Business intelligence (BI) solutions for enterprise companies, when employed correctly, can reveal new opportunities for growth and expose business strategies your team may never have considered before.
Then again, many organizations fail to leverage these benefits because business leaders fail to understand the jargon surrounding the data industry. The onus for these misunderstandings falls on two groups: The businesses who oversell what data can do, and the media which sometimes inflates industry news and obscures good reporting with heavy jargon.
Long story short, a more robust understanding of BI can help enterprises make the right decisions about data solutions. Below are a handful of terms you are likely to encounter and what they mean.
Ad Hoc Query: Some BI software and data analytics solutions allow users to ask one-off questions and receive answers on demand. This is different than method of reaching out to teams of data scientists to pull questions from the data, which is often much slower.
Automated Analysis: This data analytics option automatically finds hidden insights in your data and answers questions you may not have considered asking in the past. With a few tweaks in direction, a good machine-learning solution should anticipate questions and provide quality answers.
Behavioral Analytics: In the Coen brothers’ classic Oh Brother, Where Art Thou?, smooth-talking con artist Everett says, “It's a fool that looks for logic in the chambers of the human heart.” Perhaps he’d never heard of behavioral analytics. This functionality allows companies to gather information about consumer actions and anticipate future trends.
BI Governance:According to Forrester Research, BI governance is a key component in data governance that allows (or disallows) data user permissions, how it can be used and when it can be used. This siloed approach might work for some companies, but others opt for “democratization” which allows anyone in the organization to peer into the data.
Contextual Data: As the name implies, contextual data provides a fuller picture into various elements of business data. For instance, a typical customer record might contain a name, address, phone number and email adjoined with buying habits and online activity.
Data Modeling: For those who don’t know, data modeling is a process which structures complex software system design into an easily understood diagram of how data ought to flow. Think of it as a blueprint or flowchart illustrating the relationships between data.
Data Visualization: Data can be visualized in many different ways including charts, graphs or embedded dashboards. The goal is to take data points and create an at-a-glance graphic so you can better understand trends.
Data Warehouse: Your data warehouse is a rational repository that captures structured data from multiple sources within a company so that it can be used by another BI solution.
Gap Analysis: Data analytics isn’t a ‘set it and forget it’ solution; it takes a good deal of oversight to ensure quality findings. A gap analysis studies whether the data gathered by the company meet business expectations.
Key Performance Indicator: Speaking of performance, KPI is a business metric that determines whether BI analytics is reaching its goals and objectives.
Self-Service BI: As you might suspect, this approach empowers users (not just data scientists) to access and analyze data sources. However, this is still centralized and governed by IT.
Snapshot: You might hear this term a lot. A snapshot is a view of data at any particular moment in time.
SQL: Structured Query Language, or SQL, is a standard language for relational database management systems. In other words, SQL is a language that allows users to talk to databases.
Now that you understand the argot surround BI, you can make more informed decisions about your data solutions. Good luck!