Is Machine Learning the Solution to Your Business Problem?

The term Machine Learning (ML) is defined as ‘giving computers the ability to learn without being explicitly programmed’ (this definition is attributed to Arthur Samuel)Another way to think of this is that the computer gains intelligence by identifying patterns and data sets on its own, improving output accuracy over time as more data sets are examined. Since ML can be a challenging solution to implement, we’ve put together some foundational steps to assess the feasibility of building an ML solution for your organization: 

1. Identify the problem TYPE 

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Automation: Implementing automation without learning is appropriate when the problem is relatively straightforward. These are the kinds of tasks where you have a clear, predefined sequence of steps currently being executed by a human, but that could conceivably be transitioned to a machine.

Machine Learning: For the second type of problem, standard automation is not enough – it requires learning from data. Machine learning, at its core, is a set of statistical methods meant to find patterns of predictability in datasets. These methods are great at determining how certain features of the data are related to the outcomes you are interested in.

2. Determine if you have the right data

The data might come from you, or an external provider. In the latter case, make sure to ask enough questions to get a good feel for the data’s scope and whether it is likely to be a good fit for your problem. consider your ability to collect it, its source, the required format, where it is stored, but also the human factor. Both executives and employees involved in the process need to understand its value and why taking care of its quality is important. 

3. Evalute Data Quality and Current State

Is the data you have usable as-is, or does it require manual human manipulation before introducing into the learning environment? A solid dataset is one of the most important requirements for building a successful machine learning model. Machine learning models that make predictions to answer their questions usually need labeled training data. For example, a model built to learn how to determine borrower due dates to improve accurate reporting needs a starting point from which to build an accurate ML solution. Labeled training datasets can be tricky to obtain and often require creativity and human labor to create them manually before any ML can happen.

4. Assess Your Resources

Do you have the right resources to maintain your ML solution? Once you have an appropriate question and a rich training dataset in hand, you’ll need people with experience in data science to create your models. Lots of work goes into figuring out the best combination of features, algorithms, and success metrics needed to make an accurate model. This can be time-consuming and requires consistent maintenance over time.

5. Confirm Feasibility of ML Project

With the four previous steps for assessing whether or not ML is right for your organization, consider the responses. Is the question appropriate for building an ML business solution? Is the data available, or at least attainable? Does the data need hours of human labor? Do you have the right skilled team members to carry out the project? And finally, is it worth it – meaning, will the solution have a large impact, financially and socially? 

It’s important to consider these key questions when assessing whether or not Machine Learning is the right solution for your organization’s needs. Connect with our ML experts today to schedule your free assessment. 

How can Artificial Intelligence and Machine Learning Help with DevOps?

How can Artificial Intelligence and Machine Learning Help with DevOps?

Artificial Intelligence (AI) and Machine Learning (ML) have both become integral parts within the world of DevOps because of their ability to help developers break free from the chains of manual labor. DevOps is all about breaking down siloed developmental walls, and there is no doubt that AI and ML can help teams achieve their goal. With the combination of both these practices, efficiency and productivity can be further enhanced by providing additional performance to businesses.

How will Artificial Intelligence and Machine Learning Drive DevOps in the Future?

AI and ML are undoubtedly the best ways to drive efficiency and growth within processes; however, they do come with their own set of problems. The idea behind the implementation of these practices is to help organizations achieve their targets; however, what’s difficult is the fact that the application of the technologies into a company’s workflow might not be as easy as it seems.

To get AL and ML up and running within your business, you’ll need creative developers, who are well versed with the nuances of the two practices. Given this knowledge, it might be preferred to state upfront that the implementation of AI and ML will initially be quite a tedious task and that the learning curve would be slower than usual.

The above does not negate the fact that DevOps developers can still gain a lot of traction by adopting the essential features of Artificial Intelligence and Machine Learning within their day to day functions.

Through the successful implementation of AI and ML, management can expect to make rapid decisions, which can significantly benefit the business and further lead to improved profitability within the company.

