Data Management: The Gateway to Advanced AI

Big data analytics, data management, big data technologies, artificial intelligence (AI), cyber security, business intelligence analytics, cloud systems, data integration, information technology. These are buzzwords most executives and industry professionals have heard before, but many are still exploring the impact of what these tools can do for a company. A Boston Consulting Group survey of 13,000 people found that 44% of leaders say that they have received training to sharpen their skills and stay relevant when it comes to data management and artificial intelligence. There are vast differences in perceptions of AI between those in leadership roles and employees on the front lines, however. While the potential of AI and big data is amazing, there is a great need for training and upskilling in this new era of technology. 

Before artificial intelligence can positively impact a company, there must be business analytics and data management processes in place that give the AI somewhere to start. With good leadership and training, a company can learn to work with analytics to optimize strategies. As artificial intelligence and big data technologies continue to grow and mature, it’s crucial for organizations to add experts such as data scientists to help the organization thrive and 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, they represent unique elements of analysis. Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. The term “big data” is used to define extremely large and complex data sets that exceed the capabilities of traditional data processing.

Big data analytics includes the processes and technologies that organizations use tp analyze large volumes of information and uncover patterns, trends, and behaviors. This can include analyzing things like: 

• User behavior on websites and apps
• Email conversion rates
• Advertising click-through rates
• Customer feedback behaviors in CRM software

These steps are important to creating paths for AI integration later down the path. 

For example, a company might use big data to gain deeper insights into their customer demographic. Information about their website traffic, orders, and click-throughs originating from women aged 25–40 can be obtained using conventional analytics. However, big data analytics surpasses the capabilities of conventional data-processing software due to its sheer scale and complexity. In this context, AI can also be used to extract deeper interpretations and make correlations that may have never crossed a data analyst’s mind. 

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 receiving their intelligence through analytical dashboards, in structured ways that make it easy to perform data analysis. Ratios, percentages, and averages are relational measures that enterprises can look at their data and understand it more thoroughly. However, issues can arise when nobody in an organization has the technical knowledge to interpret the data from its unstructured form into 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. Organizations across all industries can leverage predictive analytics to optimize services, allowing management to use the data they’ve obtained and the understanding it’s given them to make predictions about future behavior. Think of it as a “what if” scenario for human interaction, providing the opportunity 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 aged 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 these details about their audience helps them make decisions for the future. Artificial intelligence machine learning takes this idea of predictive analytics a step further.

Artificial intelligence machine learning takes this idea of predictive analytics a step further. Beyond generating ideas, artificial intelligence machine learning gets smarter as time goes on. AI is an 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 aged 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. AI could revolutionize e-commerce and help the U.S. reach its digital potential. 

AI machines with the capability to learn don’t need breaks or rests, and can constantly work, learn, automate data, and grow. This means that organizations could ensure they have constant streams of new information, data sets, and smarter machines. These AI systems can work on large complex problems and procedures; some AI researchers are even currently working on ways to help combat energy shortages for the country. 

Organizations need to prepare in multiple ways to be ready for the impact of AI integration. They need to have computers and systems that are ready to handle such intense computing, plus storage systems that can handle the big data output, flexibility, and access to the data. Add in the ability to scale according to the business growth, and the need for an employee or group that truly understands how artificial intelligence systems work becomes even more obvious. Since most business executives aren't in a position to learn how AI 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—and 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 organizations 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.

As big data analytics and AI continue to advance, the demand for skilled IT managers and personnel who can effectively interact with these technologies is growing. Unfortunately, the existing “IT skills gap” highlights the continued need for professionals proficient in data, analytics, artificial intelligence, and computer systems across various industries. Prioritizing STEM and IT education in business and government is essential to addressing the shortage of qualified candidates for the thousands of technology jobs that need to be filled. Consequently, AI and big data analytics upskilling initiatives are crucial in every industry, demanding experienced AI-savvy professionals to drive business growth.

Now is the perfect time to get a degree in data management and bring your skills and expertise to the artificial intelligence field. WGU’s College of Information Technology can help you learn about data management platforms, big data technologies and sources, AI innovations, data quality, data governance, and everything else you need to confidently secure a position as a data manager. You’re also able to learn how to interface with many computer programs and manage your time—qualities that are crucial for data managers. Our online, competency-based education model allows you to progress through courses as quickly as you master the material, potentially saving time and money. Don’t wait any longer to get in with an industry that is only going to keep growing.