We are living in the age of data. As technology continues to evolve, massive amounts of information are being generated and collected every second of every day. But how exactly does “big data” differ from regular old data? What makes big data deserving of its own term and field of study?
This guide will examine the key distinctions between standard data and big data across factors like volume, velocity, variety, veracity, and value. We will also explore the technologies that have emerged to store, process, and extract insights from large-scale datasets. Let’s dive in!
Contents: Data vs Big Data
What is Regular Data?
Data simply refers to structured information that is gathered and analyzed for various business purposes. This includes common data types like:
- Customer transaction records
- Website traffic metrics
- Sales figures
- Inventory databases
These standard forms of data are managed in traditional relational databases and data warehouses. They are analyzed using conventional business intelligence tools and data visualization dashboards.
Some key properties of regular data:
- Structured format (numeric, textual, etc)
- Organized into rows and columns
- Fits neatly into tables
- Queried using SQL
- Data size in MBs or GBs
- Analyzed with Excel, Tableau, Power BI
Regular data serves many core business functions. But it has limitations in scale and complexity. Let’s explore how big data builds on regular data.
Defining Big Data
The main differences between regular data and big data come down to the “3 Vs”:
Let’s examine what each of these elements means for big data.
Big data involves absolutely massive volumes and scale. It is measured in terms of:
|Data Volume Comparison|
|Megabyte||1 MB = 1 million bytes|
|Gigabyte||1 GB = 1 billion bytes|
|Terabyte||1 TB = 1 trillion bytes|
|Petabyte||1 PB = 1 quadrillion bytes|
|Exabyte||1 EB = 1 quintillion bytes|
Processing datasets of this size requires tremendously powerful and scalable information architectures.
Big data is often generated continuously and needs to be processed and analyzed in real-time. The speed and frequency at which new data is produced brings challenges in analyzing it quickly to extract timely insights.
Some examples of high velocity big data:
- Live stock market data feeds
- Social media postings
- Sensors or machine data
- Web and mobile app activity data
Big data draws from a wide variety of data sources and formats, including:
- Structured transaction data
- Unstructured text, images, audio, video
- Semistructured data like XML or JSON
- Machine and sensor data
- Real-time streaming data
Wrangling messy, inconsistent, and heterogeneous data types requires flexible data processing capabilities.
In addition to the core 3 Vs, two more Vs are often cited as properties of big data:
- Veracity – issues around data consistency, quality, and accuracy
- Value – ability to derive business value and meaningful insights
Next let’s explore why big data necessitates its own technologies and techniques.
Why “Big Data” Needs its Own Term
The massive volume, high velocity, and wide variety of big data make it impossible to work with using traditional data tools. Entirely new technologies have emerged to store, process, and analyze big data both efficiently and cost-effectively.
For example, big data requires:
- Massively parallel processing – Computations done simultaneously across thousands of servers
- Distributed file systems – Data storage across nodes in a cluster
- NoSQL databases – Flexible, non-relational data models
- Machine learning – Pattern recognition and predictive analytics
Some examples of popular big data technologies:
- Hadoop – Open source framework for distributed storage and processing
- Spark – Engine for large-scale data processing
- MongoDB – Leading NoSQL database
- Amazon Web Services – Cloud infrastructure for big data
These technologies power big data capabilities like:
- Analyzing data from many sources
- Identifying trends and patterns in real-time data
- Applying machine learning to large datasets
- Personalizing recommendations
- Detecting anomalies or fraud rapidly
- Optimizing costs for data storage and processing
Let’s compare some key pros and cons of big data vs regular data.
|Big Data Pros||Big Data Cons|
|Derive value from massive datasets||Requires advanced analytics skills|
|Real-time analysis and insights||Complex technology ecosystem|
|Flexible data models||Significant computing resources|
|Sophisticated machine learning||Data veracity challenges|
|Innovation and competitive advantage||Security and privacy concerns|
While big data unleashes new potential, it also brings big challenges! Proper governance and management is crucial.
