Solidifying Cybersecurity with Big Data Analytics

Solidifying Cybersecurity with Big Data Analytics
The continuously growing IT infrastructure is not only providing numerous opportunities to businesses but is also opening the thresholds for raised cybercrime. The flow of data is everywhere on every device we use. This data, if not encrypted to safeguard from hackers, can bring about severe consequences.

The increasing reach of the IoT and computer machinery has made human lives much more comfortable than they were before. These devices use a flow of information to communicate and execute tasks. However, leak or misuse of such data can yield lucrative gains to hackers; this is one of the reasons why cybersecurity has emerged as one of the most important prerequisites to IT infrastructure. Scientists are continuously working towards strengthening the IT horizon so that they may become immune to external threats that cause a breach of information.

Companies have come across employing one of the most successful strategies such as Big data Analytics (BDA). Big Data Analytics comes in handy when it comes to tackling data threats; it involves an automated processing approach of examining vast and varied sets of data spreading across various servers on different computers. It hunts for patterns and trends of characteristic data and then analyzes for probable misfit that might cause a system disturbance. Organizations nowadays are also using BDA to explain and predict customer preference to attract more massive sales. BDA is two shots from one bullet— it can help reshape the framework of a business target, and it enables technicians to analyze, detect, and terminate probable cybercrime threats. The latter feat is achieved through minor reprogramming in the system software itself while keeping other operations intact.

Having said enough, let’s explore some of the primary ways through which BDA can help in boosting cybersecurity:

Identifying Unusual Behavior
Big data analytics helps analyze big chunks of data through an automated process of continuously analyzing the data, which, if given to a person, will take infinitely to examine with no guarantee of accuracy. Due to the vastness of data generated every second across the world, it becomes a time-consuming process for even machines if the data is bombarded. BDA takes on separate small bits of big data and analyzes the entire big dataset gradually, separating valid data from the threats; this not only makes the process less tedious but also decreases the chances of errors.

Cybersecurity experts often find it challenging to spot abnormalities accurately due to a varied spectrum of data. People often jump across different networks, and that makes manual data analysis very difficult. BDA distinguishes normal behavior from abnormal ones very quickly and proposes recommendations for the betterment of data flow. The more it indulges in complex data analysis, the better its structures become in tackling abnormalities. Through increased smart detection, it can quickly detect malware without any false alarms.

Tackling Malware Attacks
Cybersecurity is not ensured only through detection of malware—proper treatment of malware is also required for ensuring safety. Big data analytics can be customized to detect and respond to malware and other information threats automatically. At the hour of need, BDA can prevent an information breach through automatic cutting off the flow of information to the device that has supposedly originated the suspicious threat. It can additionally prompt automatic messages to devices that indulge in possible suspicious activities. DBA can also send a detailed report of suspicious activity to both the user and service provider. These quick actions ensure blocking of potential threats and security of confidential data.

Preparing Systems for the Future
Even tackling malware is not enough. Experts say it is better to prevent than to cure data breaches. With BDA’s smart analytics, engineers can formulate frameworks that can detect future disturbances and avoid them at their very emergence. For this purpose, BDA conducts network monitoring besides continuously analyzing the big data; it finds out probable threat cases and prepares systems to safeguard against them in advance.

Customer information is one of the main concerns of big companies and breaches may lead to severe consequences. The current leak of information from Facebook is the latest example of this sort. With the help of big data analytics systems can easily track and remove the sources of cybercrime.

The ever-growing information flow in the public domain will keep attracting hackers to steal or infect data; therefore, it is advisable that organizations employ big data analytics as it offers other benefits besides securing data systems from hackers.

