What to Consider for your Insight Engine Rollout: Hands-on Testing with Company Data

Once a company identifies its use cases and defines its success criteria, it is critical to do hands-on testing of the product using actual company data.

Part three of the blog series on what to consider for your insight engine rollout will focus on the third step when weighing your options for an information insight solution – hands-on testing with company data.


What is a Proof of Concept (POC)?

A proof of concept (POC) is one of those terms that means exactly as it sounds. A POC is evidence that a product actually works and the functionalities are sound. This evidence comes after giving it a test run or, in other words, a pilot. Many businesses preach what they can do, but it is always necessary to see for yourself before turning your money over to them. Doing so helps clients separate vendors they wish to work with from ones they may not and sets the foundation for further decision-making on a product and implementation.

Step Three: Hands-on Testing with Company Data

The POC should go beyond sales slides and demo videos, involving the customer's data and actual use to test the solution. There are only so many tutorial videos to watch before getting your hands on the product becomes necessary. A POC that includes the existing customer data can identify problems right from the beginning, and results can serve as a basis when integrating into the live operation – rather than using makeshift data for the sake of trial.

One factor that should not be brushed off is the quality of the existing company data. Especially in AI implementation, data quality is vital. Data garbage can be a significant complication to AI and machine learning testing. Data garbage could be classified as incomplete data, has various versions in different sources, is inconsistent, or has spelling/punctuation errors, making it difficult for an insight engine to generate accurate and relevant results to a query.

Bad data quality is avoidable before hands-on testing, and a detailed examination can solve these issues in a short time. Here are some steps:

1. View and Understand Data

  • Which data sources are available in the company and which are relevant to the POC?
  • When considering your solution for information insight, it is always beneficial to ensure the number of data sources can be expanded if necessary.

2. Data Cleansing

  • Existing data needs to be inspected to determine which files are needed and which are not.

3. Process and Link the Data

  • The better the product, the less manual effort is required. Mindbreeze is capable of automatically recognizing correlations between your information while making them visible to the user with the ability to provide feedback.


Be sure to tune in for part four on “involving the users” and contact us with any questions!

Latest Blogs

Discovering Business Insights with Vector Search and Mindbreeze InSpire

Jeremy Wise

Vector search is changing the way we extract meaning and value from data. Coupled with Mindbreeze InSpire’s 360-degree view capability, vector search empowers organizations to uncover deeper insights from within every department of their organization.

The Power of Prompt Engineering in Enhancing Natural Language Question Answering (NLQA)

Jeremy Wise

One of the critical techniques that significantly enhances the performance of LLMs in NLQA tasks is prompt engineering. Prompt engineering involves crafting and refining the input prompts given to LLMs to elicit the most accurate and relevant responses.