Big Data and Cloud Computing – Challenges and Opportunities

Big Data and Cloud Computing - Challenges and Opportunities

Two of the biggest IT trends making the rounds in today’s day and age include cloud computing and Big Data. Have you wondered what happens when you combine the two? Something positive may come out of it, while other times, not so much. Let’s explore and see what opportunities and challenges the union of these two worlds present to the users.

Big Data and cloud computing: bridging the gap

Big Data and cloud computing each have their own set of rules and specifications. However, when used together, they are capable of simplifying large datasets, offering value to businesses in a variety of industries and of different sizes. Big Data and cloud computing each offer their own set advantages while also including their own inherited challenges. Nevertheless, both technologies are still in the process of evolution, which is a double-edged sword in each case.

Regardless, there are plenty of companies using the two technologies in tandem to bolster how they operate. The combination of the two offer a number of benefits as they can increase a business’ revenue, while also reducing costs effectively. The cloud is all about providing infrastructure, and Big Data offers data solutions. Together, they can provide organizations with an advantage over the competition.

Advantages of using Big Data and cloud computing together

Agility: Lugging around heavy servers to store data is no longer feasible in the present. In other words, if one has to setup a traditional, physical infrastructure, chances are you will end up burning a hole in your pocket. With cloud computing, setting up the infrastructure is easy, convenient and more hands-off. Cloud computing caters to businesses’ growing need for data management, which makes it easier for companies to optimize how they utilize their resources.

Elasticity: What is one of the best features of cloud computing? The ability to scale servers up and down as data needs change has to be one of them. Data is a volatile variable as it can look completely different from one minute to another. A good service provider needs to be able to accommodate these storage changes at a moment’s notice. This way, an organization can alter their storage space as their data needs change.

Data processing and efficiency: Data can be structured or unstructured, depending on the source it stems from. Data from social media is usually unstructured. Such data needs an efficient system to process it and to derive meaningful insights from it. Cloud computing can be seen as the answer to such problems. When Big Data is used to amass information from social media, cloud computing can be used to efficiently process this data and unearth meaningful insights that can adequately address a business’ needs.

Cost reduction: Cloud computing is a great solution for enterprises looking to make use of their technological tools, especially when on a limited budget. Big However, data platforms can be a little expensive, especially when it comes to managing such data. However, cloud computing makes it so customers need to pay only for the resources they use, with no upfront capital expenditure. It’s also worth considering that as a business’ servers scales up or down based on its data needs, it will only have to pay for the storage space it uses thanks to the cloud.

Simplifying complexities: Cloud computing is well known for its ability to automate business operations. With the various components available to users, there is no shortage of options for organizations looking to reduce the complexity of their operations, while also automating repetitive tasks.

Challenges of using Big Data and cloud computing together

Security concerns: There are security concerns to consider with the union of these technologies, as organizations begin to wonder how to safeguard sensitive, customer information against hackers and fraudsters. Addressing the cloud security risks inherent in cloud computing requires a shared responsibility model. Cloud service providers are responsible for the Security of the cloud, while customers are responsible for Security in the cloud.

Conclusion
The cloud has become the go-to option for organizations looking to beat the competition and benefit from the immense technological advancements the cloud provides. Once such advancements are successfully mastered, there is a wonderful opportunity for organizations to reduce costs, use their technologies tools and manage their data. The end result is often is organizations are able to meet their business goals, making the combination of the cloud and Big Data an ideal one.

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5 Ways Data Analytics Can Help Drive Sales For Your Business

5 Ways Data Analytics Can Help Drive Sales For Your Business

Companies that concentrate on their customers can attain incredible sales figures for several reasons. The current generation of consumers is not impressed with traditional advertising strategies. Instead, they want to feel valued as an individual, meaning they want the company to anticipate the customers’ needs in a personalized an accurate manner. And this level of personalization is something that data analytics can help a company achieve.

As customers’ demands grow, so should your product line. By predicting your customers’ needs, you can get ahead of the game, ensuring that your customer conversion and retention rates remain high. Having said this, it’s necessary to study your competition and how other players in your industry are using customer data to their advantage. If data is not used in a targeted and actionable manner, there’s a good chance your brand will lose traction in your space, paving the way for the competition to increase their share of the market at the expense of yours.

How can data analytics drive sales for your company?

1) Enabling segmentation: Segmentation is the key to building a well-catered product line. One size does not fit all in business these days; if you are trying to sell your products to every population segment, your marketing strategy will likely fail to hit the mark with some customers. A product’s popularity varies based on multiple factors, including buying habits, age, sex, product usage, etc. Through effective segmentation, you can personalize your marketing strategy according to the needs of a specific customer segment. This technique can help you go a long way in deciding what product is best-suited for which customer group, expanding your customer base and increasing your company’s sales figures.

2) Product development: Customization is the key to a good sales strategy. In order to stay ahead of the competition, companies need to compile and analyze data about their customers. Examining customer feedback is an effective way to determine how to sell your product as you can use this information to inform your marketing strategy. Using this information allows companies to work out any inefficiencies in their sales strategies, ensuring they emerge victorious in the competition. For example, companies such as Netflix and Amazon look at the viewer response rate for their shows and use this data to decide which shows to highlight on the homepages of their streaming services.

3) Help customers decide what they want: The best thing about data analytics is its power to make accurate predictions. By using strong predictive algorithms, companies can forecast what customers might want in the future. Not only is this an integral part of the sales prediction model, but it can also go a long way in helping retain the right customers. Customers might want to order the products being showcased and continue doing business with a company if the products they suggested appeal to them, which ultimately bolsters a company’s revenue and profitability. This approach helps companies retain customers as they are constantly reminded of products they may find appealing.

4) Pricing the products right: Pricing plays an integral role in helping companies put their products forward to the customers. Some industries can be extremely competitive, which means that a company can fail if it does not have the right data to inform them what the right prices are for each product. Through the use of data-driven pricing strategies, the right price can be easily unearthed by analyzing the competition, making it possible for companies to set the right prices on the right products. Pricing decisions can also be influenced based on customer spending patterns, allowing the company’s sales teams to ensure a price that is both profitable and affordable is set to a product.

5) Email campaigns: There is no denying that emails are one of the best ways to reach your customers. Having said this, one could also say that analytics can be used to capture which email subject phrases and words are most likely to capture the interest of the customers. This can only be achieved through the use of analytics. Understanding response rates, as well as the best days and times to send emails are useful ways to determine how to advertise products or services to customers via email campaigns.

Analytics can help businesses understand their data, while also helping them conduct a deeper dive into the ways and means in which this data can be used to enhance business operations. The more a company understand the data, the more effectively it can be used. Due to this very reason, it is important for businesses to use the right tools and unearth the right insights in order to inform their decision-making. When done right, this leads to higher customer retention rates and a higher profit margin.

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Six Secrets to Big Data Success

Six Secrets to Big Data Success

Big Data has played a role in helping a number of industries immeasurably, as its role in the business world is becoming more important with each passing day. However, even though the utility of Big Data in a professional setting is immense, there are very few organizations capable to utilizing the technology at an optimal level to boost their operations.

A large number of companies fear that they will make mistakes with the technology, which stops them from moving forward with Big Data analytics and maximizing its value. This is because, when used poorly, Big Data analytics can make false predictions for the future. However, when implemented correctly, Big Data offers a lot of upside to an organization. Combine its capabilities with a focused vision and a competent team, and there is a good chance the technology will bolster your company’s operations and profitability.

Keep reading as we will help you develop such vision with our insights on how you can be successful with Big Data.

1. Skills matter more than technology

It’s no secret that without the right technological tools, it is nearly impossible to succeed in a growingly competitive and sophisticated business world. Nevertheless, technology alone is not enough to help you attain this success—having the skills to operate the technology properly is also needed. While talking of Big Data, your team’s skills are far more important than the technology itself since technical ability has a very small role to play in Big Data analytics. The Big Data analyst must have know how to come up with right business questions, developing a clear forward path to make the best of the technology. The analyst must also be competent enough to parse and analyze the unstructured data through pattern recognition and hypothesis formation. Eventually, the analyst should know how to use the appropriate statistical tools to generate a predictive analysis. It is not necessary for the analyst to have all these qualities before joining the organization. Instead, the organization must conduct workshops every now and then to update analysts on the latest uses of Big Data to add value to your business.

2. Run necessary pilots

Big Data Is generally adopted by firms that want a predictive analysis of market trends that they can use to to plan for their future. Such predictions are not always unearthed in a manner that ends up being useful to your organization. If the predictive data cannot be applied to your business, Big Data will not yield the fruits of success that you seek. Therefore, it is highly advisable that when looking for data-based predictions, you should run a pilot to determine whether your predictions can be applied to improve your systems or not. Doing so will not only help you rectify your errors, but will also help you redefine your prediction in a manner that better suits your market needs. Furthermore, running a pilot will also reveal any weak points on your plans from their inception through the execution of them. Thus, one pilot will strengthen the quality of your operations, as well as the overall strategies of your business.

