35% of businesses are already Leveraging AI's many benefits - Start with your Data

Successfully Leveraging AI’s Many Benefits Starts With One Thing: Data

By Kurt Stein

April 17, 2023

Artificial intelligence is on everyone’s minds these days. It shows, too: The use of AI in business is becoming more prevalent every day. In fact, 35 percent of businesses are already using AI, and 42 percent are exploring their options for implementing AI in the future.

Given the incredible benefits AI offers, these statistics are no surprise. Deployed successfully, AI can help businesses of every size automate business processes, increase engagement with customers and employees, and gain insights through data analysis.

However, as helpful as AI can be, many small businesses struggle to adopt AI. In fact, large businesses are twice as likely to adopt and implement AI as small businesses are. I believe the reason for this is that many small business owners don’t have the time, resources, or experience to deploy AI successfully.

At DCT Strategy, we hope to change that. We know that AI can help small businesses maximize revenue and increase market share. We also know that the key to accomplishing these objectives starts with understanding the AI space more deeply.

To help you with that, I’ve put together a series of articles addressing some of the most common concerns and questions we hear from our small business clients about AI adoption and deployment. (The first article in the series, which contains a broad overview of the process of AI deployment, can be found here .) In this article, we’ll take a deep look at one of the most important steps in the deployment process: data discovery and cleanup.

The Importance of Data

To leverage the benefits of AI, you must have data. But not just any data: Specifically, you need a clean, organized data lake.

?A data lake is the centralized repository of all your data. While it is possible to store your data as-is, taking the time to structure it appropriately often means the difference between success and failure.

To understand why, think of an Excel spreadsheet. Let’s get even more specific: Let’s say that you are using an Excel spreadsheet to help analyze your customer’s buying habits. Without rows, columns, and pivot tables, the data in the Excel spreadsheet will just be a bunch of useless numbers. Sure, the data is there, but without context and categories, it’s meaningless to you.

The same thing holds true for AI. If your data lake isn’t clean and organized, or if it’s missing context, even the best AI tools in the world will struggle to make sense of it. And if they can’t make sense of it, they won’t be able to achieve your objectives.

Cleaning Up Your Data Lake

So, where does the data that makes up your data lake come from? Most companies store data in a variety of locations: databases, hot storage devices, and cold storage devices are some of the most common.

Finding all the places where your data is stored isn’t always an easy task. Indeed, data discovery is one of the most time-consuming parts of the AI-deployment process—it can take so much time, in fact, that it’s not uncommon for large companies to spend millions on this step. Of course, it shouldn’t cost you, as a small business owner, anywhere near that amount! However, the fact that large businesses are willing to spend so much on this phase is illustrative of two things: how difficult it can be to find data, and how important it is to do so completely.

So, how do you go about finding your data? First, ask yourself if you have the right internal resources to accomplish the task. Do you have somebody who understands where your data is and knows how to access it??

Once they’ve found your data, do they know how to sample it? Do they know how to determine whether it’s accurate? Do they have the experience and knowledge to scrub it if necessary? The answer to all of these questions must be yes to move forward successfully.

Moving Forward

Even if members of your team have the knowledge required to find and clean up your data, there is one other factor to consider before moving forward: Do they have the time to devote to these tasks? Remember, the time they spend on data discovery and cleanup is time they aren’t spending on their other duties, which means either those responsibilities go unfulfilled or someone else has to step in to handle them.

If pulling an existing team member off their normal work to do this is not feasible (and often, it isn’t), or if you don’t have people with the expertise necessary to ensure you have a good data lake, then your best solution is to hire an expert who can help you through the process.

Ideally, the person you hire should be a data scientist. They should also have access to a team of people who understand the full stack of your business: your computers, infrastructure, cybersecurity, databases, and so on. They need experience with AI modeling, and they should have worked with all different systems.

It’s imperative for your success that they understand how to use various AI tools to help you meet your objectives, too. Finally, they must understand how to test and manage the model after deployment to make sure it’s working.?

Before hiring anyone, discuss their experience with them. Make sure they are knowledgeable about each of these areas. The AI space is constantly changing; whoever you choose to work with should understand that, and along with experience, they should also prioritize staying on top of all advancements in the field.

Getting a Good ROI

Whether you choose to hire someone or not, implementing AI can be expensive. Yes, the benefits are often worth it, but only if it’s done right. So, how do you measure your ROI?

It comes down to understanding your use case—in other words, what you are trying to do. Are you using an AI tool to improve customer retention? Then you need to define metrics and KPIs around that goal. Are you trying to increase how much each customer spends in each transaction? Then define metrics around that.

Once you have defined your metrics, you can test the AI model to make sure it’s producing the outcomes you want. If it isn’t, you may need to revisit your data or tweak the model. This, too, is a place where an expert can be of great value. Because they understand what tools are available and how to use those tools to create the outcomes you want, they can make any adjustments necessary to ensure you achieve the outcomes you want.

If you want to learn more, have a question you want to see covered in a future article, or if you are interested in learning about how DCT Strategy can help you successfully deploy AI, send me a message via LinkedIn or reach out to me through my company’s website . And be sure to check out the next article in the series, where we look at how you can assess whether your company is ready to implement AI.

#artificialintelligence #business #digitaltransformation #dctstrategy #entrepreneur