In today’s hyper-competitive, data-rich environment, enterprises need to collect relevant information and make strategic decisions extremely quickly. That kind of agility requires reducing the cycle time from gathering data to deriving insights. Legacy analytics processes, however, aren’t designed to deliver results so fast. Delays often leave enterprise stakeholders waiting on IT to deploy additional resources for analytics projects, which compromises business agility and puts innovation on hold.

Cloud-centric analytics infrastructure – what we call the modern data stack – keeps enterprises competitive by automating analytics-related data engineering and delivering advanced analytics capabilities. Until recently, these capabilities were out of reach for many organizations. With more and more enterprises embracing the modern data stack, let’s take a look at the differences between traditional and modern analytics infrastructures.

Limitations of legacy analytics

What prevents the traditional business intelligence and data analytics function from responding to calls for more speed and agility? A reliance on legacy technology stacks, often managed by siloed, disparate teams tasked with building data pipelines and managing on-premise storage and compute requirements. These teams need to dedicate considerable effort to manual coding, SQL-based ETL design and maintenance, building semantic layers, and designing complex star and snowflake schemas.

All of this delays the desired BI outcomes, which appear on the far right in the diagram below:

In short, data teams are spending valuable time and resources managing legacy data integration infrastructure rather than serving up business insights from critical data.

In addition to the human cost it imposes, legacy data infrastructure is:

• Difficult to procure
• Challenging to use
• Costly to procure and maintain
• Time-intensive to build (often a months-long project)

The biggest weakness of legacy data infrastructure is a lack of adaptability to change, which runs counter to the demands of the modern enterprise. New reporting needs are constantly emerging; data source schemas and APIs frequently change; source data systems are added, altered and deleted on a regular basis; and data-savvy managers ask new questions of the data that the business must be able to answer. These issues can interrupt enterprise development cycles that often span 12–18 months.

Advantages of a modern data stack

By embracing a modern data stack, data teams can rapidly provide business decision-makers with the data and insights they need — and with time to insight optimized, enterprises can better respond to the rapidly changing needs of the market.

A modern data stack is centered around a cloud data warehouse or data lake environment. It includes cloud-based tools that support data pipeline development and analytics reporting and visualization.

The following diagram shows a typical modern data stack with data sources, data connectors, a data warehouse and a BI tool.

This paradigm allows data engineers, data analysts and data architects to focus on mission-critical projects that create business value, while cloud services handle basic data engineering tasks like pipeline maintenance and schema design. Fivetran, for example, provides prebuilt, zero-maintenance data connectors for over 150 data sources, including databases, SaaS applications, files and APIs. Data arrives in the target destination query-ready, enabling shorter time to insight for data analytics.

Learn how F5 Networks adopted a modern data stack and optimized its time to value.

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