Modernizing Your Big Data Architecture: Key Considerations

July 20, 2016 Kelly Hopkins Schupp

Here’s a look into the future of the enterprise data ecosystem: the modern data architecture will have a managed data lake at its core. It will be fed by various structured data sources, real-time data streams, such as from the Internet of Things, and unstructured data like emails, videos, photos, audio files, presentations and more.

All of the data will be stored in this centralized repository—whether in the cloud or on premises or a hybrid— where it can be transformed, cleansed and manipulated by data scientists and business users. Then, prepared datasets can be fed back into a traditional enterprise data warehouse for business intelligence analysis, or to other visualization tools for data science, data discovery, analytics, predictive modeling and reporting.

Data management, data management, data management

Some say that a data lake strategy doesn’t deliver sufficient value for the time and money spent. Gartner has stated that a major obstacle is that enterprises don’t realize the level of skill required to adequately leverage the data lake concept. However, we emphasize that the key to realizing value from a data lake is appropriate data management and governance that provides the essential data visibility, reliability, security and privacy that can then allow broader access to data by multiple users (with permissions).

3 simple phases for data lake implementation

We simplify a successful data lake implementation by dividing it into three distinct phases:

  1. Enable the lake: Build the lake and determine how you will ingest, organize and catalog your data.
  2. Govern the data: This involves data quality rules, automation workflows, as well as data security.
  3. Engage the business: Deliver the data to more end users, including business end users, to maximize its value—“democratizing” access to your data. This involves implementing tools that make data discovery, enrichment and provisioning very intuitive for less-technically savvy business users.


Don’t go it alone: Find the right tools and expertise

Transitioning to a modern data architecture to enable advanced analytics and data science is a complicated process. That’s why we provide the Zaloni turnkey data lake solution for our customers, which leverages our data lake management software platforms and tools, our knowledge about the quickly evolving big data ecosystem, and the specialized technical skills we bring to the project.

Most importantly, we help ensure that enterprises truly derive value from their data lakes, helping to implement specific business use cases and making big data management and analytics more efficient and cost-effective. 

About the Author

Kelly  Hopkins Schupp

Kelly Schupp is Vice President of Marketing for Zaloni. Kelly has 20 years of experience in the enterprise software and technology industry. She has held a variety of global marketing leadership roles, and previously worked at IBM, Micromuse and Porter Novelli. Kelly serves as Zaloni’s brand steward and is deeply passionate about the impact of data-driven marketing.

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