This article will give an introduction to the world of Data and AI at a high-level glance. The article explains the identification of how the evolving use of Data has presented new opportunities for various organizations. The article explores which Data and AI service can be used on Azure, in relation to addressing certain business need.
In this article we will answer the following questions:
- What is Data and AI?
- What are use cases for Data and AI?
- What are the benefits of using Data and AI?
- What services on Azure are available in relation to Data and AI?
- What are real-life customer cases that use Data and AI?
You can also attend our free workshop where you will get an introduction to Data & AI.
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What is Data and AI?
What does Data and AI mean to you?
Although we get exposed to it on a daily basis, there's a whole lot of unclarity and vagueness surrounding it.
An example:
Have you ever checked your balance account before you would go to a supermarket and purchased something, when after, you immediately can check where the money went and what you paid? Some banking appliances will even bucket the Data for you into categories such as groceries, utilities, transport et cetera. You can even see predictions of your spent or future transactions/standing orders to come.
We take it for granted, but it's only possible through capturing the necessary/correct Data, transforming it, and use an algorithm or AI model to predict. To make it all look interesting, visualization tools help with creating the graphs to show your analyses. As you can see, there's a whole lot more on the backend which we don't see, but what definitely is happening when you make the transaction at the grocery store.
So, what is Data then?
According to the Cambridge Dictionary Data is defined as follows:
“Information, especially facts or numbers, collected to be examined and considered and used to help decision-making or information in an electronic form that can be stored and used by a computer”
Data comes in many forms and shapes from everywhere. We can make it look very complicated when we talk about Data, but basically, when I may be so freely to put an analogy, it's like the ingredients for a dish.
What we need for cooking is:
- Kitchen (i.e. a space with cupboards where you could cook)
- Appliances (i.e. a stove, fridge etc.)
- Kitchenware (i.e. utensils, pots/pans, plates etc.)
- Sizing (i.e. amount of people that is cooked for)
- A dish (i.e. a recipe)
- Ingredients for a dish
- Budget (the costs you’d like to pay for the dish)
- Skills (i.e. chef, beginner etc.)
How this relates to Data is to see it as follows:
- Kitchen = the Data platform
- Appliances = the Data services
- Kitchenware = the tools
- Sizing = storage capacity
- Dish = problem statement
- Ingredients = Data
- Budget = spenditure
- Skills = skills
When you’ve defined a dish, or in Data terms, a problem statement, you check how much you’re willing to spend. Then you see if you need to prep some ingredients (i.e. Data) in advance, like making a spice bag or in Data terms, applying preprocessing to Data. You should outline the skills you have within your team, are they developers, Data analysts, etc.? When it’s time to get grocery shopping decide which supermarket to go to. In Data terms, you’re identifying the Data locations of your Data. Once that decision is made, which ingredients are you going to buy? In Data terms, what Data are we getting (i.e. structured/unstructured/semi-structured), and in which formats are we getting the Data?
So, what is AI then?
According to the Cambridge Dictionary, AI is defined as follows:
“The study of how to produce machines that have some of the qualities that the human mind has, such as the ability to understand language, recognize pictures, solve problems, and learn.”
Data is the foundation for using AI. It sounds very cryptical but it’s trying to simulate, extend and expand the human mind. In statistics for example you have something called an Anova table, which back in the days, was done by hand to understand the mechanism behind it. However, if you wanted to check when worldwide a fraudulent transfer had occurred, it’s a whole lot of Data to go through manually. Now, a task like this can be performed by machines. In cooking it’s the way you’re going to create your dish, which is a whole process.
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What are the use cases for Data and AI?
I believe the use of Data and AI can be found in almost any industry.
Below you find 2 examples where Data and AI have been used:
1. When we look at the banking example that was mentioned, before, Data and AI is used to fight financial crime. Let’s say someone has somehow found out how to gain access to your banking account and deducted money, you’d like that situation to be prevented. Since examples of this happen worldwide, there’s a lot of Data available to create machine learning models to prevent this type of fraud based on anomalies (say, suddenly you’re missing 10.000 euros when normally you’d spend only a 100). Using AI, you can enhance knowledge graphs to find relationships between disparate entities and identify suspicious behavior within financial systems.