3. Formulate targeted analysis

It is imperative that the data you compile from the market is raw and unstructured. The amount of data available is expected to grow eightfold over the next five years, according to Gartner, most of which will be unstructured. Keeping this in mind, organizations must ensure they are ready to parse and analyze the data in a manner that will be beneficial to your business. Targeted analysis is key as one dataset may be used to unearth insights about multiple topics, while other pieces of information may not need to be extracted as they may not be relevant to your goals. Know what you’re hoping to achieve before extracting insights from your datasets, and then proceed to analyze the data. Having the right technological tools beforehand that you can use to store and analyze data is key. Always keep a backlog with indices for relevant interpretations of the data, so that when you need to extract information from the same dataset in future, it will be readily available for any future analysis.

4. Extract the best data possible

Even a small dataset can sometimes prove to be effective in developing predictions, while it is also equally possible for big sets of unstructured data to lead you nowhere. Aim to always narrow the focus of the data you compile for analytical purpose without compromising the robustness of the predictions. Going this route will save you plenty of time, while also helping you attain an accurate and actionable prediction. Don’t continue running massive sets of unstructured data in the hope that it will definitely lead you to a robust prediction as this is a waste of your time.

5. Keep predictions within your organization’s operational ability

Do not aim for predictions that lie outside the ability of your firm. Not all organizations are equipped with the skills and technological prowess to make the most of your predictions, so make sure your predictions are targeted within your means. Most organizations have a limited amount of wiggle room and the challenge is to come up with predictions that your organization is comfortable with. Do not exhort unnecessary operational pressure on your organization because it will only hamper the pace and confidence of your workers.

6. Be adaptive

The best results in Big Data analytics are achieved when the most actionable predictions happen to be affordable for your firm. As discussed earlier, don’t place an unnecessary burden on your firm in the hopes of achieving the best prediction possible. Instead, bring adaptive changes to your firm slowly in a way that will help it accommodate the best of ideas. When these ideas match the capabilities of your firm, great results will be only an arm’s reach away.

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How Big Data is Changing the Healthcare Sector

How Big Data is Changing the Healthcare Sector

The healthcare sector is progressing rapidly, expanding both its reach and challenges. With an increased patient-doctor ratio, organizations must find a way to tackle the chaotic situation—a better management tool to handle the workload efficiently. Primitive book-keeping provides no scope for rapid scanning and locating a particular patient’s record; this results in delayed attention to the patient and decreases in severity. With broad adoption of the latest technology in the medical field, it is time that organizations enhance the overall healthcare system.

Therefore, healthcare firms should embrace newer technologies that help facilitate better and faster resolutions to patient’s problems, while extending a scientifically-advanced atmosphere — using Big Data and Analytics helps organizations achieve these goals. These are the significant ways in which Big Data can help the healthcare sector flourish:

Patient Health Tracking

It so happens that doctors generally want to analyze the patient’s health history before exploring anything new. But, due to disorganized data-keeping, the patients themselves are not ready to furnish the health-related documents accumulated over the years. Big Data easily tracks the entire history of the patient’s health including all minor/ significant operations undergone; it has revolutionized the whole paradigm by introducing statistical analysis that predicts and warns about the future possible occurrences.

Internet of Things aided by Big Data is a further leap in this revolution. From tracking heartbeats and sugar levels to breathing patterns and distance walked, smart wearables help provide more transparent data that can serve as a basis for medical assistance. Creating a unified database containing the citizen’s health history would enable health systems to fetch data in seconds, saving crucial time and human resources.

Increased Efficiency

With patient’s data a few clicks away, healthcare firms can obtain the entire history of the patients in seconds, making it easy for both patients and doctors — apart from saving time, this leads to reduced cost. The hands needed to keep the manual records, the data carrier, the data traveler and the data analyst, would all be required to put in their working hours. However, Big Data eliminates the mediating costs as well as the consumed time, resulting in a more efficient healthcare environment.

Making Predictions

Digitized data and statistical representation not only helps analyze the current situation but also assists in making predictions; this gives the healthcare sector an edge over the potentiality of certain diseases. The pattern of the disease will help the doctors make plans for the patient in advance—certainly rewarding in situations where the time is everything for the patient. Doctors can operate with better insights concerning the health state of a patient in a customized healthcare strategy.

