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Breaking Down Business Intelligence​

Business intelligence is a broad term that encompasses the technology and techniques that companies use for data analysis. Get a better grasp on business intelligence, BI tools, and where AnswerRocket fits in with this BI guide.

What is Business Intelligence?

The term business intelligence has been around since the 1800s. In the modern era, it’s a fairly simple umbrella term that encompasses many complex business processes and activities.

Business intelligence (BI) refers to technologies and services used to analyze business data and uncover actionable findings and insights for executives and other corporate users to leverage.

Essentially, business intelligence seeks to make sense of big data, not only managing the sheer volume but also generating strategy-focused takeaways.

Forrester explains the business intelligence definition well, stating, “Business intelligence (BI) is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information. It allows business users to make informed business decisions with real-time data that can put a company ahead of its competitors.”

Every business is searching for the same thing — a competitive advantage and sustainable stability. With business intelligence, companies position themselves to strive for that goal.

State of AI in BI and Analytics Thumbnail


The State of AI in Analytics & Business Intelligence

AI is a key component in advanced analytics. In this eBook, we break down how AI is currently being implemented into business intelligence platforms and what to look for in an analytics solution.

Learn how to discern the value of different AI implementations and what to expect as the market develops in accordance with advanced, autonomous analytics.

Business Intelligence and Data

With the rise of omnichannel marketing, companies have access to more data than ever before. The customer journey is no longer a simple circular pathway, but a complex web of interactions. With each opportunity for engagement comes an outlet to receive more data. At the same time, the diverse ways in which businesses can interact with their customers can lead to incredibly complex data sets.

How do business intelligence solutions accommodate such data? Well, it depends on how the data is structured.

Structured Data

Structured data is data with a definitive format and length, usually stored in a database. Examples of structured data include numbers, dates, or groups of words called strings. Structured data can be stored predictably in columns or rows, like that of a spreadsheet. As a result, it’s much easier to process than unstructured data.

Unstructured Data

Unstructured data is information that does not mold well to a database and is not formatted consistently. A common example of unstructured data is text, which is produced and collected in a large variety of formats (email, word documents, social media posts). The majority of most business data is unstructured.

What Data Structure Means for BI

Data structures operate on more of a sliding scale than binary categories. Semi-structured data, for example, refers to data that is largely unstructured but may be labeled with structured meta-data.

Business intelligence tools can automate the analysis of structured and semi-structured data to provide insights in seconds. Structured data also holds immense potential in the hands of BI with machine learning capabilities, as these tools can analyze thousands of data combinations to determine complex data relationships and pinpoint the source of trends.

When BI tools can do the heavy lifting of structured data, business people can spend more time focusing on setting strategy and analyzing unstructured data from a human perspective.

BI for unstructured data is far more complicated and requires transforming unstructured data into a data model before it can be analyzed by a BI interface.

Business Intelligence Software Basics

Business intelligence software is designed to accomplish many necessary functions for its users. Here’s a breakdown of what the technology typically can accomplish, broken out by role, as well as some of the best BI software features you’ll encounter as you research the solution that is best for your company.

Data Scientists and Analysts

At its core, BI software should make the jobs of data scientists and analysts far easier. Reporting should be simpler. To start with, a business intelligence platform needs to let you connect to your data, no matter where it is. Whether your data is on-premises, cloud-based, or a mix of both, you should be able to access it. From Salesforce to Excel to Microsoft SQL to Hadoop and beyond, pick a platform that connects with all of your critical data sources.

Once you’re confident that you’ve selected a platform that will work with your various data sources, look for an option that checks the following boxes:

  • It is designed to make prepping and modeling data easier.
  • The advanced analytics features are user-friendly and accessible for those who are capable of taking full advantage.
  • The software enables you to create interactive, customized reports for your business’s unique needs.
  • Reports and visual analytics are readily accessible and digestible for non-technical users.

