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Relationship between Big Data, Data Science and ML

Data is all over the place. Truth be told, the measure of advanced data that exists is developing at a fast rate, multiplying like clockwork, and changing the manner in which we live. Supposedly 2.5 billion GB of data was produced each day in 2012.

An article by Forbes states that Data is becoming quicker than any time in recent memory and constantly 2020, about 1.7MB of new data will be made each second for each person on the planet, which makes it critical to know the nuts and bolts of the field in any event. All things considered, here is the place of our future untruths.

Machine Learning, Data Science and Big Data are developing at a cosmic rate and organizations are presently searching for experts who can filter through the goldmine of data and help them drive quick business choices proficiently. IBM predicts that by 2020, the number of employments for all data experts will increment by 364,000 openings to 2,720,000

Big Data Analytics

Big Data

Enormous data is data yet with a tremendous size. Huge Data is a term used to portray an accumulation of data that is enormous in size but then developing exponentially with time. In short such data is so huge and complex that none of the customary data the board devices can store it or procedure it productively.

Kinds Of Big Data

1. Structured

Any data that can be put away, got to and handled as a fixed organization is named as structured data. Over the timeframe, ability in software engineering has made more noteworthy progress in creating strategies for working with such sort of data (where the configuration is notable ahead of time) and furthermore determining an incentive out of it. Be that as it may, these days, we are predicting issues when the size of such data develops to an immense degree, regular sizes are being in the anger of different zettabytes.

2. Unstructured

Any data with obscure structure or the structure is delegated unstructured data. Notwithstanding the size being colossal, un-organized data represents various difficulties as far as its handling for inferring an incentive out of it. A regular case of unstructured data is a heterogeneous data source containing a blend of basic content records, pictures, recordings and so forth. Presently day associations have an abundance of data accessible with them yet lamentably, they don’t have a clue how to infer an incentive out of it since this data is in its crude structure or unstructured arrangement.

3. Semi-Structured

Semi-structured data can contain both types of data. We can see semi-organized data as organized in structure however it is really not characterized by for example a table definition in social DBMS. The case of semi-organized data is a data spoken to in an XML document.

Data Science

Data science is an idea used to handle huge data and incorporates data purifying readiness, and investigation. A data researcher accumulates data from numerous sources and applies AI, prescient investigation, and opinion examination to separate basic data from the gathered data collections. They comprehend data from a business perspective and can give precise expectations and experiences that can be utilized to control basic business choices.

Utilizations of Data Science:

  • Internet search: Search motors utilize data science calculations to convey the best outcomes for inquiry questions in a small number of seconds.
  • Digital Advertisements: The whole computerized showcasing range utilizes the data science calculations – from presentation pennants to advanced announcements. This is the mean explanation behind computerized promotions getting higher CTR than conventional ads.
  • Recommender frameworks: The recommender frameworks not just make it simple to discover pertinent items from billions of items accessible yet additionally adds a great deal to the client experience. Many organizations utilize this framework to advance their items and recommendations as per the client’s requests and the significance of data. The proposals depend on the client’s past list items

Machine Learning

It is the use of AI that gives frameworks the capacity to consequently take in and improve for a fact without being unequivocally customized. AI centers around the improvement of PC programs that can get to data and use it learn for themselves.

The way toward learning starts with perceptions or data, for example, models, direct involvement, or guidance, so as to search for examples in data and settle on better choices later on dependent on the models that we give. The essential point is to permit the PCs to adapt naturally without human mediation or help and alter activities as needs are.

ML is the logical investigation of calculations and factual models that PC frameworks use to play out a particular assignment without utilizing unequivocal guidelines, depending on examples and derivation. It is viewed as a subset of man-made reasoning. AI calculations fabricate a numerical model dependent on test data, known as “preparing data”, so as to settle on forecasts or choices without being expressly modified to play out the assignment.

The relationship between Big Data, Machine Learning and Data Science

Since data science is a wide term for various orders, AI fits inside data science. AI utilizes different methods, for example, relapse and directed bunching. Then again, the data’ in data science might possibly develop from a machine or a mechanical procedure. The principle distinction between the two is that data science as a more extensive term centers around calculations and measurements as well as deals with the whole data preparing procedure

Data science can be viewed as the consolidation of different parental orders, including data examination, programming building, data designing, AI, prescient investigation, data examination, and the sky is the limit from there. It incorporates recovery, accumulation, ingestion, and change of a lot of data, on the whole, known as large data.

Data science is in charge of carrying structure to huge data, scanning for convincing examples, and encouraging chiefs to get the progressions adequately to suit the business needs. Data examination and AI are two of the numerous devices and procedures that data science employments.

