The most common Business Intelligence questions
What is business intelligence?
In short, Business Intelligence (BI) is about examining business data to help people make informed decisions. It focuses on reporting, dashboards, and data visualization and storytelling. To achieve this, it uses software to extract, transform, and load data from diverse sources. Larger firms that are ‘data-driven’ are likely powered by enterprise business intelligence.
This makes BI applications vital. BI gives companies insight into users, market trends, and their own in-house projects. This helps them find new chances for growth and lowers risks. Also, by looking at past, current, and predictive views of data, BI helps leaders grasp key performance indicators (KPIs) so they can see if their actions are truly working. If not, then BI can help them find the KPIs that can guide the business better.
BI plays a crucial role in data-driven cultures. It fosters deeper inquiry into how the business works.
What does a data analyst do?
A day in the life of a data analyst can look akin to a day for a BI worker. Both of these techies may use tools such as Google data analytics. However, these jobs are not the same.
Both in-house and remote data analysts go deeper than people in BI. While BI and data analysis focus on gaining insight from data, data analysts tend to have more technical skills. For instance, they may use Python to forecast and to automate data analysis.
This means that day-to-day, these techies are hands-on with data. They collect, process, and interpret it. This uncovers key insights, trends, and patterns that can guide the business.
So what is a big data analyst? Someone who uses a slightly more technical skill set to transform raw data into easy-to-grasp reports and visualizations. This helps businesses make informed decisions. Like BI, it spurs refinement in strategies and improves outcomes. Unlike BI, it uses a little more technical know-how to get there.
What are the daily activities of a business intelligence analyst?
BI analysts’ days revolve around turning business data into insights. That means their daily tasks involve data collection, analysis, and visualization.
They gather data from sources such as databases, spreadsheets, and external APIs. They clean and organize this data and ensure accuracy. Then, they find trends, patterns, and correlations to answer specific business questions.
Next they make dashboards and reports to explain the data. They use many tools to do this, but a common business intelligence tools list includes Tableau, Power BI, and Qlik Sense. This makes their insights easy to understand.
But collaboration is also key. BI analysts often liaise with diverse colleagues to uncover their needs and provide tailored insights. They might hold meetings to discuss findings, receive feedback, and adjust analyses.
In addition, the field of BI never stays still. That means BI workers need to stay up-to-date on industry trends and new BI tech.
Is business intelligence the same as data science?
While they both handle data, they are not the same. Data science (DS) is more technical than BI. In fact, it is even more tech-heavy than data analysis. DS unites statistics and computing to build predictive systems. This means that data scientists focus on working with coders in their day-to-day work. They’re often hands-on with code in a way that BI workers aren’t.
BI is about storytelling and making business data clear to humans. People working in BI talk with colleagues to find out what’s needed. They then apply business frameworks such as marketing funnels to address these needs. This helps leaders grasp what the business is doing and backs up the findings with hard numbers. This makes BI a more communication-centric field.
DS, on the other hand, is about building and honing machine learning models. Often, these models are user-facing. Think of the video or product recommendations you get on your go-to platforms, for instance. The systems behind those suggestions were built and trained by data scientists.
Because of this, the tools for each field also differ. BI uses Excel, Tableau, Power BI, and SQL. Data scientists favor R, Snowflake, Redshift as well as Python and its libraries such as Pandas, Scikit Learn, and Torch.
Why is customer intelligence so important to companies?
Business intelligence reporting can take many forms, one of which informs customer intelligence (CI). These insights are key to companies because of what they fuel:
- PersonalizationCI allows companies to tailor products and messages to enhance experiences and increase engagement.
- Customer retentionCI data can help companies find pain points and address issues promptly, leading to improved satisfaction among their users.
- Effective marketingCampaigns based on CI yield higher conversion rates and return on investment because they can mesh with customer tastes and behaviors.
- Resource optimizationCI helps companies direct resources to high-value users and find the places that most need extra investment.
- Risk mitigationCI helps firms foresee shifts in markets and customer tastes. This makes them more prepared for change down the line.
How is artificial intelligence related to business intelligence?
These fields are quite distinct. Artificial intelligence (AI) uses machine learning to mimic human problem solving, decision making, and more. BI looks at business data and gains insight from it.
AI can inform BI, though. Here are a few ways:
- AutomationAI can automate routine tasks such as data cleaning and report generation. This saves BI workers time.
- Data explorationAI can explore data to unearth hidden relationships. It can also suggest areas for further examination.
- PersonalizationAI-driven BI can tailor info for users, whether they are executives, managers, or frontline employees.
- Real-time insightsAI helps BI systems process data in real-time. This can offer up-to-the-minute insights that are crucial for timely decision-making in fast-paced business environments.
- Continuous learningAI-powered BI systems can learn from new data inputs and adjust their algorithms over time. This improves the accuracy and relevance of insights.
Business Intelligence careers
Business intelligence jobs
So now that you know what BI is all about, let’s talk about the BI jobs out there. Often, there’s much more to them than you might find in a business intelligence analyst job description. BI has tons of tech careers open for you, and each has its own unique flavor. And, to top it off, entry-level BI salaries are quite good. Of course, BI analyst jobs have their own quirks. You can see that in detail above as we talk about business analysts vs data analysts.
But you might still wonder about business intelligence vs business analytics. Are they the same thing? The job titles and descriptions you might find on career sites can seem confusing.
Here, we’ll help clear things up for you. There are two key BI roles to know. Their daily tasks reflect what you can expect to do in most BI jobs. And just to answer a question in advance: yes, you can find remote jobs in BI. This is tech we’re talking about, after all.
Let’s dive into the two main types of BI roles.
BI developer
People in these roles design and maintain BI reports that cater to business needs. That means they spend a good deal of time talking to stakeholders and leaders.
A large portion of their work involves extract, transform, and load processes. This includes getting data from diverse sources, structuring it, and storing it for later analysis. This ensures data accuracy, consistency, and quality. Also, data security is key to their work. This is especially true with cloud BI. Because of this, BI devs make sure that data is safe and that their firms stay in line with laws and regulations.
To do all this, BI devs use BI tools like Tableau, Power BI, or QlikView to design dashboards, reports, and visualizations. This helps stakeholders quickly grasp insights, trends, and patterns to guide them to well-informed decisions.
BI devs can also help document data models and reports. This ensures that knowledge and data standards cascade across their teams. They may also provide training to users so that non-tech staff can navigate and interpret the BI solutions.
BI administrator
Let’s start out by talking about BI devs vs BI admins. While they both focus on BI data, their realms of work differ. Where a BI dev creates the tools for business data analysis, BI admins are more operational. For instance, they handle user access, security, and system maintenance. So they are also vital to proper BI at a company.
They control who has access to distinct levels of data and reports. This helps keep the firm in line with local law and keeps data secure. To do all this, they set up user profiles to ensure that only approved people can access sensitive data.
They also monitor the performance of BI platforms. They find and resolve issues that could hinder data retrieval and analysis. This may involve tuning databases and using caching mechanisms to enhance system responsiveness.
In addition, they might also help with maintenance and troubleshooting. They can lend a hand with software updates, patches, and backups. In the event of tech glitches or failures, they tackle issues promptly to minimize disruptions.
Of course, collaboration is also vital for people in these roles. They work closely with BI devs, analysts, and business stakeholders. Through frequent meetings, they align on business requirements to match their BI work with the firm’s goals and needs.
Data analyst jobs
Here we get to one more key thing to talk about: business analytics vs data analytics (DA) in terms of jobs. Above, we discussed how BI and DA differ in focus. Now that we know a little bit about BI jobs, we can look at those in DA. Like in BI, you can find remote data analyst jobs easily. Also like BA, entry-level data analyst salaries are quite respectable.
There are many paths you can choose within DA. Each one is unique, so let’s go into the main career routes people in DA tend to choose. See which one of the three described seems best for you.
Product analyst
This combines data analysis, strategic thinking, and a focus on the user. These people play a vital role in bridging the gap between user needs and product development. They collect and inspect data, feedback, and market trends to unearth insights to guide product upgrades.
Day-to-day, they work with people across varied teams such as managers, designers, engineers, and marketers. They dig into user actions, usage patterns, and metrics to see what’s working and what’s not. Where things aren’t going to plan, they apply data to unearth how to fix the issues.
Skills in storytelling are vital. Product analysts need to convince diverse groups of people of their findings and recommendations. They do this by crafting visualizations, reports, and presentations to explain complex insights.
This role also involves evaluation and adaptation. Product analysts track the impact of changes and new features. They then measure success against KPIs. This process helps refine product strategies and ensures that the product evolves to meet user expectations.
Marketing analyst
This is one more of the remote data analytics jobs you can find hiring across career sites. This role offers a blend of delving into data, looking into buyer behavior, and forward-thinking decision-making. Marketing analysts dive into data from many sources such as campaigns, website traffic, social media, and market trends. This helps them gain insights that steer promo plans.
These techies inspect data to spot patterns, trends, and correlations. They gauge the impact of ad campaigns and assess market tastes to fine-tune ad tactics. These insights then help their coworkers tailor messages to target the right people.
As such, working across diverse teams is key. People in these roles work closely with creators and sales pros. They provide data-driven insights that inform choices in content, distribution channels, and promo activities. Their work also helps companies set goals and track the success of marketing actions.
As in other jobs discussed above, an eye for detail and a knack for storytelling are vital. In this role, people translate complex data findings into recommendations for non-technical stakeholders. They create reports, dashboards, and visualizations that make the data clear. This then informs the steps a firm takes.
The field requires adaptability, though. Marketing and tech are known for how quickly they can change. Because of this, marketing analysts stay up-to-date on new tools, methods, and trends to ensure their insights are germane and useful.
Finance analyst
As you might guess, people in these roles are also key. They explore financial statements, economic trends, and market data to unearth patterns and trends that can inform fiscal roadmaps.
As the name suggests, people in these roles focus on data analysis. They check out fiscal reports to observe revenue, costs, and profit margins. This helps them assess the company's financial health. Using this data, they can locate areas where the firm can reduce spending, enhance revenue, and optimize.
As in the role described above, close collaboration is key. In this role, people work with diverse teams. They provide fiscal insights that support decision-making. This can take the form of checking the viability of a new project or predicting the financial outcomes of a business expansion.
This means that these people share complex data with non-financial teammates. To do this, people in these roles need top-notch skills in communication and visualizing data. Often, this job involves building reports that break down data into clear insights. This is all done to help leaders grasp the fiscal impacts of their choices.
Of course, problem-solving skills are also key. Finance analysts troubleshoot anomalies and suggest fixes. They may also forecast financial trends and outcomes to guide long-term strategic planning.
They are also highly valued. People with these skills will find it easy to land a job. But to stay at the top of their game, they must stay current with regulations, trends, and next-gen tech. Still, the role can be quite rewarding. It offers a blend of number crunching, strategic thinking, working with diverse teams, and direct impact on a firm's financial success.
Business Intelligence applications
So now that we’ve discussed business intelligence vs data analytics, let’s get a little more in-depth. BI can be used in diverse sectors, and it can have a real impact on a firm’s success. This means that the tools people use for BI truly matter. For instance, it’s key for people in BI to use the best power BI dashboards.
In fact, the applications of BI range so widely that BI experts have to know a wide range of tools and approaches. This includes not only dashboards but also databases and data manipulation and visualization tools.
That might sound like a lot, but you can get a handle on all of it
in just four months. But before you get there, let’s take a closer look at the tech we’re talking about.
Working with databases
BI is mostly about taking business data and making it easy to grasp for non-techie people. But, although that sounds like a soft-skills heavy role, some hard skills are also key. For instance, knowing how to work with databases will make any BI pro even more attractive to employers.
This is because many companies now use cloud business intelligence. This means the data you will be using will be stored on databases. So, if you want to be part of the latest enterprise BI teams, knowing the ins-and-outs of how to extract data is vital.
Effective database management is crucial to ensure accurate, accessible, and secure data. After all, this data is the backbone of BI work. It is what allows BI analysts to craft the analyses and presentations that positively impact the business at large.
Let’s look at databases a little more closely.
Postgres
Postgres, short for PostgreSQL, is an open-source database system known for its advanced features and robust performance. It has gained widespread use among coders, businesses, and organizations.
It can be applied in diverse ways. This can range from simple web-based tools to complex data warehousing. For instance, its extensibility allows users to create custom data types, operators, and functions. It also focuses on data integrity. It offers various tools for this, including multi-version concurrency control.
All this is key for people in BI. It allows them to tailor the database to their needs. Custom functions, data types, and extensions can be vital to the daily work of someone in BI because these robust features let them extract the right info. This helps them dig into large datasets and uncover insights. After all, finding new ways of looking at business with data is what BI is all about.
As it is open source, people can help augment it and expand what it can do. This leads to frequent updates, security patches, and new features. In essence, it is an enterprise-grade database system that strikes a balance between traditional relational databases and modern, diverse data management needs. It is a core tool for data storage, retrieval, and analysis that can fuel BI workflows.
Other Databases
While Postgres is a great tool, it’s not the only database out there. In fact, once you’re working in BI in the real world, your company might use one of many different databases. This will likely depend on what the database will be used for and the project’s scale. In any case, here are five you might see:
- Microsoft SQL ServerThis is widely used for data storage, retrieval, and analysis. It offers crucial BI features such as seamless integration with Power BI and SQL Server Analysis Services.
- Oracle DatabaseThis enterprise-grade database is known for scalability and performance. It's often chosen because it can handle large and complex datasets. Its robust nature is something BI techies deeply value.
- MySQLThis open-source database is widely used because of its ease of use and low cost. You will most likely see it on smaller BI projects. For instance, it’s a common database used for web apps.
- Amazon RedshiftThis is a tool offered by Amazon Web Services. It’s tailored for large-scale data analytics. This makes it well suited for BI, as it can tackle massive volumes of business data.
- Microsoft Azure SQL DatabaseThis cloud-based database is part of the Microsoft Azure ecosystem. Because of this, it works seamlessly with Microsoft BI tools like Power BI. It is also known to be scalable and flexible.
Manipulating data
Once you can work with databases, you can start playing with data. There are a few core tools that help BI experts accomplish this. Let’s look at a few of them.
SQL
Structured Query Language (SQL) is a programming language that coders use when working with relational databases. It is the prime way that people talk to these repositories. As such, BI experts rely on it to manipulate and query data. SQL lets them extract, transform, and analyze data to derive meaningful insights.
The SELECT statement helps them retrieve data from tables, apply filters, and use groups to narrow down results. They can join tables to combine data from diverse sources and create a comprehensive dataset for analysis.
Transforming data is also a key aspect of BI. SQL's functions allow BI workers to collect, calculate, and format data as needed. Aggregation functions like SUM, AVG, and COUNT are used to derive summary statistics, while math functions support calculations for further analysis.
SQL also helps BI analysts cleanse data with filters. They can remove irrelevant records using WHERE conditions. They can then use SQL to create new tables or views that outline data for reports. This streamlines complex queries.
SQL aids in time-series analysis and finding trends. BI analysts can use SQL's date and time functions to extract relevant time periods and observe data changes over time.
Knowing this language allows BI workers to extract insights from databases. It is the language that lets them turn data into meaning and inform their organization’s decision-making process.
Google Sheets and Excel
These two spreadsheet tools are common among people who work with data. Of course, this includes people in BI. They use Google Sheets and Microsoft Excel for data analysis and visualization. These tools allow BI workers to import, clean, and structure data from varied sources. They can sort, filter, and transform data using built-in functions.
For instance, pivot tables and charts help them boil down and visualize data trends. People in BI use these features to create lively reports and visuals. They highlight key insights to make it easier for stakeholders to grasp complex data.
Both platforms also support external data sources and APIs. This allows BI analysts to fetch real-time data for reports. This helps them track metrics and KPIs.
For more in-depth analysis, people in BI can use add-ons and scripting languages to automate tasks, create custom functions, and build more complex analytical models.
Both of these tools help BI workers process, analyze, visualize, and share data. They cover a range of analytical needs. Because of this, these two spreadsheet apps are key in BI work.
