This is a comprehensive guide to Big Data Analytics with its use cases, architecture, examples and comparison with Big Data and Data Science:
Big data analytics has gained traction because corporations such as Facebook, Google, and Amazon have set up their own new paradigms of distributed data processing and analytics to understand their customer’s propensities for value extraction from big data.
In this tutorial, we explain big data analytics and compare it against Big Data and Data Science. We will cover the necessary attributes that businesses need to have in their big data strategy and the methodology that works. We will also mention the latest trends and some use cases of data analytics.
As shown in the below image, Analytics requires one to use IT skills, business skills, and data science. Big data analytics is at the center of making use of values from big data, and it helps in deriving consumable insights for an organization.
What You Will Learn:
- What Is Big Data Analytics
- Big Data Vs Big Data Analytics Vs Data Science
- What Every Big Data Analytics Strategy Should Have
- Data Pipeline And Process For Data Analytics
- Big Data Analytics Architecture
- Current Trends In Data Analytics
- Big Data Analytics Uses
- Frequently Asked Questions
What Is Big Data Analytics
Big Data Analytics deals with the use of a collection of statistical techniques, tools, and procedures of analytics to Big Data.
Recommended Reading => Introduction To Big Data
It is the analytics that helps in extracting valuable patterns and meaningful insights from big data to support data-led decision making. It is because of the emergence of new data sources such as social media, and IoT data that big data and analytics have become popular.
This trend is giving rise to an area of practice and study called “data science” that encompasses the techniques, tools, technologies, and processes for data mining, cleaning, modeling, and visualization.
Big Data Vs Big Data Analytics Vs Data Science
A comparison between big data, data science, and big data analytics can be understood from the below table.
|Basis||Big Data||Data Science||Big Data Analytics|
|Tools & Technologies||Hadoop Ecosystem, CDH, Cassandra, MongoDB, Java, Python, Talend, SQL, Rapid Miner||R, Python, Jupyter, Data Science Workbench, IBM SPSS, Tableau||Spark, Storm, Knime, Data Wrapper, Lumify, HPCC, Qubole, Microsoft HDInsight|
|Work roles and skills||Storage infrastructure maintenance, data processing, and Knowledge on Hadoop and its integration with other tools.||Data transformation, Data Engineering,Data Wrangling,Data Modeling, and Visualisation||BI and Advanced Analytics, Statistics, Data Modeling, and Machine Learning, Math skills, Communication, Consulting.|
|Designations||Big Data Architect|
Big Data Developer
Big Data Engineer
Machine Learning Engineer
|Big Data Analyst
Business Intelligence Engineer
Business Analytics Specialist
Data Visualisation Developer
|Approx. Average Yearly Salary in USD||100,000||90,000||70,000|
Suggested reading =>> Data Science Vs Computer Science
What Every Big Data Analytics Strategy Should Have
A well defined, integrated, and comprehensive strategy contributes to and supports valuable data-driven decision making in an organization. In this section, we have listed the most critical steps that needs to be considered when defining a big data analytics strategy.
Step 1: Assessment
An assessment, already aligned with business objectives, requires involving key stakeholders, create a team of members with the right skill set, evaluate policies, people, process, and technology & data assets. If required, one can involve customers of the assessed in this process.
Step 2: Prioritization
After the assessment, one needs to derive use cases, prioritize them using big data predictive analytics, prescriptive analytics, and cognitive analytics. You can also use a tool such as the prioritization matrix and further filter the use cases with the help of feedback and input from key stakeholders.
Step 3: RoadMap
In this step, it is required to create a time-bound roadmap and publish it for everyone. A roadmap needs to include all the details regarding complexities, funds, inherent benefits of the use cases, and mapped projects.
Step 4: Change Management
Implementing change management requires one to manage data availability, integrity, security, and usability. An effective change management program, using any existing data governance, incentivizes activities, and members based on continuous monitoring.
Step 5: Right Skill Set
Identifying the right skill set is crucial to the organization’s success amid current trends in the industry. Therefore, one needs to follow the right leaders and bring educational programs to educate critical stakeholders.
Step 6: Reliability, Scalability & Security
The right approach and effective big data analytics strategy make the analytics process reliable, with effective use of interpretable models involving data science principles. A big data analytics strategy needs to also include aspects of security right from the beginning for a robust and tightly integrated analytics pipeline.
