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SAS Conducts India’s Largest Analytics Forum in Mumbai

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SAS Conducts India’s Largest Analytics Forum in Mumbai

This May, India is making preparations to host the largest analytics forum in association with SAS – the top notch player in the world of business analytics. The conference on machine learning, AI, IoT, fraud management and customer experience hosted by SAS Forum India is going to be held on Tuesday, 15th May 2018 at Renaissance Mumbai Convention Centre Hotel, Mumbai.

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In its eight year, SAS Forum India is a highly acclaimed knowledge sharing platform wherein business consultants, users and industry honchos come together and meet every year to share crucial knowledge and experience in regards to Analytics. It offers incredible learning and networking experience, while focusing on the imposing role of Analytics across versatile industry domains.

Data is like a new currency impacting the world around – it’s being used right from dealing with humanitarian issues to solving intricate business challenges. In this scenario, the scope of data scientists becomes limitless. They are ushered into countless opportunities to establish connection with their customers, along with developing new profound experiences in the technology world.

Rightfully so, this year’s Forum is going to talk about the latest trends in machine learning, cognitive computing, artificial intelligence, Internet of Things, fraud intelligence, risk management, IFRS9 compliance and customer experience. Industry stalwarts and thought leaders hailing from various business verticals are expected to exchange nuanced notions about each concept to set off breakthrough outcomes and bang open doors of new possibilities.

However, this year the theme is Inspire the Extraordinary, and some of the influential speakers are noted below:

  • Mrutyunjay Mahapatra – Deputy Managing Director, State Bank of India
  • Daniel Zeo Jimenez – Regional Research Director, IDC Asia/Pacific
  • Rahul Shandilya, SVP and CIO – Customer Experience and Product Development, Mahindra & Mahindra
  • Goutam Datta – Vice President – Technology – ICICI Lombard
  • Mridul Sharma, CIO, IndusInd Bank
  • Sudip Banerjee, CTO, Reliance Capital and many more

The summit is also going to screen live demos of diverse analytics SAS solutions, including SAS Viya, the open, next-gen and cloud-ready solutions that help tackle analytics challenges, right from experimental to mission-critical.

Commenting on the occasion, Noshin Kagalwalla, Managing Director, SAS Institute India Pvt. Ltd  was found saying, “New age machine Learning & Deep Learning techniques that form the fulcrum of AI are now opening a whole new world of possibilities for businesses. At the SAS India Forum this year, we are delighted to be joined by some of the best and brightest leaders in analytics who will enlighten you on how to leverage these emerging technologies to succeed in the Analytics Economy.”

Now, as you are reading this blog we are sure you are interested in SAS certification courses. DexLab Analytics offers best SAS analytics training Delhi for aspiring candidates at decent prices. Check out the course details now!

The blog has been sourced from – http://www.dqindia.com/sas-host-indias-largest-analytics-forum-may-15-2018-mumbai

 

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How Blockchain Technology is Transforming these Four Popular Industries

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How Blockchain Technology is Transforming these Four Popular Industries

Blockchain technology is the next big thing. It is defying industry norms and altering the manner in which industries implement new projects. The decentralized nature of blockchain technology is the key to its success. Blockchain is transforming every organization through its secure and decentralized protocols, protected peer-to peer applications, and a new approach towards distributed management.

Here are some everyday industries that blockchain technology is revamping.

  • Finance:

There are all kinds of opinions regarding how cryptocurrency is impacting macroeconomics pertaining to the financial sector. The rapidly increasing demand for Bitcoin signals a flourishing future for cryptocurrency. In 2017, ICOs (Initial Coin Offerings), which are means of crowd funding centered on cryptocurrency, raised more money than venture capital investments. Cryptocurrencies, like Bitcoin, Ethereum and Ripple are improving their speed for processing transaction fees, and will be able to contend with speed of transaction for credit card companies in the near future. Bitcoin permits people to transfer money across borders instantaneously and at low costs. Many banks, such as Barclays, are set to use blockchain technology to facilitate speedier business procedures.

