Business intelligence (BI) isn't slowing down: Gartner's 2019 Hype Cycle (available to clients) shows that BI/analytics is the top tech investment that most business leaders make. But when it comes to business value, there is still much work to do: In Gartner's 2018 Marketing Data and Analytics survey, 66% of respondents said they're not using data as effectively as they'd like to.
GetApp's own research shows that small and midsize businesses (SMBs) share the same problem. Perhaps that's why they don't invest in BI and analytics software at the same rates as their enterprise-level peers.
When we surveyed nearly 500 leaders at businesses with 500 or less employees earlier this year, we found that one in five (22%) reported using BI software to manage data. By contrast, more than double that amount (45%) said they use Microsoft Excel to manage data that they use to make decisions.
Confusion about BI's value doesn't help: A Google search for "what is business intelligence" yielded 779,000,000 results at the time of publication. So, we've answered four common questions about BI to help explain how your business can use it.
Business intelligence involves using data to make business decisions. It's an umbrella term that references a range of tasks, from data mining to predictive analytics. It includes tools, applications, and best practices used to make decisions based on data.
Likewise, business intelligence software is a broad category. At its core, BI software helps business leaders use data to track key performance indicators (KPIs). So, the BI tool(s) of your choice should be able to collect and analyze data from a range of sources.
Your goal of using BI software should be to view your KPIs through the lenses of historical analysis and future forecasting. If software allows you to share data analysis with your colleagues via dashboards and reports, you can use it for business intelligence.
Learn more: See which core features BI software should have
Business intelligence benefits businesses of all sizes. BI has historically been something you'd see more often in enterprise-sized companies. But this wasn't because BI doesn't help small businesses; it had more to do with SMBs lacking the resources (time, money, employees, etc.) to support and sustain BI initiatives.
There's a reason why data scientists command such high salaries: Managing data isn't cheap or easy. In the not-too-distant past, big companies were more likely to have the software and data storage needed to invest in BI.
That put them in a stronger position to hire employees (i.e., data scientists) who could manage these technologies full-time.
But in more recent years, cloud computing has made business intelligence more affordable for small companies. It lacks the licensing fee of on-premise tools, takes less time to deploy, is more mobile-friendly, and is updated with new features more often.
Using BI software with dashboards and analytics can give smaller businesses the insights they need to scale in less time. And even if they can't afford data scientists, small business leaders can train their team leads to make data-based decisions using the right tools.
You can apply BI tools, applications, and best practices to data in all areas of your business, regardless of which industry you're in.
Let's say you want to convert more sales leads in less time. You'll need to start by learning how long it takes the average sales lead to move through your funnel.
Then, to make well-informed choices, you would need a range of data about customer interactions per lead, from emails and phone calls to social media messages.
Most customer relationship management (CRM) software solutions track these data points for sales and customer service teams; BI software helps you take it to the next step.
Once you've found and accessed all the data points you'll need, you can export them all into BI software. The first step here is to clean your data, a crucial process that "purifies" it for use in analytics.
BI software has a host of analytics features to analyze your KPIs in more detail. These range from real-time data on the volume of leads you're nurturing to tracking historical data on where they're most likely to drop out of the funnel. Then, you can plug those data points into graphs and charts to visualize them for stakeholders.
By reviewing historical data about sales leads, businesses can build more accurate forecasts. This sets the right expectations for stakeholders and managers alike. It also allows leaders to assess past patterns (such as time to close a sale or seasonal drops in sales), then adjust their strategies to get better outcomes.
No. "Big data" references large amounts of data that are too big for traditional data processing tools. It falls under the broader business intelligence umbrella.
Managing big data demands a range of tools, resources, and processes. That's because the amount of data created each day presents a host of challenges for business leaders. These issues range from the type of data created (such as structured vs. unstructured) to not having the right tools, processes, and infrastructure in place for big data management.
As one example, research from Accenture recently found that most IT professionals surveyed don't believe their enterprise networks can support big data deployments. When asked why that's the case, 45% cited "demands for bandwidth, performance, etc. outpacing the ability to deliver" as a key culprit.
Business intelligence helps you manage your data and control its quality. This is important since your data's quality can decrease as its volume increases. Without the right tools and team members to clean, analyze, visualize, and report on that data, you don't stand much chance of gaining value from it.
Note: GetApp published this article's first version on February 10, 2017. We've updated it to share new knowledge about business intelligence, as well as new research about how businesses make decisions using data.