To add a futuristic touch to the world of DevOps, AI and ML can help manage large volumes of data and solve computational problems. AI will eventually become the sole driver to assess, compute, and ease decision making within DevOps environments.

What is Artificial Intelligence’s Influence on DevOps?

Artificial Intelligence is the changing face of DevOps; it can change the way DevOps teams develop their tools, deliver their production goals, and deploy the changes within their functions. AI can mainly help developers improve an application’s efficiency, and enhance business operations.

To understand the influence of both practices, it’s best to summarize:

Improved Data Accessibility
Within the DevOps environment, data access is a big concern. However, this issue is addressed, when AI releases critical data from its formal storage place. Through the use of AI, data can be collected from different sources and made available in a single spot, which can then further be used for different types of analysis and production uses.

Greater Ease of Implementation
AI is all about self-implementing systems; this means, the transition of processes from human run systems to mechanical systems is seamless and smooth. When it comes to assessing human efficacy, one can understand how quickly system complexity is driven out.

Effective Use of Resources
Through the use of Artificial Intelligence, resources can be managed effectively, and judiciously, wherever needed.

How can Artificial Intelligence and Machine Learning be Applied to Optimize DevOps?

Organizations have come a long way, especially when it comes to technical transformations. DevOps and its implementation is no stranger to this concept. Couple the ideas of AI and ML with your organization’s technology hierarchy, and you can rest assured that you have a winning solution on your hands.

AL can also help create complex data pipelines which feed data into app development models. By the dawn of 2020, if predictions are to be believed, AI and ML will take the lead, and digital transformation will see the launch of a new technical era. However, like the two sides of a coin, even AI and ML don’t come without their own set of issues and drawbacks. To derive maximum benefit out of a DevOps structure, a customized DevOps stack is needed.

AI and ML, as futuristic concepts, have taken over the world of technology by storm. The combination of the two languages can go a long way in ensuring a steady ROI for an organization while enhancing the working of IT operations. Efficiency can take an all-new stage, and productivity can reach another level, if DevOps, AI, and ML can be fused together into one dependent model.

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The Future of Machine Learning

Technology is innovating and revolutionizing the world at a rapid pace with the application of Machine Learning. Machine learning (ML) and Artificial Intelligence (AI) might appear to be the same, but the reality is that ML is an application of AI that enables a system to automatically learn from data input. The functional capabilities of ML drive operational efficiency and capacity automation in various industries.

Technological Innovation for Convenience
Workforce handling is tedious and less productive; this is where Artificial Intelligence has lucratively overcome the age-old system of manual labor. With the world moving at such a fast pace, monitoring has become a constraint for most organizations; for this very reason, Artificial Intelligence and Machine Learning are used more as tools of convenience rather than just pieces of technology.

We have seen how accounting systems have replaced ledger books. At the same time, processes have been set up to align machines with organizational requirements effectively to balance everyone’s demands.

However, with the way Artificial Intelligence is advancing, it seems this technology is quickly going to change the way processes are functioning. Not only trends on social media will be affected, but even marketing will see a complete makeover through the use of Artificial Intelligence.

The Effect on Various Fields
When it comes to Artificial Intelligence, everybody wants a taste of it. From marketing experts and tech innovators to education sector decision-makers, Artificial Intelligence holds the capability to pave the path for a healthy future. Artificial Intelligence has been designed to provide utmost customer satisfaction. To derive maximum results from the nuances of AI customer-centric processes will need to align their business metrics to the logic of this latest technology.

As Big Data evolves, machine learning will continue to grow with it. Digital Marketers are wrapping their heads around Artificial Intelligence to produce the most efficient results by putting in minimal efforts. The entire algorithm and the build of Artificial Intelligence will be used to predict trends and analyze customers. These insights are aimed at helping marketers build patterns to drive organizational results. In the future, it seems like every basic customer need would be taken care of through fancy automation and robotic algorithms.