Also Read

Big-data Analytics for Raising Data-Driven Enterprise
How Big Data Is Changing the Financial Industry
Big Data Empowers AI & Machine Learning

Big-data Analytics for Raising Data-Driven Enterprise

Big data Analytics for Raising Data Driven Enterprise
Big data can indeed unveil paths for unprecedented growth, for they provide a clear view of the current scenario; it sets a base for how organizations can build upon that data to make better plans and execute them accordingly. One of the numerous benefits of a data-driven organization is utilizing a digital record to store customer behavior and then using that information to develop better strategies. Although the process is a bit challenging, those who learn to tackle the hurdle take their organizations toward a market-ready and competitively secure setup.

While it is extremely beneficial to make decisions based on data-driven insights, many organizations still struggle to understand the optimum use of their big data; as a result, they overlook the potential big data has in transforming their organization. Investments put in data analytics has indeed increased over the past years which indicates the growing awareness of big-data (or DataOps) benefits; however, churning out of all the benefits that DataOps can provide is a feat mostly unachieved by many organizations. They face difficulties when leveraging big-data and end up underestimating big data’s potential. Organizations require orientation and planning for execution of big-data to achieve the best outcomes possible.

Understanding DataOps
DataOps, is a revolutionary way of managing data that promotes high-efficiency communication between data, teams, and systems. DataOps runs parallel to the benefits that DevOps provides. DataOps garners the data of organizational process change, realignment, and available technology to facilitate a professionally well-cultured relationship between everyone who handles data – data scientists, engineers, developers, business users, etc. – allowing all users to have swift access to the target data.

Because of creating data-driven enterprise, three essential properties are associated with DataOps:

Volume: Big data takes systematic record of massive scale business transactions, social media exchange, and information flow from machine-to-machine or sensor data.

Velocity: DataOps or Big-data analytics proposes timely data stream at high speed.

Variety: The Data collected forms totality in the form of a spectrum representing the full Data register. The data often comes in various formats such as structured, numeric in the traditional database or the unstructured text documents, video, audio, email, or stock ticker data.

With these varied capacities of big data, organizations must implement DataOps on a larger scale. It’s not just monetarily beneficial but also sets a smooth foundation for a variety of allied processes. The utilization of big data is even more important than just getting a grasp of the data. An organization with proper utilization of comparably fewer data points will leave behind an organization with poor utilization in the race of optimal business solidarity and growth. A data-driven enterprise, thus, entertains various privileges that other firms don’t such as:

Cost Reduction: Big data tools such as Hadoop and Cloud-Based Analytics help in reducing costs drastically especially when the data is extensive. These tools help organizations use the big data more effectively through locating and retrieving the data efficiently.

Time-Saving: The high velocity, at which data travels in a DataOps model cuts the usual long hours into small segments and renders the organization an opportunity to use the spare time for further growth of the enterprise. Tools like Hadoop and In-Memory Analytics identify the target sources immediately and make quick decisions based on the learnings.

Product Development: Having customer data in hand the enterprise can efficiently analyze the market forces and act accordingly. Creating product that satisfies the customer’s needs is one of the most common strategies that firms embrace nowadays.

Foreseeing Market Conditions: Big data analytics renders the most accurate analysis of market conditions. By keeping a record of customer purchasing behavior and likewise data, the enterprise makes itself ready for coping with future market forces and planning accordingly.

Controlling Reputation: Big data tools can also help enterprises do sentiment analysis such as review and rating analysis. Organizations can get a clear insight of their current outlook and aim at propagating the positives while marring down the negatives.

Creating and operating in a data-driven enterprise seems to be a fundamental choice for the organizations nowadays. DataOps approaches allow businesses to manage big data in the cloud through automation; this inculcates a culture of a self-service model that unfolds a variety of benefits for both, the organization, and the customer.

Also Read

How Big Data Is Changing the Financial Industry
Big Data Empowers AI & Machine Learning

Big Data Empowers AI & Machine Learning

Big Data Empowers AI & Machine Learning
Recent decades have witnessed a rapid growth in technological advancement. From raising budget-tight efficiency to rendering the smart sensing technology, IT industries not only contest for the top spot but also play a vital role in transforming the world as we perceive it. Artificial Intelligence (AI) is not an unusual term nowadays, but the importance bestowed upon it is somewhat undernourished. Coupling the technology with other recent technological advancements, AI can be optimized at even higher levels. Big data is another growing area whose full potential is still unknown. So far, IT has de-duced numerous benefits of big data interplay, but, those seem to be just a fraction of the lucrative repertoire big data has in its lap.

