Artificial intelligence is everywhere—even in places you might not expect. How do you think Uber is able to match you with a driver so quickly? How does Netflix know which movies you might want to watch next?
Many enterprises use AI to power nearly every interaction you have with their platform. Uber, for example, uses their arrival time prediction algorithm, DeepETA, to connect you with a nearby driver in a short amount of time, to calculate your food delivery window with a high degree of accuracy, and to suggest routes with less traffic.
The result? Happier customers and more efficient operations.
These flashy, AI-powered innovations wouldn’t be possible without data to support them. Collecting data, analyzing it, and using it are key to an organization’s success.
While enterprise data analytics is a huge topic to cover, it’s built on a simple foundation: Turning data into insights, and turning those insights into revenue.
In short, the “AI” piece of your enterprise data analytics strategy enabled data alchemy. The specifics behind how AI turns zeroes and ones into gold can get complicated, but in this article, we’ll break it down to make it more manageable.
First, let’s cover the basics of data analytics and then get into how AI specifically can help you level up your enterprise data strategy.
What Is Data Analytics?
Data analytics is the process of using computerized tools to derive valuable business insights from data.
For example, think back to when you ran your first enterprise: that awesome lemonade stand when you were barely taller than the table the pitcher stood on.
Remember that scorching day that business was booming, and you ran out of lemonade mix? Your parents likely explained that a single container of lemonade mix won’t cut it when your clientele is super thirsty. The next day, you got two containers instead of one and kept selling your sweet, sour syrup for an extra three hours.
That was data analytics at its finest. You took data, analyzed it, and then made a decision that boosted profits. So, what can AI bring to your curbside table?
The Role of AI in Data Analytics
Nowadays, you could use AI-powered APIs to design a system that tells you the optimal street corner to place your lemonade stand based on traffic patterns, average vehicle speeds, and how long they spend at the closest stoplight.
You could also throw in a risk assessment tool that accounts for crime rates in the immediate area to make sure you choose the safest location, as well as a system that collates weather-related data to choose the most profitable week of the year to launch your venture.
And that’s just the beginning—a drop in the pitcher. While regular enterprise data analytics tools help automate the process of collecting and organizing data so you can quickly analyze it, AI can take your process to the next level by providing recommendations for the best ways to improve business outcomes based on the data it gleans.
Pieces of an Enterprise Data Analytics Strategy
You can break down the basic elements of your enterprise analytics strategy into four categories:
- Descriptive Analytics—what happened in the past
- Diagnostic Analytics—why it happened
- Predictive Analytics—what’s likely to happen in the future
- Prescriptive Analytics—what you should do to replicate, improve on, or change business outcomes
We’ve compiled a separate blog post that goes more in-depth on common advanced analytics techniques and breaks down what each technique is and how you can use it to make an impact.
TL;DR: Using advanced data analytics goes beyond giving you a snapshot of what’s going on at your organization (though it certainly does that, too!). It enables you to monitor operations, identify trends, and make better decisions for your business.
The Power of People
To leverage your most valuable resource, the people in your organization, you must advocate for a data-first culture. Then, like a tree, you have to nurture, water, and fertilize that culture until its branches extend throughout your organization.
This often requires a combination of providing enterprise data analytics tools for a variety of internal systems and teams, from marketing to product development to HR—and rewarding those who use it to produce business-boosting insights.
While basic data analysis tools and engaged, motivated people can help surface effective insights, with the help of AI, you can do much more, especially if your business stakeholders, analysts, and engineers can collaborate using a multi-persona data science platform.
Here’s what the AI difference looks like in the context of business transformation.
How AI Is Transforming Data Analytics for Enterprises
Business intelligence is the past; artificial intelligence is the future. In other words, business intelligence, which involves gathering and using data, can be effective, but on its own, it’s too reactive to keep up with the pace of 21st-century business. AI, on the other hand, can gather and process huge volumes of data, provide insights, and make suggestions for how to improve your business—with minimal human intervention.
Jewelry powerhouse Chow Sang Sang boosted conversion rates on their website by 49% using artificial intelligence. Their AI-powered system not only recommends jewelry to customers based on their tastes, but it can also tell when a customer is hesitant about making a purchase and offer a coupon at the exact right moment.