Healthcare Sector
The healthcare industry is one of the widely reckoned industries in the world today. Simply put, it has the maximum effect on today’s society. Through the use of Artificial Intelligence and Machine Learning, doctors are hoping to be able to prevent the deadliest of diseases, which even includes the likes of cancer and other life-shortening diseases.

Robots Assistants, intelligent prostheses, and other technological advancements are pushing the health care sector into a new frenzy, which will be earmarked towards progressing into a constantly evolving future.

Financial Sector
In the financial sector, it’s vital to ensure that companies can secure their operations by reducing risk and increasing their profits. Through the use of extensive Artificial Intelligence, companies can build elaborate predictive models, which can successfully mitigate the potential of on-boarding risky clients and processes; this can include signing on dangerous clients, taking on risky payments, or even signing up on hazardous loans.

No matter what might be the company’s requirement, Artificial Intelligence is a one-stop shop when it comes to preventing fraudulent activities in day to day operations – this, in turn, will lead to money savings possibilities, profit enhancement and risk reduction within every organizational vertical.

Robotics
We are steadily heading towards a future that will be marked complete with the rise of robotics and automation; this is not going to be restricted to the medical sector only; intelligent drones, manufacturing facilities, and other industries are also going to be benefited by the rise of robotics. Artificial Intelligence methodologies like Siri and Cortana have already seen the light of day – this is just the beginning. More and more companies are going to take these capabilities to a new level.

As more and more military operations begin to seek advantages from the likes of mechanized drones, it won’t be long before e-commerce companies like Amazon start to deliver their products through the use of drones. The potential is endless, and so are the possibilities. In the end, it is all about using technology in the right manner to ensure the appropriate benefits are driven in the right direction.

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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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The Rise of Machine Learning-as-a-Service

The Rise of Machine Learning-as-a-Service
Machine learning and artificial intelligence have become two of the most important words to reckon; their use has become imperative in almost all industries of today. With the introduction of machine-learning-as-a-service, data science has become a focus of the masses within a short span of time. While machine learning is a part of artificial intelligence, it is widely regarded as the process through which self-iterating algorithms are run to analyze vast spectrums of data, in the absence of around-the-clock the clock human supervision.

As machine learning takes precedence over other technologies, machine learning-as-a-service (MLaaS) has come up to meet the growing demands of data-driven industries. MLaaS is a set of services which are offered to companies so that they can access and obtain the benefits of Machine Learning without having to hire a data scientist to do the necessary footwork. As cloud technology is gaining momentum, more companies are outsourcing their data needs to be able to benefit from the advantages of MLaaS.

Why is Machine Learning so Important?
As discussed above, Machine Learning is all about running algorithms to achieve desired data-driven conclusions. Such models, which are equipped with the knowledge of machine learning, are adept at forecasting trends, creating real-time analyses, and performing accurate predictions based on user data. Given its adaptive nature, machine learning can grow from past mistakes and outcomes, which ordinarily help drive future positive results.

No matter the realm, machine learning can do it all. From fraud detection, to price optimization, to crime prevention, there is no end to the capabilities of this advanced technology. For companies looking at optimizing their day to day services, MLaaS is the best data optimization solution. MLaaS is offered as a Cloud-based service and consists of automatic learning tools, which learn as they go. These options can be used in the Cloud, or even in a more hybrid fashion, as per the need of the hour.

The Current Situation of MLaaS Implementation
If you think that MLaaS options are new entrants in the market, you cannot be further from the truth. The technology is not new; Microsoft, Amazon, Google Cloud and IBM have already been providing customized services to their customers. These tech conglomerates offer an excellent platform to their customers; wherein organizations can then create their personalized machine learning algorithms without having to get into the know-how of the technology.

While a majority of big, financially sound companies are making the most use of these platforms, the trend certainly seems to be changing rapidly. With the entry of new MLaaS companies, even small and medium-sized companies are raking the benefits from machine learning services. Since qualified data scientists are scarce, more companies are beginning to traverse on this path to make their data ends meet.

