Artificial Intelligence(AI) has been the hype lately, with OpenAI’s evolution and adoption by customers for various use cases. In my perspective, AI is going to be powerful in the “Analytics” space due to the way data, data, data everywhere! So, being able to smartly indulge AI on top of the enterprise data warehouse system is like mining Gold against it. Do you agree? We know there are four types of data analytics: descriptive analytics (what happened?), diagnostic analytics (why did this happen?), predictive analytics (what will happen?), and prescriptive analytics (what should we do?).

It would be interesting to try to understand how Generative AI can help in this space. Hence, I wanted to share my research and view of how AI helps in addition to these four categories. My research would focus on Business Intelligence tools (MicroStrategy, Salesforce ~ Tableau, Microsoft Power BI) and how they adapt AI-utilizing products for Analytics use cases.

Descriptive Analytics

Descriptive Analytics can show “what happened” and is the foundation of data insights. It looks into past data to understand what happened and find relative patterns and trends. We can think of a scenario of “Data Augmentation,” meaning to generate synthetic data that can help us understand various scenarios of business cases we deal with. Here in “Data Augmentation,” ~ Generative AI can help produce or generate synthetic data that can augment existing datasets, allowing for a more comprehensive analysis.

Generative AI can generate synthetic data that mimics real data without the challenges that come with collecting it.

MicroStrategy

We can look at how this is being applied or planned to be utilized in MicroStrategy (a product used by customers in the Analytics space). MicroStrategy had shared in MicroStrategy World 2023 Conference as a project called “Project Data Forge.”

MicroStrategy Project Data Forge AI  (Credit: MicroStrategy)

It basically utilizes “Generative AI” for data stewardship or analysts to generate dummy augmented data for data analysis. Isn’t that awesome!? When we go back to 2000-2020, If we need to load data in an enterprise data warehouse in our Development or QA region – It is a long process (such as working with DBA & performing data masking or figuring out a strategy for the development team to proceed).  Another scenario is working with the ETL team to understand source systems, developing a pipeline, and then loading the data into data warehouse systems (database) for the Business Intelligence (BI) team to build customer reports or dashboards.

Now, with this “Project Data Forge” concept by “MicroStrategy,” ~ It will be a game changer to generate dummy data based on business needs to continue with the development of reports or dashboards for customers. The ETL team can process the data from the source system and develop the pipelines to get it loaded into the target system (EDW – Enterprise Data Warehouse) for the BI team to process it later.

Diagnostic Analytics

Diagnostic analytics addresses “why things happened.” Common diagnostic analytics techniques/insights include drill-down, data discovery, data mining, and correlations.

Tableau

This can be explained using the functionality within Tableau, a Business Intelligence Analytics product serving various customers across various domains. For this stream of analytics, Tableau built an in-built AI functionality named “Tableau Explain Data.” ~ It automatically provides AI-driven explanations for the value of a data point with a single click. It provides a deeper data exploration with statistical models applied behind the scenes to explain the details of the data for easier understanding for business users or analysts studying their data.

URL: https://www.tableau.com/solutions/ai-analytics/augmented-analytics#explain-data

Another functionality that Tableau added to their product is “Data Stories,” which utilizes AI built into it. Data Stories adds automated plain-language explanations to your dashboards in seconds. Isn’t it amazing to utilize AI to provide information about dashboards based on your own data to users in simple language? 

Predictive Analytics

Predictive Analytics helps businesses “See the future” and predict “what is likely to happen.” (i.e.) Let’s say we have seen the last five years of sales for an eCommerce giant in our database (Enterprise Data warehouse). With Predictive analytics, we can predict future sales based on historical patterns and trends seen in the business data. Below are the common processes in Predictive analytics.

1. Decision Trees
2. Neural Networks
3. Regression Models

Generative AI is different from Predictive AI. Generative AI can help design product features, while Predictive AI can forecast consumer demand or market response for these features. In my experience, Predictive Analytics is an AI-based strategy. It is called Predictive AI by default when we do deeper analyses with historical data and predict the future using the models.

Tableau

Tableau introduced an AI feature focused on predictive analytics & it is called “Einstein Discovery.” With Einstein Discovery, you can create trusted, predictive ML (machine learning) models without writing a single line of code. Usually, organizations tend to utilize a data science team to work on ML models and then infuse the data into the ML models tuned by data scientists to derive future predictions for their business. Here, with this feature of Einstein Discovery, it is being handled using AI instead of Data Scientists.

MicroStrategy

MicroStrategy had “Advanced Analytics” with AI. It automatically provides precise forecasting, allowing users to better anticipate market trends and optimize future strategy. It uses AI to predict based on historical data trends/patterns and provides business users or analysts with the right forecasting recommendations within the MicroStrategy application.

Prescriptive Analytics

Prescriptive Analytics, analytics driven by AI systems, helps companies make decisions and determine “What they should do next” based on the Predictive analytics outcome. (i.e.) Prescriptive analytics can tell us what we should do about it rather than describing or diagnosing something that’s happened or predicting what could happen. It is currently closer to reach via AI to make it happen based on the growth of Generative AI and large language models.

I haven’t seen any Business Intelligence tools reach this stage of Prescriptive Analytics yet. I hope we get there by 2030!

A few great examples shared on Bernard Marr’s site are below:

1. Customer Loss Prevention
   • Predictive Analytics can derive Customer Churn Score (i.e.) How likely are they to stop being customers?
   • Proactively advise on steps that could be taken to reduce the likelihood of the customer leaving via Prescriptive Analytics.
2. Delivery Logistics ~
   • How long will it take drivers on each route to complete their deliveries? ~ Predictive Analytics
   • Most Efficient Route that each driver should take in order to complete their deliveries quickly and safely ~ Prescriptive Analytics

Credit: https://bernardmarr.com/generative-predictive-prescriptive-ai-what-they-mean-for-business-applications/

Security & Governance with AI

Generative AI was attracting many business use cases when ChatGPT was popular during its initial release. To be successful, an organization needs to consider implementing the right Generative AI Governance. Generative AI Governance refers to the principles, policies, and practices designed to ensure the responsible and ethical use of Generative AI technologies.

MicroStrategy

One good reference to it is analytics/business intelligence tools like MicroStrategy, which is fundamentally defined from its metadata-based model approach. MicroStrategy has security and governance built into the application at various levels, such as Data, Object, User, Security Roles, Security Filter, DB Connection Mapping, Project Access, etc.

With the above layers of security applied to the data being accessed by business users, Generative AI ~ adds better governance and security to customer requests. Below are the references for the Architecture of MicroStrategy security and Governance for Generative AI.

Tableau

Tableau AI is built on top of the Einstein Trust Layer and inherits all of the security, governance, and trust capabilities.

• Zero-data retention policy
• Dynamic Grounding with Secure Data Retrieval
• Prompt defense
• Data Masking
• Toxicity Scoring
• Audit

URL: https://help.tableau.com/current/tableau/en-us/tableau_gai_einstein_trust_layer.htm

I hope this helps people aspiring to utilize Generative AI in the Analytics/Business Intelligence space to reference it.