Data has always been at the heart of a business’s success, whether you look back to the limited demographic information available in the 1940s or the plethora of information we have at our fingertips now. The more a company can leverage data, the better that company will perform. Throughout the 2010s, startups sought to change the way we look at data with the help of artificial intelligence.

The thinking was simple: what if machines could scour billions of terabytes of information to help businesses make logical conclusions about who could benefit from their products and services? Without the help of AI, humans would have no hope of fully leveraging all the data at their disposal.

When you hear the words “artificial intelligence,” you may get a little nervous and picture movies like Terminator. However, AI isn’t scary at all and is relatively useless without human help.

One of the best examples of this is AlphaGo, a computer that matches wits with players in the game of Go. Without professionals to play against, AlphaGo would be unable to match the intelligence needed to put up a significant fight. It’s only thanks to expert Go players who play against AlphaGo that it has become a genius-level Go opponent.

With human help, AI can parse terabytes of data with ease and with accuracy. Without humans to lay the groundwork and tell the AI what data implies, AI could not hope to be truly effective. AI learns much like humans do; without a teacher, AI would not correctly analyze data.

In the early days of smart data, there were quite a few problems that needed to be overcome. For instance, having a lot of data isn’t the same as having a lot of useful data. When you leave it up to a new AI to determine what data is helpful and what is inconsistent, you’ll run into some issues.

For example, think about gender demographics most of the year, then consider them around Valentine’s Day. Most of the year, men are not buying jewelry, flowers, and chocolates. When Valentine’s Day comes around, the number of men purchasing those items as gifts skyrockets.

A new AI might incorrectly assume that men have suddenly become interested in those items and urge your business to continue marketing to that demographic after Valentine’s Day. It takes the intuition of a human to tell the AI what’s really going on.

On top of that, without dedicated platforms to access and manage smart data, the utility of the data is limited.

It quickly became clear that dedicated smart data platforms are crucial to the success of businesses working with smart data. These new platforms have the capacity to support huge loads of data as well as more complicated data types than older platforms. One of the most necessary advancements is quick insights, which allows businesses to efficiently pivot when new data is analyzed.

There are three essential terms to understand in the smart data platform world: data management, data engineering, and data science. Each process is crucial to the success of a smart data platform.

Data management encompasses everything regarding the acquisition, storage, processing, and application of data. For businesses, data management must also consider quality management as well as the security of your information.

Data quality management is one of the tougher aspects of data management, as it has to do with whether your data is “good” or “bad.” There are a few factors that determine whether you have a good or bad dataset, including integrity, timeliness, accuracy, and consistency. Each of these factors must be analyzed so you know whether a particular dataset is worth looking at.

Data engineers are the framework builders in the world of smart data. Without the right pipelines, raw data can be practically unusable by data scientists. Data engineering is one of the areas where the combination of AI and human wisdom is crucial. Without the right amount of each, the data may not be as effective as it could be.

Finally, data science is where all your information is analyzed and interpreted. In a lot of ways, data scientists must be excellent statisticians, as principles like clustering, classification, and comparison are at play in both fields.

All of that information does you no good unless there are real-world applications for your business. Luckily, there are many ways to use smart data to your advantage.

Industrial companies, for example, use smart data for predictive maintenance. With smart sensors on their machines and devices, smart data can indicate when components are likely to break down. This allows the company to get as much out of a machine as possible, then replace a few parts quickly and get back to work, reducing downtime and increasing efficiency.

Smart data also has benefits in traditional areas. Targeting and customer segmentation are made much simpler with smart data, and it isn’t hard to use a smart data platform to leverage your insights into conversions. AI helps make the customer experience better for your audience, which leads to more sales for you.

Smart Data vs. Big Data

An area where some people get confused is in the difference between smart data and big data. While big data is great for storing vast quantities of data and noticing global trends, there is a ton of data to analyze. Because of the sheer quantity, big data often comes to outdated conclusions.

Smart data through a platform like Oracle enterprise data management, on the other hand, can analyze and interpret data in real-time. This allows companies to pivot quickly and stay on top of the latest trends.

Now that you understand what you need to know about smart data platforms, step up your game and get your products into the right hands.