What is AI Analytics & How it Works
by Dhiren Patel, Co-founder & CPO
What is AI Analytics & How it Works
by Ramesh Panuganty, Founder & CEO
- AI Analytics Explained
- How AI Analytics Works
- Why is AI Analytics important?
- Core Components of AI Analytics
- Top Benefits of AI Analytics
- Top Industry Use Case Examples of AI Analytics
- Fraud Detection (Banking & Financial Services)
- Inventory Management (Retail, high tech, manufacturing, industrial)
- Predictive Maintenance (manufacturing, data centers, telecom)
- Customer service automation (across multiple industries)
- Transformative Power of AI Analytics in Every Business Dimension
AI Analytics Explained
Up until recently, enterprise data analytics was simple reporting of KPI behavior over time. Business users will have access to dashboards and reports that indicate trends of sales, costs, profitability, and other operating metrics over time. Overtime, this simple reporting of KPIs have become more user friendly with prettier charts and dashboards. In a static industry situation, simple reporting of historical KPIs was good enough but many competitive industries struggled to capture and respond to market dynamics. As computing and data processing advanced in recent years, ability to process large amounts of data using statistical models has really improved in a step function way. This has helped organizations leverage statistical models to generate insights that are beyond the routing statis reports of KPI behavior.
How AI Analytics Works
Due to significant improvement in compute power and sophistication of data platforms, organizations are now able to deploy advanced statistical models, rules, and decision trees to generate advanced insights, automate processes and improve operational efficiencies. These advanced models, rules and decisions trees are collectively referred to as “Artificial Intelligence”. When Artificial Intelligence (AI) is applied to generate advanced insights such as anomalies, outliers, trends, seasonality, time series forecasting and clustering, collectively they are called “AI Analytics”. From a business user’s standpoint, these new insights help understand customer behavior better, improve operational efficiency, automate processes, and react quickly and accurately in a dynamic market situation.
Why is AI Analytics important?
Take an example of online retail company which is working with a dynamic market situation that gives their customers literally infinite competing choices. Tracking sales, churn or customer satisfaction post facto would be too late in this type of scenario. By the time you realize the underlying issues and possible causes, it may be late, and the customer may have moved on. Retaining customer is not just a topline benefit but also a bottom-line benefit due to very high customer acquisition costs for new revenue. Most industries are facing some kind of digital transformation that is making it imperative that they get ahead of trends and causal factors by analyzing data at a more granular level in real time. This is where the power of AI analytics can be impactful for the long-term viability of the business.
Core Components of AI Analytics
Understanding of business domain
Any number of sophisticated AI models will do no good if they are applied to wrong business use case or are applied with wrong objectives in mind. Having clarity on what are dependent and independent variables and what are the realistic objectives given the quality and granularity of data is paramount before any AI analytics is built and operationalized.
AI Models & Necessary Infrastructure
AI models can come in many different forms such as deep learning models, neural networks or standard machine learning models delivering pattern recognition and recommendations. Models are as good as the training data that they are trained on. It is vital that a workstream is created upfront to assimilate and organize all relevant data via reliable data pipelines in a single datastore. It is also important to make sure that a reliable cloud data store and compute environment are provisioned before any training or production workstream are initiated.
AI Analytics Consumption
Many organizations make the mistake of ignoring the business user when they are working on their AI Analytics initiatives. How an eventual business user consumes AI Analytics insight is a crucial problem to focus on upfront. One critical component in this area is NLP Search for a typical business user to access data and all the related advanced AI analytics insights. Entire front end of the traditional BI / analytics is transforming into the NLP search driven interface. Innovative audio-visual stories integrating traditional, and AI powered insights is another possible avenue to explore too.
Top Benefits of AI Analytics
Improved Customer Experience
Prompting customer with proactive suggestions rather than reacting to their complaints goes a long way to create a happy customer experience. With real time analysis of granular data using sophisticated models, businesses can now understand possible pain points or possible areas of improvements ahead of time. This can be a big dimension to create happy and loyal customers.
Traditional analytics platform involved lots of manual workflows starting from data management to report creation. With AI Analytics, many of these erstwhile manual tasks can either be automated or eliminated. This has the potential to save significant resources while also improving the quality of insights.
AI Analytics can help you reduce churn with proactive issue resolution, improve revenue with recommendation engine, invest in correct products based on richer analysis of trends and reduce overall cost of ownership of analytics ecosystem. All these collectively have the potential to offer significant competitive advantages to the business.
Top Industry Use Case Examples of AI Analytics
Fraud Detection (Banking & Financial Services)
Combining multiple data stores and applying constantly evolving AI algorithms in real time can help financial institutions reduce fraud. This will not only help customer satisfaction but also help with lost dollars due to identity frauds.
Inventory Management (Retail, high tech, manufacturing, industrial)
As mentioned earlier, businesses can now integrate plethora of data sources (internal and external) and apply sophisticated models to analyze overall economy, regional economic trends, consumer behavior, seasonality, and sales trends to make the right products in right amounts and possibly at the right moment. Power of AI analytics not only can help you save inventory costs but also can help you build the right product to generate significant topline sales.
Predictive Maintenance (manufacturing, data centers, telecom)
One of the significant implementations of AI Analytics is “Internet of Things” (IoT). Sensors attached to machines and data captured from data centers can help you analyze patterns of machine behavior to predict possible machine failures ahead of time. This can reduce machine downtime and reduce maintenance costs.
Customer service automation (across multiple industries)
With the advancement of large language models (LLMs) and generative AI, it is possible automate and improve customer service and proactively resolve customer issues before they get to the contact center.
Transformative Power of AI Analytics in Every Business Dimension
Irrespective of industry vertical or business function, AI-powered Analytics has the power to transform your business. It can deliver topline benefits and bottom-line savings. It can help you win customers or retain customers. It can help you improve the efficiency of your operations. What was not possible in the past is enabled now due to the advances in computing power, availability of data and sophistication of AI models. It is critical to keep the focus on use cases, business objectives and the consumer of AI-powered analytics. Goal is to have the most powerful AI Analytics and make it easily consumable by the business user with NLP Search interface or generative AI powered interactive audio visuals.Start Your Free Trial