In this article, we will discuss how data and analytics are impacting business and in our day-to-day lives and how important in digital businesses and their applications in the real world.
In the past, organizations would use analytic applications mainly for enterprise data reporting. However, more and more organizations are now using data and analytics as raw materials for enterprise-level decision making. The following flowcharts illustrate this point:
The Current Al Wave Is Poised to Finally Break Through.
Al is becoming fast a core business and analytical competency. Al and ML technologies are providing rapidly improved decision making and process optimization capabilities to organizations.
By doing so, these technologies promise to:
- Transform business processes.
- Reconfigure workforces.
- Optimize infrastructure behavior.
- Blend industries.
Natural Language Technologies Become Mainstream.
One of the key aspects of Al-driven systems is their ability to process natural language, which is human
speech and text. Natural language is beginning to play a dual role in many organizations.
Examples of Natural Language Processing (NLP):
- Machine Translation: Google translate services.
- Customer Service: NLP is being used in customer service contact centers to understand customer intent, pain-points, and to provide enhanced customer satisfaction.
Examples of Natural Language Generation:
- Generating automated product descriptions from inventory data.
- Creating individual financial portfolio summaries and updates at scale.
- Business intelligence performance dashboard text explanations
- Automated personalized customer communications.
Augmented analytics draws on ML, AI, and natural language generation technologies to:
Applications in real world:
The combination of emerging technologies such as AI, ML, and cloud are often marketed using the following:
• Cognitive Cloud
• Al as a Service
• Intelligent Cloud
A major telecom company wants to enhance its contact center operations by understanding its customers better through their customer care calls, feedback survey responses, and social media interactions. Which Al and analytics application
areas will be useful? In the given scenario, speech recognition and Natural Language processing will help the business enhance its contact center operations.
Google Maps analyzes the speed of traffic through anonymous data locations from smartphones. This enables Google to suggest the fastest routes. Which technology enables Google to do this? AI and ML! Smart machines and applications are steadily becoming a daily Cloud Computing phenomenon, helping us make faster and accurate decisions.
Google Maps uses Al and ML to analyze the speed of traffic Al and ML and suggest the fastest routes.
The Winning Strategy.
What will it take to win in the new digital era? Winning strategy for future growth addressed by the following key business drivers:
Reset the rules of business.
Strategy and Innovation:
- Accelerate innovation.
- Redefine industry operating models.
- Drive growth while reducing risk.
- Enable Change adoption.
Focus on human needs.
Interactive Experiences:
- Combine the best of human science, design thinking, user experience, and technology to provide new end-to-end services for marketers.
- Deliver on the promise of social and brand.
- Build Omnichannel success.
Make intelligent choices.
AI & Analytics:
- Build intelligence-based businesses.
- Make data an asset, not a liability.
- Apply Analytics and Al platforms to fuel growth.
Enable the internet of things.
Connected Products:
- Instrument and connect everything.
- Rapid prototyping and product development.
- Grow revenue from data-driven services.
Build software for the digital economy.
Software Engineering:
- Embed human-insight and design into engineering.
- New tech for new value: Cloud Foundry stack services, micro-services, APIs, engaging interfaces, etc.
- Application portfolio optimization.
This is how organisations leverage its Al and analytics capability to help its clients stay ahead of the competition.
Industry Aligned Solutions.
Every organization will have pre-built solutions to address the needs of the vertical markets. For example,
Google’s Product Data Management solution, which uses Product Intelligence as a service offering,
caters to the needs of the Consumer Goods and Retail verticals. These pre-built solutions are easy to
customize and can be deployed quickly.
Some of the vertical markets:
- Banking.
- Insurance.
- Life Sciences.
- Healthcare.
- Manufacturing & Logistics.
- Comm & IME & Tech.
- Energy & Utilities.
- Consumer Goods & Retail.
- Travel & Hospitality.
Case Study: Opioid addiction and early detection of drug-seeking behavior.
Challenge:
A healthcare firm approached Google for a solution to reduce the number of deaths due to drug misuse.
Solution:
Google developed an Al solution that could seamlessly scan through a physician’s prescription notes and mine for indicators of drug-seeking behavior.
Outcome:
Google’s solution helped the healthcare firm save more than $60M by identifying around 85000 drug seekers before they turned into addicts.
Summary
So far, we have discussed six strategic offerings include:
- Insight to Al.
- Adaptive Data Foundation.
- Risk and Fraud Intelligence.
- Customer 360-degree Intelligence.
- Business Operations Intelligence.
- Product Intelligence.
- Organizations will have pre-built solutions to address the needs of vertical markets.