Many organisations rely on data to inform their decision-making, from their operations to overall business strategy. Making the right decisions will ensure a competitive edge and set your business up for long-term success. Businesses can analyse their data through two primary ways – a traditional business intelligence platform or a data discovery tool. While both methods have their advantages, it is important to understand the benefits and limits of each when deciding which method is the right fit for your business. We explore both of these methods below.
What is Traditional Business Intelligence?
At a basic level, business intelligence (BI) refers to various processes, methodologies and technologies used to interpret an organisation’s raw data. BI can involve data mining, data visualisation, and other data tools, infrastructure and best practices. They aim to help businesses make more data-driven and evidence-based decision-making.
A business should aim to have a comprehensive view of their data and its use. This will enable you to eliminate inefficiencies and quickly adapt to your industry’s changing market environment. At the core of BI is achieving business goals by tracking performance against these goals through data gathering, data analysis and decisions about the next steps based on the insights.
There are several related activities, including:
- data mining: using statistics, databases and machine learning to detect trends in data sets;
- reporting: sharing data analysis with stakeholders so they can draw conclusions and make decisions;
- querying: BI pulling answers to data-specific questions from the data sets;
- statistical analysis: exploring the findings of preliminary data analysis by using statistics to explain how a trend happened and why; and
- data visualisation: converting data findings into visual representations like charts and graphs.
Two key examples include Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM). An ERP is a business management software that enables businesses to use, store and analyse data for various business activities, such as:
- human resources;
- manufacturing and sales; and
- operational planning.
A CRM is a technology used to manage and analyse customer interactions to improve the client’s experience within the organisation.
What is Data Discovery?
In contrast to traditional business intelligence, a data discovery tool is more time effective, as you do not need to integrate data into an enterprise data warehouse before you analyse it. Data discovery is best understood as a business intelligence architecture that creates data using multiple sources. It involves bringing together disparate, siloed data sources to be analysed.
Data discovery is useful for businesses that collect large amounts of data from customers, markets, suppliers, production processes, etc. Data can flow from online and traditional transaction systems, social media, or mobile devices. Accordingly, making sense of the hidden insights by untangling trends through data discovery is invaluable to a business.
Data discovery is increasingly relying on artificial intelligence (AI), otherwise known as smart data discovery. Algorithms can run against data sets, identify potential relationships, and generally accelerate data analysis.
Some of the uses for data discovery include the following:
- identifying the cause of customer churn;
- analysing product failures by looking at returns and failures;
- identifying promotional failures; and
- identifying price leakages due to, for example, excessive discounting.
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What’s the Difference?
The key differences between traditional business intelligence and data discovery can be summarised as follows:
- Business intelligence is best for answering questions about existing knowledge. For example, you can count the number of product sales in the previous month. On the other hand, data discovery can uncover trends, which is often useful for making predictions. For example, based on the previous data, you can predict the number of product sales in the coming month.
- A business intelligence system takes months to build due to the need for an enterprise data warehouse. However, you can build a data discovery tool within a few days.
- There can be different versions of truths when integrating multiple data sources through a data discovery tool. A business intelligence platform will only have one version of the truth.
- A business intelligence system provides a broader and more detailed view into the processes and strategies of an aspect of the organisation. Data discovery is best for answering one-off questions for data with a short shelf-life.
Key Takeaways
Data discovery is not a direct replacement for traditional business intelligence. Each has its benefits, and an organisation can employ them based on the circumstances and needs at the time.
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Frequently Asked Questions
Business intelligence (BI) refers to various processes, methodologies and technologies used to interpret an organisation’s raw data. BI can involve data mining, data visualisation, and other data tools, infrastructure and best practices.
Data discovery is a kind of business intelligence architecture that creates data using multiple sources. It involves bringing together disparate, siloed data sources to be analysed.
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