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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.
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.
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.
How Big Data Analytics Works
Big data analytics aggregates data from multiple channels to provide a more holistic view of the data source.
- First, the raw data is cleaned to remove errors, inconsistencies, and duplicate entries.
- Next, special software tools are used to uncover hidden patterns, correlations, and anomalies within the data.
- 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.
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:
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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References
- Power BI – Data Visualization | Microsoft Power Platform (Microsoft)
- We’re changing the way you think about data (Tableau)
- What is Cross-Selling? – Salesforce (Salesforce)