However you look at it, it is clear that business intelligence is deeply embedded in the roles and responsibilities of analytics departments, so you must put your best forward with an advanced and capable software solution.


The right BI software can be a huge win for marketing teams. The better a marketer understands past and current performance, the better they can strategize for driving customer acquisition. Data is the key to that enhanced understanding.

Business intelligence technology should help pull together your data, run various marketing analytics, and report on the findings, all as quickly as possible — and preferably — in real time.

From social and web analytics to customer data, your BI solution should be ready and able to meet your marketing needs while answering your marketing questions.


Sales is metrics driven. Your sales team is going to be asking questions like “Have we reached our monthly sales quota?” and “How is our region performing compared to other regions?” You need a solid BI solution in order to answer those questions effectively and efficiently.

Look for software that can help you perform sales pipeline analytics, win-loss analysis, opportunity studies, and KPI tracking. Then, you can leverage your enhanced data capabilities to identify opportunities to shorten the sales cycle and grow revenue. 

Generally speaking, your entire sales team should be able to leverage dashboards and scorecards to track performance against targets and business requirements. Pick a platform that equips your team with the visibility it needs to close deals and bring in revenue.

Financial Analysts

Business intelligence provides a huge opportunity for financial analysts. Make a greater impact given your time and resources with an advanced BI solution.

Those in finance need to track professional success metrics like company growth compared to competitors, percentage growth over time, profitability, market share, and more.

While looking to convert raw data into those metrics, your average financial analysis team is also going to be simultaneously struggling with forecasting errors, uncertainty, disparate data, and lack of collaboration and communication.

Business intelligence technology can help your team solve for those challenges while tracking the right metrics.

State of AI in BI and Analytics Thumbnail


The State of AI in Analytics & Business Intelligence

AI is a key component in advanced analytics. In this eBook, we break down how AI is currently being implemented into business intelligence platforms and what to look for in an analytics solution.

Learn how to discern the value of different AI implementations and what to expect as the market develops in accordance with advanced, autonomous analytics.

The Best Business Intelligence Tools

The market is full of business intelligence solutions, which is great for buyers. While the selection process can be intimidating and getting buy-in from leadership comes with its own set of challenges, developments in business intelligence software help make the case for the new investment.

If you’re looking to see what analytics platforms are out there, below you’ll find a BI tools list that highlights some of the best offerings in the industry.


AnswerRocket is an AI-powered data analyst for everyone, packaging BI with artificial intelligence to create a next-level solution for the business user.

More like Google search than a traditional BI tool, no technical skills are necessary to operate AnswerRocket. Any business user with access can type their natural-language question and receive an answer in seconds, complete with data visualizations. From there, a user can drill down into the data and uncover actionable insights thanks to the solution’s AI capabilities.

Bottom line: AnswerRocket is an accessible and AI-powered BI platform that automates insight generation to give you a competitive advantage.

Schedule a demo today.


Tableau is a classic data visualization platform that’s well-regarded and used across industries. It is designed primarily for data-savvy ‘creators’ to build data visualizations for ‘consumer’ business users.

Compared to AnswerRocket

Although there are numerous small differences between the platforms, there are two primary distinctions that help set the solutions apart.

First, AnswerRocket is an augmented analytics solution, meaning AnswerRocket leverages AI to provide integrated functionality that investigates data and finds outliers, trends, and other insights that might not be visible on the surface but are important to users.

Second, Tableau is not self-service for the business user. Using Tableau requires a critical level of technical expertise, whereas AnswerRocket is designed to be accessible to the average business person.

Tableau has recently introduced Ask Data, which is a step toward self-service. That said, it is keyword driven and still in its infancy, compared to AnswerRocket’s search box that is battle-tested from five years on the market and fully responsive to natural language queries.

Click here for a more complete analysis of Ask Data.

Microsoft Power BI

Power BI offers a selection of business analytics tools created to enable insights across departments and levels at organizations.