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  • Cloud Computing:

The evolution of cloud has outmoded hard drives, which was the popular choice for transferring files from one computer to another, even a few years ago. Blockchain-based companies, like Akash, want to seize this opportunity and create an open market place where cloud computing costs are determined by demand and supply, instead of centralized, fixed prices. Most large-scale data centers depend on idle computing power. Akash Network makes idle server capacity available for cloud deployments. This system enables users to ‘’rent’’ idle computing power and providers to generate revenue from their idle power. Developers specify their deployment conditions in a file that is posted on the Akash blockchain. Providers capable of fulfilling these conditions bid on it. Low bid wins; after this parties go off chain to allocate workload in Docker containers. Akash tokens are then transferred from tenant‘s wallet to provider’s wallet.

  • Online Gaming:

The online sports industry is embracing the blockchain technology. An increasing number of developers in the world of e-Sports are employing blockchain technology and cryptocurrencies. Leading fantasy sport companies, like MyDFS, permit their users to create virtual arrays of real players and obtain winnings through tokens. In-app purchase is the newest monetization model for Smartphone app games. Blockchain technology is also advantageous for e-Sports betting platforms. The tech constructs a secure environment for low fee betting that is free from the control of a central party.

  • Decentralized Governance:

One of the most famed features of blockchain is decentralization. The thought of decentralized, autonomous organizations is no doubt very fascinating, but they are very difficult to establish. A hierarchical structure, where one person or group tends to dominate, is very natural. However, new and advanced frameworks are facilitating decentralized platforms to function effectively. An example of such a framework is DAOstack, which is striving to build a platform that enables collectives to self-organize around similar goals and interests. It is a platform that authorizes emerging organizations to select suitable governance model that will work for them and execute the same through DAOstack’s technological protocol. DAOstack’s founding principle is collaboration- it aims to provide a setting where goals of individuals can work in harmony with goals of a group.

The ‘’blockchain boom’’ is driving breakthroughs for a range of industries. This is just the beginning, though. As this tech evolves, it will enable rapid progress across every industry.

To read more blogs on emerging technologies, follow DexLab Analytics; it is a premier institute providing data science certification courses in Delhi. Do take a look their data analytics certification courses.

 

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A Comprehensive Article on Apache Spark: the Leading Big Data Analytics Platform

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A Comprehensive Article on Apache Spark: the Leading Big Data Analytics Platform

Speedy, flexible and user-friendly, Apache Spark is one of the main distributed processing frameworks for big data in the world. This technology was developed by a team of researchers at U.C. Berkeley in 2009, with the aim to speed up processing in Hadoop systems. Spark provides bindings to programming languages, like Java, Scala, Python and R and is a leading platform that supports SQL, machine learning, stream and graph processing. It is extensively used by tech giants, like Apple, Microsoft and IBM, telecommunications industry and games organizations.

Databricks, a firm where the founding members of Apache Spark are now working, provides Databricks Unified Analytics Platform. It is a service that includes Apache Spark clusters, streaming and web-based notebook development. To operate in a standalone cluster mode, one needs Apache Spark framework and JVM on each machine in a cluster. To reap the advantages of a resource management system, running on Hadoop YARN is the general choice. Amazon EMR and Google Cloud Dataproc are fully-managed cloud services for running Apache Spark.

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Working of Apache Spark:

Apache Spark has the power to process data from a variety of data storehouses, such as Hadoop Distributed File System (HDFS) and NoSQL databases. It is a platform that enhances the functioning of big data analytics applications through in-memory processing. It is also equipped to carry out regular disk-based processing in case of large data sets that are unable to fit into system memory.

Spark Core:

Apache Spark API (Application Programming Interface) is more developer-friendly compared to MapReduce, which is the software framework used by earlier versions of Hadoop. Apache Spark API hides all the complicated processing steps from developers, like reducing 50 lines of MapReduce code for counting words in a file to only a few lines of code in Apache Spark. Bindings to well-liked programming languages, like R and Java, make Apache Spark accessible to a wide range of users, including application developers and data analysts.

