Today’s future-focused businesses increasingly turn toward business intelligence to drive sustainable business growth. With it, the market size of Business Intelligence is burgeoning.
Case in point: The global BI market size was valued at USD 29.42 billion in 2023 and is expected to hit USD 63.76 billion by 2032 – up from USD 31.98 billion in 2024 at a CAGR of 9.0% during the forecast period of 2024-2032.
This surge in demand is attributed to BI’s high-end capability to extract actionable insight from raw data. A critical part of leveraging BI effectively is the implementation of a well-structured Business Intelligence workflow.
How you design your BI workflow can make or break the game!
What is Business Intelligence and its importance?
Business intelligence is a descriptive tech-driven process that enables businesses to make better decisions based on current business data. It aims to foster the implementation of technologies and strategies to facilitate the extraction of actionable insight from raw data (both structured and unstructured).
These outputs are used to measure the performance of business actions against some established key performance indicators (KPIs). BI makes it effortless for businesses to determine their next actions and steps depending on these performance data, thereby augmenting decision-making.
However, traditional BI which is often retrospective, lacks real-time data analytics and only uses structured data. As a result, it can lead to errors, and delays in data processing, thus, ultimately decreasing ROI.
On the other hand, advanced BI tools equipped with modern technologies such as AI, ML algorithms, big data analytics, etc., can effectively debottleneck traditional BI tools. They support a unified data model, automated data integration, real-time data processing, and a wide range of connectors and integration options. In short, to one-up your competitors by making more informed decisions, adopting advanced BI tools stands as a strategic imperative.
Business Intelligence Workflow
Business intelligence workflow is the systematic and structured approach to data collection, integration, analysis, and visualisation to facilitate the process of extracting meaningful insights from raw data.
BI is the entire concept of leveraging data to acquire insights and make more informed decisions within a company. On the other hand, BI workflow is part of a BI that details the steps involved in efficient insight extraction. In short, BI is the wider topic that includes all tools and strategies whereas, BI workflow focuses on the detailed process of managing and processing data effectively within that strategy.
Benefits of Implementing BI Workflow
Implementing a BI workflow enables organisations to dive deeper into their business data across all channels they exist. Thus, they don’t miss out on any valuable insights or risks their competitors may use to outpace them.
Let’s go through how implementing BI workflows can benefit a business:
Complete Data Insights
Having an effective BI workflow in place means you can efficiently consolidate, integrate, clean, and process data from all sources your business presents. The cleaner and more accurate data you have, the better your business decision will be. Needless to mention, getting a complete overview of how your business is with data and extracting insight from it effectively would give you a tangible advantage over your competitors.
Holistic View for Decision-Making
A BI workflow helps you get all data across all departments at your fingertip. Effective analysis and processing of this data gives you a holistic view of your business processes. Thus, you can efficiently identify trends, hidden patterns, customer preferences, and all key market dynamics to optimise your business operations.
Advanced Analysis for Future Planning
When you use an advanced BI tool to build your BI workflow instead of going to the manual route, you can access advanced data analytics techniques, such as predictive and prospective models, multidimensional analysis, etc. Leveraging advanced techniques not only facilitates the identification of trends and patterns or foreseeing customer demands, but it also offers suggestions on your prospective actions. Thus, you can customise your service/products based on your analysis to meet future market demands and gain a competitive edge.
Steps in Building an Effective BI Workflow
An effective BI workflow is critical to strengthening decision-making and achieving sustainable organisational goals. Let’s go through the key steps to build a successful BI workflow:
Step 1: Choosing the Right BI Tool
While you can go with the manual approach, building a BI workflow using an advanced BI tool has its own advantages. The manual approach requires human intervention while performing data processing tasks such as data collection, integration, cleansing, and analysis using disparate tools.
Using siloed systems in data processing often adds error, makes the process highly arduous, and can significantly affect your decision-making. This is where a BI tool comes into play. The advanced data analytics capabilities, real-time data processing, and advanced data visualisation with modern BI tools help dig deeper into your data for augmented decision-making.
While choosing your BI tool, consider factors, such as scalability, compatibility with different data sources, data integration capabilities, etc. Some high-end BI tools are Tableau, Power BI, Qlik, etc.
Step 2: Define Business Goals and KPIs
Before you start your BI workflow designing process, ensure you strategically set our business objectives and the key performance indicators (KPIs) you would use to measure your business performance. It will also help ensure your BI workflow aligns with your established business goals.
While setting up your goals and KPIs, ensure getting all stakeholders involved onboard. Putting their heads together would help them understand what your business needs regarding data sources, data analysis, and visualisation. By gathering this information, you can design a BI workflow tailored to your business needs and specifications.
Step 3: Data Collection
The first step of designing a BI workflow is to collect data from both internal and external sources a business presents. Internal sources are webpage forms, payrolls, and customer information, whereas, external sources include social media data, competitor analysis, market analysis, etc.
Step 4: Data Integration and Preparation
Next up is data integration and preparation. Consolidate data from all sources into your data warehouse – a centralised repository that facilitates bi activities, more specifically data analytics.
To ensure only clean and high-quality data is fed into the system for data analysis, ensure the cleansing, standardisation, and transformation processes are executed precisely.
