How Self-Service Has Rewritten Data Management for Analytics

I joined Informatica just a few weeks ago to lead marketing for our big data management portfolio. And there is no better way to dive into the Informatica community than to start blogging. So that’s what I’m going to do! I hope you’ll join me for the ride as we discuss the future of big data management for analytics.

While Informatica is new to me as an employer, our marketplace is not new to me. Cloud, big data, and data security have been part of my DNA throughout my almost 20-year career in the software industry. I’ve worked with hundreds of customers around the world to understand their business goals so that I can develop the best solutions for their needs. The one common link across all these goals is that it’s data—intelligent data—that fuels transformative business decisions. And giving business analysts access to that data—quickly—so they can speedily discover new insights using self-service analytics is your biggest competitive differentiator.

Data isn’t a static entity—something that you collect, dump somewhere, and then shake awake when you want to use it. The way the data is managed for delivery and analytics should not only keep up with business trends, it should also accurately predict how the business will want to use it. And trusted data should be ready for any and all use cases.

The journey of data management for analytics

Joining Informatica gives me a chance to reflect on the progress of data management for analytics. It began with a journey to support such precise needs as financial reporting and forecasting using structured data in data warehouses. Later, businesses wanted to experiment with structured, semi-structured, and unstructured big data for predictive analytics using data lakes. Today, businesses accelerate digital transformation initiatives using active data marketplaces.

Self-service analytics is one of the biggest drivers of change in data management for analytics because it shifts the role of data management from solely IT’s responsibility to a joint IT and business responsibility. And when data management is fueled by AI and machine learning for self-service analytics, users crush the time it takes to discover transformative insights.

What has happened to the data lake?

But with progress comes limitations. Take for example, data quality for analytics. Some data lakes have become such dumping grounds for all kinds of data, that some users feel they better trust the predictions they get from feeding $1 into a fortune teller machine than they do of the predictions derived from data in a data swamp. While you wouldn’t use big data from a data lake for financial reporting where you need to be penny-perfect, you still want to trust the data. And this is where data governance comes in.

Findability and accessibility

The purpose of a data lake is so that your data scientists and data analysts can quickly find and access the data they need for experimentation and to test hypotheses. When you govern the data, your users can more quickly and successfully discover and access trusted data. And because you’re protecting the data, you can ensure data privacy for compliance.

But why stop there? What if there could be a storefront for trusted high-quality data that curates structured, unstructured, and semi-structured big data for fast analysis? Enter the data marketplace—a storefront for big data that’s distributed across your whole enterprise, regardless of whether the data is in data warehouses or data lakes across multiple clouds, on-premises, or hybrid environments.

Data marketplaces puts self-service analytics on steroids because users have the power to find, access, and prepare enterprise data on their terms, without having to wait for IT.

Better collaboration for analytics

Data marketplaces combine a standardized and industrialized process for curating raw data assets into trusted information. And because they foster an active and collaborative approach to analytics insights, data consumers can quickly and easily shop for the relevant and actionable data they need. At the heart of a data marketplace is the data catalog, which makes all trusted enterprise data discoverable and accessible for any purpose.

I’ll be discussing data management trends for analytics and more in this blog, so please stay tuned. Next, I’ll discuss if data lakes have demonstrated business value, and then we’ll dive a bit more into data marketplaces.

Until then, check out our white paper “The CDO’s Guide to Intelligent Data Lake Management,” which gives nine principles for managing data lakes.