Spark RDD:

Resilient Distributed Dataset is a programming concept that encompasses an immutable collection of objects for distribution across a computing cluster. For fast processing, RDD operations are split across a computing cluster and executed in a parallel process. A driver core process divides a Spark application into jobs and distributes the work among different executor processes. The Spark Core API is constructed based on RDD concept, which supports functions like merging, filtering and aggregating data sets. RDDs can be developed from SQL databases, NoSQL stores and text files.

Apart from Spark Core engine, Apache Spark API includes libraries that are applied in data analytics. These libraries are:

  • Spark SQL:

Spark SQL is the most commonly used interface for developing applications. The data frame approach in Spark SQL, similar to R and Python, is used for processing structured and semi-structured data; while SQL2003-complaint interface is for querying data. It supports reading from and writing to other data stores, like JSON, HDFS, Apache Hive, etc. Spark’s query optimizer, Catalyst, inspects data and queries and then produces a query plan that performs calculations across the cluster.

  • Spark MLlib:

Apache Spark has libraries that can be utilized for applying machine learning techniques and statistical operation to data. Spark MLlib allows easy feature extractions, selections and conversions on structured datasets; it includes distributed applications of clustering and classification algorithms, such as k-means clustering and random forests.  

  • Spark GraphX:

This is a distributed graph processing framework that is based on RRDs; RRD being immutable makes GraphX inappropriate for graphs that need to be updated, although it supports graph operations on data frames. It offers two types of APIs, Pregel abstraction and a MapReduce style API, which help execute parallel algorithms.

  • Spark Streaming:

Spark streaming was added to Apache Spark to help real-time processing and perform streaming analytics. It breaks down streams of data into mini-batches and performs RDD transformations on them. This design facilitates the set of codes written for batch analytics to be used in stream analytics.

Future of Apache Spark:

The pipeline structure of MLlib allows constructing classifiers with a few lines of code and applying Tensorflow graphs and Keras models on data. The Apache Spark team is working to improve streaming performance and facilitate deep learning pipelines.

For knowledge on how to create data pipelines and cutting edge machine learning models, join Apache Spark programming training in Gurgaon at Dexlab Analytics. Our experienced consultants ensure that you receive the best apache spark certification training.  

 

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Developing a Big Data Culture is Crucial for Banks to be Customer Centric

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Developing a Big Data Culture is Crucial for Banks to be Customer Centric

It is important for banks to be customer centric because a customer who is better engaged is easier to retain and do business with. In order to provide services that are valued by customers, banks need to exploit big data. When big data is combined with analytics it can result in big opportunities for banks. The volume of banking customers is on the rise, and so is their data. It is time for the banking sector to look beyond traditional approaches and adopt new technologies powered by big data, like natural language processing and text mining, which help convert large amount of unstructured information into meaningful data that can lead to valuable insights.

Switching to big data enable banks to get a 360-degree view of their customers and keep providing excellent services. Many banks, like the Bank of America and U.S. Bank, have implemented big data analytics and are reaping its benefits. Rabobank, which has adopted big data analytics to detect criminal activity in ATMs, is ranked among the top 10 safest banks in the world.

Big Data’s Advantages for the Banking Industry:

  • Streamline Work Process and Service Delivery:

Banks need to filter through gazillions of data sets in order to provide relevant information to a customer, when he/she enters account details into the system. Big data can speed up this process. It enables financial institutes of spot and correct problems, before they affect clients. Big data also helps in cost-cuttings, which in turn lead to higher revenues for banks.

In case of erroneous clients, who tend to go back on their decisions, big data can help alter the process of  service delivery in such a manner that these clients are bound to stick to their commitments. It allows banks to track credit and loan limits, so that customers don’t exceed them.

Cloud based analytics packages sync in with big data systems to provide real-time evaluation. Banks can sift through tons of client information to track transactional behaviors in real time and provide relevant resources to clients. Real-time client contact is very useful in verifying suspicious transactions.

