What is Self Service Analytics?
In the age of Big Data, business users working outside the realm of the data specialist need to access and finesse data that is aggregated from disparate sources. Not that long ago, almost all data analytics functions were performed by skilled data scientists and data scientists. Regular business users usually only accessed data via BI products and automated dashboards.
Pros and Cons: Business Users Vs. Data Professionals
Self service analytics supports have a valid reason to advocate for broader analytics access: the explosion of data and data sources in recent years has led to higher demand for skilled analysts and data scientists. While these fields are correspondingly growing in popularity, there are still not enough bodies to fill the heightened demand. In addition, adding data analysts is expensive and can add another step between raw data and time to insight for business decision makers -- especially for simpler queries.
Self-service detractors naturally point to the risk of misinformed analysis by users who are not trained in data science. This could could lead to poor decision making or even serious business strategy errors, especially if an organization has weak or non-existent data governance policies.
Regardless of one's position, self service analytics is a reality in today's data-driven business environment. Therefore, having the right tools and platforms in place can greatly increase both the performance and business value that a self-service analytics approach brings to democratizing data and data access.
Enabling Self Service Analytics with Snowflake
Snowflake’s built-for-the-cloud data warehouse provides a scalable, zero management, data warehouse platform to power data-as-a-service. Snowflake's groundbreaking technology eliminates complexity and simplifies the data pipeline, making self-service access to data and insights possible for any data pro -- data scientists and business users alike.
Snowflake delivers higher performance over other data warehouses even as data volume and concurrent users increase. All users can combine complex data from disparate sources. speeding and simplifying data access and analysis. something impossible with traditional on-premise and cloud data warehouses.