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In this blog, we have highlighted the difference between augmented analytics and predictive analytics.
Whether you own a business or hold a key position in any company, you have one thing in common – magnanimous amounts of data to deal with! Adding to this is that ‘the stack of data keeps on increasing every day.’ Every business today needs to hold and analyze a massive amount of data practically for each and every aspect of business be it customer acquisition, lead conversion, or customer success.
Companies and their data teams need to collect data to make the right decisions for the company. But do you think only collecting the relevant data would be enough to make the right decisions? Absolutely not!
The collected data needs to be analyzed in the right way to extract precise, value-adding insights from it. Traditional business intelligence tools stand ineffective to get the job done. To make informed decisions and extract actionable insights hidden in the huge sets of data, companies need advanced, AI-driven systems.
Artificial Intelligence has surely surpassed our imagination in providing powers to technology. Due to its unprecedented abilities of computing and processing large piles of data it has emerged as the powerful driving force behind modern technologies. One of the most recent impacts of AI is Augmented Intelligence. Augmented intelligence helps humans to leverage huge data and make quick informed decisions.
If you are into data analytics you must have come across the term Augmented Analytics which is based on Augmented Intelligence. Another term that might seem to overlap with the concept of Augmented Analytics in Predictive Analytics. Though they both are similar in many aspects they are two different concepts and play a different roles for any business or organization.
In this write-up, we will be discussing Augmented Analytics and Predictive Analytics, the two advanced analytics tools. First, we will introduce both the tools separately and then we will proceed towards a comparative analysis of the two.
The term Augmented Analytics was coined in 2017 to describe an advanced approach to data analytics that uses the combination of machine learning and artificial intelligence. This means that Augmented Analytics is the technique of data analysis that uses machine learning as well as artificial intelligence to provide the user with actionable insights.
One of the biggest challenges for businesses today is not only the hefty amount of data but their unstructured form. Businesses collect data about their customers from various sources. But these raw data are useless unless they are not structured well to extract actionable insights. The massive amount of unstructured data is not only hard to analyze but may also lead to a faulty decision. Enter Augmented Analytics – solving data handling challenges.
Augmented Analytics can help the user in data preparation, insight generation as well as insight explanation. It helps the data team in automating the rote and repetitive parts of data analysis such as data exploration and preparation. This automation allows the team to interpret results and dive deep into data much faster.
The automation process of Augmented Analytics starts from gathering data and works through preparing and cleaning data to building analytics data and generating actionable insights for the data team.
Predictive Analytics too is an advanced approach to data analytics. Unlike Augmented Analytics, Predictive Analytics does not give an answer to what changed in business data and the reason behind it. It rather helps in identifying patterns in data and predicting whether an event is likely to happen again. It uses historical data and predicts possible future outcomes using a variety of statistical techniques.
Predictive Analytics is actually a sub-category of data analytics. It works with the combination of machine learning, historical datasets with statistical modeling, and data mining to discover hidden patterns in the data. It predicts probable future outcomes for a business or organization based on the data about what happened in the past.
It is a very powerful and effective tool for predicting customer behavior, assessing risk, forecasting sales, etc. It focuses on providing answers to business questions by analyzing the huge dataset and identifying the possible range of outcomes.
Now that you understand Augmented Analytics and Predictive Analytics well. Let us do some comparative analysis between the two.
Augmented Analytics is the term that is used to describe the analytics approach that uses Artificial Intelligence and Machine Learning to automate the process of analysis and make data clean and accessible for the data team so that they can draw actionable insights. It answers the question that what has changed in the business data and why that change has occurred.
On the other hand, Predictive Analytics is the sub-category of advanced analytics that uses numerous statistical techniques to forecast the probable outcome based on the historical data. It reads the hidden patterns in data and predicts whether a certain event may re-occur in the future and at what time.
Augmented Analytics is very useful for modern businesses. It simplifies the process of gathering, identifying, analyzing, and visualizing business data in order to draw actionable insights. It allows businesses to recognize hidden patterns and trends in their data so that they can make informed decisions. Augmented Analytics can be used to identify hidden factors that are driving changes in the business.
On the other hand, Predictive Analytics is used to determine what is most likely to happen in the near future based on the data of the past i.e., historical data. It is used to analyze unmanageable data sets and identify hidden patterns, relationships, and associations.
Augmented Analytics supports human intelligence by automating repetitive tasks so that quick and smart decisions can be made. It can boost the outcomes of marketing efforts of a business manifold and it can be used to enhance business operations by integrating it into the business process.
While Predictive Analytics can be used for tasks like sales forecasting, customer targeting, market study, risk assessment, financial reporting, etc.
Augmented Analytics uses Artificial Intelligence and Machine Learning to automate various aspects of data. It begins from the stage of gathering the data and ends at generating actionable insights for the user while taking care of every aspect falling in between viz. preparing and cleaning data and building an analytics model. On the other hand, Predictive Analytics uses Machine Learning, statistical techniques, data mining to predict the future outcome on the basis of class, category, or numerical value of historical data.25
Both of these tools are designed to make things easier for humans and provide competitive advantages to businesses. They do not only speed up the process but also improve the accuracy of the results. Both differ in their strengths but both of them are useful in making better business decisions with their data.
Rohan has over 11 years of experience in client services, marketing and hospitality field. Previously, he was head of digital marketing for a hi-tech mobile application. Rohan is driven by new challenges and the possibility of making an impact on individuals and businesses.
Published May 26, 2022, Updated May 15, 2023
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