  • Customer Segmentation:

Big data help banks understand customer spending habits and determine their needs. For example, when we use our credit cards to purchase something, banks acquire information about what we purchase, how much we spend and use these information to provide relevant offers to us. Through big data, banks are able to trace all customer transactions and answer questions about a customer, like which services are commonly accessed, what are the preferred credit card expenditures and what his/her net worth is. The advantage of customer segmentation is that it enables banks to design marketing campaigns that cater to specific needs of a customer.  It can be used to deliver personalized schemes and plans. Analyzing the past and present expenses of a client helps bank create meaningful client relationships and improve response rates.

Let’s Take Your Data Dreams to the Next Level

  • Fraud detection:

According to Avivah Litan, a financial fraud expert at Gartner, big data supports behavioral authentication, which can help prevent fraud. Litan says, ‘’using big data to track such factors as how often a user typically accesses an account from a mobile device or PC, how quickly the user types in a username and password, and the geographic location from which the user most often accesses an account can substantially improve fraud detection.’’

Utah-based Zions Bank is largely dependent on big data to detect fraud. Big data can detect a complex problem like cross-channel fraud by aggregating fraud alerts from multiple disparate data sources and deriving meaningful insights from them. 

  • Risk Management:

Financial markets are becoming more and more interconnected, which increases their risk. Big data plays a pivotal role in risk management of financial sector as it provides more extensive risk coverage and faster responses. It helps create robust risk prediction models that evaluate credit repayment risks or determine the probability of default on loans for customers. It also aids in identifying risk associated with emergent financial technologies.

Hence, banks need to adopt a big data culture to improve customer satisfaction, keep up with global trends and generate higher revenues.

For credit risk management courses online, visit DexLab Analytics. It is a leading institute offering credit risk analytics training in Delhi.

 

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain.  The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

Let’s Take Your Data Dreams to the Next Level

At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

 

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

  • Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

  • Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

  • Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product.  It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

  • Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

Let’s Take Your Data Dreams to the Next Level

Advantages of using Python Machine Learning:

  • Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
  • Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
  • Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
  • Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

For Machine Learning training in Gurgaon, join DexLab Analytics– it is the best institute to learn Machine Learning Using Python.

 

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10 Key Areas to Focus When Settling For an Alternative Data Vendor

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10 Key Areas to Focus When Settling For an Alternative Data Vendor

Unstructured data is the new talk of the town! More than 80% of the world’s data is in this form, and big wigs of financial world need to confront the challenges of administering such volumes of unstructured data through in-house data consultants.

FYI, deriving insights from unstructured data is an extremely tiresome and expensive process. Most buy-sides don’t have access to these types of data, hence big data vendors are the only resort. They are the ones who transform unstructured content into tradable market data.

Here, we’ve narrowed down 10 key areas to focus while seeking an alternative data vendor.

Structured data

Banks and hedge funds should seek alternative data vendors that can efficiently process unstructured data into 100% machine readable structured format – irrespective of data form.

Derive a fuller history

Most of the alternative data providers are new kid in the block, thus have no formidable base of storing data. This makes accurate back-testing difficult.

Data debacles

The science of alternative data is punctured with a lot of loopholes. Sometimes, the vendor fails to store data at the time of generation – and that becomes an issue. Transparency is very crucial to deal with data integrity issues so as to nudge consumers to come at informed conclusions about which part of data to use and not to use.

Context is crucial

While you look at unstructured content, like text, the NLP or natural language processing engine must be used to decode financial terminologies. As a result, vendors should create their own dictionary for industry related definitions.

Version control

Each day, technology gets better or the production processes change; hence vendors must practice version control on their processes. Otherwise, future results will be surely different from back-testing performance.

Let’s Take Your Data Dreams to the Next Level

Point-in-time sensitivity

This generally means that your analysis includes data that is downright relevant and available at particular periods of time. In other cases, there exists a higher chance for advance bias being added in your results.

Relate data to tradable securities

Most of the alternative data don’t include financial securities in its scope. The users need to figure out how to relate this information with a tradable security, such as bonds and stocks.

