7 Questions to Ask When Evaluating Business Intelligence Software
The Quest to Become Data-Driven
The stakes to transform into a data-driven business have never been higher. From the sales floor to the C-Suite, every department wants to use data to its advantage. 88% of executives say  they feel the urgency to invest in big data initiatives — and it makes sense. Companies that embrace analytics and business intelligence (A&BI) continually outperform those that don’t. Data-driven companies are 23 times more likely to acquire customers .
Companies that embrace analytics and business intelligence continually outperform those that don’t.
The shift to embrace data has led to record A&BI spending. 55% of companies report investing $50M or more into big data initiatives . No matter how large your BI budget is, choosing the right software is crucial to your success. But let’s face it: The business intelligence software landscape is gigantic. With hundreds of vendors out there, knowing who to evaluate and what to look for in a solution can feel overwhelming.
If you’re starting to evaluate software, you may be unsure where to start. What’s the difference between all these BI software vendors? Which capabilities and features are important to have? How should you approach the process? What’s changed since your last BI purchase?
In the following pages we explore the top 7 questions every company should ask during the evaluation process — and break down some of the top BI trends and features to consider along the way.
A Brief History of Analytics and Business Intelligence Software
If you’re new to the world of business intelligence, or revisiting after a hiatus, it’s helpful to have a solid background of where we’ve been — and where we’re headed.
Dashboards reigned supreme
Business intelligence has historically focused on tracking and curating data, usually in the form of dashboards or reports. Early BI tools were designed to surface high-level insights in dashboards on a scheduled basis, but lacked real-time reporting capabilities.
These dashboard-focused tools usually answered pre-determined questions executives raised in advance. If questions changed, or additional information was required to make decisions, the dashboards needed modification. Only technical people with knowledge of SQL or other coding languages could build them, which was handled by a member of the data or IT team — effectively making them the data gatekeepers.
On-prem constraints stunted insights
In the past, implementing a business intelligence solution meant building an on-premise data center and hiring an army of IT talent to manage it. Because data storage and compute was relatively expensive compared to today, data analysis was limited.
Outdated data was often deleted to save on massive storage costs, preventing long-term historical analysis. Computing and resource constraints, combined with business teams’ inability to dig into the data behind these dashboards and reports, limited ad hoc analyses to situations when it was absolutely necessary — leaving many questions unanswered. And without the ability to ask those questions on the fly, mission critical insights remained hidden.
The datasphere looks very different today. The volume, variety, and velocity of data produced is unparalleled. Thanks to the internet, mobile devices, and the Internet of Things (IoT), more data gets created and collected than ever before. In the time it takes to walk to the water cooler and back, more than 3.8 million queries are submitted to Google and close to $1M is spent online .
We now live in a data economy
The velocity and volume of data shows no signs of slowing down. IDC expects the global datasphere to surpass 175 zetabytes by 2025 . The pace data and customer needs change in today’s business environment requires the right tools to keep up — tools that can provide all employees with real-time access to relevant data and insights.
amount of digital data generated by 2025
Annual size of the global datasphere
This new data economy is powered by the modern cloud data warehouse (CDW). Modern CDWs collect data from any source and scale elastically to support nearly infinite users and ad hoc analytic workloads. This includes support for structured and unstructured data such as JSON. Storage and compute costs have also come way down, meaning historical data doesn’t have to be tossed, and technology can meet — and even exceed — the demand for insights.
It’s no surprise that analysts expect 83% of enterprise analytical workloads to be cloud-based this year. But despite the wealth of data available and many opportunities to harness it to drive decisions, 73% of companies fail to put it to use . Meanwhile, decision makers can’t access this data or uncover insights in a timely or efficient manner.
So where do you go from here? You need a BI tool built to thrive in the new data economy. But how do you know which one to choose?
of companies fail to put data to use
7 Must-Ask Questions for Evaluating BI Tools
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