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The Power of Data: How the Industry Has Changed After Adding Data

The volume of data is expanding at an enormous rate, each day. No more are 1s and 0s are petty numerical digits, they are now a whole new phenomenon – known as Big Data. A fair assessment of the term helped us understand the massive volume of corporate data collected from a broad spectrum of sources is what big data is all about.

A recent report suggested that organizations are expected to enhance their annual revenues by an average of $5.2 million – thanks to big data.

More about Data, Rather Big Data

Back in the day, most of the company information used to be stored in written formats, like on paper. For example, if 80% of confidential information was kept on paper, 20% was stored electronically. Now, out of that 20%, 80% was kept in databases.

With time, things have changed. Across the business domain, more than 80% of companies store their data in electronic formats nowadays, and at least 80% of that is found outside databases, because most organizations prefer storing data in ad hoc basis in files at random places.

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Now, the question is what kind of data is of crucial importance? Data, that impacts the most?

With that in mind, we’ve three kinds of data:

  • Customer Data
  • IT Data
  • Internal Financial Data

The Value of Data

For companies, data means dollars – the way data costs companies’ their time and resources, it also leads to increased revenue generation. However, the key factor to be noted here is – the data have to be RELEVANT. Despite potential higher revenues through advanced data skills and technology implementation, an average enterprise is only able to employ 51% of total accumulated and generated data, and less than 48% of decisions are based on that.

To say the least, unlike before, today’s organizations gather data from a wide array of sources – CCTV footage, video-audio files, social networking data, health metrics, blogs, web traffic logs and sensor feeds – previously companies were not as efficient and tech-savvy as they are now. In fact, five years ago, some of the sources from which data is accumulated did not even exist nor were they available on corporate radar.

With the rise of ingenious and connected technologies, companies are turning digital. It hardly matters if you are an automobile manufacturer, fashion collaborator or into digital marketing – being connected digitally and owning meaningful data is all to cash on. You can structure intricate database just with consumers’ details, both personal and professional, such as age, gender, interests, buying patterns, behavioral statistics and habits. Remember, accumulating and analyzing data is not only productive for your company but also becomes a saleable service in its own way.

Make Data the Bedrock of Your Business

Data has to be the life and blood of business plans and decisions you want to make. Ensure your employees learn about the value of data collection, make sure you align your IT resources properly and keep pace with the latest data tools and technologies as they tend to keep on changing, constantly.

Embrace the change – while physical assets are losing importance, data appears to be the most valuable asset a company can ever have.

For big data hadoop certification in gurgaon, look no further than DexLab Analytics. With the right skills in tow and adequate years of experience, this analytics training institute is the toast of the town. For more information, visit our official page. 

 

The blog has been sourced from:

https://www.digitaldoughnut.com/articles/2016/april/data-may-be-the-most-valuable-asset-your-company-h

https://www.techrepublic.com/blog/cio-insights/big-data-cheat-sheet/

https://www.techrepublic.com/article/the-3-most-important-types-of-data-for-your-business

 

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Analytics of Things is Transforming the Way Businesses Run

Analytics of Things is Transforming the Way Businesses Run

As Internet of Things (IoT) invades every aspect of our lives, big data analytics is likely to be utilized for many more things other than solving business problems. This growing popularity of big data analytics, which is altering the way businesses run, has given birth to a new term- ‘ Analytics of Things’.

Much before big data was identified as the most valuable asset for businesses, enterprises had expressed need for a system that could handle an ‘information explosion’. In 2006, an open source distributed storage and processing system was developed. This system called Hadoop spread across commodity hardware and encouraged the nurturing of many more open source projects that would target different aspects of data and analytics.

Growth of Hadoop:

The primary objective with which Hadoop was developed was storing large volumes of data in a cost effective manner. Enterprises were clueless how to handle their ever increasing volumes of data. So, the first requirement was to dump all that data in a data lake and figure out the use cases gradually. Initially, there used to be a standard set of open source tools for managing data and the data architecture lacked variety.

Prior to adopting big data, companies managed their reporting systems through data warehouses and different types of data management tools. The telecom and banking industry were among the first to step into big data. Over time, some of them completely shifted their reporting work to Hadoop.

