Data Security

Big Data be Integrated into Your Business to Improve Output
admin
August 28, 2024
Big Data

How can Big Data be Integrated into Your Business to Improve Output?

Nowadays, information usage is soaring. This information, dubbed Big data, has expanded too large and complicated for typical data processing methods.

Companies are potentially utilizing Big data to enhance customer service, boost profit, cut expenditures, and update existing operations. This shows that the impact of Big Data on businesses is enormous and will remain impactful in the coming years.

But do you know from where these affecting Big Data come?

Big data is generated mainly by three sources:

Business:

Companies produce massive amounts of data on a daily basis. Some examples include financial data like invoices, billing and transaction data, and internal and external documents like business letters, reports, production plans, and so on. Big data generation is vital for enterprises transitioning from analog to digital workflows.

Communication:

Communication is the data that one generates as an individual. Social media blogging and microblogging are all vital communication data sources. A new photo, a search query, and a text message contribute to the growing volume of big data.

IoT:

Sensors integrated with IoT system produces IoT data. Smart devices use sensors to gather data and upload it to the Internet—for example, CCTV records, automated vacuum cleaners, weather station data, and other sensor-generated data. Overall, big data can be called massive data collections obtained from different sources. It can be utilized to find patterns, links, or trends to analyze and anticipate them.

Big data can be used to enhance security measures. Businesses and individuals use free VPNs and proxies to protect their data. They both depend on big data because it supports strengthening the technology.

Now, let’s get into the details of how businesses can potentially use big data to improve their operations and boost productivity.

How do businesses use big data?

Big data applications have multiple uses. Also, we can easily see various businesses employ the technology for different objectives. Insights collected are often used to make products and services more efficient, relevant, and adaptive for individuals who use them.

The applications of big data are:

Catching security defects:

With things getting online, data breaches and theft are among the most common problems as digital systems are getting complicated. Big data can be used to find out potential security troubles and analyze trends—for instance, predictive analytics catch illegal trading and deceitful transactions in the banking industry. Comprehending the “normal” trends permits banks to discover uncommon behavior quickly.

Comprehending more about customers:

This is one of the most critical and typical big data applications. Companies extract vast amounts of data to analyze how their customers behave and their choices. This enables them to predict the goods that customers desire and target customers with more relevant and personalized marketing.

One of the best examples is Spotify. The company also utilizes artificial intelligence and machine learning algorithms to motivate customers to continue connecting with the service. Spotify finds related music to design a “taste profile” as you listen and save your favorite tracks. Using this information, Spotify can suggest customers new songs based on their earlier choices.

Product invention:

Comprehensive data collection and client demand analysis can also be used to forecast future trends. Companies can utilize big data analytics to transform collected insights into new goods and services. It allows them to predict what their clients need. The corporation can offer data-driven proof for production based on customer demand, popularity, and interest. Instead of waiting for clients to tell their needs, you can fulfill their demands beforehand. Besides this, being more innovative than competitors is also a plus point for businesses.

Create marketing strategies:

Well, we are pretty familiar with the fact that a small marketing blunder can cost a lot to a company. A marketing that does not resonate with the target demographic might end up creating disaster. However, the availability of more specific data makes marketing more secure but complex.

This lets you gather information on how people respond to your advertising and allows you to create more personalized campaigns. This increased focus allows the marketing team to make a more precise approach, turn more effective, and reduce cost load.

Do you think big data is a big risk game in a business?

Till now, it’s very clear that big data provides enormous opportunities. Businesses flourishing in different sectors can take advantage of the available data. However, it could not be a smooth journey as various challenges are involved with this analytics method.

The accuracy concern:

This will also allow you to start combining data streamlining from a vast range of sources and formats. The challenge then comes to knowing which information is valuable and reliable and how to crack that information meaningfully. However, “cleaning” of data is a part of the big data sector; it is not without complication.

The price barrier:

Welcoming and adopting the world of big data carries several drawbacks. There are many aspects to be considered here- the hardware and the software. One must consider data storage and systems for managing enormous amounts of data. Furthermore, data science is increasing rapidly, and those who understand it are in high demand. The fee for recruits or freelancers can be high. Lastly, developing a big data solution that meets your company’s needs demands significant time and money.

The security challenge:

The challenge of safely storing such a large amount of data generated from collecting such a large amount. Therefore, Cybersecurity is another essential concern as data privacy and GDPR grow more vital.

The bottom line

We can easily conclude that Big data is fetching enormous benefits to many companies belonging to different sectors. Therefore, companies may thrive in the digital economy by effectively analyzing and managing flooding data. There may be many hindrances in integrating big data into business infrastructure. Still, the initial investment overcomes the rewards and advantages offered by big data and its potential application in the business. Therefore, spending time deciding whether to go for big data or not will surely land you at a loss.