Data science, Big data, and AI are probably the most sought after areas in the business at the present time. A mix of the correct ranges of abilities and genuine experience can enable you to verify a solid profession in these slanting areas.

In this day and age of huge data, data is being refreshed considerably more every now and again, frequently progressively. Moreover, much progressively unstructured data, for example, discourse, messages, tweets, websites, etc. Another factor is that a lot of this data is regularly created autonomously of the association that needs to utilize it.

This is hazardous, in such a case that data is caught or created by an association itself, at that point they can control how that data is arranged and set up checks and controls to guarantee that the data is exact and complete. Nonetheless, in the event that data is being created from outside sources, at that point there are no ensures that the data is right.

Remotely sourced data is regularly “Untidy.” It requires a lot of work to clean it up and to get it into a useable organization. Moreover, there might be worries over the solidness and on-going accessibility of that data, which shows a business chance on the off chance that it turns out to be a piece of an association’s center basic leadership ability.

This means customary PC structures (Hardware and programming) that associations use for things like preparing deals exchanges, keeping up client record records, charging and obligation gathering, are not appropriate to putting away and dissecting the majority of the new and various kinds of data that are presently accessible.

Therefore, in the course of the most recent couple of years, an entire host of new and intriguing equipment and programming arrangements have been created to manage these new kinds of data.

Specifically, colossal data PC frameworks are great at:

  • Putting away gigantic measures of data:  Customary databases are constrained in the measure of data that they can hold at a sensible expense. Better approaches for putting away data as permitted a practically boundless extension in modest capacity limit.
  • Data cleaning and arranging:  Assorted and untidy data should be changed into a standard organization before it tends to be utilized for AI, the board detailing, or other data related errands.
  • Preparing data rapidly: Huge data isn’t just about there being more data. It should be prepared and broke down rapidly to be of most noteworthy use.

The issue with conventional PC frameworks wasn’t that there was any hypothetical obstruction to them undertaking the preparing required to use enormous data, yet by and by they were excessively moderate, excessively awkward and too costly to even consider doing so.

New data stockpiling and preparing ideal models, for example, have empowered assignments which would have taken weeks or months to procedure to be embraced in only a couple of hours, and at a small amount of the expense of progressively customary data handling draws near.

The manner in which these ideal models does this is to permit data and data handling to be spread crosswise over systems of modest work area PCs. In principle, a huge number of PCs can be associated together to convey enormous computational capacities that are similar to the biggest supercomputers in presence.

ML is the critical device that applies calculations to every one of that data and delivering prescient models that can disclose to you something about individuals’ conduct, in view of what has occurred before previously.

A decent method to consider the connection between huge data and AI is that the data is the crude material that feeds the AI procedure. The substantial advantage to a business is gotten from the prescient model(s) that turns out toward the part of the bargain, not the data used to develop it.

Conclusion

AI and enormous data are along these lines regularly discussed at the same moment, yet it is anything but a balanced relationship. You need AI to get the best out of huge data, yet you don’t require huge data to be capable use AI adequately. In the event that you have only a couple of things of data around a couple of hundred individuals at that point that is sufficient to start building prescient models and making valuable forecasts.

Big Data Analytics Tools

Big Data is a large collection of data sets that are complex enough to process using traditional applications. The variety, volume, and complexity adds to the challenges of managing and processing big data. Mostly the data created is unstructured and thus more difficult to understand and use it extensively. We need to structure the data and store it to categorize for better analysis as the data can size up to Terabytes.

Data generated by digital technologies are acquired from user data on mobile apps, social media platforms, interactive and e-commerce sites, or online shopping sites. Big Data can be in various forms such as text, audio, video, and images. The importance of data established from the facts as its creation itself is multiplying rapidly. Data is junk if the information is not usable, its proper channelization along with a purpose attached to it.
Data at your fingertips eases and optimizes the business performance with the capability of dealing with situations that need severe decisions.

Interesting Statistics of Big Data:

What is Big Data Analytics?

Big data analytics is a complex process to examine large and varied data sets that have unique patterns. It introduces the productive use of data.
It accelerates data processing with the help of programs for data analytics. Advanced algorithms and artificial intelligence contribute to transforming the data into valuable insights. You can focus on market trends, find correlations, product performance, do research, find operational gaps, and know about customer preferences.
Big Data analytics accompanied by data analytics technologies make the analysis reliable. It consists of what-if analysis, predictive analysis, and statistical representation. Big data analytics helps organizations in improving products, processes, and decision-making.