A new strategy, where Big Data is employed in AI, turns out to be a total game changer. Best in its class, Big Data, which uses customer and organization generated information to help firms make better decisions concerning efficiency and cost-effectiveness, meets one of the best technological feats that humankind has achieved—AI, and we can all guess the possible results.

AI can perform such complex tasks which involve sensory recognition and decision-making that ordi-narily require human intelligence. The advent of robotics has further introduced an autonomy that re-quires no human intervention in the implementation of those decisions. Such a technology when paired with Big Data, can rise to unforeseen immensities that we cannot presently articulate. Howev-er, some of the primary outcomes of this merging are as follows:

Soaring Computational power
With continually emerging modern processors, millions of bits of information can be processed in a second or less. Additionally, graphics processors also contribute exponentially to the rising CPS (calcu-lations per second) rate of processors. With the help of Big Data analytics, the processing of big vol-umes of data, and the rendering of rules for machine learning, on which AI will operate, is possible in real time.

Cost Effective and Highly Reliable Memory Devices
Memory and storage are the essential components of any computing machine, and their health de-termines the overall strength of the computer. Efficient storage and quick retrieval of data are critical for a device to work smartly, even more so for AI.

Memory devices such as Dynamic RAMs and flash memories are increasingly in demand for they make use of information merely for processing and not for storage. Data, thus, doesn’t become centralized in one computer but is instead accessed from the cloud itself. With the aid of Big Data, memories of more precise knowledge could be built, which will inevitably result in better surface realities. Addition-ally, the ready cloud which indulges into this large-scale computation is used to produce the AI knowledge space. With the better memory of information, indeed, higher AI learning will be imparted along with reduced costs.

Machine Learning From Non-Artificial Data
Big Data is proven to be a source of genuine business interaction. Big data accumulated for analytics provide a better grounding for prospects of actions and planning of the organizations. Earlier, AI was used to deduce learning from the samples fed in the storage of the machine, but with Big Data analyt-ics it is now possible to provide machine learning with “real” data which helps AI perform better and more accurately.

Improved Recognition Algorithms
With technological advancements, it has become possible to program AI machines in such a way that they can make sense of what we say to them almost as if they were humans. However, humans can produce an infinite set of sentences through combinations based upon underlying linguistic and per-ceptive analysis. Big Data is also capable of empowering AI in the same way as it can form algorithms that the human brain possesses. The voluminous data renders a broad base for building algorithmic analysis, which in turn enhances the quality of AI perception. Alexa, HomePod, Google Home, and other virtual assistants are good (if not the best) examples of improved recognition in AI.

Promoting Open-Source Programming Languages
In the past, due to cloud unavailability (thereby unavailable Big Data), AI data models could use only simple programming languages. These scripting languages such as Python or Ruby where excellent for statistical data analysis, but with the help of Big Data, additional programming tools for data can be uti-lized.

With the introduction of new developments in technology such as Big Data, the scope, and future of AI has been soaring in new dimensions. With the merging of Big Data analytics and AI, we can create a highly efficient, reliable, and dependable in its nature AI defined infrastructure.

Also Read

How Big Data Is Changing the Financial Industry
Why is Big Data Analytics Technology so Important

How Big Data Is Changing the Financial Industry

How Big Data is changing the Financial Industry
Big Data is the talk of the town these days; not only has it ushered in the next generation of technology, but it has also modified the way businesses and financial institutions are performing their day to day activities.

Financial institutions are always on the lookout to enhance their day to day operations while keeping their competitiveness intact. Let’s have a quick look at analyzing the top 5 financial trends which are quickly taking over the financial industry and paving the path for modernizations.