Next let’s look at some industry examples of big data in action.
Big Data Use Cases by Industry
Organizations across many industries are harnessing big data to drive business results. Some examples include:
- Analyzing customer shopping behavior
- Personalized recommendations and promotions
- Optimized pricing and markdowns
- Fraud pattern detection
- Real-time trade analytics
- Automated algorithmic trading
- Risk modeling and regulatory compliance
- Fraud monitoring with machine learning
- Patient profile analytics
- Treatment pattern recognition
- Precision medicine and clinical decision support
- Disease outbreak tracking
- Analyzing consumption patterns
- Personalized content recommendations
- Ad targeting and digital campaigns
- Sentiment analysis on social media
- Predictive maintenance on equipment
- Supply chain optimization
- Quality control with sensor data
- Automation and robotics optimization
The applications are endless! Let’s examine the business value proposition of big data.
The Business Value of Big Data
If managed properly, big data can drive tangible business outcomes like:
- Cost reductions – through operational optimization
- Risk reductions – by detecting fraud and anomalies
- New revenues – through product innovation and personalized offerings
- Improved customer experiences – with targeted engagement
Some examples of big data business value:
- A manufacturing firm uses sensor data to optimize processes and reduce costs.
- An insurance firm monitors claims patterns to detect fraudulent activity faster.
- A streaming service analyzes viewing habits to recommend new shows customers will like.
- A retailer tracks buying data to notify customers of sales on items they seem to like.
Deriving insights from big data can lead to data-driven decision making and competitive advantages. But it requires skills, governance, and the right analytics tools.
Best Practices for Managing Big Data
Here are some best practices to consider when implementing big data analytics:
- Start with business goals – Don’t collect data for data’s sake. Focus on high-value use cases.
- Build a data-driven culture – Change management matters. Get stakeholders bought into data-based decision making.
- Tool consolidation – Reduce complexity by limiting the number of platforms and tools.
- Data governance – Establish processes for data quality, security, lifecycle management.
- Iterative approach – Start small, demonstrate quick wins, then expand.
- Communication and storytelling – Share compelling data narratives across the organization.
- Hybrid architectures – Blend cloud infrastructure with on-premises systems.
- Partnerships – Augment internal skills by working with external analytics consultants.
With the right strategy, big data can transform an organization. But it requires executive sponsorship, change management, trust in data, embracing analytics, and sound data governance.
Key Differences: Big Data vs Regular Data
Let’s summarize the key differences between big data and regular data:
|Factor||Regular Data||Big Data|
|Velocity||Batch processing||Real-time processing|
|Variety||Structured data||Variety of structured, semi-structured, unstructured data|
|Technology||Traditional RDBMS/SQL||NoSQL, distributed systems, machine learning|
|Analytics||Conventional BI and visualization||Advanced analytics, data science|
|Value||Operational support||Strategic, transformative insights|
While regular data serves core business functions, big data unlocks disruptive new potential through advanced analytics applied at an enormous scale.
Conclusion: Data vs Big Data
In summary, big data represents a new era defined by the volume, velocity, and variety of massive datasets. New technologies have emerged to store, process, and analyze big data in order to uncover valuable insights.
When managed properly, big data can enable data-driven decision making, optimize processes, reduce costs, identify new revenue opportunities, improve customer experiences, and drive competitive advantages. However, it also poses new challenges around data veracity, security, privacy, and requiring advanced skillsets.
Organizations need robust data governance, strategic vision, and change management to tap into the full potential of big data. Though the challenges are real, the business value unlocked by leveraging massive amounts of data makes big data analytics an essential capability in today’s increasingly competitive, data-driven business environment. By embracing big data and combining it with traditional sources, companies can gain a more holistic view of the business and customers.
The future will inevitably bring even more data and complexity. But with the right strategy, governance, technology and talent, organizations can turn big data into big opportunities.