The importance of big data analytics and its tools for Organizations:

  1. Improving product and service quality
  2. Enhanced operational efficiency
  3. Attracting new customers
  4. Finding new opportunities
  5. Launch new products/ services
  6. Track transactions and detect fraudulent transactions
  7. Effective marketing
  8. Good customer service
  9. Draw competitive advantages
  10. Reduced customer retention expenses
  11. Decreases overall expenses
  12. Establish a data-driven culture
  13. Corrective measures and actions based on predictions
Insights by Big Data Analytics

For Technical Teams:

  1. Accelerate deployment capabilities
  2. Investigate bottlenecks in the system
  3. Create huge data processing systems
  4. Find better and unpredicted relationships between the variables
  5. Monitor situation with real-time analysis even during development
  6. Spot patterns to recommend and convert to chart
  7. Extract maximum benefit from the big data analytics tools
  8. Architect highly scalable distributed systems
  9. Create significant and self-explanatory data reports
  10. Use complex technological tools to simplify the data for users

Data produced by industries whether, automobile, manufacturing, healthcare, travel is industry-specific. This industry data helps in discovering coverage and sales patterns and customer trends. It can check the quality of interaction, the impact of gaps in delivery and make decisions based on data.

Various analytical processes commonly used are data mining, predictive analysis, artificial intelligence, machine learning, and deep learning. The capability of companies and customer experience improves when we combine Big Data to Machine Learning and Artificial Intelligence.

Big Data Analytics Processes

Predictions of Big Data Analytics:

  1. In 2019, the big data market is positioned to grow by 20%
  2. Revenues of Worldwide Big Data market for software and services are likely to reach $274.3 billion by 2022.
  3. The big data analytics market may reach $103 billion by 2023
  4. By 2020, individuals will generate 1.7 megabytes in a second
  5. 97.2% of organizations are investing in big data and AI
  6. Approximately, 45 % of companies run at least some big data workloads on the cloud.
  7. Forbes thinks we may need an analysis of more than 150 trillion gigabytes of data by 2025.
  8. As reported by Statista and Wikibon Big Data applications and analytic’s projected growth is $19.4 billion in 2026 and Professional Services in Big Data market worldwide is projected to grow to $21.3 billion by 2026.

Big Data Processing:

Identify Big Data with its high volume, velocity, and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis.

Big Data Processing

Data processing challenges are high according to the Kaggle’s survey on the State of Data Science and Machine Learning, more than 16000 data professionals from over 171 countries. The concerns shared by these professionals voted for selected factors.

  1. Low-quality Data – 35.9%
  2. Lack of data science talent in organizations – 30.2%
  3. Lack of domain expert input – 14.2%
  4. Lack of clarity in handling data – 22.1%
  5. Company politics & lack of support – 27%
  6. Unavailability of difficulty to access data – 22%
  7. These are some common issues and can easily eat away your efforts of shifting to the latest technology.
  8. Today we have affordable and solution centered tools for big data analytics for SML companies.

Big Data Tools:

Selecting big data tools to meet the business requirement. These tools have analytic capabilities for predictive mining, neural networks, and path and link analysis. They even let you import or export data making it easy to connect and create a big data repository. The big data tool creates a visual presentation of data and encourages teamwork with insightful predictions.

Big Data Tools

Microsoft HDInsight:

Azure HDInsight is a Spark and Hadoop service on the cloud. Apache Hadoop powers this Big Data solution of Microsoft; it is an open-source analytics service in the cloud for enterprises.

Pros:

  • High availability of low cost
  • Live analytics of social media
  • On-demand job execution using Azure Data Factory
  • Reliable analytics along with industry-leading SLA
  • Deployment of Hadoop on a cloud without purchasing new hardware or paying any other charges

Cons:

  • Azure has Microsoft features that need time to understand
  • Errors on loading large volume of data
  • Quite expensive to run MapReduce jobs on the cloud
  • Azure logs are barely useful in addressing issues

Pricing: Get Quote

Verdict: Microsoft HDInsight protects the data assets. It provides enterprise-grade security for on-premises and has authority controls on a cloud. It is a high productivity platform for developers and data scientists.

Cloudera:

Distribution for Hadoop: Cloudera offers the best open-source data platform; it aims at enterprise quality deployments of that technology.

Pros:

  • Easy to use and implement
  • Cloudera Manager brings excellent management capabilities
  • Enables management of clusters and not just individual servers
  • Easy to install on virtual machines
  • Installation from local repositories

Cons:

  • Data Ingestion should be simpler
  • It may crash in executing a long job
  • Complicating UI features need updates
  • Data science workbench can be improved
  • Improvement in cluster management tool needed

Pricing: Free, get quotes for annual subscriptions of data engineering, data science and many other services they offer.