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Evolution of big data architecture:

Big data tools have witnessed drastic evolution. This encouraged enterprises to employ a new range of use cases on big data using the power of real-time processing hubs. This includes fraud detection, supply chain optimization and digital marketing automation among other things. Since Hadoop’s birth in 2006, big data has developed a lot. Some of these developments include intelligent automation and real-time analytics.

To keep up with the demands for better big data architecture, real-time analytics was incorporated in Hadoop and its speed was also improved. Different cloud vendors developed Platform as a Service (PaaS) component and this development was a strong driving force behind big data architectures becoming more diverse.

As companies further explored ways to extract more meaning from their data, it led to the emergence of two major trends: Analytics as a service (AaaS) and data monetization.

AaaS platforms provided a lot of domain experience and hence gave generic PaaS platforms a lot more context. This development made big data architecture more compact.

Another important development came with data monetization. Some sectors, like healthcare and governance, depend heavily on data collected through a range of remote IoT devices. To make these processes speedier and reduce network load, localized processing was needed and this led to the emergence of ‘edge analytics’. Now, there is good sync between edge and centralized platforms, which in turn enhances the processes of data exchange and analysis.

The above mentioned developments show how much big data has evolved and that currently a high level of fine-tuning is possible in its architecture.

Often enterprises struggle with successful implementation of big data. The first step is to define your big data strategy. Instead of going for full blown implementation, undertake shorter implementation cycles.

It is highly likely that our future will become completely driven by big data and ground-breaking innovations like automated analysts and intelligent chatbots. Don’t be left behind. Enroll for big data Hadoop certification courses and take full advantage of the power big data holds in today’s world of work. The big data Hadoop training in Gurgaon ensures that every student becomes proficient enough to face real challenges in the industry. Enroll now and get flat 10% discount on all big data certification courses.

 

Reference: www.livemint.com/AI/bRwVnGBm6hH78SoUIccomL/Big-Data-Analytics-of-Things-upend-the-way-biz-gets-done.html

 

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Rudiments of Hierarchical Clustering: Ward’s Method and Divisive Clustering

Rudiments of Hierarchical Clustering: Ward’s Method and Divisive Clustering

Clustering, a process used for organizing objects into groups called clusters, has wide ranging applications in day to day life, including fields like marketing, city-planning and scientific research.

Hierarchical clustering, one the most common methods of clustering, builds a hierarchy of clusters either by a ‘’bottom up’’ approach (Agglomerative clustering) or by a ‘’top down’’ approach (Divisive clustering). In the previous blogs, we have discussed the various distance measures and how to perform Agglomerative clustering using linkage types. Today, we will explain the Ward’s method and then move on to Divisive clustering.

Ward’s method:

This is a special type of agglomerative hierarchical clustering technique that was introduced by Ward in 1963. Unlike linkage method, Ward’s method doesn’t define distance between clusters and is used to generate clusters that have minimum within-cluster variance. Instead of using distance metrics it approaches clustering as an analysis of variance problem. The method is based on the error sum of squares (ESS) defined for jth cluster as the sum of the squared Euclidean distances from points to the cluster mean.

Where Xij is the ith observation in the jth cluster. The error sum of squares for all clusters is the sum of the ESSj values from all clusters, that is,

Where k is the number of clusters.

The algorithm starts with each observation forming its own one-element cluster for a total of n clusters, where n is the number of observations. The mean of each of these on-element clusters is equal to that one observation. In the first stage of the algorithm, two elements are merged into one cluster in a way that ESS (error sum of squares) increases by the smallest amount possible. One way of achieving this is merging the two nearest observations in the dataset.

Up to this point, the Ward algorithm gives the same result as any of the three linkage methods discussed in the previous blog. However, as each stage progresses we see that the merging results in the smallest increase in ESS.

This minimizes the distance between the observations and the centers of the clusters. The process is carried on until all the observations are in a single cluster.

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Divisive clustering:

Divisive clustering is a ‘’top down’’ approach in hierarchical clustering where all observations start in one cluster and splits are performed recursively as one moves down the hierarchy. Let’s consider an example to understand the procedure.

