Build powerful and responsible AI solutions with Azure

As organizations assess safely reopening and continue navigating unexpected shifts in the world, getting insights to respond in an agile and conscientious manner is vital. Developers and data scientists of all skill levels are inventing with Microsoft Azure AI’s powerful and responsible tools to meet these challenges.

Operating safely

To help organizations operate safely in today’s environment, we are introducing a new **spatial analysis **capability in the Computer Vision Azure Cognitive Service. Its advanced AI models aggregate insights from multiple cameras to count the number of people in the room, measure the distance between individuals, and monitor wait and dwell times. Organizations can now apply this technology to use their space in a safe, optimal way. For instance, RXR, one of New York City’s largest real estate companies, has embedded spatial analysis in their RxWell app to ensure occupants’ safety and wellness.

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Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

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Unlocking the Power of Data And AI for Businesses

The latest reports show that the Artificial Intelligence market was worth $40.2 billion in 2020. Experts attribute this growth to the increasing business adoption of AI. Data is the biggest asset that a company has in 2021. A comprehensive data strategy ensures the company can derive valuable insights that enable better decision-making.

What is the role of AI in business? There are countless examples of AI in business for forecasting and predicting values. For example, AI solutions enable financial companies to oversee stock price fluctuations and take advantage of trading opportunities. MetaStock is one such solution that provides analytical tools based on AI and data to predict stock prices.

Another approach of data and analytics that companies take is to forecast their budget, expenses, and revenues. It involves using historical data and transactional patterns to identify a company’s budget. Big data provides solutions to specific business problems through Artificial Intelligence models.

This article will focus on the following topics – 

• AI capabilities in business
• What is intelligent analytics?
• Latest trends in data analytics
• Business cases for AI

Let’s have a look at these topics in detail.

AI Capabilities in Business 

Harvard Business Review suggests that AI can do three significant things for your business. It can – 

1. Automate business processes
2. Gain valuable insights
3. Improve customer engagement

When people ask, “Why is data important for AI,” the answer is that all AI solutions require data. The foundation of AI analytics is historical or real-time data. With the help of data, AI can deliver the above three capabilities.

AI enables business process automation with the help of Robotic Process Automation. RPA bots can work with triggers, while smart bots can identify things that need to stay or eliminate during automation. 

The biggest role of AI in business is to generate and deliver valuable insights. When there’s tons of data, patterns are bound to emerge. The power of data in business is brought to its best use when companies can use any kind of data to improve business results. It usually happens by finding opportunities that are only seen through data and analytics. 

Artificial Intelligence drives customer experience by learning from the environment. Smart bots and AI algorithms deliver better customer service by understanding the conversation in real-time. Several companies use AI customer experience bots to enhance service delivery to their audience.

Read More: A CTO’s guide for AI Implementation

What is Intelligent Analytics?

Intelligent analytics is a branch of Artificial Intelligence. Companies use intelligent data and analytics to uncover meaningful insights. The purpose is to derive value from collected business data.

INSIGHTS – that’s the keyword here. Intelligent analytics is a part of the overall data strategy. It showcases the business that even their top management might sometimes miss. It includes new market segment opportunities, product differentiation parameters, honest product feedback, actual growth rate or products, expense and income status for a particular time period, and much more. 

There are three different types of intelligent analytics – 

• Descriptive analytics
  It focuses on using historical and real-time data to identify what happened during a time period. Examples of AI in business for descriptive analytics include understanding the new customers acquired, sales during a period, and more.
• Predictive analytics
  It focuses on predicting future values for the company. The Machine Learning algorithm uses past values and identifies values that are likely to occur in the future. Examples include predicting the budget for the next year, identifying the number of potential customers, and more.
• Prescriptive analytics
  It focuses on determining the right path that companies should take based on the insights generated from data. It highlights the action steps that will bring the desired results. Examples include price changes based on the historical demand of a product. 

Latest Trends for Data Analytics

As companies move towards modern applications of data analytics, everyone asks the same questions – “How can I use AI in my business?”. We will address the answer in the next section. For now, let’s understand the latest trends in data analytics that make AI possible for businesses. 

1. AI SaaS
   Data solutions are increasingly available as Software as a Service product. Companies can use off-the-shelf software to identify patterns by inputting data in different columns. There are software-based on multiple business functions, while some are common for all processes. However, if you want an enterprise-grade analytical tool, custom AI solutions are more effective.
2. Natural Language Processing
   AI machines are now learning on their own. More and more companies are experimenting with unstructured data through natural language processing. Intelligent bots now have conversations as real as humans do. It brings a new age of AI systems that can interact using emotions, feelings, and everything that humans have.
3. Extended Reality
   Data analytics combined with extended reality is generating unfathomable results. Through AR and VR, companies are creating virtual simulations – but combining it with the AI capabilities for predicting future environments, opportunities and making plans to understand how to solve future challenges. Extended reality delivers an AI customer experience that highly improves the customer satisfaction rate.
4. Interactive Visualizations
   Intelligent analytics is simplifying data representation for companies. There are interactive dashboards through which all stakeholders can better understand the data. It gives a personalized view of all the data points and showcases how it is performing. Interactive visualizations are useful for identifying visual patterns that might be missed otherwise.

