By IT Brew Staff
less than 3 min read
Definition:
Organizations are ingesting more data from more sources than ever before, and must figure out how to best mine that data for actionable insights. For example, an e-commerce company must determine the nuances of customer behavior and predict potential product success from a tide of structured and unstructured data, including (but not limited to) social media postings, point-of-sale data, customer surveys, shipping trends, and more.
Business intelligence is critical to those efforts. BI tools fall into a handful of categories, including:
- Open-source: For teams that want to analyze data and craft dashboards without having to purchase expensive software licenses.
- Cloud-based: Often delivered via third-party BI tool vendors.
- On-premises: Software that runs on a company’s own servers instead of the cloud, allowing for the analysis of sensitive data.
While employees might harbor fantasies of instantly opening a BI tool and spinning up a comprehensive dashboard that predicts future performance, effective BI tooling rests on a couple of distinct pillars, including data quality, whether that data is easily accessible (i.e., not spread across multiple systems), and whether the organization has the right talent, including data scientists and analysts, to successfully configure the tools to deliver the correct results.
BI tools that present complicated data in easily understandable formats can help stakeholders make better decisions. The graphs and visualizations produced by many tools can make nuanced issues more easily understood, particularly by employees who might not understand all the underlying issues. AI features may also help teams draw connections between seemingly disparate pieces of data, or make predictions about trends.