Consider the distance matrix given below. First of all, the Minimum Spanning Tree (MST) needs to be calculated for this matrix.

The MST Graph obtained is shown below.

The subsequent steps for performing divisive clustering are given below:

Cut edges from MST graph from largest to smallest repeatedly.

Step 1: All the items are in one cluster- {A, B, C, D, E}

Step 2: Largest edge is between D and E, so we cut it in 2 clusters- {E}, {A., B, C, D}

Step 3: Next, we remove the edge between B and C, which results in- {E}, {A, B} {C, D}

Step 4: Finally, we remove the edges between A and B (and between C and D), which results in- {E}, {A}, {B}, {C} and {D}

Hierarchical clustering is easy to implement and outputs a hierarchy, which is structured and informative. One can easily figure out the number of clusters by looking at the dendogram.

However, there are some disadvantages of hierarchical clustering. For example, it is not possible to undo the previous step or move around the observations once they have been assigned to a cluster. It is a time-consuming process, hence not suitable for large datasets. Moreover, this method of clustering is very sensitive to outlietrs and the ordering of data effects the final results.

In the following blog, we shall explain how to implement hierarchical clustering in R programming with examples. So, stay tuned and follow DexLab Analytics – a premium Big Data Hadoop training institute in Gurgaon. To aid your big data dreams, we are offering flat 10% discount on our big data Hadoop courses. Enroll now!

 

Check back for our previous blogs on clustering:

Hierarchical Clustering: Foundational Concepts and Example of Agglomerative Clustering

A Comprehensive Guide on Clustering and Its Different Methods
 

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How Hadoop Data Lake Architecture Helps Data Integration

How Hadoop Data Lake Architecture Helps Data Integration

New data objects, like data planes, data streaming and data fabrics are gaining importance these days. However, let’s not forget the shiny data object from a few years back-Hadoop data lake architecture. Real-life Hadoop data lakes are the foundation around which many companies aim to develop better predictive analytics and sometimes even artificial intelligence.

This was the crux of discussions that took place in Hortonworks’ DataWorks Summit 2018. Here, bigwigs like Sudhir Menon shared the story behind every company wanting to use their data stores to enable digital transformation, as is the case in tech startups, like Airbnb and Uber.

In the story shared by Menon, vice president of enterprise information management at hotelier Hilton Worldwide, Hadoop data lake architecture plays a key role. He said that all the information available in different formats across different channels is being integrated into the data lake.

The Hadoop data lake architecture forms the core of a would-be consumer application that enables Hilton Honors program guests to check into their rooms directly.

A time-taking procedure:

Menon stated that the Hadoop data lake project, which began around two years back, is progressing rapidly and will start functioning soon. However, it is a ‘’multiyear project’’. The project aims to buildout the Hadoop-based Hotonworks Data Platform (HDP) into a fresh warehouse for enterprise data in a step-by-step manner.

The system makes use of a number of advanced tools, including WSO2 API management, Talend integration and Amazon Redshift cloud data warehouse software. It also employs microservices architecture to transform an assortment of ingested data into JSON events. These transformations are the primary steps in the process of refining the data. The experience of data lake users shows that the data needs to be organized immediately so that business analysts can work with the data on BI tools.

This project also provides a platform for smarter data reporting on the daily. Hilton has replaced 380 dashboards with 40 compact dashboards.

For companies like Hilton that have years of legacy data, shifting to Hadoop data lake architectures can take a good deal of effort.

Another data lake project is in progress at United Airlines, a Chicago-based airline. Their senior manager for big data analytics, Joe Olson spoke about the move to adopt a fresh big data analytics environment that incorporates data lake and a ‘’curated layer of data.’’ Then again, he also pointed out that the process of handling large data needs to be more efficient. A lot of work is required to connect Teradata data analytics warehouse with Hortonworks’ platform.

Difference in file sizes in Hadoop data lakes and single-client implementations may lead to problems related to garbage collection and can hamper the performance.

Despite these implementation problems, the Hadoop platform has fueled various advances in analytics. This has been brought about by the evolution of Verizon Wireless that can now handle bigger and diverse data sets.