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How to Address Data Management Challenges in IoT Using Fabrics
admin
August 7, 2023
Data Security, IoT

How to Address Data Management Challenges in IoT Using Fabrics

Whenever we talk about data management, the whole conversation remains incomplete if we do not mention the most important aspect related to data management: the Internet of Things, IoT networks. Today, everything is connected, and all credits go to IoT networks. From smart towns to industrial sensors, our world is interconnected with smart devices, and the volume of data generated has reached unbelievable proportions. This is advantageous for our digital transformation initiatives but carries a parallel increase in vulnerability to data piracy, cyber attacks, and privacy infringements.

The amount of data generated is directly proportional to the higher stakes regarding safeguarding it. This raises the need for data protection measures in IoT ecosystems, which has now become a significant challenge for organizations. It has also necessitated robust data management strategies to guarantee IoT data’s integrity, security, and privacy.

However, enterprises are still making errors. They emphasize more on expanding IoT and are least interested in making the data streams safer and more authentic. More comprehensive IoT networks assure more users and faster streaming, yet they lack in terms of data protection.

Critical data management challenges in IoT

In the domain of IoT, significant data challenges emerge, including security risks, privacy concerns, data authenticity, and data proliferation. Security risks create a constant threat, as IoT devices are vulnerable to breaches, unauthorized access, and tampering, potentially resulting in data leaks and network attacks. 

Safeguarding privacy is crucial due to the collection and transmission of personal data by IoT devices containing sensitive information like location, health data, and behavioral patterns. 

Securing data integrity and authenticity is difficult in IoT environments, as changes often lead to erroneous decisions and compromise system reliability. 

Besides this, the sheer volume of data created by IoT devices can overcome traditional management systems, making it necessary to have sufficient storage, processing, and analysis strategies in a timely and cost-effective way. As per the ‘State of IoT Spring 2023’ report released by IoT Analytics, the worldwide count of operational IoT endpoints rose 18% in 2022, reaching 14.3 billion connections. 

How can data fabrics handle these challenges?

Data fabrics are essential in allowing scalable data management in IoT ecosystems. They provide valuable support in different aspects of IoT data management. They play a vital role in privacy protection by using data masking techniques that pseudonymize or anonymize sensitive information.

By substituting original values with masked or randomized data, the identity of individuals or devices remains safe, diminishing the threat of data breaches.

Data fabrics also allow access control, restricting access to authorized personnel or systems. Encryption also improves security by shielding transmitted or stored data from unauthorized access. Data fabrics offer an extra layer of security against attackers by integrating encryption with masking.

In addition, data fabrics support data minimization by reducing the amount of sensitive data stored or transmitted, using masked or aggregated data instead.

  • Data integration and aggregation: Data silos create a  significant challenge in IoT, as they can cause data duplication, loss, or inaccessibility by different systems. Data fabrics can support breaking down data silos by offering a unified view of data across the IoT ecosystem. Data is created from different sources and in diverse formats; data fabrics can enable the integration of this data into a suitable view. This allows organizations to comprehend their IoT data landscape and make informed decisions. Data fabrics can collect and merge this data in real-time, offering a compressed and contextualized view of the IoT environment. This collected data can be used for real-time analytics, irregularity detection, and predictive modeling, allowing organizations to derive valuable insights and make proactive decisions.
  • Data processing and analytics: Data fabrics offer processing power, permitting IoT data to be analyzed and changed into actionable intelligence. By using distributed computing and parallel processing, data fabrics can handle IoT data’s high volume as well as velocity. This empowers organizations to conduct complex analytics on the gathered IoT data, like machine learning algorithms, extracting valuable patterns, trends, and correlations. 
  • Data management and quality: Data fabrics offer a management layer guaranteeing data quality, consistency, and compliance. As we know, IoT data comes from different sources and devices, and it is necessary to ensure data integrity and reliability. Data fabrics can implement data management policies, perform data validation and assure data quality standards are fulfilled, thereby enhancing the reliability and trustworthiness of IoT data.
  • Scalability and flexibility: IoT establishment often includes multiple devices creating data at a high frequency. Data fabrics are designed to be scalable and flexible, enabling organizations to manage the high intensity of IoT data and acclimate future growth. They are seamlessly scaled horizontally, adding more resources as required and adapting to evolving IoT infrastructures and data requirements.

Not just this, data fabric tools also enable real-time data processing and help in decision-making. In IoT systems, real-time responsiveness is essential for upcoming maintenance, monitoring, and dynamic resource allocation applications. Data fabrics can process and analyze data in real-time, allowing organizations to take prompt actions based on IoT insights.

