Skip to content

When data gets too big: why you need structured data

We live in a data-driven world. The amount of digital data created each day is mind-boggling – over 2.5 quintillion bytes daily! With data coming from everywhere – social media, devices, sensors – businesses can‘t afford to ignore it. But massive volumes of scattered, disorganized data are useless. The key is structuring it for analysis.

Let‘s explore big data, why structure matters, and how to put that data to work!

The relentless growth of big data

Experts forecast global data generation to soar from 33 zettabytes in 2018 to 175 zettabytes by 2025. That‘s like every person creating over 5 times more data daily!

Driving this exponential growth are plummeting storage costs, Internet ubiquity, and technologies like IoT sensors. Unstructured data makes up the bulk, including:

  • Social media – 500 million tweets, 4 petabytes of Facebook data daily
  • Messaging – 294 billion emails sent each day
  • Videos – Over 500 hours uploaded to YouTube every minute
  • Images – 95 million photos and videos shared on Instagram daily

This unstructured data explosion strains companies relying on traditional databases. It demands new strategies to store, process, and extract value.

Why structure matters for big data

Let‘s compare structured and unstructured data. Think of structured data as neatly organized inside tables or databases, while unstructured sprawls across documents, emails, audio, video.

Structured data provides:

  • Consistency – formatted data models
  • Accessibility – easily searched and queried
  • Analyzability – simple to aggregate for insights

Unstructured data tends to be:

  • Messy – difficult to interpret data
  • Costly – requires extensive processing
  • Siloed – trapped in isolated systems

For example, a customer support call center receives 1 million emails annually. Putting these conversations into a queryable database multiplies their usefulness. Structured data unlocks vital analytics into customer sentiment, common issues, optimal responses.

According to IDC, organizations leveraging unstructured data could boost productivity by up to 430%!

Structuring big data in 4 key steps

Turning vast volumes of scattered, messy data into structured treasure isn‘t easy. Success requires an end-to-end approach:

1. Data collection

First, the relevant data must be identified and extracted from sources like websites, apps, devices, documents. For example, an e-commerce site may web scrape competitor prices daily.

2. Cleaning

Next, scrub irrelevant info, fix inconsistencies, deduplicate, and validate the data. Data quality critically impacts later analysis.

3. Modeling

Then, organize the data – design database schemas and taxonomies. Structure varies by goals – relational databases for transactions, data warehouses for business intelligence, graph databases for relationship analysis.

4. Loading

Finally, populate the structured databases. For big data, distributed storage like Hadoop combined with Spark for processing is often used.

Turbocharge analysis with structured data

Structured data enables powerful techniques like:

Machine learning – Models uncover hidden insights and predict future trends based on vast amounts of historical data. Structured data is essential for training.

Data mining – Sophisticated querying combined with statistical analysis reveals buried patterns and correlations in large structured data sets.

Network analysis – Studying structured data on relationships lets analysts identify key nodes, clusters, and network vulnerabilities.

Leading big data practitioners point to structure as the prerequisite for advanced analytics. A McKinsey study found that firms leveraging techniques like machine learning outperform rivals by up to 30%!

Real-world examples

Structured data delivers concrete benefits across industries:

  • Retail – Structured point-of-sale data merged with customer info fuels personalized promotions.

  • Manufacturing – Sensor time-series data analyzed to predict equipment failures before they occur.

  • Finance – Structured credit card transaction data transformed into robust fraud detection models.

  • Healthcare – Unstructured clinical notes and reports structured using NLP to accelerate research.

  • Government – Billions of unstructured web pages made searchable through knowledge graph techniques.

The common thread? Structure opens the door to turning data into insights!

Key takeaways

  • Big data is scaling exponentially across structured, unstructured, and semi-structured forms.
  • Unstructured data represents an vast underutilized asset for most organizations.
  • Structuring big data is crucial to enable advanced analytics like ML and data mining.
  • A workflow of data extraction, cleaning, modeling and loading drives structuring success.
  • The benefits of structured data are real – from productivity gains to competitive advantage.

The data deluge shows no signs of slowing. Organizations must embrace strategies to structure this valuable asset or risk falling behind. Turn big data into your big advantage with the power of structure!

Join the conversation

Your email address will not be published. Required fields are marked *