The Smart Assistant that Constantly Improves Your Data

Summary:

• Businesses are prioritizing high-quality data for smarter decision-making.
• GenAI capabilities make it easier for users to engage with internal data, drive greater usage, and free up the data team for more strategic work.
• Users can easily find and update missing or incorrect data without involving others.
• The interactive dialogue feature helps users access and blend internal data for analysis.
• GenAI capabilities in Tamr allow data consumers to interact with and improve data in real time so they can get more out of their internal data.

As businesses work to become truly data-driven, the emphasis has moved beyond merely gathering information to prioritizing its accuracy, relevance, and value. More and more, organizations are realizing that high-quality data is far more than just a resource—it’s the cornerstone of smarter, faster decision-making that fuels innovation and provides a decisive competitive edge.

But one of the biggest challenges organizations face is getting data consumers to actively engage with the data and help validate or improve its quality. Today, when users spot inaccurate, incomplete, or inconsistent information, they ignore it or call the data team. And while the data team can help fix the incorrect data, doing so takes time away from other, more strategic work.

But what if there was a better way for data consumers to interrogate the data in real-time and fix it themselves? The good news is, there is.

New advances in GenerativeAI (GenAI) make it possible for data users to interact with the data, ask questions about it, and quickly find answers in real time. Think of it like ChatGPT, but for your data. Let's take a look at how it works in Tamr.

Prompt #1: Help me understand more about Customer X

Consider this scenario: an analyst is conducting a "know your customer" analysis on Customer X. As they review the information available about Customer X, they notice that the registration number is blank. Knowing this number would help them assess if this is a new customer, an existing customer, or an entity with a red flag.

In the past, tracking down the registration number would be a tedious process that often involved multiple people or teams within the organization. But using GenAI capabilities within Tamr, the user can now search other internal data (and potentially, external data) to see what registration numbers already exist on similar entities already known by the organization. Suppose the analyst finds that a registration number already exists for the entity. In that case, they can use it to continue their analysis and give feedback that connects the two records. They answer their business question AND their organization's data gets better.

Prompt #2: I need a slice of data

Picture this: a data scientist at a financial services firm is working on an analysis to understand the impact of higher interest rates on the financials of automotive parts suppliers. They go into Tamr to grab a list of companies that supply automotive parts.

Using the GenAI capabilities, the data scientist can ask a question such as "what data do we have about automotive suppliers" and in response, the system can ask follow-up questions to better clarify the kind of information the data scientist needs. For example, the system may reply "We have one million companies that are classified as ‘auto parts manufacturers’ or ‘auto makers.' Would you like both lists?" At this point, the data scientist can reply yes or no. Then, based on that response, the system can follow-up again, asking if they want US or global companies, as well as the format in which they want the data delivered to them.

This interactive dialogue enables the data scientist to engage with the organization's data in real-time and interact with the system in a way that gives them greater insight into the data they need for their analysis. And because the interaction is intuitive and familiar, the data scientist is more likely to use it.

Prompt #3: Help me enrich this file

An analyst is tasked with understanding the ROI of sponsoring a tradeshow. Marketing shared a lead list from the tradeshow organizer and asked the analyst to provide insights into the types of companies and contacts in attendance, including if they are new prospects or existing customers. Here's how the dialogue plays out.

Analyst: "Here is a lead list from a recent tradeshow. Many fields are missing or clearly wrong. Can you fix them?"

Tamr: "I can help with that. We have information about 1,675 of the 2,222 companies and 4,311 of the 7,831 contacts. Would you like to see a sample of the fixed records or the full output?"

Analyst: "A sample."

Tamr: "Here is a sample. Would you also like the full output?"

Analyst: "Yes, please provide the full output as a CSV."

Tamr: "Here is the CSV with the fixed records. What would you like me to do with the records that weren't found in the database? I can add them to the master list or ignore them."

Analyst: "Please add them to the master list."

Tamr: "Done. Is there anything else that I can help you with?"

This conversational interaction makes it easy for the analyst to tap into internal data sets in real-time to enrich the data and make it more complete. It also makes it easier to append new data to the organization's central data assets, ensuring everyone can benefit from new data, regardless of how or where it's captured from.

It's Like ChatGPT...But For Your Data

According to Wavestone's 2024 Data and AI Leadership Executive Survey, many respondents agreed that "[GenAI] is more potentially transformational than any other technology in a generation," and they view it as "a top organizational priority," with 90% indicating they are increasing their GenAI investments.

Clearly, organizations today have a rich set of internal data. But many times, users don't realize that the answer they are looking for exists somewhere else in the organization. By leveraging the power of advanced GenAI capabilities, Tamr makes it easy for data consumers to ask questions and gain insight beyond the information available in a dashboard. Using an intuitive, interactive ChatGPT-like interface, data users can ask questions within Tamr, enabling them to tap into the treasure trove of information buried deep within their internal data and enrich it using resources already available within the organization.

Not only do these capabilities enable the organization to get more value out of their internal data, but they also help them avoid the costs associated with querying external sources. Further, these capabilities make it easier for users to engage with the data which, in turn, drives greater usage of the data, while also freeing up the data team to focus on more strategic work.

To learn more about how Generative AI is transforming data management and driving greater engagement with data, join us on Thursday, September 26 at 11:30am ET for a webinar entitled "The Pitfalls of Self-Service Analytics & Why the Virtual Chief Data Officer is the Game-Changer." Register now.