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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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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.

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

 

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Industry Use Cases of Big Data Hadoop Using Python – Explained

Industry Use Cases of Big Data Hadoop Using Python – Explained

Welcome to the BIG world of Big Data Hadoop – the encompassing eco-system of all open-source projects and procedures that constructs a formidable framework to manage data. Put simply, Hadoop is the bedrock of big data operations. Though the entire framework is written in Java language, it doesn’t exclude other programming languages, such as Python and C++ from being used to code intricate distributed storage and processing framework. Besides Java architects, Python-skilled data scientists can also work on Hadoop framework, write programs and perform analysis. Easily, programs can be written in Python language without the need to translate them into Java jar files.

Python as a programming language is simple, easy to understand and flexible. It is capable and powerful enough to run end-to-end advanced analytical applications. Not to mention, Python is a versatile language and here we present a few popular Python frameworks in sync with Hadoop:

 

  • Hadoop Streaming API
  • Dumbo
  • Mrjob
  • Pydoop
  • Hadoopy

 

Now, let’s take a look at how some of the top notch global companies are using Hadoop in association with Python and are bearing fruits!

Amazon

Based on the consumer research and buying pattern, Amazon recommends suitable products to the existing users. This is done by a robust machine learning engine powered by Python, which seamlessly interacts with Hadoop ecosystem, aiding in delivering top of the line product recommendation system and boosting fault tolerant database interactions.

Facebook

In the domain of image processing, Facebook is second to none. Each day, Facebook processes millions and millions of images based on unstructured data – for that Facebook had to enable HDFS; it helps store and extract enormous volumes of data, while using Python as the backend language to perform a large chunk of its Image Processing applications, including Facial Image Extraction, Image Resizing, etc.

Rightfully so, Facebook relies on Python for all its image related applications and simulates Hadoop Streaming API for better accessibility and editing of data.

Quora Search Algorithm

Quora’s backend is constructed on Python; hence it’s the language used for interaction with HDFS. Also, Quora needs to manage vast amounts of textual data, thanks to Hadoop, Apache Spark and a few other data-warehousing technologies! Quora uses the power of Hadoop coupled with Python to drag out questions from searches or for suggestions.

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End Notes

The use of Python is varied; being dynamically typed, portable, extendable and scalable, Python has become a popular choice for big data analysts specializing in Hadoop. Mentioned below are a couple of other notable industries where use cases of Hadoop using Python are to be found:

 

  • YouTube uses a recommendation engine built using Python and Apache Spark.
  • Limeroad functions on an integrated Hadoop, Apache Spark and Python recommendation system to retain online visitors through a proper, well-devised search pattern.
  • Iconic animation companies, like Disney depend on Python and Hadoop; they help manage frameworks for image processing and CGI rendering.

 

Now, you need to start thinking about arming yourself with big data hadoop certification course – these big data courses are quite in demand now – as it’s expected that the big data and business analytics market will increase from $130.1 billion to more than $203 billion by 2020.

 

This article first appeared on – www.analytixlabs.co.in/blog/2016/06/13/why-companies-are-using-hadoop-with-python

 

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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.

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

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Predicting World Cup Winner 2018 with Big Data

Predicting World Cup Winner 2018 with Big Data

Is there any way to predict who will win World Cup 2018?

Could big data be used to decipher the internal mechanisms of this beautiful game?

How to collect meaningful insights about a team before supporting one?

Data Points

Opta Sports and STATS help predict which teams will perform better. These are the two sports companies that have answers to all the above questions. Their objective is to collect data and interpret it for their clients, mainly sports teams, federations and of course media, always hungry for data insights.

How do they do it? Opta’s marketing manager Peter Deeley shares that for each football match, his company representatives collects as many as 2000 individual data points, mostly focused on ‘on-ball’ actions. Generally, a team of three analysts operates from the company’s data hub in Leeds; they record everything happening on the pitch and analyze the positions on the field where each interaction takes place. The clients receive live data; that’s the reason why Gary Lineker, former England player is able to share information like possession and shots on goal during half time.