Step 5: Data Modeling and Analysis
The next step is to apply advanced data analytics techniques such as predictive modelling to turn data into valuable insights.
Step 6: Visualization and Dashboarding
One of the most significant steps in designing a BI workflow is to create visualisation and dashboarding of the data analytics results. Again, cashing in on an advanced BI tool instead of taking the manual route is recommended.
These tools feature high-end data visualisation through interactive dashboards, visuals, graphs, and charts to make it effortless for businesses to pull insights from massive troves of datasets, and pinpoint patterns and anomalies – in a short time. It fosters transparency across all departments within an organisation while also facilitating real-time cross-departmental collaboration.
Step 7: Collaboration and Sharing
Enable dros-departmental collaboration to allow stakeholders to access, share and collaborate on results and data-driven insights in real-time. Fostering a collaborative environment within an organisation helps augment communication, facilitate report sharing, and get a quick understanding of KPIs and performance metrics.
With the manual approach that relies on emails, meetings, or printed reports, you can enable collaboration to some extent. However, using these disparate processes in silos lacks version control, and makes data sharing arduous.
The best option is to leverage the collaboration features of advanced BI tools that allow stakeholders to share insights, data, dashboards, and reports in a central system for real-time collaboration. In addition, you can also foster transparency, team alignment, data literacy, and accountability in data processing by using advanced BI capabilities such as auditing, commenting, shared workspace, etc.
Step 8: Training and User Adoption
Now that you have your analytics result in hand it’s time to ensure the results are accurately deciphered. In addition, you also need to foster a culture of leveraging advanced analytics in all your business processes across all departments. To enable data-driven decision-making and encourage the adoption of BI tools, you need to train your staff on their BI best practices.
Step 9: Monitoring and Performance Optimisation
Continuous monitoring of workflow performance, and performance optimisation is paramount to fortify operational efficacy.
Step 10: Feedback and Iteration
The last step is to iterate and enhance the performance of your bi workflow by collecting feedback for your stakeholders, and users. A culture of continuous improvement can ensure you get the most out of your BI workflow.
How to Choose the Best BI Tool
You will find the market bustling with BI tools with bells and whistles. To get the best bang for the buck, look for the following criteria in a BI tool:
- Cloud Data Warehouse Support: Cash in on a BI tool that can seamlessly connect with cloud data warehouses. Optimised for scalable BI and data analytics, these types of data warehouses are great for businesses that experience an influx of data flowing in.
If you frequently deal with complex analytical queries, go for a BI tool with support for the cloud data warehouse. Tools like Tableau (connects to 14 data warehouses) and Mode (20) support integration with multiple data warehouses. - Library and Support for Custom Views: When it comes to informed decision-making, interactive data visualisation comes second to none. Opt for high-end BI tools that feature a library of vast native visualisations, along with support for built-in and customisable visualisations.
- Cost: Before splurging your money on a tool, evaluate if its features align with your business specifications and the targets you want to achieve. Key factors impacting the cost of a BI tool include licence models, additional high-end functionalities, user type, etc. Tools like Power BI, Sigma, Sisense, etc., come with different price tiers. Choose the one that offers the best compromise between features and cost.
- Modelling with Versioning Control: Using BI tools equipped with version control is critical to drive scalability in data analytics. To enable seamless collaboration and project development, choose tools like Looker, Periscope Data, etc., that integrate with versioning control platforms like Git.
- Automated Report Generation: Choosing a tool with no automatic report generation and real-time notification capabilities is a big fat NO! For a more streamlined decision-making process, cash in on tools like Mode or Power BI that can schedule report delivery via Slack or email.
Challenges with BI Workflow
- Data Integration Complexities: One of the key challenges organisations face while executing their BI efforts is integrating a vast amount of data from multiple sources often plagued by inaccuracy and inconsistent data quality.
The issue is further enhanced when they deal with data of various formats. However, modern BI tools come with advanced data integration capabilities such as data connectors, ETL processes, and data transformation capabilities for seamless data integration from multiple sources and transforming it into a standardised format. - Lack of Scalability: Organisations that spend on BI tools without taking the possibility of expanding their business often grapple with scalability issues. This is because, as businesses grow, the volume of data they generate and receive also expands. Traditional BI tools lack scalability and fail to process a sheer volume of data efficiently.
- Data Governance Challenge: Having no robust data governance policy in place can pose a significant challenge to a BI initiative by affecting the accuracy of analytics outcomes. Inaccurate and inconsistent data-based insights can mislead analytics teams, and lead to non-compliance, affecting BI processes and decision-making. Incorporating robust data governance principles in your BI initiatives can help ensure data integrity, data quality, and compliance with regulatory standards.
Business Intelligence Workflow Wrap Up!
As business is getting increasingly data-driven, more focus is placed on designing a robust BI workflow.
Looking ahead, BI is fast getting a boost, by transforming its processes with advanced technologies. For example, more focus is now placed on equipping BI with advanced technologies such as artificial intelligence, machine learning algorithms, Natural Language Processing (NLP), Big Data analytics, etc. Adopting these technologies would help businesses harness the full potential of data.