Benefits of MLaaS for Companies and Organizations
Just like SaaS (Software as a Service), MLaaS is hosted by a vendor, which means outsourcing is going to help you reduce your expenses drastically. Since many organizations do not have the infrastructure or the funds to host their own data storage servers, they seek the help of MLaaS vendors to do their bidding. Storing vast amounts of data can be a costly affair especially for small and medium-sized businesses (SMBs). What can be better than bringing in vendor managed platforms for data management, and letting them do all the data-driven algorithms? For this very reason, many MLaaS companies offer scalable technology, which can help SMBs pick and choose as per their requirements.

With so much riding high on this technology, there is a lot of scope for machine learning to progress within the near future. The capacity for expansion is limitless, which means companies are becoming more competitive in the market. MLaaS helps small and medium-sized business improve their technology, enhance their services, and lower their overall operational costs.

As the list of benefits increases so does the need for these platforms in everyday functioning within these companies. As more businesses begin to seek the services of machine learning, there is an inherent scope of expansion, as companies start to look for greater benefits from their machine learning partners.

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Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Understand How Artificial Intelligence and Machine Learning Can Enhance Your Business

Automation is the key to success; every company is expanding on this domain’s expertise, as organizations take on a more global approach. Given the problems of decision making, learning, and the need for adaptability when understanding data, data scientists introduced the concept of Machine Learning within the realm of Artificial Intelligence. These practices have been able to bring about a radical change in modern business efficiency.

Artificial Intelligence is commonly a platform which performs tasks intelligently, without incurring the need for human intervention. On the other hand, Machine Learning is an exclusive part of the Artificial Intelligence world, which encapsulates the know-how and the logic behind making the concept of Artificial Intelligence a real success story. Through the use of Machine Learning, machines can be taught to work more sensibly, thereby allowing them to recognize different patterns and understand new circumstances with ease.

Machine Learning has come to be used extensively, especially when it comes to providing analytical solutions to the world of consumers and technology. Through large systems of data, Machine Learning has been able to drive solutions, which help create a more data-driven approach towards solving problems.

How Artificial Intelligence is Changing Enterprise Applications

Corporate enterprises are showing a growing interest in the field of Artificial Intelligence and Machine Learning. From IBM’s Watson to Google’s DeepMind to AWS’s multiple Artificial Intelligence services, there is a lot of activity happening in the market these days.

Other features of Machine Learning include the likes of Deep Learning, computer vision and natural language processing (NLP). With all these innovations languages in place, computers can enhance their functionalities, including pattern recognition, forecasts, and analytical decision-making.

By incorporating Artificial Intelligence and Machine Learning techniques in day to day functions, large enterprises can automate everyday tasks and enhance their overall efficiency in the long run.

Here are some ways in which Machine Learning techniques are helping enterprises enhance their efficiency:

Improving Fraud Detection: Fraud detection has become the need of the hour, as more and more companies are investing heavily in these new capabilities. With more companies falling prey to fraudulent practices, there is an imminent need to be ahead in the game of fraud detection. With Artificial Intelligence and Machine Learning in place, companies and organizations can extensively direct their resources towards enriching their fraud prevention activities, to help isolate potential fraud activities.

Loss Prediction and Profit Maximization: When it comes to deriving insights from heaps of data, there is nothing better than Machine Learning to prevent loss prediction and maximize profits. The stronger the techniques, the more foolproof the loss prediction methodologies would become in the long run.

Personalized Banking: In this era of digitization, everything is automated. For this reason, banks often seek to deliver customized, top notch, personalized experiences to their customers to keep loyalty intact. By leveraging their data, banks can aim to unearth customer needs and fulfill them with the utmost precision and dedication.

Robotic Financial Advisors: Portfolio management has become the talk of the town these days, especially since robotic financial advisors have stepped into the game. Clients can benefit immensely by this advancement, since the right opportunities are mapped with their portfolio needs and demands. Robotic applications are easy to merge with services such as Alexa and Cortana, allowing banks to provide exceptional service to their customers. Through this integration, financial institutions can hope to acquire new customers and also offer more individualized services to existing customers.