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In fact, companies now want the data lake platforms to encompass more than Hadoop. The future systems will be ‘’hybrids of on-premises and public cloud systems and, eventually, will be on multiple clouds,’’ said Doug Henschen, an analyst at Constellation Research.

Large companies are very much dependent on Hadoop for efficiently managing their data. Understandably, the job prospects in this field are also multiplying.

Are you a big data aspirant? Then you must enroll for big data Hadoop training in Gurgaon. At Dexlab industry-experts guide you through theoretical as well as practical knowledge on the subject. To help your endeavors, we have started a new admission drive #BigDataIngestion. All students get a flat discount on big data Hadoop certification courses. To know more, visit our website.

 

Reference: searchdatamanagement.techtarget.com/news/252443645/Hadoop-data-lake-architecture-tests-IT-on-data-integration

 

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Why Portability is Gaining Momentum in the Field of Data

Why Portability is Gaining Momentum in the Field of Data

Ease and portability are of prime importance to businesses. Companies want to handle data in real-time; so there’s need for quick and smooth access to data. Accessibility is often the deciding factor that determines if a business will be ahead or behind in competition.

Data portability is a concept that is aimed at protecting users by making data available in a structured, machine-readable and interoperable format. It enables users to move their data from one controller to another. Organizations are required to follow common technical standards to assist transfer of data instead of storing data in ‘’walled gardens’’ that renders the data incompatible with other platforms.

Now, let’s look a little closer into why portability is so important.

Advantages:

Making data portable gives consumers the power to access data across multiple channels and platforms. It improves data transparency as individuals can look up and analyze relevant data from different companies. It will also help people to exercise their data rights and find out what information organizations are holding. Individuals will be able to make better queries.

From keeping a track of travel distance to monitoring energy consumption on the move, portable data is able to connect with various activities and is excellent for performing analytical examinations on. Portable data may be used by businesses to map consumers better and help them make better decisions, all the while collecting data very transparently. Thus, it improves data personalization.

For example, the portable data relating to a consumers grocery purchases in the past can be utilized by a grocery store to provide useful sales offers and recipes. Portable data can help doctors find quick information about a patient’s medical history- blood group, diet, regular activities and habits, etc., which will benefit the treatment. Hence, data portability can enhance our lifestyle in many ways.

Struggles:

Portable data presents a plethora of benefits for users in terms of data transparency and consumer satisfaction. However, it does have its own set of limitations too. The downside of greater transparency is security issues. It permits third parties to regularly access password protected sites and request login details from users. Scary as it may sound; people who use the same password for multiple sites are easy targets for hackers and identity thieves. They can easily access the entire digital activity of such users.

Although GDPR stipulates that data should be in a common format, that alone doesn’t secure standardization across all platforms. For example, one business may name a domain ‘’Location” while another business might call the same field ‘’Locale”.  In such cases, if the data needs to be aligned with other data sources, it has to be done manually.

According to GDPR rules, if an organization receives a request pertaining to data portability, then it has to respond within one month. While they might be willing to readily give out data to general consumers, they might hold off the same information if they perceive the request as competition.

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Future:

Data portability runs the risk of placing unequal power in the hands of big companies who have the money power to automate data requests, set up an entire department to cater to portability requests and pay GDPR fines if needed.

Despite these issues, there are many positives. It can help track a patient’s medical statistics and provide valuable insights about the treatment; and encourage people to donate data for good causes, like research.

As businesses as well as consumers weigh the pros and cons of data portability, one thing is clear- it will be an important topic of discussion in the years to come.

Businesses consider data to be their most important asset. As the accumulation, access and analysis of data is gaining importance, the prospects for data professionals are also increasing. You must seize these lucrative career opportunities by enrolling for Big Data Hadoop certification courses in Gurgaon. We at Dexlab Analytics bring together years of industry experience, hands-on training and a comprehensive course structure to help you become industry-ready.

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For a Seamless, Real-Time Integration and Access across Multiple Data Siloes, Big Data Fabric Is the Solution

For a Seamless, Real-Time Integration and Access across Multiple Data Siloes, Big Data Fabric Is the Solution

Grappling with diverse data?

No worries, data fabrics for big data is right here.

