doing so, the AI interprets the data and gathers insights on social, economic, and cultural factors associated with humans across the world. 

Furthermore, by understanding human behavior, Instagram can enhance its platform to promote cultural diversity. 

7. Increasing Engagements with Instagram Bots 

Instagram has remained relevant over the years because of its engagement and interaction potential. One of the things that have made interaction so effortless on the app is Instagram Bots

Using these bots, the algorithm can identify who is more likely to interact with a particular content. This way, posts are pushed to interested users, and users’ activities are automated efficiently. 

8. AI-Powered Automatic Caption on IGTV

Since introducing AI-powered automatic captions for IGTV, Instagram has transformed into a big data corporation. This technology was created to assist individuals with hearing impairments in keeping up with the rest of the world.

With this new caption feature on IGTV, your audience can read your thoughts rather than hear them. This implies that consumers may watch IGTV without cranking up the volume. It’s a good idea to make IGTV more user-friendly.

 Image by Andrea Piacquadio @Pexels

9. Crisis Communication 

Instagram is leveraging Big Data to perform crisis communication. The Ebola outbreak, Hurricane Sandy, the California wildfires, and the COVID-19 pandemic all prove this. Point. Images are integrated into the Instagram feed to keep people informed about current crises and disasters. They do this to assist the victims and link them with their loved ones.

10. Description Tailored for Visually Impaired People  

To wrap this up, we have to mention how Instagram has used Big Data and AI to revolutionize image recognition. This technology makes Instagram accessible to people with visual impairments. 

Due to the popularity of this social media app, many people with visual impairments are feeling left out. Naturally, these people feel frustrated and depressed when they can’t use the app seamlessly as others do.

So, the good news is that Instagram is leveraging artificial intelligence to enable descriptions for visually impaired people. The technology can recognize and analyze photos to automatically generate descriptions. 

With screen readers, visually impaired users can browse Instagram profiles and explore pages. There’s also the “

Alt Text

” feature designed to help those posting gain more control over these descriptions. 

Image by Mikhail Nilov @Pexels

Conclusion

Instagram has established its way into becoming one of the best social media apps globally. All thanks to the constantly upgraded features that keep its users engaged and entertained. 

Instagram is using Big Data and Artificial Intelligence to improve every aspect of its platform. So, this has helped increase the number of active users and daily posts on the social network. 

Challenges Of Big Data Analytics

Introduction to Challenges of Big Data Analytics

Data is a very valuable asset in the world today. The economics of data is based on the idea that data value can be extracted through analytics. Though Big data and analytics are still in their initial growth stage, their importance cannot be undervalued. As big data starts to expand and grow, the Importance of big data analytics will continue to grow in everyday personal and business lives. In addition, the size and volume of data are increasing daily, making it important to address big data daily. Here we will discuss the Challenges of Big Data Analytics.

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According to surveys, many companies are opening up to using big data analytics in their daily functioning. With the rising popularity of Big data analytics, it is obvious that investing in this medium will secure the future growth of companies and brands.

The key to data value creation is Big Data Analytics, so it is important to focus on that aspect of analytics. Many companies use different methods to employ Big Data analytics, and there is no magic solution to successfully implementing this. While data is important, even more important is the process through which companies can gain insights with their help. Gaining insights from data is the goal of big data analytics, so investing in a system that can deliver those insights is extremely crucial and important. Therefore, successful implementation of big data analytics requires a combination of skills, people, and processes that can work in perfect synchronization with each other.

With great potential and opportunities, however, come great challenges and hurdles. This means that companies must be able to solve all the hurdles to unlock the full potential of big data analytics and its concerned fields. When big data analytics challenges are addressed in a proper manner, the success rate of implementing big data solutions automatically increases. As big data makes its way into companies and brands around the world, addressing these challenges is extremely important.

Major Challenges of Big Data Analytics

Some of the major challenges that big data analytics programs are facing today include the following:

Uncertainty of Data Management Landscape: Because big data is continuously expanding, new companies and technologies are developed every day. A big challenge for companies is to find out which technology works bests for them without introducing new risks and problems.

The Big Data Talent Gap: While Big Data is growing, very few experts are available. This is because Big data is a complex field, and people who understand this field’s complexity and intricate nature are far from between. Another major challenge in the field is the talent gap that exists in the industry

Getting data into the big data platform: Data is increasing every single day. This means that companies have to tackle a limitless amount of data on a regular basis. The scale and variety of data available today can overwhelm any data practitioner, which is why it is important to make data accessibility simple and convenient for brand managers and owners.

Need for synchronization across data sources: As data sets become more diverse, they must be incorporated into an analytical platform. It can create gaps and lead to wrong insights and messages if ignored.

Getting important insights through the use of Big data analytics: It is important that companies gain proper insights from big data analytics, and it is important that the correct department has access to this information. A major challenge in big data analytics is bridging this gap in an effective fashion.

