How many BI tools does an organization really need? - Pyramid Analytics
Organizations typically use multiple BI tools—often four or more across departments—due to the belief that specialized analytics are needed per function, but this fragmented approach can undermine data trust and decision-making consistency, whereas a single decision intelligence platform that integrates data preparation, analytics, and data science can provide comprehensive BI capabilities and establish a unified "single source of truth."
On average, organizations own four or more different analytics tools. Is this necessary?
According to Forrester, “25% of organizations use 10 or more BI platforms, 61% of organizations use four or more, and 86% of organizations use two or more.” The main reason for all these different tools is a widely held belief that different departments and functions within an organization require their own specialized analytics tools. This belief is so ingrained that it has become an accepted “best practice” to take a department-by-department approach to analytics.
However, deploying so many different analytics solutions throughout an organization can erode people’s trust in data. There’s a concern that decisions are being made with incomplete data. For example, finance, marketing, and sales may each work with their own data, raising the question of whether everyone is making decisions based on the same underlying truths. This challenges the goal of establishing a “single source of truth.”
Beyond departments, organizations also adopt separate tools for data preparation, data science, and reporting, making the situation even more complex.
The truth is… you can get complete BI capabilities from a single decision intelligence platform.
Recent insights reveal that it is possible to meet all analytical needs in a single platform. While a departmentalized, best-of-breed approach is effective in some areas of business, analytics is an exception. Trust in data erodes when there are several data prep tools, standalone data science workbenches, and numerous analytics and reporting tools for different people in different departments.
Decision intelligence offers complete capabilities in one platform. A decision intelligence platform is purpose-built to combine all three capabilities—data preparation, business analytics, and data science—into a single, unified solution. With such a platform, organizations do not need all those separate tools. Decision intelligence addresses the shortfalls of current fragmented approaches that frustrate leaders responsible for data and analytics strategies and cause organizations to overspend on the implementation and upkeep of too many BI tools.
Rather than requiring a department-by-department approach, a decision intelligence platform can tap into data wherever it lives and bring tailored analytics and reporting straight to the point where decisions are made. This enables the establishment of a single source of truth.
With decision intelligence, organizations can combine data prep, business analytics, and data science into a single unified platform that allows for sophisticated analytics and data science without writing a line of code, while maintaining governance and security.
A no-code, point-and-click experience ensures that everyone, from data novices to data scientists, can get value from the data analytics experience.
The widely held beliefs about BI and analytics are addressed in the "Mythbusting Business Intelligence vs. Decision Intelligence" guidebook, which breaks down myths into categories related to data, people, and analytics. The guide shows how, with a new approach, organizations can move past limiting beliefs and take analytics to the next level through decision intelligence.