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Let’s play a quick game of “you prefer.” Would you rather manually sift through 6,000 rows of data in a spreadsheet to guide your next business strategy or have software do it for you?

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If you prefer the former, you’re probably a data scientist (or masochist). If you’re like me, you get frustrated when you see uncategorized data and prefer to see an organized report or a series of data visualizations. Either way—we both need business intelligence.

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So what is business intelligence, and how can you use it to make business decisions easier? In this guide, I break down why you need to implement a business intelligence strategy and what it looks like.

Business intelligence (BI) refers to the processes and tools used to collect, organize and analyze data to determine how a business is performing and how it can improve.

Don’t panic—BI doesn’t mean your job as a business executive or data analyst is being taken over by a robot. Compiling the data into a report is only half the battle. Your job is to gain insight from that data and make sensible and humane decisions. BI simply automates the tedious process of data extraction and analysis, making your work easier—and more effective.

Think of business intelligence as your company’s data whisperer—it takes the cacophony of raw numbers and orchestrates them into a symphony of actionable insights. Only…a little less thrilling. Let’s break down this not-so-magical but totally practical process:

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Typically, data science professionals will interpret the results of business data analysis before BI makes the language less technical. Essentially, BI simplifies insights from data analytics into more descriptive business terms.

Think of it like the difference between a study conducted on a technical health topic and an article published about that study. The results of the study may sound like a foreign language, but the article explains its implications and provides recommendations for readers to take home.

Let’s say you’re not excited about the prospect of manual data analysis and interpreting the results. Here are some reasons BI can make your system work better.

The use of BI is almost unlimited. The first step to doing this is deciding what business objectives you want to achieve. Do you want to compare the performance of your sales channels, use data from past marketing campaigns to inform your next one or create a new HR dashboard with employee utilization data?

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For many BI uses, you can rely entirely on trusted internal data, but some issues require external data. For example, you might want to take data published in a trade journal into consideration when implementing BI.

Example: That same clothing company might collect additional data from customers based on their buying patterns to identify the styles they like best and better market those styles to them.

Once all of your data sources are gathered, you’re ready to choose tools to transform, analyze, and learn from the data. The platform you use will depend on your goals for BI, which is why determining those in Step 1 is important.

Example: A clothing company might use a BI solution with powerful analytics capabilities to break down the meaning of customer data about their interests and buying habits.

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Leave it to robots to identify patterns and make predictions from data. Most BI tools are built to perform descriptive and predictive analysis, which can shed light on where your business stands. For example, your BI platform might look at employee turnover history and make predictions about which departments need the most aggressive recruiting efforts and when.

Example: A clothing company’s BI software might identify that customers consistently return a certain type of romper due to fit problems, suggesting the company should reevaluate the item’s design.

We’ve established that (almost) everyone prefers colorful charts, graphs or maps to heavy data spreadsheets. Data visualizations are especially useful when sharing findings with the rest of the team—they’re easier to digest and therefore better for motivating action toward goals.

Example: A clothing company might use BI-generated charts to reveal how much profit could be increased by better targeting customers with personalized marketing efforts.

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BI tools help show you where action can be taken, but they can’t take action for you. Develop short- and long-term strategies based on your findings to address your problem areas, whether internal inefficiencies, supply/demand issues, or lack of customer service.

Example: A clothing company might initially discontinue a generally unpopular style while gathering more customer data, then proceed to adjust its marketing strategy months ahead.

In addition to choosing a BI tool that meets your unique needs, make sure any solution you choose includes the following qualities:

Once you’ve implemented business intelligence software, automate it so you can spend time delighting your customers instead of frying your brain staring at spreadsheets.

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Here are some Quickfire FAQs, so you don’t have to wade through 2,000 words to find out whether or not you need to have a PhD in data science to use business intelligence effectively (you don’t):

The core objective of business intelligence is to turn data into actionable insights. By analyzing raw data, BI helps organizations make strategic decisions, identify opportunities for improvement and gain competitive advantage.

You don’t need a PhD to get into BI—start with some online courses that cover the basics, like data analysis and pattern recognition. Throw in some training with popular BI tools and maybe a few YouTube tutorials, and you’ll be on your way. As you progress, don’t overlook the power of a good BI community forum, where you can get insights from those who have navigated this path for a long time.

To be clear: there are seasoned BI experts out there who have spent years refining their knowledge in this area. Their depth of understanding is something to aspire to, but it is not impossible to achieve.

What Do Finance Leaders Need To Bear In Mind When Selecting Business Intelligence Tools?

Coding can be a plus when you want to make all the scientists crazy with your data, but it’s not a must. Many BI tools today are like Star Wars-themed LEGO sets—they let you build interesting data models without knowing the mechanics or science behind the Millennium Falcon. But dabbling in a little SQL or Python can definitely add some muscle to your BI game.

Luke Strauss Luke is a writer and content marketer from sunny San Diego. Since receiving a degree in International Management from Pepperdine University, she has channeled her passion for digital marketing and creative writing to create compelling content for organizations across a variety of verticals. When she’s not at her desk, you’ll find her at music festivals, thrifting or spending time with her friends and family. For businesses, BI software aims to do the heavy lifting of collecting and processing raw data into something valuable. So instead of waiting for the next report to see the latest statistics, you’ll be able to make data-driven decisions in real time. Business intelligence tools for retail make it easy for businesses to estimate growth and revenue and understand more about their consumers by collecting and organizing data.

A business intelligence strategy by itself cannot process and combine all the data into manageable chunks. When dealing with large amounts of raw data, it can be difficult to keep track of all procedures. Modern problems require answers, which is why sophisticated business intelligence tools are used to meet these urgent needs.

For a successful business in the retail market, it must focus on a number of important areas, including supplier collaboration and return policies, comprehensive return procedures, supply chain structure, item disposal and analysis.

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Stock availability, sales rate, overhead costs, marketing strategy and so on are just some of the many aspects of each focus area.

Retail businesses are increasingly using Business intelligence tools for retail

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