Data visualization tools
This was touched on above, but there are many ways to visualize data. Here’s a quick run-down of a few tools that people in BI use on a daily basis.
Tableau
This is something you’ll see BI experts use all the time. And it’s easy to see why. Tableau is a leading data visualization tool. Since people in BI are all about making data easy to digest, they often rely on this tool. It can turn complex data into insights that can spur informed actions.
It offers a user-friendly interface that allows BI analysts to connect to varied data sources, from databases to spreadsheets. Then it helps them create interactive and clear dashboards and reports.
With this tool, BI workers can explore data through drag-and-drop functions. This leads to swift analysis without asking that people in BI have advanced coding skills.
It can make various types of visualizations such as bar charts, line graphs, heat maps, and geographic maps, which aid in presenting patterns and trends. Then, to get into more detail, filters and parameters allow users to customize views based on their own criteria.
Tableau also has a basic formula language. Using it, BI analysts can embed calculations. This lets them perform complex aggregations and derive new insights.
But Tableau can also build interactive dashboards. That means analysts can make dynamic presentations that allow stakeholders to drill down into data to gain deeper insights.
In short, Tableau is a robust tool for turning raw data into meaningful insights that drive informed decision-making.
Power BI
If you’ve been looking into BI, you likely saw this coming. Power BI dashboard examples can be seen across the BI space — how to work with them, the best way to set them up, the list goes on.
But this tool is used for good reason. Microsoft Power BI is a robust platform that can transform data into compelling insights. Power BI connects to diverse data sources, from databases to cloud services, and creates interactive reports and dashboards.
With Power BI, BI analysts can also drag and drop data to construct visualizations such as bar charts, line graphs, pie charts, and maps. These visuals help them highlight trends and patterns within data.
Power BI can also model data. This allows BI analysts to link distinct datasets, enhancing the depth of analysis. Then, the Data Analysis Expressions language within Power BI augments what people in BI can do. With it, they can run complex calculations for advanced analysis.
This may sound akin to what Tableau can do, but there is a key difference. Power BI is a Microsoft product. That means it works seamlessly with Excel and Azure out-of-the-box. This powers easy collaboration and data integration. And because so many tools mesh, it is simpler to share interactive dashboards with stakeholders.
Power BI is also mobile-friendly, and it has cloud-based deployment options. This makes it easy to scale for organizations of all sizes.
Other Tools
Self-service business intelligence tends to focus on the tools described above for visualization. But, as you might expect, there are other tools as well. In your BI career, you may also encounter the following:
- Qlik SenseThis self-service tool allows users to apply associative data analysis to explore relationships dynamically. It’s also a way for BI workers to unearth and visualize data to create purpose-built dashboards.
- LookerThis is known for its baked-in analytics and focus on data exploration. It can create custom data models and interactive visualizations.
- DomoThis is a cloud-based platform for BI and data visualization. It offers real-time insights and dashboards. It can also connect to diverse data sources. Domo is easy to use, as it was designed to cater to both tech-savvy and tech-averse users.
- SisenseThis tool shines when you need to refine complex data into easy-to-grasp insights. Like other tools, you can use it to make dashboards, reports, and embedded analytics for a wide range of business users.
- Google Data StudioYou might see this in your BI career for one simple reason: it’s free. Like other tools, it lets you create custom reports and dashboards. Unlike other tools, it works seamlessly with Google products such as Google Analytics, Google Ads, and Google Drive.
Want to become a Business Intelligence Analyst?
So you’ve gotten down here, and BI sounds like something you want to pursue. Read on to find out the ins and outs of how to land a BI career.
Learn SQL
Of all the tools listed above, none is more vital than this language. If you want to know more about it, check out our section on it above. We go into the nitty-gritty of it up there. Here, we’ll instead answer why BI analysts should know SQL.
In short, it lets BI analysts access, change, and analyze data directly from databases. In a data profession, this is key. After all, people whose jobs are all about data need to master the tools that let them work with data.