Strengthening Financial Models: Data is prevalent in every industry. Financial institutions such as banks, lending institutions, trading firms, etc., produce tons of data on a regular basis. To manage such voluminous data, there is an imminent need to bring into operation a data handling language which is equipped to handle, manipulate and analyze massive volumes of information – this is where the role of Big Data comes into the picture. Financial institutions often work on different business and financial models, especially with respect to approving loans, trading stocks, etc. To make efficient working models past data trends need to be taken into consideration. The better the data relativity, the stronger the model and lesser would the risks involved. All such strategies can be derived from the use of Big Data, which in turn becomes an effective method to drive data-driven models through different financial services.

Enhanced Data Processing and Storage: Technology will never stop growing. Since the aforementioned has become an inseparable part of every organization’s life cycle, the data generated by daily operations gives way to the need of the hour storage and data processing. If one talks about the use of Big Data, the name is a clear giveaway in itself; it encompasses the use of the language, which means storing data in the Cloud or on other shared servers becomes a cinch. Thus distribution and processing come as a byproduct of storage capabilities. Cloud management, data storage, and data processing have become the words to reckon with, as more and more organizations are considering opportunities within the technical world.

Machine Learning Generates Better Returns: Financial Institutions deal with customer data on a day to day basis. Not only is such information critical, but very valuable, since it gives insights into the daily functioning of the bank. Considering the sensitivity of the data, there is a pressing need to evaluate the stored data, and protect it from fraudulent activities, while ensuring the risk is reduced drastically. Machine Learning has become an integral part of modern fraud prevention systems, which help to enhance risk management and prevent fraudsters from entering into protected domains.

Blockchain Technology: When customer data is at the fore, and financial transactions are at risk, Anti-Money Laundering (AML) practices become a topic of deliberation. Many people are beginning to give considerable importance to Blockchain technology within the financial industry forum. Blockchain possesses the ability to decentralize databases, and further link separate transaction information through code. This way, it can secure the transactions and offer an extra layer of security to the organizations dealing with sensitive data.

Customer Segmentation: Banks are always under pressure to convert their business models from business-centric to customer-centric models; this means that there is a lot of pressure to understand customer needs and place them before business needs to maximize the efficacy of banking. To facilitate the shift banks need to perform customer segmentation to be able to provide better financial solutions to their customers. Big Data helps perform such tasks with simplicity, thereby enhancing groups and data analysis.

There is no denying the fact that Big Data has increasingly taken over various industries in a short matter of time. The higher the opportunities being exploited, the better the results being displayed by banks and other financial institutions. The idea is to expand efficiency, provide better solutions, and become more customers centric. All the while decreasing the tangent of fraud and risks within the financial domain.

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Why is Big Data Analytics Technology so Important

Why is Big Data Analytics Technology so Important

Big Data Analytics Technology

Yes! Big Data Analytics, as well as Artificial Intelligence, has truly shown its importance in today’s business activities. Corporations & Business sectors are coming up with their procedures to data analytics as the aggressive landscape modifications. Records analytics is slowly becoming entrenched in the enterprise. Today, it’s a well-known behavior and desired practice for companies to apply analytics to optimize something, whether or not it’s operational performance, false detection or purchaser reaction time.

To this point, usage has been pretty easy. Maximum agencies are still doing descriptive analytics (historic reporting) and their use of analytics is characteristic-unique. But in upcoming years more business areas will follow the leaders and boom their levels of class, the use of predictive and prescriptive analytics to optimize their operations. Moreover, extra groups will begin coupling feature-specific analytics to get new intuition & observation into client journeys, risk profiles, and marketplace opportunities.

The “leading” companies were also much more likely to have some sort of cross-purposeful analytics in vicinity enabled via a common framework that enables collaboration and statistics sharing. These pass-practical views allow agencies to recognize the effect of cross-useful dynamics consisting of supply chain effects.