This article will look at these challenges in a closer manner and understand how companies can tackle these challenges in an effective fashion. Implementation of Hadoop infrastructure. Learn Hadoop skills like HBase, Hive, Pig, and Mahout.

Challenge 1

The challenge of rising uncertainty in data management: In a world of big data, the more data you have, the easier it is to gain insights from them. However, in big data, there are a number of disruptive technology in the world today, and choosing from them might be a tough task. That is why big data systems need to support both the operational and, to a great extent, analytical processing needs of a company. These approaches are generally lumped into the NoSQL framework category, which differs from the conventional relational database management system.

There is a number of different NoSQL approaches available in the company, from using methods like hierarchal object representation to graph databases that can maintain interconnected relationships between different objects. As big data is still in its evolution stage, there are many companies that are developing new techniques and methods in the field of big data analytics.

In fact, new models developed within each NoSQL category help companies reach their goals. These Big analytics tools are suited for different purposes as some provide flexibility while other heal companies reach their goals of scalability or a wider range of functionality. This means that the wide and expanding range of NoSQL tools has made it difficult for brand owners to choose the right solution to help them achieve their goals and be integrated into their objectives.

Challenge 2

The gap in experts in big data analytics: An industry completely depends on the resources it has access to, whether human or material. Some tools for big data analytics range from traditional relational database tools with alternative data layouts designed to increase access speed while decreasing the storage footprint, in-memory analytics, NoSQL data management frameworks, and the broad Hadoop ecosystem. With so many systems and frameworks, there is a growing and immediate need for application developers who have knowledge of all these systems.  Despite the fact that these technologies are developing at a rapid pace, there is a lack of people who possess the required technical skill.

Another thing to remember is that many experts in the field of big data have gained experience through tool implementation and its use as a programming model instead of data management aspects. This means that many data tool experts lack knowledge about the practical aspects of data modeling, data architecture, and data integration.

This lack of knowledge will result in less than successful data and analytical processes implementations within a company/brand.

According to analyst firm McKinsey & Company, “By 2023, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effective decisions.

All this means that while this sector will have multiple job openings, there will be very few experts who will actually have the knowledge to fill these positions effectively. While data practitioners become more experienced through continuous working in the field, the talent gap will eventually close. At the same time, it is important to remember that when developers cannot address fundamental data architecture and data management challenges, the ability to take a company to the next level of growth is severely affected. This means that companies must always invest in the right resources, be it technology or expertise, to ensure that their goals and objectives are objectively met in a sustained manner.

Challenge 3

As companies have a lot of data, understanding that data is very important because, without that basic knowledge, it is difficult to integrate it with the business data analytics program. Communication plays an integral role here as it helps companies and the concerned team educate, inform and explain the various aspects of business development analytics.

Before even going towards implementation, companies must a good amount of time explaining the benefits and features of business analytics to individuals within the organizations, including stakeholders, management, and IT teams. While companies will be skeptical about implementing business analytics and big data within the organization, once they understand its immense potential, they will easily be more open and adaptable to the entire big data analytical process.

Challenge 4

The challenge of the need for synchronization across data sources: Once data is integrated into a big platform, data copies are migrated from different sources at different rates. Schedules can sometimes be out of sync within the entire system. There are different types of synchrony. It is important that data is in sync. Otherwise, this can impact the entire process.  With so many conventional data marks and data warehouses, sequences of data extractions, transformations, and migrations, there is always a risk of data being unsynchronized.

With exploding data volumes and the rising speed at which updates are created, ensuring that data is synchronized at all levels is difficult but necessary. This is because data is not in sync. It can result in analyses that are wrong and invalid. If inconsistent data is produced at any stage, it can result in inconsistencies at all stages and have disastrous results. Wrong insights can damage a company to a great degree, sometimes even more than not having the required data insights.

Challenge 5

The challenge of getting important insights through Big data analytics: Data is valuable only as long as companies can gain insights from them. By augmenting the existing data storage and providing access to end users, big data analytics needs to be comprehensive and insightful. The data tools must help companies not just have access to the required information but also eliminate the need for custom coding. As data grows inside, it is important that companies understand this need and process it in an effective manner. With the increase in data size on time and cycle, ensuring the proper adaptation of data is a critical factor in the success of any company.

Conclusion

These are just some of the few challenges that companies are facing in the process of implementing big data analytics solutions. While these challenges might seem big, it is important to address them in an effective manner because everyone knows that business analytics can truly change the fortune of a company. The possibilities with business analytics are endless, from preventing fraud to gaining a competitive edge over competitors to helping retain more customers and anticipating business demands. In the last decade, big data has come a very long way, and overcoming these challenges is going to be one of the major goals of the Big data analytics industry in the coming years.

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