How to Assess and Adopt New Technologies Using Data-Driven Proof of Concept

[Razi_Bayati]

In this article, I share a #data-driven method for assessing and adopting new technologies and steps for creating or evaluating a strong proposal. This includes using statistical measures to evaluate the technology, such as #LLM, addressing potential risks, outlining costs, and ensuring alignment with the organization's strategy.

At Databricks, we support this methodology by:
- Offering transparent cost monitoring metrics (#system_tables), helping you understand your financial investment and compare it to the return on investment (ROI) through #usage_monitoring.
- Providing comprehensive resource policies (#compute_policies and #budget_policies) to enforce budgets and prevent unexpected costs.
- Enabling your entire organization to leverage data and AI using the #Data_Intelligence_Platform. Built on a lakehouse architecture, it offers an open and unified foundation for all data and governance needs; this will accelerate your end-user enablement journey assuring user adoption rate.
- A unified approach to governing both structured and unstructured data in any format, as well as machine learning models, notebooks, dashboards, and files across any cloud or platform through #Unity_Catalog.

With Databricks' transparent and robust platform, you can build a strong case before investing in technology—one that not only demonstrates technical feasibility but also aligns with your business strategy and investment goals, increasing the likelihood of long-term success.

How to Assess and Adopt New Technologies Using Data-Driven Proof of Concept

In today’s fast-paced business landscape, organizations are racing to adopt new technologies to stay competitive. However, not all technologies are guaranteed to deliver value unless they are thoroughly tested and validated. Without careful planning, your investments can drain resources without generating the expected return on investment (ROI). Imagine switching your marketing strategy to focus on new social media platforms without thorough market research. It might generate some interest, but it’s unlikely to fully meet customer needs and outperform your previous strategy. Adopting any new technology requires careful planning and testing to ensure it delivers the expected results.

A strong and data-driven proof of concept is the foundation for adopting technology. By providing reliable initial tests and data-driven evidence, you can build confidence among stakeholders, influence organizational strategy, and make informed decisions about future investments. But how do you run a data-driven proof of concept that leads to success?

What is data-driven proof of concept?

Investing in new technologies without properly benchmarking your key pain point and implementing data-driven proof of concept is risky and often unproductive. By validating assumptions early, you reduce the chance of costly failures and increase your likelihood of achieving a solid ROI, thereby creating a strong proposal for adopting new technologies.

A successful PoC doesn’t just validate your technical solutions; it provides the foundation for scaling projects that can deliver measurable business value. With this in mind, let’s explore the steps for designing a data-driven POC with hypothesis testing at its core.

Steps for a successful data-driven proof of concept

1. Define the Problem and Hypothesis

Before jumping into technical solutions, clearly define the problem and establish how success will be measured. A reasonable hypothesis outlines:

• The problem or benchmark: What are you trying to improve? What is the current process of solving it? How it is failing?
• The proposed solution: How does this solution challenge the status quo?
• Measurable performance metrics: What specific metrics will you track to validate the improvement?

For example, suppose you’re using a ‘statistical topic modeling’ solution to classify customer feedback into topics. The model works well in general, but it occasionally needs to be more accurate in the context of certain phrases. By switching to a large language model (LLM), which understands context better, you can improve topic accuracy. You hypothesize that the LLM-based model will perform better than the current approach.

1. Define your hypothesis: Null and Alternative hypotheses

To test this belief, you need a clear hypothesis:

• Null Hypothesis (H₀): The LLM-driven model performs no better than the existing topic model. H0: μLLM<=μTM
• Alternative Hypothesis (H₁): The LLM-driven model outperforms the current model in topic classification accuracy. Ha: μLLM>μTM

In statistical terms, your null hypothesis suggests no significant difference in performance between the two models. Your alternative hypothesis states that the LLM-driven model will capture more relevant topics by understanding context beyond word frequency.

1. Select a measurable performance metric

To validate your hypothesis, choose a measurable performance metric. Clarify the success criteria by tying it to measurable business value. Before jumping into any new technology, it is essential to define a success metric that directly ties the solution to business outcomes. In our example of adopting LLM, common choices in topic modeling include:

• Coherence score: How semantically meaningful are the topics?
• Perplexity: How uncertain is the model about the words in a document (lower is better)?
• Human evaluation: Human experts rate the quality of the topics generated.

Let’s say you choose the coherence score. It’s critical that your performance metric ties back to business outcomes, such as improved customer insights or reduced incorrect escalation,

2. Collect and Analyze Data

Run your experiments on a representative dataset. For each model (current and LLM-based), calculate the performance metric multiple times (e.g., using k-fold cross-validation) to gather a distribution of results. Following best practices in sampling ensures you’re not basing decisions on biased or incomplete data.

For example, after running both the topic modeling solution and the LLM-driven model on 100 customer emails, collect coherence scores for each and compare them.

3. Conduct Statistical Testing

Once you have the performance metrics, conduct statistical tests to determine whether the difference in performance is significant. Test statistic quantifies the difference between the observed data with new technology and the null hypothesis, allowing you to assess how extreme the observed results are under the assumption that the null hypothesis is true.

Common types of test statistics include Z-statistic, T-statistic, Chi-square statistic, F-statistic and The Wilcoxon signed-rank test. Since you’re comparing two means (performance of Topic modelling vs. LLM-driven model), you might use:

• T-tests: These tests compare the means of two models when data is normally distributed.
• Wilcoxon signed-rank test: If your data doesn’t meet the normal distribution assumption.

4. Compare with Critical Value and make a decision:

The value of the test statistic is then compared to a critical value from a statistical distribution to determine the p-value, which helps determine whether the observed data is extreme enough to reject the null hypothesis. The critical value sets a threshold; if the test statistic exceeds this, it indicates significant evidence against the null hypothesis. It incorporates the chosen significance level (alpha, usually 0.05 to have a 95% confidence level and 0.01 to have a 99% confidence level ), defining the probability of making a Type I error (rejecting a true null hypothesis).

• If the p-value from the test is less than your significance level alpha, reject the null hypothesis, concluding that the LLM-driven model performs significantly better than the existing model.
• If the p-value exceeds alpha, you fail to reject the null hypothesis, meaning there’s no significant evidence that the LLM model performs better.

Let’s assume the p-value from your t-test comes out to 0.03, if you chose 95% confidence level, test statistic results is below the accepted threshold of 0.05, you can reject the null hypothesis, concluding that the LLM-driven model performs significantly better than the existing model. However, if you choose a 99% confidence level, you will fail to reject the null hypothesis, meaning there’s no significant evidence that the LLM model performs better.

Considerations for Convincing Stakeholders

Even with statistically significant results, convincing stakeholders to invest in new technology can be challenging. Be transparent about the potential risks and rewards. In the case of adopting LLM technology you can think of:

1. Cost-Benefit Analysis

Be upfront about the financial costs of implementing an LLM-based solution. These models typically require more computational power and resources than simpler models like topic modeling. Compare these costs against the potential business value, such as improved customer insights or faster decision-making. Use platforms and technologies that are transparent with technology costs. 

2. Risks of Errors

Highlight the potential consequences of errors in AI models. For instance, if you’re analyzing medical records, a mistake in topic classification could delay critical treatment, increase healthcare costs, and damage trust in the system. Ensure that stakeholders understand the importance of accuracy in high-stakes applications. 

3. Data Access and Dependencies

Address any challenges with data access and dependencies. High-quality AI/ML models require well-documented, unbiased datasets. A unified data catalogue with comprehensive documentation on composition, collection process, and lineage map will facilitate better communication between dataset creators and consumers, improve data quality and reliability encourage the machine learning community to prioritize transparency and accountability.

4. Alignment with organizational strategy

One of the most critical factors for convincing stakeholders is demonstrating how the new technology aligns with the organization’s overall strategy and long-term goals. New technologies are more likely to succeed when designed to directly support business objectives. With a strong proposal, you can even influence the long-term vision of the company and stakeholders. 

5. Enable end users

For any technology to succeed, it’s crucial to focus on the usability and accessibility of the solution for end users. Even the most sophisticated technologies won’t add value if the intended users — such as employees or customers — cannot effectively interact with them. Demonstrate to stakeholders how the proposed solution will empower end users by being intuitive, and easy to use. Do not be afraid to add a change management section to your proposal and highlight the technical expertise gap and required effort for training. By emphasizing the user-centric design of your solution, you build confidence that it will not only deliver strong technical performance but also be readily adopted by those who use it daily, ensuring long-term success and ROI.

Conclusion

In summary, adopting a new technology can be expensive in terms of time and capital. Your success requires a well-executed data-driven proof of concept and structured hypothesis testing. By defining clear success metrics, gathering unbiased data, and performing rigorous testing, you can avoid wasting resources on solutions that don’t deliver. More importantly, you can confidently make the case to stakeholders that your AI investments will drive measurable business outcomes.