Big data analytics, data management, big data technologies, artificial intelligence (AI), cyber security, business intelligence analytics, cloud systems, information technology. These are buzzwords most executives and industry professionals have heard before, but they may not really know what they mean, and what they can do for a company. A McKinsey survey of more than 500 executives found that only 85% are “somewhat effective” at achieving their goals through data and analytics initiatives, including big data and artificial intelligence. The potential of AI and big data is amazing, but for many organizations the struggle is to start with any kind of analytics at all. Before artificial intelligence can take off and work for a company, there must be business analytics and data management processes in place that give the AI somewhere to start, and get the company into the habit of working with analytics to change their strategies. As artificial intelligence and big data technologies continue to grow and mature, it’s crucial for organizations to add team members who are well versed in using the technologies to help the organization thrive, as well as teach others about big data and AI.

Data analytics and big data analytics are actually two different things, and they're separate from artificial intelligence. While similar to each other, they represent unique elements of analysis. Data analytics is the way any data is looked at to gain information and draw conclusions. The term “big data” is simply used to define extremely large data sets. Data management is how an enterprise utilizes that insight, and make changes.

Big data analytics is the way that organizations take past information and find patterns, trends, and behaviors. This can include things like how users behave on apps and websites, to email conversion rates, advertising click through rates, and customer input behaviors from CRM software. These steps are important to creating paths for AI integration later down the path.

For example, a company may get big data analytics to understand their customer base better. Big data technologies can help them understand that in the past, most of their website visits, orders, and click-through on advertisements have been from women, age 25-40. This would help executives understand why behaviors have existed in their sales and on their website, and could help them in the future as well.

Data mining is the primary data source that delivers the insight for analytics and eventually AI in vast quantities, and it’s often unstructured data. Big data solutions include ways to structure that work, and help executives make business decisions. Executives and marketers are used to getting their intelligence through analytical dashboards, in structured ways that make it easy to perform data analysis. Things like ratios, percentages, and averages are ways that enterprises can look at their data and understand it more thoroughly. The issues arises when nobody in an organization can help get the data from its unstructured form, to understandable bites that they can use to their advantage.

Big data analytics helps enterprises obtain intelligence on patterns and trends based on history. Predictive analytics helps them understand how to use it for the future. Management is able to use the data they’ve obtained and the understanding it’s given them to make predictions about future behavior. This is a “what if” type scenario for human interaction. They are able to make changes to marketing or products based on what they have learned about their customers. This is an important next step before artificial intelligence can move forward. For example, the company that found that historically they appeal most to women age 25-40 could use that data to decide to run a targeted social media campaign to women in that age group. They could also change the look of their ads so they would be more appealing to that audience. Knowing that this is their audience based on past data helps them make decisions for the future.

Artificial intelligence machine learning takes this idea of predictive analytics a step further. Beyond just giving good ideas for what an organization can do, artificial intelligence machine learning gets smarter as time goes on. AI is a more automated type of processing, that's strictly content and data-driven. AI is able to test and analyze on its own, and help deliver results based on those tests and analyzations. The example company that found they appeal to women age 25-40 could use artificial intelligence machine learning to learn that their targeted ads performed best in suburbs of cities. It could then use their artificial intelligence to test and analyze the idea that women with children reacted and behaved best with the company, and re-tailor the ads to work with that new information.

The potential for AI is huge. Beyond just helping organizations solve problems, fully integrated artificial intelligence machine learning could cut down on mundane tasks that workers do. AI could revolutionize e-commerce and help the US reach its digital potential. AI machines with the capability to learn don’t need breaks or rests, and can constantly be working to learn and grow even more. This means that organizations could ensure they have constant streams of new information and smarter machines. These AI systems can work on large complex problems and procedures; some AI researchers are currently working on ways to help combat energy shortages for the country. This is important work that can be done with the help of artificial intelligence.

Organizations need to prepare in multiple ways to be ready for AI integration and machine learning. They need to have computers and systems that are ready to handle such intense computing, storage systems that can handle the big data output, flexibility and access to the data, ability to scale according to the business growth, and an employee or group that truly understand how the whole artificial intelligence systems work. Most executives in organizations don’t have the time, or ability, to learn how AI machine learning works at the detailed level needed. This is where a data manager comes in.

The role of the data manager is simply stated, but is a huge responsibility; with the advancements in big data technology and artificial intelligence, data managers need to be on top of changes and be ready to help their organization grow and thrive with new tech. They are responsible for helping program and run data and artificial intelligence software, interfacing with it regularly, and helping explain needed changes or implementations to executives.

With the continued advancement of big data analytics and AI, organizations are going to need IT managers and personnel who can interact with these technologies. Unfortunately, there aren’t enough qualified professionals who can do this work, which many experts are calling the “IT skills gap.” That means now, more than ever before, every industry is desperate for professionals who can work with data, analytics, artificial intelligence, and computer systems to help achieve goals. Government leaders and business leaders alike are being told to continue to put emphasis on STEM and IT learning so that the thousands of technology jobs that need to be filled will have qualified candidates.

Now is the perfect time to go back to school and get a degree in data management. and add to the artificial intelligence growth. Schools like WGU will help you learn about data management platforms, big data innovations, big data technologies, artificial intelligence, and the basics of analytics that you’ll need to walk into any organization, and come out with a data manager job. Online education allows you to get done as quickly as you can master the material. You’re also able to learn how to interface with many computer programs, and manage your time, qualities that are crucial for data managers. Don’t wait any longer to get in with an industry that is only going to keep growing!