Imagine trying to manage a library where new books appear on the shelves every second, in languages you’ve never seen before and some even vanish or change their content without warning. That’s a similar challenge businesses face when dealing with big data governance. Approximately 402.74 million terabytes (or 0.4 zettabytes) of data are created every day!
It’s a world where information flows like a mighty river, and companies are scrambling to build dams and channels to harness its power without getting swept away.
Data governance is all about organizing how people, processes, and technology work together to handle data as a valuable asset. It sets up rules for making decisions about data and who’s in charge of what. While this approach has worked well for smaller amounts of structured data, it often falls short when dealing with the complexities of big data.
As companies deal with increasing amounts of data that come in all shapes and sizes and move at lightning speed, they’re finding that their old ways of managing it don’t cut it anymore. Let’s explore five key reasons why businesses struggle to keep up with big data governance and some ways to tackle these challenges.
1. Too much data, too little time
Old methods just don’t work when dealing with data measured in petabytes or exabytes. It’s tough to track where data came from, what it means, and how it’s used when there’s so much of it.
As data grows, so does the cost of storing and processing it for governance purposes. This can put a strain on IT budgets and make it hard to keep an eye on everything. Plus, finding the correct information becomes increasingly time-consuming and complex.
To tackle this, companies should:
- Look into scalable systems for managing metadata and tools that automatically catalog data.
- Use sampling techniques and distributed processing for quality checks to help manage the scale more effectively.
- Consider graph-based tools to track data lineage to improve how to keep tabs on large-scale relationships in data.
2. Real-time data processing demands
Many businesses now work with data streams that must be processed and analyzed immediately. This speed makes it challenging to apply traditional governance processes without slowing things down.
Making decisions based on streaming data requires instant checks and validations. However, this need for speed can make it hard to be thorough. Striking a balance between data governance requirements and the demand for quick data processing is a constant challenge.
To address this, businesses can:
- Look into technologies that can process data streams with built-in governance features.
- Develop quick, rule-based checks that can be applied in real time to help maintain data quality without causing delays.
- Use a tiered approach to governance, with different levels of checks based on how critical the data is, to help balance speed and thoroughness.
3.Variety of big data types
Big data includes structured data (like neat spreadsheets), semi-structured data, and unstructured data (like social media posts, audio, video, and online reviews). This variety makes it hard to apply consistent governance policies across all data.
Traditional ways of organizing data often don’t work for unstructured or semi-structured data. Combining and governing data from many different sources, each with its own quality and format, is incredibly complex.
To handle this, organizations should:
- Use flexible ways of modeling data, like data virtualization.
- Deploy AI and machine learning to automatically classify and tag data and help manage diverse data types more effectively.
- Set up a data lake with strong metadata management to provide a unified approach to handling varied data formats while maintaining governance controls.
4. Distributed and decentralized systems
Big data systems are often spread across multiple locations or cloud environments. This distribution can lead to inconsistencies in how policies are enforced and data is managed.
When data is spread across different geographic locations, it can create legal and regulatory headaches, especially for global organizations dealing with different rules in different countries. Keeping a single source of truth for metadata and governance policies in this distributed environment is technically challenging.
To overcome these hurdles, businesses can:
- Implement a big data governance model that works at local and central levels.
- Use technologies like distributed ledgers to maintain consistent governance policies and help ensure uniformity across different locations.
- Set governance policies that are aware of geographic differences and controls for data residency and can address compliance concerns in multi-national operations.
5. Evolving technologies and architectures
Technologies and architectures like Hadoop, Spark, Snowflake, and data lakes are constantly evolving. This rapid change makes it hard to establish stable, long-term data governance frameworks. With each technological advancement, new challenges pop up, like figuring out how to govern data in edge computing or Internet of Things scenarios.
The fast pace of change in big data technologies can also lead to skills gaps in governance teams. This can result in subpar data governance implementations and increased risk. Continuous learning is necessary to keep governance practices in line with technological advancements.
To address this challenge, organizations should:
- Adopt master data management (MDM) and data governance solutions that can adapt and change.
- Invest in ongoing learning and training programs for governance teams.
- Develop a data governance strategy that anticipates technological changes to help organizations remain flexible.
Getting big data governance right
Balancing data governance and big data requirements is tricky. Businesses face a perfect storm of challenges: massive amounts of data, the need for split-second processing, a mix of data types, spread-out systems, and always-evolving tech. However, by understanding these hurdles and finding smart ways to overcome them, companies can improve data governance and big data management.
To get it right, you need to look at the big picture – considering your people, processes, and tech. It takes teamwork across different departments, speaking the same language, and agreeing on common standards. By tackling these challenges head-on, businesses can make the most of their big data while staying on the right side of the rules and making decisions they can trust.
Need help mastering and governing your big data? Speak to one of our experts today.