Thanks to the rapid rise of cloud-based systems and applications, IoT, and third-party data sources, modern businesses have a massive amount of valuable data at their fingertips. And everyone is seeking ways to make better use of that data. A business intelligence framework helps organizations make the most of their data and allows decision-makers to quickly find answers to important questions and have confidence in the accuracy of their insights. Let’s explore the benefits of using a business intelligence framework and the components that will improve your team’s ability to use data for driving growth.
What is a Business Intelligence Framework?
A business intelligence framework is a strategic approach to conducting business intelligence for better decision-making. It helps to ensure that all relevant data is brought to bear, that data is clean and validated, and that the data is being applied in context. Ultimately, a business intelligence framework is a process that allows you to make the most of your data to find optimal solutions to business problems and quickly take advantage of opportunities as they present themselves.
Benefits of Using a Business Intelligence Framework
Let’s take a deeper look at the advantages of using a business intelligence framework. Here are four powerful benefits you can expect to experience.
Better data quality
Using a consistent framework with a step-by-step process will help you ensure data quality — the accuracy, completeness, relevance, cleanliness, and freshness of your data. You won’t accidentally overlook relevant data, begin analyzing data before it’s been cleaned and verified, or use outdated data. With a business intelligence framework, decision-makers will know where to find data that’s ready for analysis. And if you’re using a cloud-native BI platform that sits on top of your cloud data warehouse (like Sigma), you’ll avoid data extracts that compromise data quality.
More accurate insights
When you have confidence in the quality of your data, you can have confidence in the quality of the insights that you derive from it. And with a framework that prompts decision-makers to fully explore the data — digging into the data to answer follow-up questions — all the angles of an issue can be considered.
A business intelligence framework improves the reporting process by making it actionable. Because a framework emphasizes following an organized process, business questions are clearly defined and insights can be applied in a practical manner.
A business intelligence framework also results in faster decision-making since it’s optimized for speed (while preserving accuracy). Business moves quickly, and decision-makers need to access insights just as quickly. This speed is accelerated further when you use a BI solution that allows users to work in a spreadsheet-like interface, automating SQL generation.
5 Components of a Business Intelligence Framework
While there’s no one correct way to implement business intelligence, an effective framework will include the following components.
- Clarify the question.
Focusing on one question at a time will make your BI process more organized and will allow you to more easily identify which data is relevant to each question. The answers you find will likely bring up additional questions, which you can then explore to gain a deeper understanding of the issue at hand. It’s also helpful to make a list of all the questions related to a decision and work through them systematically.
- Identify relevant data sources.
Starting with your initial question, consider all the data sources that may offer relevant information. These data sources will likely include a variety of databases and SaaS solutions, but also may include your website analytics, social media platforms, advertising platforms, ERP system, and even public data sources such as census data, weather data, and public health data.
- Optimize the data pipeline.
Raw data comes in a variety of formats and is often riddled with inconsistencies and errors. The data pipeline process extracts data from various sources and transforms, tests, and documents data so it’s ready for analysis. The data may be loaded into a data lake or warehouse before or after transformation. Your data pipeline can be optimized for speed and security by using tools like Fivetran and dbt.
- Explore the data.
With a cloud-native analytics tool like Sigma, you have direct, governed access to all of the live data inside your cloud data platform. You can then explore the data to identify insights related to your business question — joining data sources, calculating, filtering, and sorting. Conducting root cause analyses and what-if scenario modeling will allow you to uncover deeper insights.
- Create visualizations.
Visualizing the data is where the magic happens. Visualizations distill data into graphics that provide a clear understanding of complex relationships within the data. There are many ways to visualize data, including charts and graphs, scatterplots and diagrams, matrices and heatmaps, and geographic maps. Ideally, your visualizations will be interactive, allowing authorized viewers to drill beyond the surface level data and into the underlying granular data to answer new questions or for further analysis.
Implement a BI Framework with Sigma
Sigma seamlessly integrates with popular data warehouses and platforms like Amazon Redshift, Google BigQuery, and Snowflake as well as with data pipeline tools like Fivetran and dbt. And with a familiar spreadsheet-like interface that auto-generates the necessary SQL, Sigma makes it possible for users with varying technical skills to easily participate in the business intelligence process. At the same time, data experts and BI analysts can dive into the SQL whenever they like.
With Sigma, decision-makers can get insights when they need them and collaborate to solve more complex issues or explore opportunities that span departments. Most significantly, Sigma enables your company to be data-driven without the need to hire a big team of data experts and BI analysts.
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