Predictive and Prescriptive Analytics

Whilst descriptive analytics continues to be the maximum popular shape of analytics today, it is no longer the satisfactory manner to advantage a competitive side. Businesses that want to move beyond “doing business through the rear-view mirror” are the use of predictive and prescriptive analytics to decide what is going to possibly arise. Prescriptive analytics has the delivered advantage of recommending movement, which has been the number one gripe approximately descriptive and predictive analytics. The forward-searching abilities enabled through predictive and prescriptive analytics allow groups to plan for possible outcomes, excellent and bad.

Armed with the in all likelihood styles predictive and prescriptive analytics screen; agencies can identify fraud faster or intrude sooner when it seems that a consumer is set to churn. The mixed foresight and timelier action help corporations force extra sales, reduce risks, and improve consumer delight.

Artificial Intelligence (AI)

Artificial intelligence (AI) and gadget learning culture take analytics to new ranges, figuring out previously undiscovered patterns which can have profound outcomes on a commercial enterprise, consisting of identifying new product opportunities or hidden dangers.

Machine intelligence is already constructed into predictive and prescriptive analytics equipment, dashing insights and enabling the analysis of well-sized probabilities to determine the greatest route of movement or the first-rate set of alternatives. Over the years, extra state-of-the-art forms of AI will find their way into analytics systems, similarly enhancing the rate and accuracy of selection-making.

Governance and Security

Groups are supplementing their information with third-celebration records to optimize their operations, comprehensive of adapting useful resource degrees primarily based at the expected level of consumption. They are also sharing statistics with users and companions who necessitate robust governance and a focal point of safety to reduce information misuse and abuse. However, protection is turning into an increasing number of the complex as more ecosystems of records, analytics, and algorithms interact with every other.

Given latest excessive-profile breach instances, it has emerged as clean that governance and safety have to be applied to information at some point in its lifecycle to reduce facts-associated risks.

Developing Statistics

Facts volumes are developing exponentially as agencies connect to statistics outside their internal structures and weave IoT devices into their product lines and operations. Because the records volumes continue to grow, many groups are adopting a hybrid records warehouse/cloud strategy out of necessity. The businesses maximum in all likelihood to have all their records on-premises keep it there due to the fact they’re involved in security.

Groups incorporating not gadgets into their enterprise strategies are both adding an informational element to the bodily products they produce or including sensor-based total information to their existing corpus of statistics. Depending on what is being monitored and the use case, it could be that every piece of information does no longer have value and no longer each issue calls for human intervention. While one or each of those things are authentic, aspect analytics can help identify and remedy as a minimum some common issues routinely, routing the exceptions to human decision-makers.

Top 7 Technologies to Unleash the Full Potential of Big Data

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When I was wondering “how big is ‘Big Data’?”, I stumbled across an excellent description saying, “A Big Data can be as big as a million of exabytes (1,024 petabytes) or a bazillion of petabytes (1,024 terabytes) containing billions and trillions of records from people worldwide”. And that’s amazing!

Big data is massive and exploding!! Hundreds of companies worldwide are springing up with new projects to extort the full potential of Big Data – that of rapid extraction, loading, transformation, search, analysis and share massive data sets.

Here we go top 7 open source technologies to bring out the best of Big Data that you should start adopting today.

Apache Hive 2.1: If you want your applications to run 100 times faster, Apache is your solution. Apache Hive is Hadoop’s SQL solution. The latest release features performance enhancement keeping Hive as the only solution for SQL on petabytes of data over clusters of thousands of nodes.

Hadoop: One of the most popular MapReduce platforms, Hadoop is a robust enterprise-ready solution to run Big Data servers and applications. For this, you need YARN and HDFS for your primary data store.

Spark: Yet another no—brainer, Spark offers easy-to-use technologies for all Big Data Languages. It is a vast ecosystem that is growing rapidly providing easy batching/micro-batching/SQL support.

