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Business Intelligence (BI) software is any application designed to automate the retrieval, analysis, reporting, and presentation of business performance data, in order to help organizations understand and visualize their key strengths and weaknesses.
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Collecting data has become crucial for the success of any business, but the challenge lies in managing its ever-growing volume.
Business intelligence (BI) software can help by integrating data from multiple sources into a single platform. The BI tool can then extract actionable insights such as customer buying habits or market trends to help your business make informed, data-driven decisions.
Below, you’ll find everything you need to know about business intelligence software so you can choose the right BI platform for your small business.
Here’s what we'll cover:
Business intelligence (BI) software is technology that helps organizations analyze their data assets to make informed decisions, improve processes, and drive business objectives. The solution’s signature function is sharing data analysis—in the form of dashboards and reports—with teams. With an eye on key performance indicators (KPIs), historical analysis, and future forecasting, BI grants organizations a vantage point to see a holistic view of their business.
As a business owner, you might have questions about sales, finances, or other KPIs. To answer all such queries, you need to gather data, analyze it, and determine what actions you can take in response that will help move the business forward. Business intelligence tools help you do just that.
Here are some key functions of BI tools:
Collect data: BI tools extract structured and unstructured data from a source, prep it for analysis, and store it in a central location.
Run queries: Analysts and other professionals run queries against the data to discover trends, patterns, or inconsistencies that shed light on a business' current state.
Create visuals: Business intelligence tools present actionable insights in the form of interactive dashboards, charts, maps, and graphs.
The first step toward selecting BI software is deciding on traditional vs self-service BI. Traditional BI has long been confined to big corporations because of its cost and complexity. Modern BI (aka self-service) is what has emerged as cloud and digital technologies have tilted BI solutions towards user-friendly and accessible options.
Highly controlled analytics environment where analytical knowledge is mandatory to make use of data.
Expertly trained data scientists create static reports and share them with the team.
Analytics reports, while of high quality, are slow to surface and bottlenecked by IT workload.
Requires highly sought after and expensive data analytics skills sets, which are hard to find given data scientist and other veteran IT hire shortages.
Direct control over servers offers more robust security.
More customization options, though limited by staff aptitude for programing languages and ability to code integrations between software platforms.
Open analytics environment where user-friendly, intuitive, and highly interactive tools allow anyone to perform valuable analysis of data.
Enables data citizens—users who are not data scientists by trade—to create value with business data and BI despite lacking formal training in analytics and statistics.
Much easier to scale, as BI practitioners can be recruited from your existing staff.
Frees up veteran data scientists to work on more complex, value added projects.
Contributes to real-time insights, allowing any worker to rapidly personalize data queries and manipulate analysis filters.
Reallocates IT resources to line of business (LOB) processes, as well as to the departments and users directly facing business challenges.
Key question to ask your BI vendor before you buy: How will your solution deal with my organization’s primary data type, as well as large volume datasets, and how will it maintain my data’s security and availability?
The terms business analytics and business intelligence are often used interchangeably. Though there is some overlap, business intelligence is descriptive while business analytics is predictive/prescriptive.
BI tools help you analyze the current state of your business based on past activities. BI software simply presents existing raw data in a way that is readable and interpretable by non-technical end-users.
On the other hand, business analytics software helps businesses make predictions about the future and requires the expertise of data science professionals to interpret the predictions and develop advice.
Key question to ask your BI vendor before you buy: Will I have to provide extensive training to my staff to be able to use your BI solution?
When used effectively, business intelligence tools can help spot market trends, identify bottlenecks, and track employee productivity.
Here are some of the key benefits of BI software:
Faster, improved decision making
Better understanding of customer behavior
Early identification of problems and solutions
Identification of emerging business trends
Track sales, marketing, and finance KPIs
Once you have decided to leverage BI for your business, you should start planning your BI implementation strategy to help with both software adoption at your business and to avoid any roadblocks.
