Most businesses, whether you are in Retail, Manufacturing, Specialty Chemicals, Telecommunications, consider a 10% market capitalization increase from 2020 to 2021 outstanding. But what would you say to your shareholders when they found out your competitors’ market capitalization grew 35%? A recent McKinsey report dove into the divergence between retail’s laggards and winners and found […]
Most businesses, whether you are in Retail, Manufacturing, Specialty Chemicals, Telecommunications, consider a 10% market capitalization increase from 2020 to 2021 outstanding. But what would you say to your shareholders when they found out your competitors’ market capitalization grew 35%?
A recent McKinsey report dove into the divergence between retail’s laggards and winners and found if there is one message in the retail sector’s stock market performance since the pandemic’s start, it is that investors flocked to companies with strong technology capabilities.
Why? Technology drives the ability to use enterprise data to make choices, decisions and investments – which then produce competitive advantage. An example from retail: higher fidelity demand forecasting in a large leading global grocery retailer produced a 5% to 7% increase in sales by minimizing out-of-stocks and a 30% to 50% reduction of average out-of-stocks in stores – that’s millions of dollars for most retailers.
Thousands of our customers across all industries are harnessing the power of their data in order to drive insights and innovation. Once companies are able to leverage their data they’re then able to fuel machine learning and analytics models, transforming their business by embedding AI into every aspect of their business.
As you proceed with your own AI business transformation journey, there are 3 things to consider:
Much of the AI done today in the enterprise is modeling in the cloud, but when we look at many of the exciting use cases around real-time AI inference, we see huge potential for business value. In Retail or Consumer applications like biometrics, voice recognition, or autonomous vehicles, the potential to inject AI into emerging data-intensive applications within the enterprise is enormous. It’s estimated that 90% of the historical cost of AI applications is inference and it’s been a blocker in terms of adoption because it’s just been too expensive and difficult. The combination of Cloudera’s Hybrid Data Cloud and cutting edge compute power is dramatically lowering the cost of enabling this type of work. We’re seeing improvements in speed and accuracy for existing enterprise AI apps like fraud detection, recommendation engines, supply chain management, drug provenance; and increasingly, the consumer-led technologies are bleeding into the enterprise in the form of autonomous factory operations (e.g. robots), AR/VR in manufacturing (quality), power grid management, automated retail, IoT, Intelligent call centers – all powered by AI – the list of potential use cases is virtually endless.
We all lived through 2020, and now in 2021 we recognize the world has changed. Everyone’s algorithms are off, some examples:.
So relying upon the past for future insights with data that is outdated due to changing customer preferences, the hyper-competitive world and emphasis on environment, society and governance produces non-relevant insights and sub-optimized returns. Quality data needs to be the normalizing factor. To get back on track, we need clear insights from our data, and we need it now. We need to reset our assumptions and quickly iterate. AI will be a key factor in the coming decade and it will be different than the last ten years. Last decade, much of leveraging AI was a science project comprising highly skilled data scientists and data teams. In the coming years, AI will be infused into every application, business process and machine.
Work to democratize the access of the right data across the entire enterprise by embedding data within applications and allowing different groups across the enterprise to independently access, manage, and combine different data types for insights that are relevant to them. Then back this up by embedding compliance and security protocols throughout the insights generation cycle. Invest in maturing and improving your enterprise business metrics and metadata repositories, a multitiered data architecture, continuously improving data quality, and managing data acquisitions.
The above considerations, among many others, have been developed with extensive interaction with companies like NVIDIA, Accenture, 3soft and others. Dive deeper into this discussion at “Transforming Innovative Ideas into Data Driven Insights”, where we will continue talking about:
Register for this executive event, here.