Business Intelligence Automation

Over the years, conventional business intelligence has consistently powered data-driven decision making, enhancing customer satisfaction, increasing efficiency, and driving growth.

However, in today’s fast-moving, competitive, and data-heavy business landscape, relying on conventional business intelligence causes more setbacks than benefits. The businesses gaining the most from business intelligence are the ones implementing automation.

Business Intelligence Automation (BIA) is the process of integrating automation technologies into traditional business intelligence workflows. Thanks to BIA, you improve data accuracy, increase decision-making efficiency and speed, and optimize operational costs.

To enjoy these benefits, here is how to implement Business Intelligence Automation. We also highlight common missteps businesses make during the automation process, saving you the frustrations of failed or ineffective automations.

Implementing Business Intelligence Automation

1. Define objectives and specify key performance indicators (KPIs)

Review business processes to find those that heavily rely on data insights. Perhaps you have a conventional business intelligence structure in place. Reference it to find the most critical business processes that rely on data insights. Why the most critical ones? You need an initial focus point or to create a prioritization list before automation. Once you have a list in place, define or reference the objectives of the business operations at the top of the list. For instance, if it is the sales, you can aim to improve sales. Then, specify key performance indicators. These are the metrics you’ll monitor to determine whether you are achieving the objectives, driving decision-making. Ensure the KPIs are specific and measurable. Reflecting on the example objective, relevant KPIs may include average order value, conversion rates, and weekly or monthly sales growth. With the objective and KPIs in mind, it becomes easier to determine the data to analyze, extracting focused insights.

2. Specify data requirements and obtain the data

Based on the set business objectives and defined KPIs, figure out the type of data needed to achieve the objectives. With the availability of AI models capable of analyzing semi-structured and unstructured data, worry less about being limited to working with structured data. After determining the type of data needed, define the source and how to collect the data. You can source data from internal databases or external sources like public databases or competitor websites. Yes, you can unlock and scrape websites of various competitors to collect necessary data to give you a competitive edge. Finally, ethically collect the needed data. If you are sourcing data from multiple sources, use data integration tools to automatically pull data to a specific storage point. If you need to use sensitive or private data, obtain consent to avoid legal trouble. Also, handle and store the data securely to prevent access by unauthorized individuals.

3. Design BI workflows and identify areas to automate

So far, you have clearly defined objectives and KPIs, including the necessary data from which you’ll extract insights. Now, proceed to sketch and design the flow of data from its raw state to the analysis point, and finally to meaningful information (insights). The design should also demonstrate how the business intelligence workflow integrates with other automated business operations. Design the business intelligence workflow with scalability and flexibility in mind. Consider using cloud-based data storage to support growing data volumes. Also, implement ETL pipelines to handle data extraction, transformation, and loading while accommodating increasing complexity. Once the design is ready, analyze it to determine the sections you can automate. For example, you can automate data extraction from the storage point or other external sources, data processing and transformation, and data analysis.

4. Choose automation tools and automate the workflows

When it comes to choosing automation tools, many businesses prefer AI or machine learning enabled tools. This is because of their capability to uncover deeper trends and patterns from large data volumes. It is also possible to create a custom AI model to fully automate the intelligence workflow. Select tools with a set budget in mind. Investing in automation tools, especially AI models, may become costly over time. So, ensure to optimize spending on the tools to avoid financial stress. After choosing the right automation tools, implement the business intelligence workflow. Then, set up and automate data visualization and reporting to communicate insights or inform other business operations without manual intervention. Pro tip: Consider setting up alerts and notifications systems within the business intelligence workflow to monitor sudden changes or tell when a certain KPI is achieved. This way, you can capture both positive and negative changes as the automated systems analyze data.

5. Develop a training and adoption plan before launching the automation

Define the responsibilities and roles of various stakeholders such as the IT teams, data analysts, business managers, and other users of the automated workflow. Then, define what training or support each needs to understand the tools and the generated insights, maximizing the benefits of the automation setup. To ensure compliance, accountability, and consistency in using the automated system and decision-making, establish governance guidelines. These are principles or rules guiding the stakeholders on how to control and manage the automated setup. Finally, put together policies for security, data quality, and compliance to sustain the integrity of the data flowing within the automated system before making it available for use.

6. Oversee and enhance the setup

As business objectives and priorities change, it is critical to continuously assess the performance of the automation. Oversee data quality and integrity and review business intelligence workflows to find and address bottlenecks. This way, you ensure the automation is aligned with current business objectives and consistently delivers accurate and actionable insights. Moreover, as you monitor the automation, be on the lookout for common challenges like change management, integration complexity, security and compliance, and cost overruns. Prepare in time to mitigate the impact of these and any other anticipated automation challenges.

Closing Words

As a result of automating business intelligence workflows, businesses can now cut down on manual reports and make quicker decisions. The routine tasks like data processing, transformation, analyzing, reporting, and visualization are now automated, freeing up time for businesses to focus on strategic decision-making. And, it gets better! If you are still stuck with a conventional business intelligence system, use this guide to automate it. While at it, remember to approach this strategically because it is a long-term investment.