Next-Era Digital Traveling: Through the use of recommendation engines, travelers can experience the new recommendations for their travel aspirations. Organizations can play a role by allowing customers to converse with chatbots, which are created through the use of Artificial Intelligence and Machine Learning. As predicted by Gartner, by the year 2020, 25% of all customer service operations will rely on virtual assistant technology to make their business ends meet.

Detailed Maintenance: Through the help of predictive maintenance, industries like aviation, transportation, and manufacturing are expecting to be able to provide the best customer service in the market. Through the use of predictive models, such industries can accurately forecast prices and predict their losses, thereby, reducing any redundancies in the future.

With digitization paving the path of the future, there is a bright scope for companies and organizations which are investing heavily in these new age technologies of Machine Learning and Artificial Intelligence. Third party consulting services such as Idexcel are ready to help companies looking to take their first step with industry leading consulting and cloud-advisory services.

As we progress through the years, what should be interesting to note are the changes we will get to see in the various industries, as every sector aims to provide exceptional customer service to their customers in multiple ways.

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How Your Small Business can Benefit from Machine Learning

How Your Small Business can Benefit from Machine Learning

The practice of Machine Learning (ML) is no longer an exotic concept for businesses. No matter if you have a small business or a Fortune 500 enterprise; the chances are that you can benefit from the nuances of AWS Machine Learning. While prominent organizations have different ways of using Machine Learning than their smaller siblings, there is a multitude of ways in which even small businesses can benefit from Machine Learning techniques.

What Exactly is Machine Learning?

Machine Learning is a type of artificial intelligence which uses programs, algorithms, and data to drive learning and automation. Under normal circumstances, it’s something you most likely already encounter on a day to day basis. For example, if you are using software like Amazon’s Alexa, Microsoft’s Cortana, Google’s Assistant, or Apple’s Siri, then you have already had a taste of the power of Machine Learning.

On the other hand, Machine Learning and Artificial Intelligence can be used in businesses as well; it isn’t just for asking about the latest weather conditions. For instance, many websites are making use of chatbots to assist customers. Businesses, irrespective of their size, are using the likes of Machine Learning to help customers while driving efficiency and monitoring social media accounts.

How can AWS Machine Learning Help Business?

Amazon has established themselves as a leader in customer service and operations. Such execution can be found with the Amazon Web Services (AWS) Machine Learning tool. These data learning devices are aimed at catering to data scientists, researchers, developers and even small businesses who are enthusiastic to use Machine Learning to their advantage.

The extent of Machine Learning advantages is not limited to just the essentials. Solutions such as Amazon Comprehend and AWS DeepLens are some of the top-notch services being provided by Amazon these days. Through these services, developers can inherit the ability to use neural networks to gain insight with regards to computer vision projects.

Developers can also train chatbots, which can cater to a customer’s specific incoming request. Machine Learning and Artificial Intelligence can even be utilized to organize a website’s content, as various defined logical algorithms come into play. A small business can also coordinate their website’s inventory using artificial intelligence.

If you are running a small business, and feel as though you don’t wish to dapple in artificial intelligence alone, then you can count on the consulting services of companies such as Idexcel. Experienced teams are always available to help businesses of any scales accomplish their goals and increase their cloud repertoire.

How does AWS Machine work with Small Businesses?

Small businesses often need to use predictive models to enhance their revenue and sales models. One of the ways to improve these models is through the use of machine learning. Entrepreneurs, who are running small businesses, often don’t have sufficient time or the resources to sift through massive data and derive intelligent decisions out of it; this is where machines learning techniques come to the rescue.

Such business owners can benefit immensely from the use of AWS Cloud-based services and AWS Machine Learning. The vast amount of data which is collected can be sorted, sifted, and analyzed to deliver helpful business-related insight efficiently.