Reducing Errors

It is known that human error is bound to take place, no matter how much care is placed while working with data. The calculations, the sorting, and the interpretive analysis all require precise attention. With increased workloads (or even otherwise) workers may commit errors. Big Data reduces this error-rate by employing scientific and mathematically correct equations—equally robust every time they are applied. Big data can also be used to sort unrelated prescriptions added faultily in a patient’s record. So, Big data can take care of not just avoiding errors but can also of rectifying them.

Progressive Approach

Adoption of Big data in the healthcare sector is not only a problem-solving tool but rather a way of growing operation. What use will expensive equipment and the latest medicines have if they don’t have a compatible platform to perform? A progressive environment consists of forces that work in cohesion, leading to an optimum output, all within the shadow of efficient operating. An environment which is readily embracing other advancements will show no progress if all improvements are not adequately attended. Big Data not only eases the healthcare procedures but also helps in the advancement of the infrastructure as a whole.

Predicting the possible disease level, analyzing and representing data statistically, reducing the doctor-patient gap, and cutting down costs and time are all sign of progressive development. Without the help of Big Data, the healthcare sector would possibly never achieve this goal.

Challenges are there in implementing Big Data fully in the healthcare sector, but there won’t be achievements without starting the process. To fully utilize the wealth of scientific intelligence, using Big Data seems unavoidable. If implementing it introduces so many positives to the sector and then why not apply it?

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How Big Data is Changing the Business World and Why it Matters

How Big Data is Changing the Business World and Why it Matters

The future is here, and Big Data is ushering new advents within the technology world at a steady pace. Over the last two years, Big Data has changed the very outlook of companies and the way they store data; it allows precise manipulations on large volumes of data, and it’s been revealed that every day 2.5 quintillion bytes of data are produced; this number will only increase in the future.

Every company, irrespective of their size, generates data; this might be customer information, employee data, or even sales data. No matter what type of data you have, it plays an important role when it comes to improving your quality of services. Here are a few ways in which Big Data is changing the face of businesses these days:

Enhanced Business Intelligence: A set of tools, business intelligence (BI), designed to help analyze the company. BI and Big Data go hand in hand; they have come to complement each other when it comes to handling business-related operations. As data insights drive a majority of the companies and businesses, there is a lot to look forward to regarding Business Intelligence. The higher the scope of BI, the better the business insight.

Better Targeted Marketing: When one talks about Big Data, the idea is to look at the benefits which can be achieved through data manipulation. Through the use of Big Data, targeted marketing has become a thing of the present and the future. Target marketing has helped businesses achieve their long-term goals, with efficiency and excellent results. Through high accuracy, companies can meet the demands of their perceived customers and develop their marketing strategies more effectively. It’s almost like preempting the needs of your customers and basing your products on these needs. The level of marketing and customer satisfaction goes up a notch, thereby leading to better sales and higher revenue.

Happy Customers, Satisfied Customers: Companies and businesses serve customers at all times; a happy customer is a loyal, satisfied customer. But how does one ensure their customers are happy at all times? Simply put, a business has to do all it takes to satisfy their customers’ needs and work towards fulfilling them. To pursue your customer’s needs, there are only two ways to move forward: either wait for your customer to come forward and express his/her needs or preempt the needs beforehand and work on them to enhance customer service. Big data helps in the latter; if a business can understand their customer’s needs, it can immensely benefit from a better customer service and a satisfied customer base.

Driving Efficiencies within Internal Processes: Data is the backbone of every business; it is essential to create efficiencies within their internal processes. By driving efficiencies, a company can garner momentum within their operations and get a lot of success in their day to day endeavors. The idea is to be able to maximize profits while keeping customer needs in mind. Through the use of Big Data, processes can be made more efficient without compromising on the customer service needs. The idea is to create a subtle balance between the business and customer needs to be able to drive the business forward in the right direction.

Cost Reduction: Big Data is well equipped to provide the required information for businesses to help reduce costs. Through the use of this predictive science, previous trends monitoring and event predictions, companies can predict events and strategize according to the given resources and needs. Cost reduction is a long-term goal, and can’t be achieved in a day, or a week; it has to be planned over a period, keeping in mind past trending factors, future occurrences, and how customers would behave to a particular enhancement. The idea is to ensure proper cost standards are established, so that cost reduction is no longer a fable, but a well-established practice.

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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.

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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.

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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.

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