There are good businesses, and there are businesses that succeed. Often what separates the two is the degree to which business intelligence (BI) is embraced.
BI is a critical aspect of modern business operations, it refers to the use of data analytics tools and techniques to transform raw data into actionable insights that can make informed decisions. With the explosive growth in data in recent years, there is an increasing need for businesses to make sense of the vast amounts of data they generate to remain competitive.
One of the most significant benefits of BI is that it helps businesses identify patterns and trends in their data. By analysing this information, organisations can better understand their customers, operations, and market trends. These insights can then be used to develop strategies that help businesses stay ahead of the competition in addition to identifying areas of inefficiency, leading to more effective resource allocation and cost savings.
With the amount of airtime currently given to Artificial Intelligence (AI), it may sound like it is the ultimate solution to solving all these challenges. However, before a company considers using artificial intelligence (AI) for decision making, it is important to first implement BI as a foundation. BI provides a solid framework for understanding data and making informed decisions, while AI requires more advanced technology, investment, and expertise to be used effectively.
Understanding business intelligence
Business intelligence isn’t a wholly new concept. There are mentions of the term in literature dating as far back as the 1800s. The references were often made in the context of business acumen and astute decision making in entrepreneurship. So, what’s changed from then to now? The core principle of robust decision making during business remains the same but it’s how we approach the decision-making process that has evolved. The vast advancements in how we capture and analyse data, now mean decisions can be driven by insights derived from data, rather than relying on intuition and memory alone.
Where to apply BI in your business and when to look to AI
The use cases for BI depend on your business’s needs. Some of the most common ways to use BI include:
- Sales and marketing: BI can be used to analyse customer behaviour, track sales trends, and identify opportunities for growth. It can also help monitor the effectiveness of marketing campaigns and adjust strategies accordingly.
- Operations: BI can help optimise production processes, improve supply chain management, and reduce waste. It can also monitor key operational metrics, such as inventory levels and production efficiency.
- Finance: BI can track financial performance, analyse profitability, and forecast revenue. It can also monitor expenses and identify cost-saving opportunities.
Implementing business intelligence (BI) for business decision making requires careful planning and execution to ensure that the insights gained from data analysis are meaningful and actionable. Here are some best practices for implementing BI effectively:
- Start with a clear strategy: Begin by defining your business goals and the questions you want to answer through BI. This will help you identify the data you need to collect and analyse, as well as the KPIs that will be used to measure progress.
- Focus on data quality: Ensure that the data used for analysis is accurate, reliable, and up-to-date. This may require investing in data governance practices, such as data cleansing, validation, and standardisation.
- Choose the right tools: Select the BI tools that best fit your organisation’s needs and budget. Consider factors such as ease of use, scalability, and the ability to integrate with other systems.
- Develop a data-driven culture: Encourage a culture of data-driven decision making throughout the organisation.
- Collaborate across departments: Work across departments to ensure that data analysis is aligned with business goals and objectives. This will help ensure that the insights gained from BI are meaningful and actionable.
- Monitor and adjust regularly: Regularly monitor the metrics and KPIs established through BI analysis and adjust as necessary. This will help ensure that the insights gained from data analysis remain relevant and useful.
- Ensure security and compliance: Implement appropriate security and compliance measures to protect sensitive data and ensure compliance with relevant regulations.
BI enables businesses to develop metrics and key performance indicators (KPIs) that help them track progress towards their goals. By regularly monitoring these metrics, organisations can adjust their strategy to ensure they are on track to achieve their objectives.
On the other hand, AI is a more complex technology that requires significant expertise to implement effectively. While AI can provide advanced insights into data that may not be apparent through traditional BI analysis, it is important to first establish a solid BI framework to ensure that the AI algorithms have reliable and accurate data to work with.
You may have read that AI has the potential to automate a range of processes and roles. Whether you see this as an opportunity or a risk to your business, it is unlikely that ‘real’ intelligence, provided by humans, will become obsolete. While AI can automate tasks that require surface level knowledge, it is not a replacement for human expertise, especially in fields where the domain is filled with nuance.
Creating a baseline of data literacy for BI and AI success
As data becomes an increasingly important part of business operations, it is essential for organisations to increase their data literacy. Creating a strong baseline across your workplace will dramatically improve your team’s understanding of the opportunities they can glean from data. While you don’t need your whole team to become data scientists, by encouraging them to explore continuous learning and upskilling initiatives – and giving them time within the workday to do so – you’ll increase their understanding of data driven decision making and the range of tools and processes that enables both BI and AI. With this strong baseline in place, organisations can empower employees to effectively leverage data to drive business success.
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