Business Cases for AI

Today, there are use cases of AI in almost every business process. Even in the manufacturing sector, intelligent data analytics can help build systems that can identify the maintenance schedule of equipment – keeping it safe from breakdowns. 

There are plenty of applications that are common in business. We will talk about these popular business use cases of AI, based on different functions –

• Customer Service
  Sentiment analysis is a useful application of data analytics in business. Companies can identify what customers are saying about their product. It also includes:
  
  – Customer responses
  – Call forwarding
  – Call classification
  – Intelligent routing
• Marketing
  Marketing analytics is the most significant use. It helps to determine the results that we can achieve from the marketing budget. It also includes:
  
  – Personalized marketing
  – Intelligent content
  – Contextual marketing
  – Budget forecasting
• Sales
  The sales team can identify the future sales potential by analyzing the historical data. It helps to find areas where activities need to be improved. It also includes:
  
  – Predictive sales
  – Sales chatbot
  – Appointment scheduling
  – Lead management
• Operations
  The role of AI in business operations is increasing dramatically. Companies use automation and data analytics to drive valuable insights. It also includes:
  
  – Data mining
  – Predictive maintenance
  – Operational analytics
  – Inventory optimization

Apart from this, there are various business cases in different domains like healthcare, FinTech, HR, Insurance. In every domain and area of business, AI is showing significant results.

Checkout: The Future of Artificial Intelligence Services for Business

Bottom Line

Artificial Intelligence is transforming how companies used to work. Today, intelligent analytics is a business priority. Since data is the biggest asset they have, companies want to leverage AI and derive valuable insights from that data. That’s why more and more businesses are now hiring AI developers to build intelligent solutions.

BoTree Technologies is a leading Artificial Intelligence and Data Analytics company in the US, India, and Singapore.

Contact us today to understand how we can build AI capabilities for your business.

 

Source: https://www.botreetechnologies.com/blog/unlocking-the-power-of-data-and-ai-for-businesses/

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Learn NoSQL in Azure: Diving Deeper into Azure Cosmos DB

This article is a part of the series – Learn NoSQL in Azure where we explore Azure Cosmos DB as a part of the non-relational database system used widely for a variety of applications. Azure Cosmos DB is a part of Microsoft’s serverless databases on Azure which is highly scalable and distributed across all locations that run on Azure. It is offered as a platform as a service (PAAS) from Azure and you can develop databases that have a very high throughput and very low latency. Using Azure Cosmos DB, customers can replicate their data across multiple locations across the globe and also across multiple locations within the same region. This makes Cosmos DB a highly available database service with almost 99.999% availability for reads and writes for multi-region modes and almost 99.99% availability for single-region modes.

In this article, we will focus more on how Azure Cosmos DB works behind the scenes and how can you get started with it using the Azure Portal. We will also explore how Cosmos DB is priced and understand the pricing model in detail.

How Azure Cosmos DB works

As already mentioned, Azure Cosmos DB is a multi-modal NoSQL database service that is geographically distributed across multiple Azure locations. This helps customers to deploy the databases across multiple locations around the globe. This is beneficial as it helps to reduce the read latency when the users use the application.

As you can see in the figure above, Azure Cosmos DB is distributed across the globe. Let’s suppose you have a web application that is hosted in India. In that case, the NoSQL database in India will be considered as the master database for writes and all the other databases can be considered as a read replicas. Whenever new data is generated, it is written to the database in India first and then it is synchronized with the other databases.

Consistency Levels

While maintaining data over multiple regions, the most common challenge is the latency as when the data is made available to the other databases. For example, when data is written to the database in India, users from India will be able to see that data sooner than users from the US. This is due to the latency in synchronization between the two regions. In order to overcome this, there are a few modes that customers can choose from and define how often or how soon they want their data to be made available in the other regions. Azure Cosmos DB offers five levels of consistency which are as follows:

• Strong
• Bounded staleness
• Session
• Consistent prefix
• Eventual

In most common NoSQL databases, there are only two levels – Strong and Eventual. Strong being the most consistent level while Eventual is the least. However, as we move from Strong to Eventual, consistency decreases but availability and throughput increase. This is a trade-off that customers need to decide based on the criticality of their applications. If you want to read in more detail about the consistency levels, the official guide from Microsoft is the easiest to understand. You can refer to it here.

Azure Cosmos DB Pricing Model

Now that we have some idea about working with the NoSQL database – Azure Cosmos DB on Azure, let us try to understand how the database is priced. In order to work with any cloud-based services, it is essential that you have a sound knowledge of how the services are charged, otherwise, you might end up paying something much higher than your expectations.

If you browse to the pricing page of Azure Cosmos DB, you can see that there are two modes in which the database services are billed.

• Database Operations – Whenever you execute or run queries against your NoSQL database, there are some resources being used. Azure terms these usages in terms of Request Units or RU. The amount of RU consumed per second is aggregated and billed
• Consumed Storage – As you start storing data in your database, it will take up some space in order to store that data. This storage is billed per the standard SSD-based storage across any Azure locations globally

Let’s learn about this in more detail.

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