The Importance of Repeatable, Scalable Systems in Business Intelligence and Data Science
Building repeatable and efficient systems is essential for effective business intelligence and data science strategies. In business intelligence, the goal of self-service analytics dashboards is to empower business users to explore and understand their data independently, significantly reducing the need for ad hoc analysis. With a well-established business intelligence strategy, companies can often answer 80% of incoming questions in under a minute. As your business intelligence strategy scales, the way decisions are made and the culture of the organization naturally evolve.
In the world of data science, there’s a major emphasis on adhering to sound engineering principles and making sure code is production-ready. Efficient use of resources and compatibility with the production environment are critical. As data science has advanced, specialized fields like machine learning engineering and MLOps have emerged to focus on these areas. It’s clear that both seasoned data science leaders and newcomers are prioritizing repeatability and scalability—organizations won’t fully realize the value of data science without this focus.
While repeatability is crucial, there is still a vital role for ad hoc analysis. Neglecting it can result in missed insights or an unpreparedness to handle urgent, unexpected needs, putting other deliverables at risk.
Ad hoc analysis often takes a backseat in analytics strategies, and it’s not just in the data space—“one-off” tasks are usually frowned upon by CEOs. However, when coupled with robust, systems-first strategies, ad hoc analysis still has its place. In many organizations, though, ad hoc tasks are deprioritized in favor of scalability and efficiency.
Why Ad Hoc Analysis Matters:
Ad hoc analysis provides valuable opportunities for deeper exploration, even if initially a one-time effort. Here are a few reasons why:
- Deeper Dive: Going beyond aggregated data to explore unknown key drivers can reveal new insights or features for models.
- Edge Case Exploration: Examining anomalies and outliers might uncover entirely new questions related to data collection or emerging patterns.
- Visualization: Creating custom visuals can clarify issues and enhance understanding, even if this is just a step in the analytical process.
- Challenging Assumptions: Analysts may not always be asked to challenge assumptions, but confirming or disproving widely held “facts” is critical for business accuracy.
- Documenting Non-Findings: Sometimes, the absence of findings is as significant as discovering new insights. In exploratory analysis, non-findings help avoid redundant work and shape future analyses.
- Creating New Questions: Effective analysis often uncovers new, valuable questions and hypotheses, even though they may be frustrating to those not accustomed to them.
Key to Successful Ad Hoc Analysis:
To make ad hoc analysis truly effective, remember these guiding principles:
- Define the Business Question: Be clear about the issue at hand so you’ll know when you’ve solved it.
- Make It Tangible: Set clear expectations for deadlines and the desired output. Whether it’s a quick answer by the end of the day or a comprehensive presentation by the quarter’s end, make sure everyone knows what to expect.
- Leverage Existing Resources: Don’t reinvent the wheel—check if an existing model or dashboard can address the problem or be modified to help.
- Seek Feedback Early: Involve stakeholders early in the process to ensure no critical insights are missed and avoid wasting time.
- Return to Systems Thinking: Once the analysis is complete, consider how the insights should be integrated into your broader business intelligence and data science processes.
Ad hoc analysis, while often seen as a “one-off” task, remains an important aspect of both business intelligence and data science strategies. It generates critical insights, fuels new inquiries, and plays a key role in advancing your organization’s data and analytics capabilities.