Like Tableau, Microsoft Power BI is a strong product for data-savvy users to create content for non-technical consumers of business data.

Compared to AnswerRocket

The core difference between Power BI and AnswerRocket is that AnswerRocket is designed for business users who want answers without technical help. To use Power BI effectively, a business’s technical users have to build content for non-technical users to consume.

Power BI does have a selection of features that aim to make it easier to use for business users, but those features still have significant limitations (i.e., their Q&A that only works if the user’s data resides in the cloud).


Domo is a business intelligence tool that is focused on enabling closer collaboration. Domo is a well-known solution in the BI space because of elements like its mobile accessibility and its dedication to helping users tell a story.

Compared to AnswerRocket

Domo’s similarities to AnswerRocket include mobile accessibility and a focus on easy access for the business user.

Like traditional players in BI, Domo’s relies on a drag-and-drop model and does not have natural language search capabilities. Additionally, with Domo, analytics teams cannot leverage R and Python routines as extensions to automate the analytics process.


Qlik is a BI reporting tool that focuses on democratized data and being easily embedded. Qlik has a range of solid products to choose from based on business need.

Compared to AnswerRocket

Qlik’s QlikView is their version of an analytics solution for the business user. QlikView offers a level of enhanced accessibility over historic BI platforms (they call it ‘guided analytics’) via interactive dashboards. Similar to many traditional BI tools but unlike AnswerRocket, businesses that use Qlik require data experts to produce accessible content for consumers to leverage for strategy.

One key difference in interactivity and usability is in each platforms’ employment of a search feature.

QlikView’s version can search for the names of various items in the database, which, for example, is useful for finding the name of a product. AnswerRocket’s search feature can access the entire data model, including, most importantly, measures to automatically generate visualizations from the users’ questions. This is useful for end users to get data and visualizations generated directly in response to their search.

Additionally, AnswerRocket utilizes AI algorithms to do an exhaustive analysis that offers the ability to answer not just the “what” questions users have but the “why” questions.


Sisense bills itself as agile BI for everyone and is a solid example of a modernized BI solution. It is especially popular as an embedded option.

Compared to AnswerRocket

Like AnswerRocket, Sisense can manage large and complex data sets and strives to be easy for everyone to use.

For its self-service component, Sisense has user-friendly dashboards; but, the dashboards don’t have natural language query capabilities and instead are drag-and-drop. This model works well for technical users trained in the tool to produce dashboards and reports for non-technical users, and it is most useful for regular reporting needs vs. ad hoc business questions.

In addition, while both platforms are focused on driving meaningful takeaways for the end user, AnswerRocket’s software is designed to deliver next-level insights that dig into why something is happening by leveraging AI and machine learning.

The Bottom-Line Benefits of BI

AI, BI and AnswerRocket

Artificial intelligence (AI) is a huge topic in tech. In the data and analytics space, the combination of machine learning and natural language processing are changing the way companies approach their data. It’s making it possible for business users to forecast future trends and automate analysis in a way that was previously unimaginable.

Data analytics providers have been working for years to incorporate AI technology into business intelligence tools. Now, a few years into this trend, the most established version of this pairing is augmented analytics. Gartner coined the term, explaining that augmented analytics automates insights using machine learning and natural-language generation.

AnswerRocket is a leading augmented analytics platform, leveraging AI and BI to help its end users find growth opportunities faster than the competition. With business intelligence features such as dashboards and data visualizations and AI technology that automates processes and offers deeper insights, the AnswerRocket platform is a leading solution in this new era.

AnswerRocket’s Approach to Business Intelligence

AnswerRocket brings AI, machine learning, and natural language processing to BI. With AnswerRocket, the analytical power is in the hands of the everyone, not just analysts, unlike with other business intelligence examples.

Discover how our analytics platform leverages AI and business intelligence technology to help the end user generate actionable insights and find growth.

Schedule a demo today.

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