2. Another use case where Data and AI is involved is to improve your customer’s experience with your company. Data and AI can give insights into your customer's portfolio and their satisfaction. In order to improve their experience, you can think about intelligent contact centers or chatbots. It might also be helpful to personalize towards a customer's need by personalized portfolio managed or getting proactive in offers for certain customers empowered by AI.
What are the benefits of using Data and AI?
Besides looking at what Data and AI is and what it can be used for, it’s imperative to understand why there are benefits to it.
- Insights: You will gain insights in your own Data such that you’re able to apply AI to for example perform predictions. Your Data will be ready in no time to visualize in reports that you can share with anyone.
- Flexibility: When using the Azure cloud, you gain flexibility in tools and services that you can use.
- Scaling: Products used can be scaled to your needs (i.e. up or down) or removed when you don’t need the service anymore.
- Efficiency: As with any scenario, you can use built-in business logic to launch solutions in shorter periods that could range from weeks instead of months.
- Skillset variety: You’ll have access to various frameworks for the different skill sets you have in house such as developers, Data Scientists, Data engineers, etc., where products can be used for any level of experience.
- Responsible and Secure AI: Even if the world of Data and AI and what we can achieve by it sounds scary, on Azure we have tools, guidelines, and services built into products to use it responsibly. With using AI responsibly, we mean for example that Data privacy is ensured, such that trust and transparency can be met with built-in security that extends from the cloud to intelligent edge.
What services on Azure are available in relation to Data and AI?
So, what services would your company need in relation to Data and AI?
To be honest, when going to the products section of Azure on the Microsoft page (Directory of Azure Cloud Services | Microsoft Azure), you might get a bit overwhelmed with all the services available for Data and AI + machine learning. However, every scenario you might have envisioned where you could use Data and AI is solvable and might use different services depending on all kinds of variables that we will discuss in-depth in the upcoming Data and AI workshop.
On a high level, it’s important to have some type of storage, a tool for transformations, Data analyses, and visualizations. If you for example want to improve customer experiences, you could do so with chatbots. For the chatbots itself, you’d might go for the Azure Bot services with LUIS. These services do need to have Data that is stored somewhere like an Azure Data Lake Storage.
However, maybe you already have in-house coding skills that would like a notebook environment where a complete platform is enabled for machine learning such as Azure Machine Learning. Again, the notebook needs to be able to read Data from somewhere, which even might be a table in an Azure SQL Database.
What are real-life customer cases that use Data and AI?
Still interested in using Data and AI but like to see some real-life implemented cases, please check out this general link: Microsoft Customer Stories
Below we discuss 2 customer cases:
1. Kodisoft
2. Royal Agrifirm Group
One case where Data and AI has been used extensively is Kodisoft.
Kodisoft, a creator of interactive restaurant technology, wanted to create a new way for engaging consumers. The company designed an innovative platform based on Windows 10 IoT and Microsoft Azure, where they’ve used Azure Machine Learning, Azure SQL Database, Power BI, and other Data and AI tools, to connect interactive restaurant dining tables with cloud services. Consumers can touch the table surface to order menu items and services, play games, and more, while the table responds intelligently with personalized themes and offers. As a result, consumers gain a digitally enhanced dining experience, and businesses benefit from more traffic and higher profits.
More on Kodisoft can be found here: Microsoft Customer Story-Bringing a new solution to the table: interactive technology transforms the restaurant industry
Another company where Data and AI services have been used in Royal Agrifirm Group.
They have used Data and AI to contribute to a responsible food chain were measurable, relevant, and sustainable value at farm, field, and in the food industry is their expertise. They needed a single version of the truth about crops and livestock feed and wanted to lead the way with setting up a Data platform during their technological transformation. They’ve used Data and AI services such as Azure Databricks and SQL server in order to go through this transformation and gain insights into overcoming the challenges of a global food chain.
If you want to know more about the success Royal Arifirm Group had please go click this link:Microsoft Customer Story-Royal Agrifirm Group: Creating a sustainable food chain with Data
Want to know more?
If you got curious and want to see how Data and AI at Intercept can help your organization move forward, please let Intercept help you by attending our free workshop about Cooking with Data and AI.
This is the first article in our 'Data and AI' series. The next topics will be:
- Data Engineering
- Data Science / Analytics
- Data Visualizations
Stay tuned for more!