The very notion of a fabric joining computing resources and offering centralized access to a set of networks has been doing rounds since the conceptualization of grid computing as early as 1990s. However, a data fabric is a relatively new concept based on the same underlying principle, but it’s associated with data instead of a system.

As data have become increasingly diversified, the importance of data fabrics too spiked up. Now, integrating such vast pools of data is quite a problem, as data collected across various channels and operations is often withhold in discrete silos. The responsibility lies within the enterprise to bring together transactional data stores, data lakes, warehouses, unstructured data sources, social media storage, machine logs, application storage and cloud storage for management and control.  

The Change That Big Data Brings In

The escalating use of unstructured data resulted in significant issues with proper data management. While the accuracy and usability quotient remained more or less the same, the ability to control them has been reduced because of increasing velocity, variety, volume and access requirements of data. To counter the pressing challenge, companies have come with a number of solutions but the need for a centralized data access system prevails – on top of that big data adds concerns regarding data discovery and security that needs to be addressed only through a particular single access mechanism.

To taste success with big data, the enterprises need to seek access to data from a plethora of systems in real time in perfectly digestible formats – also connecting devices, including smartphones and tablets enhances storage related issues. Today, big data storage is abundantly available in Apache Spark, Hadoop and NoSQL databases that are developed with exclusive management demands.

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The Popularity of Data Fabrics

Huge data and analytics vendors are the biggest providers of big data fabric solutions. They help offer access to all kinds of data and conjoin them into a single consolidated system. This consolidated system – big data fabric – should tackle diverse data stores, nab security issues, offer consistent management through unified APIs and software access, provide auditability, flexibility and be upgradeable and process smooth data ingestion, curation and integration.

With the rise of machine learning and artificial intelligence, the requirements of data stores increase as they form the fundamentals of model training and operations. Therefore, enterprises are always seeking a single platform and a single point for data access, they tend to reduce the intricacies of the system and ensure easy storage of data. Not only that, data scientists no longer need to focus on the complexities of data access, rather they can give their entire attention to problem-solving and decision-making.

To better understand how data fabrics provide a single platform and a single point for data access across myriad siloed systems, you need a top of the line big data certification today. Visit DexLab Analytics for recognized and well-curated big data hadoop courses in Gurgaon.

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics Presents #BigDataIngestion

 
Referenes: https://tdwi.org/articles/2018/06/20/ta-all-data-fabrics-for-big-data.aspx
 

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Hierarchical Clustering: Foundational Concepts and Example of Agglomerative Clustering

Hierarchical Clustering: Foundational Concepts and Example of Agglomerative Clustering

Clustering is the process of organizing objects into groups called clusters. The members of a cluster are ‘’similar’’ between them and ‘’dissimilar’’ to members of other groups.

In the previous blog, we have discussed basic concepts of clustering and given an overview of the various methods of clustering. In this blog, we will take up Hierarchical Clustering in greater details.

Hierarchical Clustering:

Hierarchical Clustering is a method of cluster analysis that develops a hierarchy (ladder) of clusters. The two main techniques used for hierarchical clustering are Agglomerative and Divisive.

Agglomerative Clustering:

In the beginning of the analysis, each data point is treated as a singleton cluster. Then, clusters are combined until all points have been merged into a single remaining cluster. This method of clustering wherein a ‘’bottom up’’ approach is followed and clusters are merged as one moves up the hierarchy is called Agglomerative clustering.

Linkage types:

The clustering is done with the help of linkage types. A particular linkage type is used to get the distance between points and then assign it to various clusters. There are three linkage types used in Hierarchical clustering- single linkage, complete linkage and average linkage.

Single linkage hierarchical clustering: In this linkage type, two clusters whose two closest members have the shortest distance (or two clusters with the smallest minimum pairwise distance) are merged in each step.

Complete linkage hierarchical clustering: In this type, two clusters whose merger has the smallest diameter (two clusters having the smallest maximum pairwise distance) are merged in each step.

Average linkage hierarchical clustering: In this type, two clusters whose merger has the smallest average distance between data points (or two clusters with the smallest average pairwise distance), are merged in each step.

