What to look for in an ad hoc analysis tool for your modern data stack 

25 Apr, 2024 •

Ad hoc analysis

Ad hoc analysis is critical for data-driven professionals to extract insights from their data. Traditionally, that means using a graphical interface to make queries on-the-fly to the data. But as simple as it sounds, there are often a number of obstacles to getting there. 

The ‘modern data stack’ is a term used to describe the SaaS versions of traditional business intelligence stack: data integration (or extract-transform-load, ETL), data warehouse or data lake, and business intelligence layer, consisting of reporting and ad hoc analysis tool. 

The ad hoc analysis tool must: 

  1. Access the data you are authorized to query, both structured and unstructured. 

Often times, the data is buried in systems that you can’t easily find, or it’s unstructured data for example in PDFs. Graphical analytic tools often hit a wall in this situation. 

  1. Be easy to use.  

While it’s true that pictures are worth a thousand words, it’s often easier to ask a question rather than figuring out how a graphical tool works to simply pose a question. 

  1. Provide trustworthy and understandable results 

You need to know that the data is authorized for your use and reliable. Therefore, the data should ideally come from a data catalog that governs the use of the data and is being cleansed with automated data quality rules. 

  1. Handle iterative questions 

Often, analytic reports are designed by analytics teams who don’t deeply understand the data and therefore need to design general-purpose reports for constrained use cases. That’s often a problem for managers and executives who are frustrated by the obstacle to pivot or go one layer deeper to a question.  

  1. Provide robust data security and data governance 

Prioritize tools that offer robust data governance and security features, including access controls, encryption, audit trails, and compliance certifications, to protect sensitive data and ensure regulatory compliance. 

A new approach: DvSum CADDI, Conversational AI for Self-Service Analytics 

DvSum CADDI empowers professionals to simply ‘talk’ to their data. CADDI is the conversational AI layer that empowers any authorized user to ‘talk’ to the systems of record and thus achieve self-service analytics. When a query is posed, CADDI retrieves the data, analyzes it, and makes recommendations. 

Therefore, professionals gain data insights by simply talking to their data in an iterative manner without requiring any technical skills.  

Powered by GPT-3 AI from OpenAI (that powers ChatGPT) and automatically learned intelligence from data, the solution gives reliable, accurate, trustworthy results every time.  

With DvSum’s patented push-down architecture, insights are delivered securely in real-time from data sources without requiring complex infrastructure or moving data outside the environment. 

Conclusion: Try DvSum CADDI for GenAI-Powered Ad Hoc Analysis 

DvSum CADDI empowers you with an intuitive conversational interface for ad hoc analysis. Harnessing the power of GenAI, DvSum CADDI lets any employee become more autonomous and productive with self-service analytics to achieve “in-the-moment” and iterative analysis. Contact us today to learn how DvSum CADDI can help you.    

Share this post:

You may also like