Data Pipeline And Process For Data Analytics
When planning for the data analytics pipeline, there are three fundamental aspects one needs to consider. These are as follows:
- Input: Data format and selection of technology to process, it is based on data’s underlying nature .i.e. whether data is time-series and quality.
- Output: Choice of connectors, reports, and visualization depends on the technical expertise of end-users and their data consumption requirements.
- Volume: Scaling solutions are planned based on the volume of data to avoid overloading on the big data processing system.
Now let us discuss a typical process and the stages for a big data analytics pipeline.
Stage 1: Data Ingestion
Data Ingestion is the first and most significant step in the data pipeline. It considers three aspects of data.
- Source of data – It is significant regarding the choice of the architecture of big data pipeline.
- Structure of data – Serialization is the key to maintain homogeneous structure across the pipeline.
- Cleanliness of data – Analytics is as good as the data without issues such as missing values and outliers, etc.
Stage 2: ETL/Warehousing
The next important module is data storage tools to perform ETL (Extract Transform Load). Data storage in a proper data center depends on,
- Management Expertise
Some time tested tools for ETL/Warehousing in data centers are:
- Apache Hadoop
- Apache Hive
- Apache Parquet
- Presto Query engine
Cloud companies such as Google, AWS, Microsoft Azure provide these tools on pay per basis and save initial capital expenditure.
Stage 3: Analytics & Visualization
Considering Hadoop’s limitation on fast querying, one needs to use analytics platforms and tools that allow fast and ad-hoc querying with the required visualization of results.
>>Recommended Reading: Big Data Tools
Stage 4: Monitoring
Post setting up an infrastructure for ingestion, storage, and analytics with visualization tools, the next step is to have IT and data monitoring tools to monitor. These include:
- CPU or GPU usage
- Memory and Resource consumption
Some tools worth considering are:
Monitoring tools are indispensable in a big data analytics pipeline and help monitor the quality and integrity of the pipeline.
Big Data Analytics Architecture
The architecture diagram below shows how modern technologies use both unstructured and structured data sources for Hadoop & Map-reduce processing, in-memory analytic systems, and real-time analytics to bring combined results for real-time operations and decision making.
Current Trends In Data Analytics
In this section, we have listed the essential aspects to look for when implementing or following trends of big data analytics in the industry.
#1) Big Data Sources
There are primarily three sources of Big Data. These are enlisted below:
- Social Data: Data generated because of social media use. This data helps in understanding the sentiments and behavior of customers and can be useful in marketing analytics.
- Machine Data: This data is captured from industrial equipment and applications using IoT sensors. It helps in understanding people’s behavior and provides insights on processes.
- Transactional Data: It is generated as a result of both offline and online activities of users regarding payment orders, invoices, receipts, etc. Most of this kind of data needs pre processing and cleaning before it can be used for analytics.
#2) SQL/NoSQL Data storage
When compared with traditional databases or RDBMS, NoSQL databases prove to be better for tasks required for big data analytics.
NoSQL databases can inherently deal with unstructured data quite well and are not limited to expensive schema modifications, vertical scaling, and interference of ACID properties.
#3) Predictive Analytics
Predictive Analytics offers customized insights that lead organizations to generate new customer responses or purchases and cross-sell opportunities. Organizations are using predictive analytics to make predictions on individual elements at granular levels to predict future outcomes and prevent potential issues. This further is combined with historical data and turned into prescriptive analytics.
Some areas where big data predictive analytics has been used successfully are business, child protection, clinical decision support systems, portfolio prediction, economy-level predictions, and underwriting.
#4) Deep Learning
The big data is overwhelming for conventional computing. It turns out that traditional machine learning techniques of data analysis flatten out in performance with the increase in variety and volume of data.
Analytics faces challenges with respect to format variations, highly distributed input sources, imbalanced input data, and fast-moving streaming data, and Deep learning algorithms quite efficiently deal with such challenges.
Deep learning has found its effective use in semantic indexing, conducting discriminative tasks, semantic image, and video tagging, social targeting, and also in hierarchical multi-level learning approaches in the areas of object recognition, data tagging, information retrieval, and natural language processing.
#5) Data lakes
Storing different data sets in different systems and combining them for analytics with traditional data management approaches prove expensive and are nearly infeasible. Therefore, organizations are making Data lakes, which store data in their raw, native format for actionable analytics.
The image below displays an example data lake in the big-data architecture.
