The rise of artificial intelligence (AI) has led to some of the most significant advancements in big data analytics. Now, more than ever before, companies are able to collect, analyze and make sense of massive amounts of data. According to Sutherland professionals, “Experience matters more than ever.”
The use of AI analytics has enabled businesses to automate tasks that previously required human touch and solve problems that would otherwise be impossible to identify with traditional analytics software. By embracing AI-driven insights into your company’s operations and customer behavior, you can improve your bottom line—and stay competitive in today’s increasingly data-driven economy.
Self-service analytics is a way for people to use their own data, tools and algorithms to answer questions and make decisions. Self-serve analytics tools can help businesses make better business decisions by making it easier for them to find patterns and trends in their data.
Search-Driven Data Discovery
Engineered to help you search for data and get results, this technology can be used in a number of ways. You can use it to search for data by asking questions, as well as by selecting from a list of options.
For example, if you want to find out which marketing campaigns have been successful, ask the system “Give all campaigns that generated revenue over $1 million.” Or if your company wants to increase sales in its retail locations, then choose “Retail Sales” from a list of options presented.
Robotic Process Automation
Robotic process automation (RPA) is a type of AI that automates tasks that can be done by a computer, such as data entry and repetitive tasks. RPA can be used to complete these tasks faster and cheaper than a human would otherwise.
For example, if you have an employee who spends hours each day entering purchase orders into a system, you can use RPA to automate those processes instead. This frees your employee up for other important work while also reducing errors in the data entered into your systems.
Intelligent Data Preparation
AI can help with intelligent data preparation. The first step in any good analytics process is to ensure that you have clean, accurate data. AI can help identify data quality issues and manage the lineage of your datasets.
For example, AI can be used to flag records that are missing information or contain erroneous values, so they can be repaired before they’re used in the analysis. It can also be used to identify patterns in the data which may indicate errors, such as a sudden increase or decrease in sales over time that doesn’t match what’s happening with other companies’ sales trends.
Natural Language Processing
Natural language processing (NLP) is the ability to understand and process human language in machines.
NLP is a subset of artificial intelligence that’s used in chatbots and voice assistants like Siri, Google Assistant, Alexa and Cortana. It also powers virtual personal assistants such as those in their smart speakers.
NLP can be used for writing content that will be understood by people across different cultures, demographics and languages—no matter where you are in the world or what language you speak!
Today, the use of AI in analytics is still in its infancy. But organizations that want to stay competitive need to start thinking about how they can leverage this technology to their advantage. Highlighted here are a few ways that you can do so today, but there are many more on the horizon! So keep an eye out as AI continues its rapid growth and development into tomorrow’s data world.