Big Data Analytics

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What is Big Data Analytics?

Big data analytics is a complex process that involves collecting, cleaning, transforming, analyzing, and interpreting large amounts of structured, unstructured, and semi-structured data.

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The goal of this type of analytics is to discover meaningful patterns that can be used to make data-driven decisions.

Big data analytics handles much larger and more diverse data sets than traditional data analytics, so it requires more compute resources and different types of analytics platforms and data visualization tools.

To make large volumes of big data easier to manage, raw data is often cleaned and preprocessed in the cloud. To gain actionable insights in a reasonable amount of time, analysts use machine learning (ML) algorithms that can efficiently parse the data, identify significant patterns, and predict future trends based on historical information.

What is Big Data Analytics?

5 Key Takeaways

  • Big data analytics is used to make data-driven decisions.
  • It requires huge quantities of disparate data types to be cleaned and preprocessed before analysis.
  • Big data analytics is too complex for traditional data analytics tools.
  • It is used in a wide variety of industries to predict future trends and behaviors.
  • Cloud computing made big data analytics more accessible to organizations of all sizes.

What is Big Data?

Big data describes extremely large data sets that are too complex for traditional data processing software to handle. Big data is characterized by its volume, velocity, variety, veracity, and value.

Volume – the amount of data aggregated from different sources.

Velocity – the speed at which new data is created.

Variety – the types of data and data sources.

Veracity – the quality of the data.

Value – the usefulness of the data.

What is Big Data?

How Big Data Analytics Works

Big data analytics aggregates data from multiple channels to provide a more holistic view of the data source.

  1. First, the raw data is cleaned to remove errors, inconsistencies, and duplicate entries.
  2. Next, special software tools are used to uncover hidden patterns, correlations, and anomalies within the data.
  3. The findings are then shared through dashboards or reports that have data visualization components. This allows decision-makers to understand the findings and act upon them to achieve a desired goal.

Cloud computing plays an important role in big data analytics by providing the scalable infrastructure and processing power required to handle massive datasets. In business to customer (B2C) relationships, it has leveled the playing field by helping small businesses to gain valuable insights that were previously only accessible to larger enterprises.

Types of Big Data Analytics

Big data analytics is often categorized by the objective of the analytics.

Descriptive analytics
Examine data to try and understand what has happened in the past.

Diagnostic analytics
Dives deeper into the data to try and understand the cause of a past event or behavior pattern.

Predictive analytics
Uses statistical models and machine learning algorithms to forecast future outcomes based on historical data.
Prescriptive analytics
Forecasts future outcomes and also suggests actions to achieve a desired outcome.

Big Data Analytics Tools

Big data analytics requires special tools to handle the unique challenges of collecting, storing, processing, and analyzing huge amounts of data from disparate sources. The choice of tool depends on budget, the type of data that is being analyzed, and the objective of the analysis.

Tool Name Primary Use
Apache Hadoop Data processing and storage
Apache Spark Large-scale data processing
Apache Kafka Data streaming
MongoDB NoSQL database
Cassandra NoSQL database
Google BigQuery Big data analytics
Microsoft Power BI Business intelligence and data visualization
Tableau Data visualization
TensorFlow Machine learning and deep learning
Apache Hive Data warehouse software

Uses of Big Data Analytics

Big data analytics is being used to solve unique challenges and enhance decision-making in a wide variety of use cases, including:

Healthcare
Improve patient care, predict disease outbreaks, personalize treatment plans, and enhance diagnostic accuracy by analyzing electronic health records, medical imaging, genomic data, and clinical trial results.

Finance
Manage risk and detect fraud by analyzing financial market data, transaction histories, and credit scores.

Manufacturing
Optimize production processes, reduce downtime, and enhance quality control by analyzing sensor data, production logs, and supply chain data.
Transportation
Streamline routing, improve fuel efficiency, and optimize scheduling by analyzing traffic data, weather information, and vehicle telematics.
Telecommunications
Improve service by analyzing call center records, customer service interactions, and network traffic patterns.
Energy
Predict equipment failures and optimize energy consumption by analyzing intelligent sensor data from equipment and smart grids.
Government
Improve public services by analyzing citizen feedback, demographic data, and service utilization patterns.

Big Data Analytics Examples

Big data analytics has arguably had the most impact on customer relationship management (CRM).

In retail and marketing, for example, customer relationship management apps that can visualize big data analytics insights have allowed sales representatives to identify cross-selling opportunities more accurately in real time.

Here are some other examples of the way big data analytics is being used in CRM:

  • Provide a more holistic view of customer behavior by combining data from social media activity with in-store and website interactions.
  • Develop predictive models that forecast customer retention rates or customer churn rates.
  • Identify common customer issues, complaints, or areas of dissatisfaction.
  • Develop prescriptive models that suggest ways to address customer issues.
  • Measure the effectiveness of a marketing campaign across multiple customer channels.

Big Data Analytics Pros and Cons

Big data analytics offers numerous advantages by enhancing data-driven decision-making, however, it also presents challenges that organizations must address to harness its full potential.

Pros

  • Reduce reliance on intuition and guesswork
  • Anticipate customer needs more accurately
  • Identify areas for improvement
  • Reveal opportunities for new products, services, or business models

Cons

  • Potential misuse of sensitive information
  • Potential privacy violations
  • Requires analytics skills.
  • Requires data engineering and machine learning skills

The Bottom Line

Big data analytics’ meaning is used across a wide variety of industries, including retail, healthcare, finance, manufacturing, and government. Applications include personalized marketing, real-time fraud detection, risk management, supply chain optimization, and scientific research.

While big data analytics offers immense benefits, it requires careful data preparation, the right technology infrastructure, and a skilled workforce to truly harness its potential and mitigate associated risks.

FAQs

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Margaret Rouse
Senior Editor
Margaret Rouse
Senior Editor

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.