Pheonix: An SQL skin on Apache HBase, Pheonix is ideal to support Big Data use cases. It replaces regular HBase client APIs with standard JDBC APIs to insert data, create the table and send queries to HBase Data. It reduces the amount of code, allows transparent performance optimisation to the user, integrates and leverages the power of several other tools.

Zeppelin: It calls itself a web-based notebook empowering interactive data analytics. You can just plug in data/language processing back end into Zeppelin that supports interpreters like Python, Apache Spark, JDBC, Shell and Markdown.

Kafka: Kafka is a fast, durable, scalable and fault-tolerant subscribe and public system. It often replaces message brokers like AMOP and JMS as it features higher throughput, replication and reliability. It is combined with Apache HBase, Apache Storm and Apache Spark for streaming of data and real-time analysis.

NiFi: NiFi maximises the value of data-in-motion. It is designed and built to automate the data flow between systems and create secure data ingestion. Two key roles of NiFi are:
• Accelerate Data Collection and enhances movement for ROI on Big Data
• Collect, secure and transport data from IoT.

Idexcel Big Data Services is focused on dealing effectively with technologies & tools that enable efficient management of Big Data Volume, Diversity and Velocity. With massive and active client engagement spanning several verticals, we help businesses in building data analytics decision within the organisation.

That said, would you like to be another name enlisted on our happy customer directory?

Budget Strategies for Maximizing Big Data – Idexcel Big Data Roundup

1. Budget Strategies for Maximizing Big Data

Big data doesn’t necessarily come with a big pricetag. Here, an expert offers his tips for using big data on a small budget.

Got an operational problem?

Big Data will solve it! Marketing ills? Ask Big Data! Those two words have become a catchall — but data-crunching services tend to chase after enterprise-level businesses, making them out of reach for most small businesses. (Google Analytics Premium, for example, starts at $150,000 a year.) Don’t worry: Martineau says that with a strategic approach to Big Data, anyone can afford it. Continue reading…

2. Hadoop Overview: A Big Data Toolkit

Big Data isn’t new. Forbes traces the origins back to the “information explosion” concept first identified in 1941. The challenge has been to develop practical methods for dealing with the 3Vs: Volume, Variety, and Velocity. Without tools to support and simplify the manipulation and analysis of large data sets, the ability to use that data to generate knowledge was limited. The current interest and growth in Big Data, Data Science, and Analytics is largely because the tools for working with Big Data have finally arrived. Hadoop is an important piece of any enterprise’s Big Data plan. Continue reading…

3. Mendix Low-Code Mobile Dev Platform Connects IoT, Big Data and Machine Learning

Mendix today announced a new version of its low-code mobile development platform, designed to help developers build “Smart Apps” with connectors to accommodate emerging trends such as the Internet of Things (IoT), Big Data and machine learning (ML). Continue reading…

4. Marketing Technology Vendors Offer Big Success with Big Data

As you’re probably already aware, the marketing technology vendor landscape is amazingly vast. Scott Brinker, editor of chiefmartec.com, shares that in 2016 there are at least 3,874 vendors hawking their wares in the marketing technology space. And every single one of them uses data.
To that end, following are four examples of marketing technology vendors using big data effectively: Continue reading…

5. Role of Risk Audits: How the Cloud & Big Data have Changed Them

The role of auditors has been changing rapidly over the past decade. Big data is allowing them to make higher quality decisions. However, their job is also becoming more complicated, so future financial auditors will need a strong background in IT. Continue reading…

Big data, marketing and decision-making – what is it all about?

Last week I got asked if I know about big data and how you use it in digital marketing. Yes, of course, I do. I’ve been using big data for years when analysing numbers from websites and social media.

I’ve also been fortunate to speak at many conferences where some of the speakers are fully trained ‘big data ninjas’, and I’m lucky to know some of them personally.

Big data is complex information, and it feels as overwhelming as a huge waterfall. It’s only if you present big data in a meaningful way it helps you to make better decisions. Continue reading