What is Business Intelligence Technical Debt?

What is Business Intelligence Technical Debt?

Business Intelligence (BI) technical debt refers to the time spent by an analytics team in fixing data, which can be quantified between 0 and 100. It is caused by factors such as inconsistent metrics, low development efficiency, and an inability to reuse data. Technical debt can lead to lost productivity and increased costs, with data engineers spending up to a third of their time on tasks caused by technical debt.

• Inconsistent metrics: This refers to the lack of standardization in the measurement units used in data analysis, leading to confusion and inaccuracies.
• Low development efficiency: This occurs when the BI team is not able to produce results in a timely and efficient manner, leading to delays and increased costs.
• Inability to reuse data: This happens when data is not structured or stored in a way that allows for easy retrieval and reuse, leading to wasted time and resources.

How Can Technical Debt Impact Product Development?

Technical debt can significantly delay product development. When a significant amount of time is spent on fixing data and dealing with issues arising from technical debt, it diverts resources away from product development. This can result in delays that can extend to several months, impacting the overall productivity and profitability of the company.

• Resource diversion: When resources are spent on fixing data issues, they are not available for product development, leading to delays.
• Productivity loss: The time spent on dealing with technical debt results in lost productivity, as it detracts from the time that could be spent on more productive tasks.
• Profitability impact: Delays in product development can lead to missed market opportunities, impacting the company's bottom line.

What Are Some Tips to Manage Technical Debt in Business Intelligence?

Managing technical debt in Business Intelligence involves several strategies such as decommissioning legacy BI tools, freezing the creation of new analytic content on legacy BI tools, incentivizing decommissioning, and creating a data-driven culture in the organization. It also involves providing people with the necessary tools, procedures, knowledge, and responsibility, and automating selected reporting processes.

• Decommissioning legacy tools: This involves phasing out old and outdated BI tools that are no longer efficient or effective.
• Freezing new content: This involves stopping the creation of new analytic content on legacy BI tools to prevent further accumulation of technical debt.
• Incentivizing decommissioning: This involves offering incentives to encourage the use of more efficient and effective BI tools.

What Are the Different Forms of Technical Debt in Business Intelligence?

Technical debt in Business Intelligence can take several forms. It can be intentional, where development teams deliberately incur technical debt to meet deadlines or business goals. It can also result from incorrectly integrated systems, overly complex code, running outdated versions of software, or misusing the Data Warehouse as a Data Hub between operational systems.

• Intentional technical debt: This is when teams knowingly take shortcuts to meet deadlines, fully aware of the future costs associated.
• Incorrectly integrated systems: This occurs when systems are not properly integrated, leading to inefficiencies and errors.
• Overly complex code: This refers to code that is unnecessarily complicated, making it difficult to maintain and update.

How Can Secoda Help Manage Technical Debt?

Secoda is a data management platform that helps users manage technical debt. It offers features such as data search, catalog, lineage, monitoring, and governance. It also connects data quality, observability, and discovery, and offers automated workflows. By providing these tools, Secoda can help organizations manage their technical debt more effectively and efficiently.

• Data search: This feature allows users to easily find and access the data they need, reducing the time spent on data retrieval.
• Data catalog: This feature provides a centralized location for all data assets, making it easier to manage and maintain them.
• Automated workflows: This feature automates routine tasks, freeing up time for more important tasks and reducing the likelihood of errors.