Single linkage looks at the minimum distance between points, complete linkage looks at the maximum distance between points while average linkage looks at the average distance between points.

Now, let’s look at an example of Agglomerative clustering.

The first step in clustering is computing the distance between every pair of data points that we want to cluster. So, we form a distance matrix. It should be noted that a distance matrix is symmetrical (distance between x and y is the same as the distance between y and x) and has zeros in its diagonal (every point is at a distance zero from itself). The table below shows a distance matrix- only lower triangle is shown an as the upper one can be filled with reflection.

Next, we begin clustering. The smallest distance is between 3 and 5 and they get merged first into the cluster ‘35’.

After this, we replace the entries 3 and 5 by ‘35’ and form a new distance matrix. Here, we are employing complete linkage clustering. The distance between ‘35’ and a data point is the maximum of the distance between the specific data point and 3 or the specific data point and 5. This is followed for every data point. For example, D(1,3)=3 and D(1,5) =11, so as per complete linkage clustering rules we take D(1,’35’)=11. The new distance matrix is shown below.

Again, the items with the smallest distance get clustered. This will be 2 and 4. Following this process for 6 steps, everything gets clustered. This has been summarized in the diagram below. In this plot, y axis represents the distance between data points at the time of clustering and this is known as cluster height.

Complete Linkage

If single linkage clustering was used for the same distance matrix, then we would get a single linkage dendogram as shown below. Here, we start with cluster ‘35’. But the distance between ‘35’ and each data point is the minimum of D(x,3) and D(x,5). Therefore, D(1,’35’)=3.

Single Linkage

Agglomerative hierarchical clustering finds many applications in marketing. It is used to group customers together on the basis of product preference and liking. It effectively determines variations in consumer preferences and helps improving marketing strategies.

In the next blog, we will explain Divisive clustering and other important methods of clustering, like Ward’s Method. So, stay tuned and follow Dexlab Analytics. We are a leading big data Hadoop training institute in Gurgaon. Enroll for our expert-guided certification courses on big data Hadoop and avail flat 10% discount!

DexLab Analytics Presents #BigDataIngestion

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Check back for the blog A Comprehensive Guide on Clustering and Its Different Methods

 

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Fintech Companies: How They Are Revolutionizing the Banking Industry?

Fintech Companies: How They Are Revolutionizing the Banking Industry?

The world of technology is expanding rapidly. And so is the finance. Fintech is the new buzzword; and its extensive use of cutting edge algorithms, big data solutions and AI is transforming the traditional banking sector.

Nevertheless, there exist many obstacles, which fintech companies need to deal with before creating an entirely complementary system that covers the gap between both.

Ezbob and LaaS

Innovation takes time to settle, but with little effort, banks can strike gold than ever. New transparency laws and digital standards are being introduced and if banks are quicker in embracing this new technology, they can ring off success very easily. Not every fintech is determined to cause discomfort to banks, in fact a lot of fintech startups offer incredible services to attract new customers.

One of them is ezbob, a robust platform in partnership with multiple major banking institutions that streamlines an old process with cutting edge technology. This platform develops a smooth, automatic lending process for bank’s customers by sorting data accumulated from more than 25 sources in real time. Currently, it’s leading Lending-as-a-Service (LaaS) industry, which is deemed to be the future of banking sector.

LaaS is one of the key transforming agents that have brought in a new trend in the banking sector. It reflects how everyone can benefit, including customers and partners, when efficiency is improved. Real time decisions are crucial; it helps bankers turn attention to the bigger picture, while technology takes care of other factors.

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The Art of Regulations

Conversely, fintech startups should be wary of regulations. Notwithstanding the fact that technology is fast decentralizing the whole framework and disrupting institutional banking sector, fintech companies should focus on regulation and be patient with all the innovations taking place around. Banks need time to accept the potentials of fintech’s innovation but once they do, they would gain much more from adopting these technologies.

The aftermath of 2008 financial crisis have made it relatively easier for fintech startups to remain compliant and be more accountable. One of the latest regulations passed is about e-invoicing, which require organizations should send digital invoices through a common system. This measure is expected to save billions of dollars on account of businesses and governments, as well.