It allows BI techies to craft tailored queries, perform complex aggregations, and transform raw data into actionable insights. Using it, they can independently extract specific information, join data from distinct sources, and create custom reports. This lets them derive meaningful insights that drive informed decision-making within organizations.
And it’s a language that you can learn for free. In fact, in just 20 hours split across unique lessons, TripleTen can help you master its basics. Check out our SQL course
here.
Do you need a degree?
This is a common question we come across. It comes in a few forms: is there such a thing as a business intelligence degree? Do I need a master’s degree to land one of these jobs? Can I still get a BI job if I studied something else in college?
Tech, as an industry, looks at people’s skills and know-how as opposed to their background. If you can show that you know how to do the tasks, you can land the job. In fact, according to a
recent report, 59% of tech companies are thinking of eliminating college degree requirements entirely.
So if you want to start a career in BI, just gain the knowledge and master the skills. You don’t need any fancy degree to land a great BI job.
Business intelligence analytics bootcamp
To gain knowledge and skills, bootcamps are the way to go. They provide in-depth and up-to-date info on everything a BI expert will need to know. They’re focused on getting grads jobs, and that shapes how they teach. Instead of the theoretical knowledge that university computer science programs tend to prefer, industry experts at bootcamps teach the practical know-how that gets people hired in tech and keeps them thriving. Best of all, instead of demanding an expensive four-year commitment, some can be completed in as little as four months.
These intensive programs are often broken into two- to three-week sprints during which students have a set list of tasks to complete. But how and when they work on the tasks is up to them. This mirrors how the industry at large operates. That means grads come out of bootcamps not only with know-how, but also with baked-in experience with how tech works.
All of that is what we provide in our
Business Analytics Bootcamp. Our beginner-friendly course is focused on getting people the jobs they study for. We have tutors, insightful code reviewers, a community of fellow learners, and industry-seasoned experts who will help you gain the needed knowledge and skills. Then, once your studies are done, people in-the-know from tech will help you craft a catchy resume and a robust portfolio. With all of this, you’re sure to get a job. And we’re so confident in that promise that if you don’t get a job within six months of graduating, you’ll get your money back.
BI certifications
The first thing to know here is that to start out as a BI analyst, you do not need a certification. Landing an entry-level BI job without one is no problem. But if you want to keep growing in your BI career, certifications might make your resume just that little bit more convincing. As such, here are our top five recommendations for BI certifications.
- Microsoft Certified Data Analyst AssociateThis confirms skills in Microsoft Power BI and Excel. It covers data visualization, modeling, and preparation techniques.
- Tableau Desktop SpecialistThis attests to skills in Tableau Desktop. It states that the person holding it knows how to create impactful visualizations.
- Certified Business Intelligence Professional (CBIP)This is offered by The Data Warehousing Institute. It covers data analysis, data integration, and business performance management.
- Qlik Sense Business Analyst CertificationThis affirms skills in using Qlik Sense for data visualization and analysis. It attests to the holder’s abilities in creating dynamic dashboards and reports.
- Google Data Analytics Professional CertificateThe Google Data Analytics certification covers skills with Google tools. This includes data collection, transformation, visualization, and interpretation. It is a great first certification for entry-level BI professionals.
BI analyst job placement
Job placement is a cornerstone of the best data analytics bootcamps. For instance, TripleTen has a wide network of companies that it partners with to provide externships. During these learning experiences, students wrapping up their time at TripleTen join real-world projects. This gives them hands-on experience before they even start looking for jobs.
For instance, in a data-focused externship, you might find yourself cleaning, analyzing, and visualizing data that has real impact on a company’s future just like
one of our grads. You could also end up being a key voice in
guiding startups as they scale.
This experience is the exact thing hiring managers look for. That is why TripleTen makes sure to offer externships to its students. We’re focused on getting people hired in new, meaningful jobs.
Our pitch to you
Remote data analyst jobs aren’t as hard to get as you might think. The right bootcamp can help you pursue business intelligence jobs or even Google data analytics certification. It will give you skills in BI reporting and Google data analytics and expose you to Power BI dashboard examples.
This in-depth prep is what you’ll find at TripleTen. We’ll teach you business intelligence reporting so you can land that coveted BI analyst job.
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