The 5 Critical Big Data Architecture Components

Big Data Architecture Components

The business world is increasingly data-driven, with more organizations realizing the need to make a concerted investment in data management so they can better understand their customers and engage more effectively to drive increased revenue and corporate longevity. But while organizations large and small understand the need for advanced data management functionality, few really fathom the critical components required for a truly modern data architecture.

Understanding these components is necessary for long-term success with data-driven marketing because the alternative is a data management solution that fails to achieve desired outcomes. As we see it here at Redpoint, a modern data architecture has five critical components:

  • Flexibility at scale. The volume, variety, and velocity of customer data is only going to increase with time. As volume balloons and velocity accelerates, your data management solution must be able to adapt and continue to function the way it was designed. If its failover rate increases as data volumes increase, as can happen in some cases, then the solution is practically worthless for long-term use. This is one of the core challenges with some technologies that have been traditionally used for managing big data, as they weren’t built for ingesting billions of records and simply can’t scale.
  • Support for parallel and distributed processing. Time to insight is a critical metric for data management success, and parallel, as well as distributed, processing of business data dramatically reduces the length of time it takes to go from data ingestion to insight. Linear processing may be acceptable for smaller jobs, or at organizations with smaller databases, but the benefits of being able to process various types of data in parallel and in a distributed fashion becomes more important as data volumes grow.
  • Democratized data access. IT has traditionally owned business data, which in an earlier age was logical because the pace of business change was much slower. This concept of data ownership is out of date, however, especially because data analysts increasingly need to access data on a daily, and sometimes hourly, basis for analysis. Because of this, a modern data architecture, via a no-code environment, allows everyone—no matter the job role—to access business data on their own schedule.
  • Easy to use without specialized training. Data processing and analytics solutions historically have required specialized training to use well, including understanding SQL and learning a statistical language such as R or SAS. A modern data architecture eliminates these requirements and should allow you to query the data and derive insight without having to learn a coding language or take a lengthy training course on the solution’s functionality.
  • Ability to handle all data types. The variety of data types is constantly increasing, including structured, semi-structured, and unstructured data—all of which must flow through a data management solution. A modern data architecture must be able to handle all these different data types, generally through a data lake or data warehouse, and be adaptable enough to wrangle all current and future types of business data to boot.

In the data-driven business world, it’s absolutely critical that your organization has the right solution in place. A modern data architecture that contains the five critical components above is adaptable, future-focused, and will be useful for years to come. You owe it to your organization to take the time and think critically about the data management solution you either have in place now or plan to put in place and determine whether it has the right combination of factors for your long-term success.

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