It then automatically generates a message that emphasizes the value of the item they’re considering, and based on data gleaned from the customer’s behavior, decides whether or not to put up a countdown clock to encourage them to make the purchase quicker. Much better than a simple analytics dashboard, right?
While there are many applications of AI for enterprises, four key ways AI can level up your business insight system are:
- Optimizing Resources
- Enhancing Customer Relationships
- Improving Retail Performance
- Reducing Risk
Basic analytics can tell you which resources you’re using, when you’re using them, and how they’re being used. But, AI can tell you the impact adjusting the use of a resource will have on your bottom line, including if you should increase or decrease its use, the optimal time to make the switch, and how to best support its implementation.
For example, healthcare organizations can use machine learning algorithms that take into account patient needs, the amount of patients providers can handle, and the physical resources on hand.
They can then analyze that data and tell you the most efficient way to provide excellent care to as many patients as possible without taxing your resources, overcrowding your facilities, or over-working your doctors, nurses, and other professionals.
Enhancing Customer Relationships
Analytics can help you understand how many customers call a help desk for assistance, which departments they connected to, and how long the average call lasted.
Going deeper, AI can tell you the correlation between customer retention rates and how long each call lasted, the call center reps each customer interacted with, and the kind of mood your clientele was in using a linguistic analysis engine.
For example, an e-commerce company could use AI in conjunction with a customer relationship management (CRM) system and a unified communications platform to gather and analyze data about customer experiences. It can then provide insights you could use to train employees regarding:
- How long they should spend on the phone with customers
- Scripts to use that increase customer retention rates
- The most common reasons customers call, and the most effective responses to their problems
In this way, AI can help you optimize a far more valuable tool: the people that drive your organization. By equipping them with the knowledge and best practices they need to serve your customers, you can reduce churn and increase revenue.
Further, you can use AI for customer segmentation, breaking it up according to whichever demographics or buying habits you like. The insights you generate from this can be used to create custom-designed campaigns, advertisements, and offers for certain groups.
Improving Retail Performance
Especially in recent years, customer expectations have changed dramatically when it comes to the retail industry, and basic analytics simply isn’t enough to help businesses meet those rising expectations.
Trying to leverage a comprehensive array of retail system and customer data using a basic analytics system would be like attempting to juggle 1,000 balls, 200 bowling pins, and at least one chainsaw. But with AI, you don’t have to clown around with clunky, outdated analytics tools.
AI can collect and visualize data, process it, and tell you what it means for your organization. For instance, an AI system could identify buying trends in both online portals and brick-and-mortar retail locations. The system could then recommend how much you should spend on advertising—for each selling venue—to get the highest overall ROI on your marketing budget.
In highly regulated industries particularly, like insurance and finance, reducing risk is a #1 priority, and basic analytics can’t give you much more than charts and raw data. AI goes further and enables systems that can detect fraud, check for compliance, and identify and prevent errors in banking systems.
For example, an AI-powered system can detect anomalous user behavior that may indicate an attempt to steal from a business’s bank account. This can be done using geolocation factors, purchasing frequency, the size of individual transactions, and even the specific computers or devices used to access the company’s network. It can then automatically mitigate the threat.
Where Does a Multi-Persona Data Science Platform Fit in?
Sure, the opportunities that AI presents to your enterprise sound great, but how do you actually execute on them?
A multi-persona data science platform like RapidMiner not only gives everyone in your organization access to AI, it also makes actually implementing AI-powered solutions easier. How? One word: Upskilling.
You don’t need a computer science degree to use a multi-person data science platform. In fact, the nature of multi-persona is that the platform offers environments for everyone—coding for expert data scientists, an automated platform for beginners, and a visual designer for everyone in between.
With a multi-persona platform, everyone can collaborate, share insights, and build ML models that make your business better. It’s just as accessible (but a thousand times more powerful) than a basic analytics tool.
Experience the Power of AI
Whether your enterprise already has a developed enterprise data analytics strategy or is still trying to turn lemons into lemonade, AI can help you overcome roadblocks and give your organization a major competitive advantage.
Want to learn more ways that AI can have a tangible effect on your business? Check out 50 Ways to Impact Your Business with AI and start brainstorming your first use case today!