The power of automation in business intelligence
Native application evaluation
Small and medium-sized businesses (SMBs) and startups often begin operations without integrating any business intelligence systems. They mostly rely on SaaS applications, which leads them to use the native analytics that these platforms offer. Although this approach can be beneficial in the initial stages, it quickly becomes limiting for two main reasons:
Lack of control: They cannot customize metrics or reports that reflect the uniqueness of their business model or specific situations.
Inability to integrate data: By using marketing and product analytics applications separately, they are unable to bring together data from both areas to get a more holistic view of the customer journey.
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Manual business intelligence reports
Over time, the need for analytics that integrate multiple sources becomes imperative as businesses require deeper insights into their operations. Initially, many opt for the simplest approach: building their business intelligence reports manually.
The typical process an analyst follows to create a report manually includes the following steps:
. Data extraction: download CSV files from SaaS applications and databases.
. Data loading: transferring information to a database or spreadsheet.
. Data modeling: use SQL or formulas to structure the data so that the table needed for the report is generated.
. Report creation: design the report or dashboard in a spreadsheet or using a data visualization tool.
. Distribution: share the report with relevant people within the organization.
However, it also has important disadvantages:
Error-prone: manually updating reports is repetitive, making it difficult to ensure data quality and reliability. This is a critical issue, as data is only valuable if it is reliable; In many cases, having no data is preferable to having incorrect data, since basing decisions on faulty information can be disastrous.
Slowness and limitations: the constant arrival of new data makes manually updating reports a slow process. This means that it is only viable for quarterly or monthly reports, complicating the generation of weekly reports and making daily and intraday reports practically unviable. In an ever-changing business environment, organizations that do not automate their business intelligence will not be able to keep up.
Difficulty of maintenance: manual report creation often leads to dispersing business logic across multiple spreadsheets or SQL queries, resulting in code duplication. As complexity grows and errors arise, tracking and correcting these issues becomes very difficult, which can result in unreliable reporting and compromised business decisions.
Lack of security: managing security manually is risky. By emailing reports without an automated process to control access rights, you risk sending sensitive information to the wrong people.
Despite these drawbacks, many companies with a significant number of employees still rely on manual methods for their analysis. This is often due to their focus on daily operations and a misperception that analytics is an optional distraction with a long-term return. This view is wrong; Data is a critical driver of growth and must be reliable to be useful. The main function of data is to provide us with a truth that can only be achieved through proper processes.
Automation in business intelligence through pipelines
The right strategy for business intelligence involves moving away from the batch mentality and toward a pipeline perspective. Data pipelines take care of all the process necessary to ensure that reliable information is available for generating reports and dashboards. Typically, a data pipeline automatically performs the following steps periodically:
. Collects information from various sources and centralizes it in a single warehouse.
. Processes the raw data tables, making them ready for modeling.
. Adjusts and models data according to the specific needs of the organization.
. Perform checks to ensure data integrity and quality, triggering alerts if necessary.
. Determines the defined business metrics.
. Report refresh – Automatically refresh dashboards and reports.
. Send alerts and notifications to business users as appropriate.
This approach not only optimizes data management, but also improves the reliability of the information used for decision making.
The advantages of adopting this method are significant:
Reliability: when pulling data from various external sources, it is common to face issues such as missing values or schema changes. The only way to ensure data quality is through rigorous testing. Pipeline automation also includes the automation of these tests, resulting in a significant improvement in data quality and reliability compared to a manual process.
Scalability: with automation, a single analyst can manage numerous data pipelines and obtain accurate results. This level of management is practically unattainable using manual methods.
Security: security is based on a systematic approach that requires automation. Well-designed and automated business intelligence pipelines can verify whether a specific user has the necessary permissions to access, view, or download specific data, minimizing the risk of breaches. A more advanced approach is to implement row-level security (RLS), which dynamically filters rows of data in a table based on the user performing the query. This allows multiple users to be served from a single master table, reducing maintenance, while controlling access to information effectively.
Business intelligence powered by AI
Up to this point, we have covered the importance of data pipelines, which are the structural foundation for achieving robust business intelligence. However, the final stage of business intelligence, traditionally linked to dashboards, can benefit significantly from automation thanks to AI capabilities.
AI-powered analytics solutions, such as AI assistants for BI, represent an effective response to this challenge. These systems are typically presented in conversational interfaces that allow non-technical people to chat with their database and receive answers instantly. The true innovation of these systems lies in the fact that they democratize access to data selected by specialists and, at the same time, automate the discovery of information to a certain extent. Self-service analytics, facilitated by AI BI tools, has long been a desired goal in the business intelligence industry, and thanks to artificial intelligence, this goal is becoming a reality.
However, it is essential to point out a major drawback related to AI-based analytics: the large language models (LLMs) that underpin these applications can incur “hallucinations.” This phenomenon refers to when generative AI systems produce inaccurate or meaningless results and even invent answers. This clearly contradicts the purpose of business intelligence, which seeks to improve decision making based on the “truth” offered by data. Fortunately, there are some strategies that help mitigate this risk:
Context control: this involves restricting the information that is sent to the LLMs, limited only to the tables and metrics carefully selected by the data team for uses. commercials. Providing full access to the data store is not recommended.
Documenting metadata: systematically recording each asset, such as tables, columns, and metrics, is an essential practice for creating scalable data operations and is even more important for providing relevant context to AI and reducing hallucinations.
Fine-tuning: basic models are often too general and have inherent limitations. As in other fields, tailoring models to specific tasks can lead to significant improvements in results.
While it's tempting to indulge in shiny object syndrome when it comes to AI, its effectiveness depends on a rigorous discipline in data operations and a holistic approach to maximizing the available context. If you have a fragmented data infrastructure, where different components barely communicate with each other, metadata silos will form, making it difficult to implement meaningful AI-powered analytics like BI chat GPT effectively.
Conclusions
Relying on manual data management for decision-making is unsustainable: this method is slow, error-prone, and generates hidden costs. Therefore, organizations should prioritize automating their data processes for business intelligence as soon as possible, as the benefits will far outweigh the investment made. The integration of artificial intelligence also offers great potential, facilitating immediate access to actionable information for all employees, regardless of their technical or data literacy level. AI data analysts and AI assistants for BI can revolutionize how insights are accessed and interpreted. However, it is crucial to account for the significant risk of hallucinations presented by generative AIs. This risk can be mitigated through an integrated approach that provides AI with refined context and allows for effective control of the results obtained.
