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:
- It is likely that by 2020, every individual will generate 1.7 megabytes in no time.
- Every day the internet users generate about 2.5 quintillion bytes of data.
- Surveys indicate that 97.2% of organizations are investing in big data and AI.
- Netflix saves $1 billion per year on customer retention with big data.
- Profits of businesses have seen an increase of 8–10 percent and experienced a 10 percent reduction in overall cost due to 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:
- Improving product and service quality
- Enhanced operational efficiency
- Attracting new customers
- Finding new opportunities
- Launch new products/ services
- Track transactions and detect fraudulent transactions
- Effective marketing
- Good customer service
- Draw competitive advantages
- Reduced customer retention expenses
- Decreases overall expenses
- Establish a data-driven culture
- Corrective measures and actions based on predictions
For Technical Teams:
- Accelerate deployment capabilities
- Investigate bottlenecks in the system
- Create huge data processing systems
- Find better and unpredicted relationships between the variables
- Monitor situation with real-time analysis even during development
- Spot patterns to recommend and convert to chart
- Extract maximum benefit from the big data analytics tools
- Architect highly scalable distributed systems
- Create significant and self-explanatory data reports
- 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.
Predictions of Big Data Analytics:
- In 2019, the big data market is positioned to grow by 20%
- Revenues of Worldwide Big Data market for software and services are likely to reach $274.3 billion by 2022.
- The big data analytics market may reach $103 billion by 2023
- By 2020, individuals will generate 1.7 megabytes in a second
- 97.2% of organizations are investing in big data and AI
- Approximately, 45 % of companies run at least some big data workloads on the cloud.
- Forbes thinks we may need an analysis of more than 150 trillion gigabytes of data by 2025.
- 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.
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.
- Low-quality Data – 35.9%
- Lack of data science talent in organizations – 30.2%
- Lack of domain expert input – 14.2%
- Lack of clarity in handling data – 22.1%
- Company politics & lack of support – 27%
- Unavailability of difficulty to access data – 22%
- These are some common issues and can easily eat away your efforts of shifting to the latest technology.
- 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.
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.
- 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
- 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.
Distribution for Hadoop: Cloudera offers the best open-source data platform; it aims at enterprise quality deployments of that technology.
- 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
- 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.
This tool helps to make Big Data analysis easy for large organizations, especially with speedy implementation. Sisense works smoothly on the cloud and premises.
- Data Visualization via dashboard
- Personalized dashboards
- Interactive visualizations
- Detect trends and patterns with Natural Language Detection
- Export Data to various formats
- 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.
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.
- Work-flow optimization
- Instant data visualization
- Data Cleansing
- Customizable Templates
- Git Integration
- 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.
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.
- Pre-defined common reports
- Reports scheduling and sharing
- IP restriction and access restriction
- Data Filtering
- Real-time Analytics
- 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.
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.
- Performance Metrics
- Profitability Analysis
- Visual Analytics
- Data Visualization
- Customize Charts
- 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.
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.
- 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
- 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.
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.