Verdict: This tool is a very stable platform and keeps on continuously updated features. It can monitor and manage numerous Hadoop clusters from a single tool. You can collect huge data, process or distribute it.

Sisense:

This tool helps to make Big Data analysis easy for large organizations, especially with speedy implementation. Sisense works smoothly on the cloud and premises.

Pros:

  • Data Visualization via dashboard
  • Personalized dashboards
  • Interactive visualizations
  • Detect trends and patterns with Natural Language Detection
  • Export Data to various formats

Cons:

  • Frequent updates and release of new features, older versions are ignored
  • Per page data display limit should be increased
  • Data synchronization function is missing in the Salesforce connector
  • Customization of dashboards is a bit problematic
  • Operational metrics missing on dashboard

Pricing: The annual license model and custom pricing are available.

Verdict: It is a reliable business intelligence and big data analytics tool. It handles all your complex data efficiently and live data analysis helps in dealing with multiparty for product/ service enhancement. The pulse feature lets us select KPIs of our choice.

Periscope Data:

This tool is available through Sisense and is a great combination of business intelligence and analytics to a single platform.
Its ability to handle unstructured data for predictive analysis uses Natural Language Processing in delivering better results. A powerful data engine is high speed and can analyze any size of complex data. Live dashboards enable faster sharing via e-mail and links; embedded in your website to keep everyone aligned with the work progress.

Pros:

  • Work-flow optimization
  • Instant data visualization
  • Data Cleansing
  • Customizable Templates
  • Git Integration

Cons:

  • Too many widgets on the dashboard consume time in re-arranging.
  • Filtering works differently, should be like Google Analytics.
  • Customization of charts and coding dashboards requires knowledge of SQL
  • Less clarity in display of results

Pricing: Free, get a customized quote.

Verdict: Periscope data is end-to-end big data analytics solutions. It has custom visualization, mapping capabilities, version control, and two-factor authentication and a lot more that you would not like to miss out on.

Zoho Analytics:

This tool lets you function independently without the IT team’s assistance. Zoho is easy to use; it has a drag and drop interface. Handle the data access and control its permissions for better data security.

Pros:

  • Pre-defined common reports
  • Reports scheduling and sharing
  • IP restriction and access restriction
  • Data Filtering
  • Real-time Analytics

Cons:

  • Zoho updates affect the analytics, as these updates are not well documented.
  • Customization of reports is time-consuming and a learning experience.
  • The cloud-based solution uses a randomizing URL, which can cause issues while creating ACLs through office firewalls.

Pricing: Free plan for two users, $875, $1750, $4000, and $15,250 monthly.

Verdict: Zoho Analytics allows us to create a comment thread in the application; this improves collaboration between managers and teams. We recommended Zoho for businesses that need ongoing communication and access data analytics at various levels.

Tableau Public:

This tool is flexible, powerful, intuitive, and adapts to your environment. It provides strong governance and security. The business intelligence (BI) used in the tool provides analytic solutions that empower businesses to generate meaningful insights. Data collection from various sources such as applications, spreadsheets, Google Analytics reduces data management solutions.

Pros:

  • Performance Metrics
  • Profitability Analysis
  • Visual Analytics
  • Data Visualization
  • Customize Charts

Cons:

  • Understanding the scope of this tool is time-consuming
  • Lack of clarity in using makes it difficult to use
  • Price is a concern for small organizations
  • Lack of understanding in users for the way this tool deals with data.
  • Not much flexible for numeric/ tabular reports

Pricing: Free & $70 per user per month.

Verdict: You can view dashboards in multiple devices like mobiles, laptops, and tablets. Features, functionality integration, and performance make it appealing. The live visual analytics and interactive dashboard is useful to the businesses for better communication for desired actions.

Rapidminer:

It is a cross-platform open-source big data tool, which offers an integrated environment for Data Science, ML, and Predictive Analytics. It is useful for data preparation and model deployment. It has several other products to build data mining processes and set predictive analysis as required by the business.

Pros:

  • Non-technical person can use this tool
  • Build accurate predictive models
  • Integrates well with APIs and cloud
  • Process change tracking
  • Schedule reports and set triggered notifications

Cons:

  • Not that great for image, audio and video data
  • Require Git Integration for version control
  • Modifying machine learning is challenging
  • Memory size it consumes is high
  • Programmed responses make it difficult to get problems solved

Pricing: Subscription $2,500, $5,000 & $10,000 User/Year.

