4 Ways to Maximize the Value of Your Data Warehouse and Data Lake with Metadata Management

Last Published: Aug 05, 2021 |
Charles Mathew
Charles Mathew

Modernizing and consolidating data warehouses and data lakes

Businesses today are rapidly modernizing and consolidating data warehouses and data lakes in the cloud. The transformation over the past few years has been conspicuous. With the exponential growth in the amount of data being generated and with businesses and functions demanding quick access to that data, enterprises need to scale up their analytical capability. The capacity that cloud computing brings – along with elasticity, performance, and scale – are accelerating this migration. What’s less well known is the role that metadata management plays in ensuring that you’re successful and that your cloud investments return maximum value.

How Cloud Data Management Has Evolved

Sitting in an audience of about thirty decision makers primarily drawn from BFSI and manufacturing almost 10 years ago, I was witness to an engrossing conversation on the benefits of cloud computing. Back then, questions on cloud were primarily focused on security and the long-term cost benefits, especially in an environment where enterprises nearly owned all their infrastructure. I distinctly remember a cloud SME trying to convince a skeptical client with an analogy drawn from his own industry, banking. He asked the client if their depositors’ money was safe in a bank like theirs or in their homes, trying to draw a parallel that data is safe with the experts than in their own environments.

Fast forward to 2021, people don’t need convincing on the benefits of cloud. In a recent article by Deloitte, 55% of respondents called out data modernization as a reason for moving to cloud.

Enterprises are also seeing the rise of the citizen analysts. Business functions are striving to become less dependent on IT for their analytical needs. Self-service analytics has been driving this change. It is predicted that a staggering 80% of organizations will move from being IT centric to a self-service model by 2024. In such a scenario where various departments and functions demand greater access to data, organizations must be prepared to ensure this data democratization is leveraging data that is trusted and governed. Cloud data warehouses and lakes with their ability to process vast amount of information and scale up quickly have become pivotal to facilitating this and powering analytics and reporting systems in the enterprise.

Questions to Ask About Your Cloud Investments

As businesses transform and consolidate on the cloud, here are 4 key considerations to know how well you are leveraging your cloud investments.

  1. In a world of multi-cloud architectures do you have end-to-end visibility of data across clouds?
  2. Are your stakeholders being empowered with trusted data?
  3. Are you able to facilitate collaboration among users to extract value from your data?
  4. How is AI-powered automation leveraged to scale and understand data that is growing rapidly across environments?

Metadata holds the answer to the above and is foundational to understanding your data. After all, doesn’t it all start with understanding your own data? An intelligent metadata management solution like an AI powered data catalog helps quicken that understanding at enterprise scale. It brings context, enables discovery and provides an understanding of data across heterogeneous and multi-cloud environments.

Another key benefit that metadata management offers is by way of its ability to equip business stakeholders with data that is trusted and governed. As organizations enter new businesses and engage customers through new business models, the role of data that is governed and trusted cannot be emphasized enough. With the understanding of the data that metadata provides, data governance can ensure that enterprises are able to deliver the right data to the right users at the right time.   

5 steps in the lifecycle of building and operating data warehouses and lakes for cloud analytics

How Metadata Management Helps Your Data Warehouse and Data Lake Initiatives Succeed

How does metadata management play a pivotal role in the success of your cloud analytics? With metadata management you are able to:

1.  Address the needs of data management professionals across the entire lifecycle of building and operating a cloud data warehouse and lake.  Metadata management provides the foundation for enabling analytics on data that is governed, trusted and compliant. There are 5 steps in the lifecycle of building and operating data warehouses and lakes for cloud analytics: Build, Operate, Control, Consume and Measure. Each stage has its own unique challenge for the relevant data professional. Architects need to identify data, understand its usage and its impact on migration while data engineers look to manage constant changes to the data warehouse and lake, associate right context and prevent duplication. The privacy steward wants to ensure data compliance and adherence to privacy policies while data scientists and citizen analysts need quick and easy access the right data to do their jobs. Finally, a data steward needs to know how his data is being used and understand how it is performing and be able to measure its value.

2.  Gain visibility into all data across heterogeneous environments. Metadata-driven intelligence helps identify data stored across the organization’s systems to move the needed data to a cloud data warehouse or lake. With data being proliferated across multiple systems, you need a tool whose ability to scan is not limited to a few environments and that provides comprehensive visibility

3.  Facilitate collaboration and foster collective learning. In an enterprise, data has multiple consumers. An ideal solution can provide collaboration and social curation capabilities that enables data owners and subject matter experts to certify data assets and help identify data sets to be migrated. It also leverages the collective learning across the board by way of ratings, certifications, and Q&A to continuously improve contextual information, data literacy, and the usability and findability of datasets.

4. Achieve enterprise scale with the help of AI/ML. With enterprise data growing exponentially, automation is critical to understand vast amounts of data. Without AI/ML it is impossible to go through the data landscape, track lineage, assign context, find similarity, associate business terms, label the data, etc. Manual approaches simply won’t work.

I would like to encourage you to read our new eBook, “Unleash the Power of Cloud Analytics with Metadata Management,” that provides a comprehensive perspective on how you can maximize the value from your cloud data warehouse and data lake investments with metadata-driven intelligent data management, while fostering a data driven culture within the organization.

I hope to cross paths with my erstwhile skeptical client, to not convince him about cloud but to hear about the strides he would have taken by now on cloud!


First Published: Mar 29, 2021