Lakes, Hubs, and Governance: Turning Technology into Insight

Your company has invested in a cost-effective data hub system or adopted data lake-centric architecture. Fantastic! But… Do your data scientists have the autonomy to extract data from the hub or lake, and do they know what they need to compare sales markets for older products? Meanwhile, your analysts are eager to develop a predictive model for the new product launch using historical data. They find two tables that seem to contain sales data—but they’re not sure. Do they know who to contact quickly and easily for clarification and more information?

It may not seem like it, but often the answer is no, and it happens more than it should.

The advantages of a data lake or data hub-centric architecture are undeniable:

• Flexible and scalable storage;

• Near real-time access to data;

• Complete historical data retention;

• Data democratisation;

• Above all, it provides an excellent foundation for data science and machine learning, enabling access to raw data without the need for extensive preprocessing.

  
  

However, organisations that adopt this architecture with the ambition of becoming data-driven, which may have been a key factor in choosing data lake or hub technologies, often face serious challenges if there’s no control over what happens to the data:

• Data quality can suffer, turning lakes into data swamps filled with disorganised, duplicated, or inconsistent data.

• Security may be compromised if access isn’t properly managed, putting regulatory compliance at risk.

• A lack of data description and classification makes it difficult for users to recognise and understand the data, which in turn undermines the insights derived from it.

  
  

Has your company invested in top-of-the-class data lake technologies but isn’t seeing the expected return on information value? One or several of these issues may be silently limiting the value of your data assets.

Want to accelerate your ROI? The solution may lie in a systematic data governance program that clearly defines data roles, processes, and rules. Establishing responsibilities and ownership will streamline access control (who accesses what) and promote data description and classification, helping users understand the data they’re working with and improving the efficiency of data-related processes.

This will lead to:

• Higher data quality;

• Stronger security and privacy;

• Better compliance;

• Enhanced team collaboration;

• More responsible data usage;

• And, cherry on top, better analytical outcomes, enabling strategic decision-making based on trustworthy data.

  
  

Your company doesn’t need to rethink its entire tech strategy—what it needs is to strategically govern its data and unlock its full potential safely and efficiently.

Without data governance, risk is invisible until it becomes a problem. Let’s explore together how data governance can be the GPS of your data and a powerful lever for your business.

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by Rita Pinto

@ Passio Consulting