Verdict: Huge organizations like Samsung, Hitachi, BMW, and many others use RapidMiner. The loads of data they handle indicate the reliability of this tool. Store streaming data in numerous databases and the tool allows multiple data management methods.

Conclusion:

The velocity and veracity that big data analytics tools offer make them a business necessity. Big data initiatives have an interesting success rate that shows how companies want to adopt new technology. Of course, some of them do succeed. The organizations using big data analytic tools benefited in lowering operational costs and establishing the data-driven culture.

How is big data generated

Why big data analytics is indispensable for today’s businesses.

Ours is the age of information technology. Progress in IT has been exponential in the 21st century, and one direct consequence is the amount of data generated, consumed, and transferred. There’s no denying that the next step in our technological advancement involves real-life implementations of artificial intelligence technology.

In fact, one could say we are already in the midst of it. And there’s a definitive link between the large amounts of digital information being produced — called Big Data when it exceeds the processing capabilities of traditional database tools — and how new machine learning techniques use that data to assist the development of AI.

However, this isn’t the only application of Big Data even if it has become the most promising. Big data analytics is now a heavily researched field which helps businesses uncover ground-breaking insights from the available data to make better and informed decisions. According to IDC, big data and analytics had market revenue of more than $150 billion worldwide in 2018.

What is the scale of data that we are dealing with today?

  • ·It is estimated that there will be 10 billion mobile devices in use by 2020. This is more than the entire world population, and this is not including laptops and desktops.
  • We make over 1 billion Google searches every day.
  • Around 300 billion emails are sent every day.
  • More than 230 million tweets are written every day.
  • More than 30 petabytes (that’s 1015 bytes) of user-generated data is stored, accessed and analyzed on Facebook.
  • On YouTube alone, 300 hours of video are uploaded every minute.
  • In just 5 years, the number of connected smart devices in the world will be more than 50 billion — all of which will collect, create, and share data.
Social media platforms have shot up human-generated data exponentially.

As an aside, in an attempt to impress the potential here, let me state that we analyze less than 1% of all available data. The numbers are staggering!

Before we get to classifying all this data, let us understand the three main characteristics of what makes big data big.

The 3 Vs of Big Data

3 Vs of Big Data
Image Credit: workology

Volume

Volume refers to the amount of data generated through various sources. On social media sites, for example, we have 2 billion Facebook users, 1 billion on YouTube, and 1 billion together on Instagram and Twitter. The massive quantities of data contributed by all these users in terms of images, videos, messages, posts, tweets, etc. have pushed data analysis away from the now incapable excel sheets, databases, and other traditional tools toward big data analytics.

Velocity

This is the speed at which data is being made available — the rate of transfer over servers and between users has increased to a point where it is impossible to control the information explosion. There is a need to address this with more equipped tools, and this comes under the realm of big data.

Variety

There are structured and unstructured data in all the content being generated. Pictures, videos, emails, tweets, posts, messages, etc. are unstructured. Sensor-collected data from the millions of connected devices is what you can call semi-structured while records maintained by businesses for transactions, storage, and analyzed unstructured information are part of structured data.

Classification of Big Data

With the amount of information that is available to us today, it is important to classify and understand the nature of different kinds of data and the requirements that go into the analysis for each.

Human Generated Data

Most human-generated data is unstructured. But this data has the potential to provide deep insights for heavy user-optimization. Product companies, customer service organizations, even political campaigns these days rely heavily on this type of random data to inform themselves of their audience and to target their marketing approach accordingly.

Classification of Big Data
Image Credit: EMC

Machine Generated Data

Data created by various sensors, cameras, satellites, bio-informatic and health-care devices, audio and video analyzers, etc. combine to become the biggest source of data today. These can be extremely personalized in nature, or completely random. With the advent of internet-enabled smart devices, propagation of this data has become constant and omnipresent, providing user information with highly useful detail.

Data from Companies and Institutions

Records of finances, transactions, operations planning, demographic information, health-care records, etc. stored in relational databases are more structured and easily readable compared to disorganized online data. This data can be used to understand key performance indicators, estimate demands and shortage, prevalent factors, large-scale consumer mentality, and a lot more. This is the smallest portion of the data market but combined with consumer-centric analysis of unstructured data, can become a very powerful tool for businesses.

What we can do for you

Whether one is seeking a profit advantage or a market edge, carving a niche product or capturing crowd sentiment, developing self-driving cars or facial recognition apps, building a futuristic robot or a military drone, big data is available for all sectors to take their technology to the next level. Bridged is a place where such fruitful experiments in data are being utilized and we are endeavoring to provide assistance to companies who are willing to take advantage of this untapped but currently mandatory investment in big data.