Innovative and competitive

AI and alternative data analytics are dramatically changing. A lot of competition between companies urges them to stay up-to-date and innovative. In order to do so, some data vendors have pooled in a dedicated team of data scientists.

Data has to be legal

It’s very important for both vendors and clients to know from where data is coming, and what exactly is its source to ensure it don’t violate any laws.

Research matters

Few vendors have very less or no research establishing the value of their data. In consequence, the vendor ends up burdening the customer to carry out early stage research from their part.

In a nutshell, alternative data in finance refer to data sets that are obtained to inject insight into the investment process. Most hedge fund managers and deft investment professionals employ these data to derive timely insights fueling investment opportunities.

Big data is a major chunk of alternative data sets. Now, if you want to arm yourself with a good big data hadoop certification in Gurgaon then walk into DexLab Analytics. They are the best analytics training institute in India.

The article has been sourced from – http://dataconomy.com/2018/03/ten-tips-for-avoiding-an-alternative-data-hangover

 

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How is AI Shaping the Indian Job Market?

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How is AI Shaping the Indian Job Market?

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Deep Learning and AI using Python

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

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A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

Get an edge in this AI-era by enrolling yourself for the Machine Learning training course at DexLab Analytics– a leading data analyst training institute in Delhi.

 

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How Data Exhaust is Leveraged for Your Business

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How Data Exhaust is Leveraged for Your Business

Big data is the KING of corporate kingdom. Every company is somehow using this vital tech tool; even if they are not using it, they are thinking of it.

A 2017 survey says, around 53% of companies were relying on big data for their business operations. Each company focuses on a particular variant of data. Some of the data types are considered most important, while others are left out. Now what happens to the data that is kept aside?

Data exhaust can be a valuable addition for a company – if leveraged properly.

Let’s Take Your Data Dreams to the Next Level

Explaining Data Exhaust

It entirely deals with the data that is leftover but produced by the company itself. Keep in mind, when you try collect information from a specific set of data, a whole lot of information is also collected at the same time. So, many organizations might be sitting on a gold mine of data but without acknowledging the importance of that data. In instances like this, data exhaust can be very helpful across numerous business development channels.

Market Research

The best way to use data exhaust is through extensive market research. Know your audience is the key. Customers are crucial for effective marketing and product development. Nevertheless, the former involves manual research as well as analytical research, which once again leads us to analytics.

Through data exhaust, you get to know everything your customers do on your website – thus, can understand what they like better.

Cyber Security

As a potent threat, cyber crime results into potential costs to businesses all across the world. So, what role does data exhaust play? At best, it can help determine risk across different databases to develop superior cyber security plan.

Product Development

Importantly, businesses work on a plethora of projects at the same time. As a result, the issue of time crunch pops up. No one can do everything all at once, and data exhaust helps in sharpening whatever is important. Like, if your excess data says that most of your viewers visit your site through mobile device, it’s better to develop a mobile app to serve the customers better.

All Data Is Not Important

All data is not useful. Though data exhaust is useful, yet there would be times when you will come across bad data. You need to shed off those data, and get rid of data of that manner that is meaningless. Ask data experts which data to keep and which is irrelevant. Data that is of no use needs to be destroyed, because a company cannot keep trash for long.

Be Responsible for Data

Its clear data exhaust is all good and great for business, but it’s always suggestible to be cautious and responsible. There can be many legal implications, hence its suggestible to consult a data professional who have the desired know-how, otherwise things can get a bit complicated.

In this world of competitive technology, businesses have to be very careful about how they are using data to avoid any kind of negative outcomes. Be responsible and use data correctly; big data help frame a highly effective business strategy.

Looking for good big data courses? We have good news rolling your way – DexLab Analytics offers excellent big data training in Gurgaon. If interested, check out the course itinerary RN.

The blog is sourced from – http://dataconomy.com/2018/03/how-data-exhaust-can-be-leveraged-to-benefit-your-company

 

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