Big Data Analytics Uses
We have enlisted some prevalent use cases below:
#1) Customer Analytics
Big Data Analytics is useful for various purposes, such as micro-marketing, one-to-one marketing, finer-segmentation, and mass customization for the customers of a business. Businesses can create strategies to personalize their products and services according to customer propensities to up-sell or cross-sell a similar or different range of products and services.
#2) Operation Analytics
Operation analytics helps in improving the overall decision making and business results by leveraging existing data and enriching it with the machine and IoT data.
For example, big data analytics in healthcare have made it possible to face challenges and new opportunities related to optimization of healthcare spending, improving the monitoring of clinical trials, predicting and planning of responses to disease epidemics such as COVID-19.
#3) Fraud Prevention
Big data analytics is seen with the potential to deliver a massive benefit by helping to anticipate and reduce fraud attempts, primarily in the financial and insurance sectors.
For example, Insurance companies capture real-time data on demography, earnings, medical claims, attorney expenses, weather, voice recordings of a customer, and call center notes. Specific real-time details help derive predictive models by combining the information mentioned above with historical data to identify speculated fraudulent claims early.
#4) Price Optimization
Companies use big data analytics to increase profit margins by finding the best price at the product level, and not at the category level. Large companies find it too overwhelming to get the granular details and complexity of pricing variables, which change regularly for thousands of products.
An analytics-driven price optimization strategy, such as dynamic deal scoring, allows companies to set prices for clusters of products and segments based on their data and insights on individual deal levels to score quick wins from demanding clients.
Frequently Asked Questions
Q #1) Is big data analytics a good career?
Answer: It is an added value to any organization, allowing it to make informed decisions and providing an edge over competitors. A Big Data career move increases your chance of becoming a key decision-maker for an organization.
Q #2) Why is big data analytics important?
Answer: It helps organizations to create new growth opportunities and completely new categories of products that can combine and analyze industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed.
Q #3) What is required for big data analytics?
Answer: The range of technologies that a good big data analyst must be familiar with is huge. For one to master Big Data analytics, it requires an understanding of various tools, software, hardware, and platforms. For example, Spreadsheets, SQL Queries, and R/R Studio, and Python are some basic tools.
At the enterprise level, tools such as MATLAB, SPSS, SAS, and Congnos are important in addition to Linux, Hadoop, Java, Scala, Python, Spark, Hadoop, and HIVE.
Q #4) Which of the databases given below is not a NoSQL database?
Q #5) Is Cassandra a NoSQL?
Q #6) Which of the following is not the property of Hadoop?
- Open Source
- Based on Java
- Distributed processing
Q #7) Choose all the activities that are NOT performed by a Data Scientist.
- Build Machine Learning models and improve their performance.
- Evaluation of statistical models to validate analyses
- Summarise advanced analyses using data visualization tools
- Presentation of results of technical analysis to internal teams and business clients
Answer: Presentation of results of technical analysis to internal teams and business clients
Further reading =>> Key differences between Data Analyst and Data Scientist
Q #8) Which activities are performed by a Data Analyst?
- Clean up and organize raw data
- Finding interesting trends in data
- create dashboards and visualizations for easy interpretation
- All of the above
Answer: All of the Above
Q #9) Which of the following is performed by a Data Engineer?
- Integration of new data sources to the existing data analytics pipeline
- The development of API’s for data consumption
- monitoring and testing of the system for continued performance
- All of the Above
Answer: All of the Above
Q #10) The correct sequence of data flow for analytics is
- Data sources, Data preparation, Data transformation, Algorithm Design, Data Analysis
- Data sources, Data transformation, Algorithm Design, Data preparation, Data Analysis
- Data sources, Algorithm Design, Data preparation, Data transformation, Data Analysis
- Data sources, Data preparation, Algorithm Design, Data transformation, Data Analysis
Answer: Data sources, Data preparation, Data transformation, Algorithm Design, Data Analysis
Q #11) Data Analysis is a linear process.
Q #12) Exploratory Analysis is NOT
- Answer initial data analysis questions in detail
- Determine problems with the data set
- Develop a sketch of an answer to the question
- Determine whether the data is correct for answering a question
Answer: Answer initial data analysis questions in detail
Q #13) Prediction question is another name given to an Inferential question.
We covered the most important aspects of big data analytics. We explained the most prevalent use cases and the trends in the big data analytics industry to reap maximum benefits.