Some of the other reforms that have been passed recently are mainly PSD2, which has systematized mobile and internet payments, and AMLD, which is an abbreviation of Anti Money Laundering Directive. The later hurts those who don’t want to be accountable for their income, or involved in terrorism activities.

Conclusion

As closing thoughts, we all can see the financial sector has been the largest consumers of big data technology. According to Gartner, 64% of financial service companies have used big data in 2013. And the figures are still rising.

To be the unicorn among the horses, it’s high time to imbibe big data hadoop skills. This new-age skill is going to take you a long way, provided you get certified from a reputable institute. In Delhi-Gurgaon region, we’ve DexLab Analytics. It offers state-of-the-art hadoop training in Gurgaon. For more information, drop by their site now.

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The blog has been sourced from – http://dataconomy.com/2017/10/rise-fintechpreneur-matters
 

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Secrets behind the Success of AI Implanted Analytics Processes

Secrets behind the Success of AI Implanted Analytics Processes

Big data combined with machine learning results in a powerful tool. Businesses are using this combination more and more, with many believing that the age of AI has already begun. Machine learning embedded in analytics processes opens new gateways to success, but companies must be careful about how they use this power. Organizations use this powerful platform in various domains, such as fraud detection, boosting cybersecurity and carrying out personalized marketing campaigns.

Machine learning isn’t a technology that simply speeds up the process of solving existing problems, it holds the potential to provide solutions that weren’t even thought of before; boost innovation and identify problem areas that went unnoticed.  To utilize this potent tech the best possible way, companies need to be aware of AI’s strengths as well as limitations. Businesses need to adopt renewed ways of harnessing the power of AI and analytics. Here are the top 4 ways to make the most out of AI and big data.

Context is the key:

Sifting through available information, machine learning can provide insights that are compelling and trustworthy. But, it lacks the ability to judge which results are valuable. For example, taking up a query from a garment store owner, it will provide suggestions based on previous sales and demographic information. However, the store owner might see that some of these suggestions are redundant or impractical. Moreover, humans need to program AI so that it takes into account variables and selects relevant data sets to analyze. Hence, context is the key. Business owners need to present the proper context, based on which AI will provide recommendations.

Broaden your realm of queries:

Machine learning can offer a perfect answer to your query. But, it can do much more. It might stun you by providing appropriate solutions to queries you didn’t even ask. For example, if you are trying to convince a customer to take a particular loan, then machine learning can crunch huge data sets and provide a solution. But is drawing more loans your real goal? Or is the bigger goal increasing revenues? If this is your actual goal, then AI might provide amazing solutions, like opening a new branch, which you probably didn’t even think about. In order to elicit such responses, you must broaden the realm of queries so that it covers different responses.

Have faith in the process:

AI can often figure things out that it wasn’t trained to understand and we might never comprehend how that happened. This is one of the wonders of AI. For example, Google’s neural network was shown YouTube videos for a few days and it learnt to identify cats, something it wasn’t taught.

Such unprecedented outcomes might be welcome for Google, but most businesses want to trust AI, and for that they seek to know how techs arrive at solutions. The insights provided by machine learning are amazing but businesses can act on them only if they trust the tech. It takes time to trust machines, just like it is with humans. In the beginning we might feel the need to verify outputs, but as the algorithms give good results repeatedly, trust comes naturally.

2

Act sensibly:

Machine learning is a powerful tool that can backfire too. An example of that is the recent misuse of Facebook’s data by Cambridge Analytica, which couldn’t be explained by Facebook authorities too. Companies need to be aware of the consequences of using such an advanced technology. They need to be mindful of how employees use results generated by analytics tools and how third parties handle data that has been shared. All employees don’t need to know that AI is used for inner business processes.

Artificial Intelligence can fuel growth and efficiency for companies, but it takes people to make the best use of it. And how can you take advantage of this data-dominated business world? Enroll for big data Hadoop certification in Gurgaon. As DexLab Analytic’s #BigDataIngestion campaign is ongoing, interested students can enjoy flat 10% discount on big data Hadoop training and data science certifications.

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References: https://www.infoworld.com/article/3272886/artificial-intelligence/big-data-ai-context-trust-and-other-key-secrets-to-success.html

 

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