Some robust platforms for managing IoT data

For handling IoT data, many platforms offer robust capabilities. One such platform is K2View, a data integration and management solution that allows organizations to merge and manage their data from various sources. Their technique pivots around micro-data management, emphasizing granular test data management instead of replicating entire datasets. This strategy streamlines operations, decreases complexity, and minimizes the risk of data inconsistencies. Organizations can overcome data silos, improve data quality, and achieve valuable insights for informed decision-making using their scalable and flexible architecture. 

For companies planning their AI move, IBM Pak is an available option. It is a pre-integrated, enterprise-grade data and AI platform that assists businesses in accelerating their journey to AI. It offers a unified view of data, streamlines data preparation and control, and allows rapid growth and deployment of AI models. It is also available on-premises or in the cloud.

There are other platforms like Talend, known for its data integration and transformation capabilities. Talend is a data integration platform that gathers, cleans, and converts data from IoT devices. It also offers a combination of connectors to other data sources, making it uncomplicated to build a data fabric. It also offers a set of data integration, quality, administration, application, and API integration capabilities. Their Fabric also supports organizations in getting trusted data promptly, improving operational efficacy, and reducing threats.

The realm of IoT- connecting everything

The Internet of Things (IoT) will become the most powerful domain in the coming years, and data fabrics will be the best solution to encounter and subdue data challenges. They empower businesses to break free from silos and gain a holistic view of their digital landscape. With the help of data fabric, real-time insights become the standard, promoting intelligent decision-making and growing businesses into new frontiers. With the adoption of this paradigm, data fabrics come out as beacons driving organizations to the vast intricacies of IoT data and unlocking endless opportunities.

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admin
December 2, 2019
Data Security

How to Overcome Looming Threats on Big Data?

The continuation in the advancement of technology generates approximately 2.5 quintillion bytes of data daily. Protection of the data is a vital responsibility of the service provider company. Data security has now erupted as a big concern and must be guarded in proficient ways.

Big data security- this term aggregates all the measures and tools which are used to safeguard both data available cloud and on-premise from malicious activities, attacks, or any thefts that could compromise their confidentiality.

Data is vulnerable:

The increase in the amount of data is directly proportional to threats like DDoS attacks, information piracy, ransomware etc. These attacks could turn even worse when companies store sensitive and confidential information like Contact Info, Identity Information, Credit Card numbers, Bank Details etc. Additionally, attacks on a provider company’s big data could cause severe financial repercussions such as losses, litigation costs, and fines or sanctions. In fact, an unauthorized user might get access and misuse your big data and sell valuable information.

Today most of the data incoming/outgoing pass through Web API. In part, we are sharing some key errors by developers that could lead an organization to significant risk and some simple techniques to mitigate this risk.

Web API mistakes:

The common mistakes made in Web API are listed below:

  • Simple authentication like username/password
  • Weak token encryption
  • Sensitive information like token, username, a password is stored in plain text in cookies
  • No data authorization. Any authorized user has access to all data

Also Read: How To Improve Web Application Security?

Best Practices to tighten the Big data Security:

A) Authentication

API security is complex and requires an explicit crystal knowledge. Many times you built an API that you want to the public, but at the same time, you do not want everybody to access it. In such cases, you need to have control over who can access the API.

For the same reason, use strong authentication like OAuth 2.0, token using HMAC Algorithm or SHA256 with an expiry date. For an additional security use whitelist source IP Address. This would avoid requests for a given token from unauthorized IPs.

Always keep a log of all authentication request with DateTime stamp and source IP.

B) Authorization

The authorization permits a user to grant or restrict permissions on functionality and data. Always implement authorization at Logic or Database Layer. i.e. each request must be authorized of ‘which data access’ is allowed.

C) Brute force

A brute force attack is a trial and approach type cyber attack with a purpose to crack a password or username or find a hidden web page or find the key used to encrypt a message. This method being old is still active and popular among hackers. In this attack attempt, attackers try different user-names and passwords or tokens.

To stop such attacks write codes in a way to auto-detect brute force attack or slow down a client if it makes the number of requests often or directly block such IP for some time.

D) Cookies

Cookies are stored in the browser cache and are easily readable. Never leave sensitive data in cookies as plain text. Use secure encryption like AES 256 with unidentifiable cookie name. Like ‘token’ can be named as ‘zeta’.

E) Set Limits

Set request limits per minute/hour. Configure alerts for flooded request from the same IP.

These are certainly best practices to endeavour the best cybersecurity and nullify the looming attacks.

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