Through the use of machine learning, small businesses can save on operating costs, while at the same time make sound decisions, and earn better profits than before. However, it is import to know that small businesses cater to customers at different stages. For this reason, it’s imperative to understand how customer behavior can change from time to time. Through predictive analytics and machine learning, such tactics can become a breeze.

No matter what the type of business you have, machine learning can come to your aid at any given point in time. From data collection to data storage and insights you can have it all; it not only helps enhance your business’s image through the use of chatbots but also helps you manage your inventory efficiently. Such is the power Machine Learning gives to its users.

For every small business owner out there, there is a unique benefit that you will get with the use of AWS Machine Learning techniques. It all depends on how you use the services to meet your company’s needs and wants at the end of the day.

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The Future of Data Science Lays within Cloud-Based Machine Learning and Artificial Intelligence

From working in solitary cubicles to working with artificial bots, automation has come a long way and has changed how the modern generation works. Today, Artificial Intelligence (AI) and Machine Learning (ML) have become regarded as the future of tomorrow’s workforce and culture. At Idexcel, we have been keeping track of these ongoing trends.

It has been said that “with great power comes great responsibility.” If we tweak this statement a bit to reflect more contemporary times, we can also assuredly say that “with great technology, comes heaps of data.” The more we progress on the path of digitization, the more massive our datasets have become, and the idea that fascinates all data scientists is linked with the emergence of AI and ML technologies – how can they be used efficiently?

Here are top 10 trends that you should look out for as they shape the direction of data analytics in our future:

Augmented Analytics: This technology broadly utilizes the power of machine learning to automate data preparation and presentation. Through the use of augmented analytics, data scientists hope to be able to aid human intelligence, to produce rapid outcomes in different data-driven domains.

Artificial Intelligence and Machine Learning: Many of us might still be living in a bubble when it comes to AI assisted work. However, the fact of the matter is, that this bubble is going to burst. With robotics and artificial intelligence taking over at an increasing pace, there is a very heavy emphasis on getting data up and running to meet organizational goals. AI and ML will be used extensively to simplify work processes through the use of Big Data analytics.

Big Data: As technology has been advancing over the years and more affordable machines have emerged, faster processing powers have been available to businesses. Now, as cloud services are taking over traditional storage methods, there is a lot to look forward to regarding the increased output of processed information. As all these sources of information generate data, there is an imminent need to draw meaningful conclusions from the data – this is where Big Data comes into the picture. As the sources for storage get defined, Big Data provides excellent methods for allowing the manipulation of stored data to draw analysis and get the ball rolling.

Cloud and Edge Computing: One can easily say that technology has reached the Clouds. Companies such as Amazon, Google, and Microsoft are providing Cloud Services to organizations for storing their day to day data. Edge Computing, which is another form of shared computing, has become the next generation’s technology. Through the means of Edge Computing, organizations can overcome connectivity and latency issues, so that the distance data has to travel is reduced significantly. Edge Computing has seen an increasing rate of growth in mobile computing, as well as in the decrease of computer hardware. The rising use of IoT-enabled devices has ushered in an era of new technology.

Predictive Analytics: As more problematic situations emerge, there is a need to develop systems which can solve problems with ease and provide meaningful solutions. Predictive analytics prove to be the solution for such issues. The better the insights, the more structured are the solutions. Such are the capabilities of predictive analytics, as they help organizations gear up to tackle the worst possible issues.

Blockchain Technology: Digital currencies, such as BitCoin, owe their very existence to Blockchain technology. Given the success rate of cryptocurrencies, there is a lot of focus on merging Blockchain technology with the world of data science. The idea is to fuse the two methodologies together, to maximize the results. Since Blockchain technology is a versatile source, it can store any digital data; it has become a well-received option with data scientists.

As more and more organizations are taking analytics and data science seriously, there is an imminent need to progress to the next level of technology. Here at Idexcel, we work with clients every day to provide DataOps Consulting and Services; it has become an inseparable part of the modern organizational structure. As we progress into the future, the thin line between business intelligence and artificial intelligence will be removed; data will become smarter than ever before.

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