How DvSum Overcomes the Pitfalls of Using GPT-4  to Interface with Your Data

This is the second of a two-part blog series. The prior blog explored the advantages and potential pitfalls of using ChatGPT and GPT-4 with your enterprise data. The following blog explores how DvSum helps you maximize the advantages of GPT-4 and overcomes the pitfalls.

The first blog of this series concluded with the following advantages and pitfalls of using ChatGPT or GPT-4 with your data:

1. Advantages 

• An easy chat interface
• Automated analysis and reporting
• Personalized recommendations 

2. Pitfalls

• Data integration
• Accuracy and reliability of the data 
• Accuracy of the data within the specific context
• User adoption 
• Data security: data access rights and protecting sensitive data

Now let’s explore what DvSum is and how it helps to maximize the advantages and overcome the pitfalls.

What is DvSum AI-Powered Chat?

DvSum makes it effortless for everyone in your organization to interact with their data, understand it, and extract reliable insights. Simply by chatting with the data. Thus, DvSum delivers data democracy, ensuring a data-driven culture with an easy, powerful, and comprehensive data solution. You achieve value on Day 1 without extensive training or setup required.

How is using DvSum different from a DIY approach with ChatGPT or GPT-4?

DvSum’s “secret sauce” is marrying AI-powered chat with an automated data infrastructure that organizes data into a unified data catalog – complete with business glossary- and constantly ensures high data quality. Ultimately, that means you attain reliable insights from all your data and the fastest time-to-value. All with no technical skills required.

How does the underlying DvSum data infrastructure work?

In a nutshell, here are the steps that the DvSum data catalog takes to bring structure and meaning to your data:

• Connect: DvSum connects to your on-premise and cloud data sources including databases, data lakes, reports, and enterprise applications.
• Catalog: It scans your data to extract metadata and uses ML to classify, link your data assets, and sets data quality rules.
• Consume: Users consume the insights from DvSum’s AI-powerd chat, with queries going to the underlying catalog. The usage and suggestions continuously curates the catalog.
• Check: DvSum continuously monitors your data and alerts on unexpected changes in data quality, volume, and distribution.

So how does DvSum help against the pitfalls identified by ChatGPT? 

Let’s take the Pitfalls of the prior blog on-by-one:

1. Data integration

i. DvSum features automated data catalog curation. 

DvSum’s sophisticated ML-driven algorithms, automatically catalog, classify, and curate data from all your data, with connectors to 200+ data sources for databases, data lakes, BI Reports, and applications. As a unified, actionable data catalog, DvSum harvests the metadata, profiles the data, classifies it on multiple characteristics, and links it to other data and to your data domains.

ii. Your data never leaves your network

With cloud solutions, you typically have to give your data to vendors and the vendor cloud security becomes a critical requirement. With DvSum, the data scanning happens inside your network and only metadata is sent outside your network to DvSum Cloud. Get the benefit of a SaaS solution without increased data protection footprint.

2. Accuracy and reliability of the data 

I. DvSum data quality rules and monitoring

Using a combination of anomaly detection and rule-based algorithms, DvSum automatically recommends the data quality rules. It can check for unexpected schema, quality, volume, and distribution of data. DvSum provides 23 out-of-the-box rule templates that include rules for master data, transactional data, and cross-system reconciliation.

ii. Cleanse & write-back

Don’t just detect data quality issues, fix the issues and write-back to the source from a single interface. With SOX-compliant features, you can write-back changes into your production sources. Specialized write-back capabilities for Oracle and SAP ERP.

iii. Workflow based orchestration

Data quality is a process. With built-in and customizable workflow templates, create an orchestrated process to review, cleanse, approve and write-back changes.

iv. Data preparation workbench

The best option for higher data quality is to create high quality data in the first place. With an Excel-like grid, your business teams can create, update, cleanse data faster and push it to the enterprise application.

v. Data quality tracking & reporting

Track data quality coverage, score, and trends over-time through simple, beautiful dashboards and reports with drag-and-drop self-service reporting.

3. Accuracy of the data within the specific context

i. DvSum business glossary of data definitions & business rules.
DvSum adds context to your data. Simply upload your current list of data domains, business terms, definitions. Using its AI-enabled classification algorithms, DvSum will automatically link the physical data elements to the glossary terms and recommend enrichments to the business glossary.

ii. Data preparation recommendations for analytics

DvSum semantically tags your data. Data relationships are inferred and ML algorithms generate cleansing recommendations. Non-technical audiences, as well as data analysts and data scientists, can quickly find the relevant data, understand its contents and quality and create their data cleansing recipe.

4. User Adoption

For anyone, chat is an easy interface. We believe both technical and non-technical audiences will highly value this alternative to traditional reporting and analysis tools. It makes sense though to monitor the usage of DvSum’s usage to see how certain aspects might be tuned over time.

5. Data security: data access rights and protecting sensitive data

i. Industry-accepted best practices and frameworks

Our security approach focuses on security governance, risk management, and compliance. This includes encryption at rest and in transit, network security and server hardening, administrative access control, system monitoring, logging and alerting, and more.

ii. Single Sign-On

If you use Single Sign-on for user access (like Azure ADFS, Okta or any SAML supported Provider), you can configure DvSum to use the same Identity Provider. If you do not use SSO, you can let DvSum manage user authentication powered by AWS Cognito.

iii.Your data never leaves your network

With cloud solutions, you typically have to give your data to vendors and the vendor cloud security becomes a critical requirement. With DvSum, the data scanning happens inside your network and only metadata is sent outside your network to DvSum Cloud. Get the benefit of a SaaS solution without an increased data protection footprint.

iv. Role-based access control (RBAC)

Allow for the separation of privileges by user role. Built-in Admin, Editor, User roles. Create your own specific roles and user groups to create finer control to your Data Intelligence platform.

v. Automatic PII data masking and filtering

DvSum only harvests metadata from your data sources. Even so, DvSum uses PII identification and masking algorithms when scanning data. So you can be sure that any PII information in your data is not extracted in the data catalog. Continue to maintain GDPR, HIPAA, and other data privacy compliances while using DvSum.

Summary

Ultimately, DvSum’s ability to combine GPT-4 with a powerful underlying data infrastructure makes all the data appropriately and easily accessible. This empowers a data-driven culture and ultimately drives data democracy ensuring that decisions are based upon trusted, real-time data.