AI is the acronym on everyone’s lips this summer. Generative AI tools have gone mainstream, and nearly everyone has heard an AI use case that piques their interest. But you’ve got questions about the cost, the security, and how to use your data because you’re a small business, not an enterprise with massive resources.
AI is a growth tool that can be a game-changer for smaller businesses. Using existing AI tools to automate and streamline processes is a force multiplier — freeing your employees to focus on more complex tasks. Being able to ask AI questions about your business holds limitless potential.
Even if your company hasn’t formally decided to embrace AI, your employees may be using it — 75% of all employees worldwide are, according to new data from a joint Microsoft and LinkedIn 2024 Work Trend Index. As a cybersecurity professional, when I read that, the first thing I worried about was data security.
Doing fundamental data governance can protect your intellectual property and your employees’ and customers’ data as you explore AI.
Data governance 101
Data governance is part of your overall security posture, often as a component of cybersecurity or modernization. It’s the set of policies, procedures and practices for handling data within the organization. Data government ensures that your data is accurate, reliable and consistent — so it can be used for business decisions and initiatives.
Data governance is an important foundation for AI because it protects the data you consider proprietary and secures other corporate data that may be exposed by open AI tools, such as calendars or the text from your latest proposal.
The process for data governance can be broken down into three steps: organize, cleanse and protect. A deeper dive into each step can help you understand what kind of resources and time will be needed to get the AI outcomes you desire.
Organize your data
Depending on your organization, you could be looking at massive amounts of data stretching back decades or more modest databases and files. So, before the data is analyzed or used, an important first step is to understand what you have and determine if it’s valuable. Certain tools can help you apply data governance by mapping your data landscape and automating tagging data to help classify it.
Organizing can help you determine how to store data, too. For example, you might have data related to your company holiday party and summer picnic. It’s important to keep but likely won’t be part of your AI efforts, so you might tag it and decide to archive it.
Cleanse your data
High-quality, clean data is the basis of accurate analytics. Your company will need it to derive meaningful insights or make data-driven decisions. Without data cleansing, your business risks using results based on inaccurate or misleading data, with potentially detrimental outcomes.
Data cleansing involves reviewing and removing inaccurate, incomplete or irrelevant data from your datasets. Data can become inaccurate over time as businesses acquire customer and prospect data with errors, duplications, or incomplete information from human error, formatting issues, and processing errors.
The data cleansing process can be different depending on the dataset but may involve removing duplicate and irrelevant data, correcting formatting and validating data. For this step, it may help to consult with an AI practitioner or data cleansing service provider. They can also assist you in determining what data to cleanse based on the AI use cases you have in mind.
Protect your data
You lock your car doors, put your money in a bank and store valuables in a safe. Protecting your data — your company’s intellectual property — demands the same security mindset. In many cases, the data you have may be that of your customers, who trust you to be stewards of their information.
Consider how data collection practices have escalated: Many companies now use software to track every action a user takes and collect information submitted through forms — including intellectual property, data from cookies, IP addresses and device IDs. Personal data that is collected and used to train AI becomes part of the AI model, meaning there’s no way to track it or undo it.
Protecting data may involve moving data to a more secure location, isolating data that will be used for AI and encrypting data. Some AI models will spell out requirements for security. Small companies want to read that fine print to make sure their data is retained in their environment and can’t be used to develop other models or used collectively.
Implementing AI
I recommend that small companies look to industry leaders for their data governance steps or build the process into their larger cybersecurity efforts. Companies like Microsoft and Google have invested in making the process easier with tools and services that accelerate the prep and get you to AI results sooner.
Once organized, cleansed and secure, what can you expect from AI? Measurable gains in:
- Productivity, by streamlining tasks, facilitating communication, or accelerating service delivery,
- Optimized operations, with greater efficiency at every level and relief from digital overload,
- And business outcomes, using your data to identify trends, see opportunities, make decisions, or answer questions.
And since we’re talking data, it might help to look at expected AI results in terms of numbers from a Work Trend Index Special Report on productivity, creativity and time:
- 70% of AI users said they were more productive
- 29% faster overall in a series of tasks (searching, writing, and summarizing)
- Nearly 4x faster catching up on a missed meeting
- Users saved 90 minutes on tasks/week
AI isn’t just for enterprises. It’s accessible and realistic for small companies to use too — if you first take the time to do proper data governance.
