In today’s hyper-competitive, data-driven environment, companies need to collect relevant information and make strategic decisions as quickly as possible. This demand for agility requires a drastic reduction in the time between data collection and the generation of information directly useful for decision making. Traditional analytics solutions are not designed to deliver results so quickly. These delays often force business decision-makers to wait for additional IT resources to be deployed for analytics projects, which compromises the agility and performance of organizations.
A new cloud-based data analytics infrastructure, known across the Atlantic as the Modern Data Stack, keeps organizations competitive by automating data integration and providing next-generation analytics capabilities. Until recently, these capabilities have been out of reach for many companies. But as more and more companies adopt this model, let’s review the differences between traditional and next-generation data analytics infrastructures.
Limitations of traditional solutions
What factors prevent traditional business intelligence and data analytics functions from meeting the new need for speed and agility? A reliance on traditional technology stacks, often managed by disparate, siloed teams whose mission is to build data pipelines and administer on-premises storage and processing infrastructures. These teams must spend a great deal of time and effort on hand-coding, SQL-based ETL design and maintenance, building semantic layers, and developing complex star schemas.
All these tasks delay the generation of business intelligence results, which appear on the far right of the diagram below.
In short, these teams waste valuable time managing a traditional data integration infrastructure, instead of generating strategic insights from the data.
In addition to its human cost, a traditional data infrastructure is:
- Difficult to obtain
- Complex to use
- Expensive to purchase and maintain
- Time-consuming to install and configure (a process that often takes several months).
The biggest weakness of traditional data infrastructures is their inability to adapt to new business needs. New reporting needs are constantly emerging; data source schemas and APIs change frequently; data sources are added, modified, and deleted regularly; and decision-makers regularly query stored data to enable the business to position itself on key topics. These issues can interrupt development cycles that often span 12 to 18 months.
The benefits of next-generation data analytics: The Modern Data Stack
By adopting a Modern Data Stack, analytics teams can quickly provide decision makers with the data and insights they need, and with shorter time-to-value, companies can better respond to rapid market changes.
A Modern Data Stack is based on a data warehouse or data lake environment in the cloud. It includes cloud-based tools that support the development of data pipelines, as well as reporting and visualization of analytics.
The diagram below provides a good example of a Modern Data Stack, which brings together data sources, data connectors, a data warehouse, and a business intelligence tool.
This principle allows analytics teams – engineers, analysts, and data architects, to focus on strategic projects that create value, with cloud services taking care of all the basic data engineering tasks such as pipeline maintenance and schema design.
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