AI-driven tools have swept the world by storm, with a universal presence in all modern business operations. However, every AI tool relies on data insight. Business intelligence and data analytics are vital for the growth and quality of an AI’s learning model.
How BI and AI Compare
Business intelligence (BI) and artificial intelligence (AI) compare by bringing together the best of human intuition and displaying it in different ways. There are similarities and overlaps between these two types of data intelligence systems, however, there are also fundamental differences that keep them separate.
How BI and AI are Applied
Artificial intelligence is the use of a computer system to compare patterns in data and mimic human responses and displays of the pattern. Business intelligence is similar in that it collects data, but, rather than mimic the human response and display that data, business intelligence collects obscure data across a system and then presents it to a human analyst in a way that simplifies it and optimizes it for direct-to-human interaction.
Why BI and AI's Goals Differ
Simply put, business intelligence is like a good news broadcast: it only tells you the facts about what data exists in the system. AI, however, is like a friendly consultant and suggests what to do with the data collected.
While the goals of the two types of intelligence are different, they pack a punch by being paired together.
Where BI Meets AI
When a business wants to harness an AI’s power to get ahead in its operations, it has to feed the learning model the right kind of data to get the job done. To do justice to your team’s expectations, the business intelligence gathering team needs to produce stellar data visuals and a crystalline path through that data. Making the data make sense is imperative. Data analytics is the AI’s school, and without clear visuals, the AI’s interpretation of data may even misinterpret business goals or brand messaging.
Why AI is Used for Automation
Before you go further, you may be wondering, what is the purpose of automating database processes with AI? While machine learning and AI can feel a bit overdone across the spectrum, the reason for advancing business operations with AI centers around efficiency. Data science professionals have noted that technological processes for housing and distributing data are evolving. Many monolithic data systems are overwhelmed with an abundance of ad hoc reporting demands.
How to Train Your AI
To get the best results from training and AI, you need a clear definition of what the AI’s purpose is and what you want to train it to do. The AI training process may be different for a generative AI, or a tool that creates text and images than it would be for the kind of machine learning tool that would automate an email flow, a customer experience feature, or some other component of e-commerce in a digital business.
Different Learning Types
AI learns in a variety of ways, and different learning techniques are emerging all the time, says the Data Science Dojo. These include as following:
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Supervised learning: IBM explained supervised learning as the category of machine learning and artificial intelligence where datasets are labeled to train algorithms to classify data and predict accurate outcomes with it.
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Unsupervised learning: this type of learning is the inverse of supervised learning, training the AI on unlabeled data instead.
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Reinforcement learning: with this type of machine learning, the agent learns by interacting with an environment.
Gathering Data Visualization
As you determine the best method for your AI model to train under, you’ll have to think ahead about how to prepare data visualization appropriately. Think of it like collecting lesson materials.
Importance of Clean Visuals
As you carry out AI training, your process will be iterative and will require a hands-on approach to keep data visuals and testing visuals clean. For example, machine learning professionals advise new trainers to keep test sets and validation sets separate so that visuals will be clean.
Designing the Dashboard
When you’re ready to display the data for machine learning, it will be time to select an interactive dashboard model design. Interactive data analytics dashboards take on a variety of forms, but the objective is similar for each one: keep the visuals simple, and easy to engage with.
When creating a final dashboard design, incorporating the following elements will advance the efficiency of the completed tool:
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Highlight Key Performance Indicators: Remember, the goal of business intelligence is to show the facts about data in a system, and the goal of artificial intelligence is to produce outcomes or make suggestions. Showing KPIs in an AI-training dashboard will train the AI to make highly metric-influence suggestions.
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Make It Interactive: Adding a splash of color, or interesting display designs can go a long way in making a data dashboard interactive.
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Build To Scale: AIs require an abundance of high-quality data. Building a business intelligence platform to grow with the AI model is important as your business operations will need room to breathe without breaks.
Quick Ethics Note:
Building on those foundational elements is key to a great AI learning model dashboard. It’s also worth mentioning baking ethics in any data you display. Using data responsibly in a visualization built for training an AI model is essential to ensuring the outcomes an AI generates are also ethical.
Why Use Wyn
The value of cutting a clear path through sometimes weedy data is unmatched. Yet, that value can also be cost-ineffective, which pressures many businesses to continue using outdated technologies for data visualization.
As business processes forge ahead with machine learning automation, a business that doesn’t have optimal data visualization is certain to be left behind in tech obsoletion, unless that business can find a cost-effective way to update data collection to suit the demands of modern reporting.
Wyn Enterprises solves the pain point of cost efficiency by eliminating hidden fees. With dashboards deployable in mere minutes, Wyn cuts through a lot of billable hours costs and saves time and efficiency in data reporting, all of which is essential for scaling to modern use case scenarios.
Summer Rippel
Summer is the Product Marketing Manager for Wyn Enterprise. She has over 13 years of experience in marketing, specifically in the technology and software fields. Summer graduated from Robert Morris Univeristy with a Bachelor of Science in Business Administration, Concentration in Marketing. You can connect with Summer via LinkedIn.