The same procedure is followed at Stats.com; Paul Power, a data scientist from Stats.com explains how they don’t rely only on humans for data collection, but on latest computer vision technologies. Though computer vision can be used to log different sorts of data, yet it can never replace human beings altogether. “People are still best because of nuances that computers are not going to be able to understand,” adds Paul.

Who is going to win?

In this section, we’re going to hit the most important question of this season – which team is going to win this time? As far as STATS is concerned, it’s not too eager to publish its predictions this year. The reason being they believe is a very valuable piece of information and by spilling the beans they don’t want to upset their clients.

On the other hand, we do have a prediction from Opta. According to them, veteran World Cup champion Brazil holds the highest chance of taking home the trophy – giving them a 14.2% winning chance. What’s more, Opta also has a soft corner for Germany – thus giving them an 11.4% chance of bringing back the cup once again.

If it’s about prediction and accuracy, we can’t help but mention EA Sports. For the last 3 World Cups, it maintained a track record of predicting the eventual World Cup winner impeccably. Using the encompassing data about the players and team rankings in FIFA 2018, the company representatives ran a simulation of the tournament, in which France came out to be the winner, defeating Germany in the final. As it has already predicted right about Germany and Spain in 2014 and 2010 World Cups, consecutively, this new revelation is a good catch.

So, can big data predict the World Cup winner? We guess yes, somehow.

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The blog has been sourced from – https://www.techradar.com/news/world-cup-2018-predictions-with-big-data-who-is-going-to-win-what-and-when

 

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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.

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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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How Big Data Is Influencing HR Analytics for Employees and Employers, Both

How Big Data Is Influencing HR Analytics for Employees and Employers, Both

HR analytics powered by big data is aiding talent management and hiring decisions. A Deloitte 2015 report says 35% of companies surveyed revealed that they were actively developing suave data analytics strategies for HR. Moreover, big data analytics isn’t leaving us anytime soon; it’s here to stay for good.

Now, with that coming, employers are of course in an inapt position: whether to use HR analytics or not? And even if they do use the data, how are they going to do that without violating any HR policies/laws or upsetting the employees?

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Health Data

While most of the employers are concerned about healthcare and wellness programs for their employees, a whole lot of other employees have started employing HR analytics for evaluation of the program’s effectiveness and addressing the gaps in healthcare coverage with an aim to improve overall program performance.

Today, data is the lifeblood of IT services. Adequate pools of employee data in conjunction with company data are aiding discoveries of the best benefit package for employees where they get best but affordable care. However, in the process, the employers need to be very careful and sensitive to employee privacy at the same time. During data analysis, the process should appear as if the entire organization is involved in it, instead of focusing on a single employee or sub-groups.

Predictive Performance Analytics

For talent management, HR analytics is a saving grace. Especially, owing to its predictive performance. Because of that, more and more employers are deploying this powerful skill to determine future hiring needs and structure a strong powerhouse of talent.

Rightfully so, predictive performance analytics use internal employee data to calculate potential employee turnover, but unfortunately, in some absurd cases, the same data can also be used to influence decisions regarding firing and promotion – and that becomes a problem.

Cutting edge machine learning algorithms dictate whether an event is going to happen or not, instead of what employees are doing or saying. Though it comes with its own advantages, its better when people frame decisions based on data. Because, people are unpredictable and so are the influencing factors.

Burn away irrelevant information

Sometimes, it may happen that employers instead of focusing on the meaningful things end up scrutinizing all the wrong things. For example, HR analytics show that employees living close to the office, geographically, are less likely to leave the office premise early. But, based on this, can we pass off top talent just because they reside a little farther from the office? We can’t, right?!

Hence, the bottom line is, whenever it comes to analyzing data, analysts should always look for the bigger picture rather giving stress on minute features – such as which employee is taking more number of leaves, and so on. Stay ahead of the curve by making the most productive decisions for employees as well as business, as a whole.

In the end, the power of data matters. HR analytics help guide the best decisions, but it’s us who are going to make them. We shouldn’t forget that. Use big data analytics responsibly to prevent any kind of mistrust or legal issues from the side of employees, and deploy them in coordination with employee feedback to come at the best conclusions ever. 

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Reference:

The article has been sourced from https://www.entrepreneur.com/article/271753

 

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

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.

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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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