Every day, companies collect data on everything from customers and products to sales and marketing performance. That’s on top of information on internal performance, such as team productivity and employee recruitment and retention. This data is only as valuable as the insights it provides to companies to make informed decisions and drive business growth.
3 Steps to Get Data Ready for AI
- Centralize your company’s data.
- Make sure your data is high quality.
- Train teams on AI and keep them current on AI trends.
The reality is that many companies are doing very little with the mounds of data they have. This is because most organizations have their data spread across several systems, channels and gatekeepers. For the most part, teams have very little insight into what data their company has. Even if they do know, they typically can’t find it or have permission to access it. This forces them to make business decisions based on gut instincts or the most senior or loudest person in the room.
Employees aren’t the only ones affected by this lack of access to data. Artificial intelligence (AI) is, too.
Without data, AI cannot run. AI leans on data to learn, adapt and continually improve the insights it offers and the decisions it makes.
According to Slingshot’s 2024 Digital Work Trends Report, data’s lack of readiness is keeping companies from implementing AI altogether: nearly half (45 percent) of employers say they haven’t yet implemented AI because their company’s data is not ready.
So, how do companies get their data in shape so they can properly support AI and experience the significant benefits that the technology offers to businesses? Here are three ways to do just that.
Ensure Data Quality
Because AI is only as powerful as the data it relies on, companies need to ensure they’re running high-quality data to the technology. Incorrect data and missing values will only result in inaccuracies in the decisions and predictions made by AI.
To properly prepare their data and ensure its reliability and accuracy, companies need to conduct a thorough assessment on it. This includes standardizing data reporting (e.g. every customer profile reports first name and last name in separate tabs), reviewing data for missing values and inaccuracies and updating it as necessary. Companies should also take the time to remove duplicate entries that can affect analyses.
Centralize Company Data
Once data is clean and free of errors, the step next is bringing together all of a company’s data in one place. When companies have their data siloed across the organization, it’s inaccessible and teams are unable to garner comprehensive insights to help inform their day-to-day work and decisions.
The same can be said for AI. The technology can’t pull data from all of a company’s different systems and channels. Data needs to be readily accessible, all in one place.
Companies need to connect their disparate data sources and bring every piece of data across departments, platforms and channels together. Once data is centralized, AI can run effortlessly and effectively and teams can ensure AI is making insights and recommendations that are based on a holistic view of the organization and projects.
While centralizing data is a priority for companies for AI, having all of a company’s data in one place is useful for employees as well. Teams know exactly where all data from across the organization lives, and can easily access and incorporate it into their workflows and decisions.
Train Teams on AI
While AI can get companies far, it’s up to teams to take insights and put them into action to drive the business growth and results that companies are after. But they can only do that if they have the right education and training around AI.
Businesses won’t see success from simply implementing AI tools into their organization. Employees need to understand how to use technology and drive action from its insights and feel confident in their ability to do so.
Currently, 72 percent of employers say their employees are at least adequately trained on AI, but only 53 percent of employees believe they are, according to Slingshot’s 2024 Digital Work Trends Report. This means that companies have some work to do when it comes to AI training.
For many employees, using AI in the workplace may be their first time using the technology. Before even diving into how to use exact tools, companies should start training off with what AI is and how it is intended to help support employees in the workplace.
From here, companies can offer training on specific tools and for specific departments. A big part of this training — and keeping employees educated — is that it should happen yearly at the very minimum. This will keep employees current on the technology and help them unlock its full potential.
Companies may be prepared to use AI, but if their data isn’t ready, they won’t be able to see the full potential of what AI can deliver. Many companies first need to clean and centralize their data before it’s ready for AI’s use. They need to also make sure employees feel confident in their AI skills, so they can garner the best results and put them into action.
By getting their data ready for AI, businesses will also begin to create a data-driven culture within their organization. This enables AI to run properly and empowers employees to use data more frequently as part of their daily work and rely on it to identify trends and make business decisions.
