Data science is a field where data scientists, and other data professionals, analyze trends in data sets to give companies operations about their performance. The current demand for data transparency in the business world creates more opportunities than ever for data scientists to gather, analyze and process data in new ways.

What data science trends will define 2023?

Data science isn’t just for data scientists. 60% of the world’s companies currently use data and analytics to improve operations, a figure destined to grow in the next year. Trends in artificial intelligence, data democratization, data-as-a-service, data governance and augmented analytics could define the direction of the data science world in 2023.

1. Artificial intelligence

Artificial intelligence (AI) might conjure images of JARVIS from the Iron Man movies for some; others might picture a never-ending row of computers with complicated switches and flashing lights. In reality, AI uses an intelligent algorithm to process data automatically and utilize it for specific purposes, which means many data practices, like gathering, can be completed by computer.

Here are some examples of the ways AI can help a business:

• Optimize customer service — Many online businesses use chatbots to make customer communication more convenient, even when a live customer service agent is available to speak with a particular customer.
• Elevate productivity — Not everyone has the luxury of delegating tasks to a large team of staff members. Businesses can use AI to complete specific tasks, allowing real employees the bandwidth to handle more complicated issues.
• Increase IT protection — Cyberattacks can cause severe damage to an online business, potentially costing that company time and money. Thankfully there are AI applications that offer protection, with processes that can access potential threats faster than a human team.

These and other assistive techniques make AI a valuable resource for companies prepared to utilize it.

2. Data governance

Data governance is a fancy way of saying data protection. Even though you might not have any external threats affecting your data today, it’s important to safeguard your data for the day when your data is threatened.

Organizations can use several techniques to protect their data. These can include:

• Establishing clear roles and responsibilities for data management and usage
• Implementing policies and procedures for data handling and access
• Conducting regular audits and reviews to ensure compliance with regulations and internal policies
• Providing training and education for employees on data governance best practices
• Implementing technical controls, such as access controls and encryption, to protect data
• Creating a process for handling data breaches and incidents
• Organizing a data classification scheme to identify and protect sensitive information
• Regularly monitoring and reporting on data governance activities.

Data governance is essential for organizations that want to protect their assets, mitigate risk and derive value from data.

3. Augmented analytics

A combination of AI technology and data analytics, augmented analytics transform how data is processed and utilized. Each of these tools are valuable on their own, but when used together, they can optimize performance across numerous industries.

The following are examples of how some industries utilize augmented analytics:

• Healthcare — Doctors can analyze individual patient data to maintain the best patient care practices at hospitals.
• Retail — Customer data helps determine items customers are likely to purchase during other months, seasons, etc.
• Hospitality — Tourism hotspots like Disney World can use data from guests to determine which rides and food items are more successful.

This form of technology has become increasingly popular among professionals and organizations in the past few years. The next time you complete a survey after an experience or purchase, consider how businesses utilize this information as data.

4. Data-as-a-service (DaaS)

Have you ever received a message on your phone, or other device, that you’re almost out of storage? You might close out the message and go about your day, but eventually, you’ll receive the same message again. Most of us store photos and other files on our smartphones, which leads to a lack of storage space. Companies like Apple offer data storage plans that allow more room on the cloud.

In the same way that customers use data storage plans, companies can use data-as-a-service programs to store information securely in the cloud.

The DaaS model encourages users to access data through the internet, a convenient way to store and retrieve data on command. Data-as-a-service is also cost-effective and agile, allowing companies of all sizes to scale their data usage plans up or down to accommodate any changes to their data storage needs.

5. Data democratization

Data science isn’t just for analysts. The increased shareability and portability of data means that more than ever before, non-data employees have access to information capable of transforming company operations.

Meet data democratization — the process of making data accessible to all relevant stakeholders within an organization, regardless of their role or department. This includes ensuring that data is accurate, complete, and up-to-date, as well as providing employees with the tools and training they need to access and use data effectively.

Here are some of the benefits this trend can contribute to businesses:

• Increased team empowerment — We all know businesses with executives that hold all the power. Sharing the data workload creates wider involvement in decision-making processes, creating team members that can now affect a company’s short- and long-term trajectory.
• More careers — Extending data responsibilities to the entire team could encourage current employees to educate themselves and further pursue data-related careers, or at least internal positions with greater data-related responsibilities.
• Customer-driven data — When we leave the technical work to experts, it might limit the amount of data they can collect. Greater data participation expands the scope of possible data collection. For example, a retail store can enlist the help of retail associates to collect customer information.

The goal of data democratization is to empower employees to make data-driven decisions, rather than relying on a small group of individuals or departments who control access to data.

Put data science to work today

This year’s most prevalent data science concepts prove another clear trend — data science is here to stay. If it’s time for your team to put the power of internal and external data to greater use, Pace can help. Our Data Science Bootcamp empowers learners of all skill levels to better collect, analyze, secure and learn from data to improve company operations.

Connect with our Admissions department to improve your team’s data capabilities today.