Step 1: Identify key metrics and select BI software. It’s important to know what it is that you want to achieve with the help of a BI tool. Are you looking to monitor sales volume or understand customers’ buying behavior? Build a shortlist of software options based on your requirements to ensure you select the right business intelligence tool.
Step 2: Analyze current data storing and sharing practices. Each department may have its own way of dealing with data. It's important to understand how data is currently handled at your business to determine how you will bring it all together moving forward.
Step 3: Establish a business intelligence team. This team should include data-driven individuals who would act as BI manager, analyst, architect, developer, and/or project manager. Together, this team will be responsible for deploying, managing, and efficiently using the BI tool.
Step 5: Train your staff to work with BI software. It’s important that anyone who will interact with your new BI sool knows how to use it. Whether they need to change the way they store and share data to ensure the tool has access or will be using the BI tool directly to extract insights, proper training ensures that everyone is prepared and ready to use the new tool.
Step 6: Conduct a test run. Before you go live, select the most important KPIs your business wants to measure and test your new BI software. See how it performs, collect feedback from users, and make any necessary adjustments.
With implementation out of the way, the next hurdle is determining which features are most important to your organization. The best practice is to make a balanced purchase: fulfilling your current needs, yet leaving room for future growth. But avoid the temptation to overbuy, because it only overwhelms your users with unnecessary functionality. Below are some common features offered by BI vendors:
Custom dashboards: Creates an overview of metrics in customizable dashboards that can be filtered by key performance indicators (KPIs) and are consumable at a glance by all manner of users.
Custom dashboards in Klipfolio
Embedded analytics: Provides users access to BI and analytics capabilities that are self-contained within the BI and analytics platform or that are available for import and integration from third-party tools.
Embedded analytics in Microsoft Power BI
Collaboration features: Helps users share and discuss information, analyses, analytic content, and visualizations via discussion threads, chat, and annotation; remote access via mobile devices is a key component and offers teams data sharing asynchronous to time and across extended geographies.
Collaboration features in Zoho Reports
Self-service data preparation: Enables access to and combines various data sources, transforms data using arithmetic, logical operators and functions, and ultimately loads the data into the self-contained storage layer.
Self-service data preparation in Tableau
Advanced analytics: Predictive modeling, data-mining, workflow builders, machine-learning capabilities, natural language processing (NLP) and platforms for creating custom models in “R,” Python, Java, and using query languages such as SQL.
Advanced analytics (chatbots) in Sisense
Key question to ask your BI vendor before you buy: What unique value does your tool offer for my primary intended use case?
*Note: The applications selected are examples to show a feature in context, and are not intended as endorsements or recommendations.
Your next critical consideration is deployment options: cloud BI or on-premise BI. This choice will sometimes cleave on available features, but the main deciding factor will come down to the variety, velocity, and volume of data your business hosts, as well as cost and infrastructure requirements.
High, upfront cost, but bulk of investment a one-time charge.
Better for managing unstructured content such as images, extremely large datasets, and data that is rapidly growing, frequently updating, or continually incoming.
Faster migration of large, legacy datasets.
Security controls are more customizable, whereas cloud-hosted data is only as safe as your vendor.
May lag behind in updates or feature parity with SaaS option.
Lower total cost of ownership (TCO) and incurred risk compared with on-premise BI.
Quick implementation time and easily scaled.
Elimination of staffing and IT real estate needed to host BI infrastructure.
Suited for remote teams with simple mobile and cross-location data sharing.
Key question to ask your BI vendor before you buy: How timely will data be surfaced for my users, and what tools are provided to share insights to fragmented or remote teams?
The scope of business intelligence is vast, but how SMBs use it will vary based on their industry and needs.
For example, BI software can help forecast sales for an eCommerce business by identifying popular and in-demand products and help determine customer satisfaction by analyzing customer reviews.
Other common use cases include:
Tracking financial data or other key metrics
Determining overall customer satisfaction
Recording customer buying behavior over time
Tracking overall business performance
Monitoring real-time sales data