What is Augmented Analytics

The practice of AI and machine learning to streamline data analytics is referred to as augmented analytics.

Augmented analytics is the use of AI to automate the job of a data scientist or specialist. AI and machine learning take care of repetitive tasks that otherwise would demand a large amount of time and effort from people trained in data analysis. In essence, augmented analytics frees humans to primarily focus on data on a contextual level. 

What are the benefits of augmented analytics?

Augmented analytics serves to simplify data analytics, so it has the potential to make many people’s jobs much easier. It also may hold the key to expanding the accessibility of data. Rather than being exclusively analyzed by trained data scientists, augmented analytics can allow employees without much data training to understand the analytics of their companies and make intelligent, informed decisions.

Simplification of data

Augmented analytics uses AI to clean and merge data. Often, companies are bogged down with large amounts of transactional data (data collected from transactions between companies, such as dates, prices, and locations of transactions). The data sources for these can be disparate and unreliable, and companies can spend many resources dealing with transactional data. With augmented analytics, AI can be used to clean up this transactional data in very little time.

Inquiry and insights

Augmented analytics can provide employees with intelligent search technology. Essentially, intelligent search technology takes the data of a company and makes it into a search engine. This way, employees who know little about data analysis can ask questions in simple language, and thus be able to make informed decisions regarding their company’s data. For example, an employee could query: “Thru sales of Q3 2023”. Intelligent search technology can provide insight to all employees of an organization and democratize data.

Faster Decision-Making

Augmented analytics can speed up the time it takes for companies to make data-driven decisions. Using intelligent search technology, any employee can contribute to discussions of data analysis. 

Reducing costs

Using augmented analytics can reduce the amount of time and money spent on analyzing a company’s data. This means a company has to hire far fewer data scientists, as augmented analytics makes data science accessible to many more employees. People do not need to spend large amounts of time organizing data and putting it into tables, reducing the hours for which companies pay their employees.

What is the difference between predictive analytics and augmented analytics?

Image: A cartoon of data scientists.
Augmented analytics assists, rather than replaces, data scientists.

Predictive analytics refers to using data to make forecasts of future trends, outcomes, and events. Predictive analytics can additionally utilize machine learning and artificial intelligence, as well as statistical models to make these forecasts. Regressive analysis is an important tool of predictive analytics. It refers to using mathematics to determine which variables impact a particular topic and the ways in which they impact it (i.e. do they have a negative or positive effect?). Regressive analysis can be utilized to analyze one or multiple variables. 

Some key uses for augmented analytics include: 

  • Supply chain analytics: Manufacturing companies often have to deal with very large amounts of data, including maintenance, production, staffing, profits, and geopolitical impacts on their business. Augmented analytics can automate all this data analysis and allow manufacturers to more easily optimize their production schedules and predict future trends.

  • Healthcare: Augmented analytics utilizes the increasing popularity of electronic medical records to optimize patient data analysis. This can include prediction of future pandemics, the popularity and manufacturing needs of life-saving drugs, and the potential costs of healthcare by insurance companies and patients.

  • Education: Primary and secondary education quality vary vastly by state and region. Augmented analytics can help determine which locations need the most financial aid and have the greatest staffing need. In addition, augmented analytics can determine which learning programs produce the highest standardized test scores in order to help schools best utilize their resources for their students.

  • Sports: Both teams and fans often use data to determine the outcomes of games, the rankings of teams, and the potential of certain players. Augmented analytics can help simplify this data and allow judgements to be made about sports strategies, injuries, and player performance, as well as financial aspects such as the sales of merchandise.

  • Entertainment: Due to the rise of streaming platforms for TV, movies, and other forms of media, competing platforms and stock market analysts alike are frequently looking to data to determine successful strategies. Augmented analytics allows entertainment companies to see what methods get them the most subscribers, which shows and movies are the most popular, and how they can get ahead of their competitors. 

Some examples of predictive analysis include:

  • Credit scores: Predictive analytics is used to analyze a person’s credit history in order to determine the amount of risk placed on borrowers for their future purchases.

  • Underwriting: Underwriting is used by insurance companies to determine the probability of having to pay a future claim for a client. For example, car insurance companies may use predictive analytics to determine higher risk for young car owners as they are more likely to get into accidents.

  • Hospitality: Predictive analytics can be used in hospitality to predict how much staff will be needed at certain times of the year. For example, it may be predicted that a hotel in a tropical country will need more staff during spring break than the middle of September.

  • Fraud detection: Predictive analysis can determine the likelihood of credit card fraud by looking at a client’s usual spending habits. If a frugal American client begins spending large amounts of money in Italy, predictive analytics can flag this as potential credit card fraud.

Augmented analytics, on the other hand, is a far more generalized tool than predictive analytics. While predictive analytics tends to use historical data in order to determine trends in the future, augmented analytics seeks to determine the current state of a company’s data. Augmented analytics seeks to figure out whether or not a change has occurred in a certain variable, how much has it changed, and what has driven that change. Augmented analytics mostly helps out data scientists and analytics professionals by completing the repetitive aspects of their jobs for them. 

Augmented analytics’ key purpose is to allow teams to understand changes in their company and help them make informed decisions in the future.

What is augmented data intelligence?

Image: a graphic of data.
Augmented intelligence has many uses outside of data analytics.

As AI has become increasingly popular, people have begun to worry about their jobs and industries being replaced with artificial intelligence. This has become such a fear that some people are hesitant to utilize any form of AI or machine learning. However, AI won’t replace human intelligence, reasoning, or intuition anytime soon. And it definitely won’t reject its human creators in order to take over the world. In fact, we should start thinking of AI as a means to enhance human intelligence rather than replace it.

This is what augmented data intelligence is. A more accurate term may be “intelligence enhancement” rather than “artificial intelligence.” Augmented analytics is a subset of augmented data intelligence.

Key differences between augmented analytics and augmented intelligence

Augmented analytics simplifies complex and hefty data in order for non-data scientists to access and understand the data. With this knowledge, the humans involved can go on to make their own decisions. Augmented analytics automates many aspects of data science, including the preparation and analysis of data. It can also reveal data trends.

Augmented intelligence, rather, refers to the broader collaboration between AI and human intelligence to enhance decision making. Augmented intelligence involves the use of AI to provide insights that complement human intelligence. 

Different forms of augmented data intelligence may include:

  • Machine learning, which refers to an AI’s ability to ‘learn’ tasks without traditional programming. In this way, it resembles a human’s ability to learn rather than a machine which needs to be fed programming in order to complete tasks. A common example of machine learning is facial recognition.

  • Deep learning, which refers to an AI’s ability to mimic human neural networks. Deep learning enables artificial intelligence to process data in a way similar to the human brain. One of the most frequent uses of deep learning is enabling AI to learn complex patterns in a way that mimics the exceptional pattern recognition of the human brian.

Augmented intelligence utilizes both machine learning and deep learning to help provide humans with data they need to make intelligent choices and decisions. Some common examples of augmented data intelligence include think tanks and virtual tutors.

The future of AI in data

Augmented intelligence and augmented analytics are growing quickly. AI technology is already helping businesses make vast amounts of progress in a much shorter time frame than when they were fully reliant on human intelligence. As of now, AI seems to be the future of data analytics. Utilizing augmented analytics can greatly benefit your business.

Do you need help navigating the world of analytics? Augmented or otherwise, or UX and AX experts are here to help you make the numbers make sense. Get in touch today to learn what UXAX can do for you.

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