Artificial intelligence, machine learning, and deep learning are buzzworthy terms in the world of business, ranging across channels from customer service to finance and beyond. Because big data is big news, companies want to implement AI to improve their businesses — but some are making the mistake of trying to layer in artificial intelligence without the basis in analytics and data that allows AI to make a true impact.
Other companies — those with a strong foundation in analytics, possessing a store of data to work from — can utilize AI with great success. According to the Harvard Business Review, “companies with strong basic analytics — such as sales data and market trends — make breakthroughs in complex and critical areas after layering in artificial intelligence.”
AI innovations like those aren’t possible without the right data and specialized data science staff who know how to use it. The following are four lessons that vanguard businesses have learned about the intersection between data and deep learning — lessons you can apply to your organization as you layer in your own artificial intelligence algorithms.
1. You have the data you need, but aren’t using it yet
If you have a successful business model making significant revenue, chances are you already have a ton of data available to you about your customers — what they’ve purchased, when they purchased it, how they prefer to be contacted — collected from common customer interactions. If you aren’t using this data or centrally collecting it for easy access, you’re wasting a resource that can add efficiency to your organization by providing a better, more personalized experience for your customers.
2. Creating new data is as important as using existing data
Too often, the traditional approach to analytics or data science is beholden to data you already have. As data science evolves, the creation of new, more applicable data and the capture of previously underutilized data are becoming more important. Business processes helped by artificial intelligence are only as good as the data you feed into that AI, which means locating and creating the right data is essential.
3. Data and information availability empowers autonomy
Historically, a business’ success has largely depended on in-company experts who knew the business model very well — high-level employees who have been with the company a long time, who understand the business and can act as resources, and who generate intuition from experience. This “executive intuition” is an incredibly valuable resource, but many employees in larger organizations do not have the experience necessary to garner this intuition, and not every employee has access to those veteran experts who do have the intuition.
Though teamwork is essential to good business, you also want everyone in your organization to be as autonomous as possible. When you use data correctly, you take the information that comes in over time, capture it, and make it available to whoever might need it. Data science can elevate executive intuition, empowering more decision makers. Then, moving forward, your organization can grow from a small group of experts making decisions based on experience and intuition to a larger group of employees making decisions based on quantitative measures.
4. Successful data science requires specialization
To properly make use of your data, you should build a functional team of specialists focusing on different aspects of data science and its applications. One data scientist with a basic understanding of different aspects of the discipline can put you in a situation where you have a jack-of-all-trades and a master of none. Recommended areas of specialization include data visualization, data cleansing, and artificial intelligence algorithm creation.
Artificial intelligence can help you be much more precise in the business decisions you make on the macro and micro levels, no matter how your business interacts with customers. To achieve that precision, though, you need to collect, create, and organize your data so that it becomes one of your organization’s greatest assets. The other greatest asset? The data science experts who make sure that your data and artificial intelligence are doing the most they can for you.
Landon Starr leads the data science organization at Clearlink, which includes the information management, advanced analytics, reporting, and conversion rate optimization teams.