All You Must Know About Data Curation in ‘23

Data growth forecast chart

Data is the lifeblood of modern organizations. But without proper curation, its value slowly decays over time.

Data curation is the process of managing data throughout its lifecycle to ensure it remains accurately documented, complete, trustworthy, accessible and actionable.

As an experienced data consultant, I‘ve seen many organizations accumulate vast volumes of data in siloed lakes and warehouses. But without curation, critical context gets lost. Valuable signals get buried in noise. Insights lie dormant and underutilized.

In this comprehensive guide, you‘ll learn everything needed to curate your enterprise data assets and unlock their full potential in 2024 and beyond.

The Growing Need for Curation

With global data expected to grow exponentially to 175 zettabytes by 2025, organizations are accumulating data faster than they can extract value from it.

Data growth forecast chart

In my consulting experience across industries like financial services, CPG and telecom, a few pain points consistently arise:

  • Data swamps accumulating in lakes – billions of files lacking context or organization

  • Vast troves of customer and operational data, yet key insights remain elusive

  • Data scientists spending over 80% of time simply finding, cleaning and organizing data

  • Ad-hoc analytical projects, but no enterprise-wide data strategy or stewardship

This data deluge requires a new approach – curating enterprise data as a strategic business asset.

What is Data Curation?

Data curation is the active process of adding value to data assets by:

  • Finding, selecting and acquiring relevant data from reliable sources

  • Organizing, categorizing and structuring data meaningfully

  • Cleaning, validating and enhancing data for accuracy and usability

  • Adding rich context through metadata, documentation and provenance

  • Retaining, archiving and preserving data over long time horizons

  • Ensuring findability, accessibility, interoperability and reusability of data

Curated data is essential for analytics-driven decision making, building accurate AI models and fostering collaboration.

Curated data enables data-driven organizations to maximize the ROI from their data assets.

Key Phases of the Data Curation Process

Curation spans the entire data lifecycle – from birth to archival. Key phases include:

1. Discover and Acquire

Identify high-value data sources, both internal and external. Data is extracted and integrated into a processing pipeline.

2. Organize and Describe

Clean, validate and categorize data assets. Descriptive metadata is added to provide context.

3. Enrich and Maintain

Continuously enhance data quality through master data management. Append external data to add context.

4. Publish and Store

Data is made available in trusted repositories with access controls. Strict backups prevent loss.

5. Archive and Preserve

Data is retained in long-term archives. Assets remain findable and accessible indefinitely.

6. Exposure and Usage

Analytics, applications and users leverage curated data to derive insights and drive decision making.

This lifecycle enables continuous curation at scale. Next we‘ll explore some real-world examples.

Data Curation in Action

Here are a few examples of how organizations leverage curated data:

Financial Services

Banks curate customer data from disparate systems to gain a 360-degree view. This powers hyper-personalization and next-best recommendations.

eCommerce

Retailers curate product catalogs with rich attributes, images and text. This improves discovery and drives sales.

Manufacturing

Sensor data from machinery is curated with metadata like location, model and year. This enables predictive maintenance.

Healthcare

Patient records are de-identified and normalized in clinical data warehouses to improve care while ensuring privacy.

Government

Census data is curated over decades. Historical context enables policy analysis and social science research.

Media

Music and video streaming companies curate massive catalogs by enhancing metadata like genre, ratings and transcripts.

Transportation

Real-time traffic and transit data from sensors is curated to optimize routing and planning.

These examples illustrate the diverse applications of curated data across domains. Next let‘s examine some key business benefits.

Why Invest in Data Curation?

Based on my experience, focusing on curation provides significant business value:

360-Degree Customer View

Curated customer data creates a single source of truth to improve personalization, recommendations and satisfaction.

Higher Quality Data

Curation enhances completeness, validity, accuracy, consistency and timeliness of data for analytics.

Accelerated Analytics

Curated data eliminates time wasted hunting, cleaning and verifying. Analysis moves faster.

Improved Data Access

Metadata catalogs make it easy to find the right data. Common models increase usability.

Machine Learning Productivity

Clean, tagged training data accelerates development of unbiased AI models.

Regulatory Compliance

Curation activities like de-identification help meet privacy regulations like GDPR.

Future-Proofing

Metadata preserves context over long timeframes despite changes in technology.

Investing in curation creates a solid data foundation that enables data-driven innovation and agility.

Overcoming Key Data Curation Challenges

While essential, curating enterprise data brings significant challenges:

Disparate Sources

Data lives across operational systems, cloud apps, social feeds and more. Consolidating it is arduous.

Velocity and Scalability

The speed and volume of incoming data requires automation to scale curation processes.

Variety

Text documents, media, time series and other data types add complexity to curation flows.

Limited Context

Raw data often lacks the documentation required to properly interpret it.

Fluidity

One-time curation isn‘t enough – data requires ongoing stewardship as it changes.

Privacy

Personally identifiable data must be properly anonymized to mitigate compliance risks.

Cost

The ROI from curation must exceed the substantial costs involved in manual processes.

These aspects make enterprise-wide data curation daunting using traditional platforms and methods. Next we‘ll explore some cutting-edge solutions.

Modern Tools and Techniques for Data Curation

Thankfully, advanced technologies now enable automated, intelligent curation at scale:

Augmented Data Management

Platforms like Alation, Atlan and Soda combine cataloging, pipelines, governance and enriched metadata.

Machine Learning and NLP

Automatically tag, classify, match entities, extract facts and translate text into usable data.

Knowledge Graphs

Semantic graphs preserve connections between data, users, computations and context over time.

Master Data Management

Create golden records of core business entities like customers, products and suppliers.

Data Lakes and Warehouses

Repository architectures that incorporate principles of FAIR data – Findability, Accessibility, Interoperability and Reusability.

Data Virtualization

Tools that provide access to integrated data, without materializing computations.

Collaborative Curation

Enable domain experts to collectively enhance dataset accuracy and context.

Blockchain Transactions

Provide immutable ledger for recording transactions and metadata.

With the right strategy, organizations can leverage these technologies to curate enterprise data at the speed and scale required in the modern data-driven business landscape.

Key Principles for Data Curation Success

Based on proven practices I‘ve observed, here are my recommendations for approaching enterprise data curation:

  • Start with business priorities – identify high-value use cases to focus curation efforts.

  • Take inventory – cataloging what data exists is an important first step before curating.

  • Assess gaps – determine what additional data is needed and how to acquire it.

  • Simplify data stores – deprecate redundant, trivial or outdated data sources.

  • Structure iteratively – progressively refine organization as understanding evolves.

  • Automate aggressively – reduce reliance on manual processes which don‘t scale.

  • Begin small, demonstrate value – pick targeted projects to validate benefits and build support.

  • Monitor usage – analyze consumption patterns to optimize curation activities.

  • Don‘t go it alone – leverage internal and external experts to create a robust data curation capability.

With a strategic approach, organizations can transform dispersed, disorganized data into highly curated assets that drive tremendous business value.

The Data-Driven Future is Curation

As data volumes continue exploding exponentially, manual curation methods will become infeasible. Businesses must embrace automated, scalable curation to remain competitive.

With the right strategy and modern tooling, any organization can curate their enterprise data assets into a high-quality, FAIR resource delivering actionable insights and powering innovation.

Curation is no longer optional – it‘s a prerequisite for survival and success in the data-driven future.

Tags: