Data Analytics Vs Predictive Analytics – Which One is Useful

Difference Between  Data Analytics and Predictive Analytics

Analytics is the use of data, machine learning, statistical analysis and mathematical or computer-based models to get improved insight and make better decisions. Analytics is defined as “a process of transforming data into actions through analysis and insight in the context of organizational decision making and problem-solving.” Analytics is supported by many tools such as Microsoft Excel, SAS, R, Python(libraries). Let us learn both Data Analytics and Predictive Analytics in a detail in this post.

There are mainly three types of analytics: – descriptive analytics, predictive analytics, and Prescriptive analytics.

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Descriptive Analytics: This type of analytics is used to summarize or turn data into relevant information. In other words, it summarized what has occurred. This type of analytics has some meaningful impact but won’t be much helpful in forecasting.

Predictive Analytics: – Predictive analytics involves advanced statistical, modeling, data mining and one or more machine learning techniques to dig into data and allows analysts to make predictions. Predictive analytics is used to forecast what will happen in future.

Prescriptive Analytics: – This form of analytics is one step above of descriptive and Predictive Analytics. With this type of analytics, we are able to predict the possible consequences based on different choices possible for an action, it can also be used to find the best course of action for any pre-specified outcome.

Head To Head Comparison Between  Data Analytics Vs Predictive Analytics (Infographics)

Below is the Top 8 Comparison Between  Data Analytics Vs Predictive Analytics

Key Differences Between Data Analytics Vs Predictive Analytics

Let’s understand few differences between Data Analytics and Predictive Analytics similarly looking terminologies –

1. Data analytics (DA) involves processing and examining of data sets in order to draw conclusions about the information those data sets consists of. Predictive analytics helps to predict the future by inspecting historical data thoroughly, detecting patterns or relationships in these data, and then conclude these relationships in time.
2. Data analytics uses tools and techniques to enable businesses to make more informed, real-time and pragmatic business decisions. Predictive analytics can predict risk and find a relationship in data not readily apparent with traditional analysis.
3. Data analytics involves finding hidden patterns in a large amount of dataset to segment and group data into logical sets to find behavior and detect trends whereas Predictive analytics involves the use of some of the advanced analytics techniques.
4. Using Data Analytics, in general, Data scientists and researchers verify or disprove scientific models, theories, and hypotheses. Whereas Predictive analytics, with increased use of specialized systems and software, help Data scientists and researchers to bring confidence into predictions and possible outcomes.
5. Data Analytics is the science of using raw data and generating purposeful information with a defined objective which brings conclusions about that information. Data Analytics uses traditional algorithmic or mechanical process to build deep insights. For example, running through a number of data sets to look for meaningful correlations between each other. Whereas Predictive analytics uses advanced computational models and algorithms for intelligently building a forecast or prediction platform, for example, a commodities trader might wish to predict short-term movements in commodities prices, collection analytics, fraud detection etc.
6. For working in Data Analytics one needs strong statistical knowledge though for working in Predictive analytics segment one needs to have strong technical knowledge along with fundamental statistical knowledge as well. He / She may be required to use and work on technological tools like SAS, R and Hadoop.
7. Data analytics is generally used for business-to-consumer (B2C) applications. Many organizations collect, stores, analyze and cleanse data associated with their customers, business partners, market competitors etc. Data Analytics is then used to study trends and patterns. Predictive analytics facilitates future decision-making. For example, a social networking site collects data related to its users regarding their interests, community likings, and others segment preferences according to a specified criterion such as age, gender and most important demographics. Predictive analytics reveals most probabilistic future product buy or preferred shopping items for such users.

Data Analytics Vs Predictive Analytics Comparision Table

Conclusion –  Data Analytics Vs Predictive Analytics

Today huge data is collected across organizations. This data could be related to customers, business partners, applications users, visitors, internal employees and external stakeholders etc. This data is churned and categorized to find and analyze patterns. Data analytics refers to various tools and techniques involving qualitative and quantitative methods and processes, which utilizes this collected data and generates an outcome which is used to enhance efficiency, productivity, reduce risk and increase business gain.  Data analytics techniques vary from organization to organizational according to their requirements.

Predictive Analytics as a subset of Data analytics is a specialized decision-making tool which uses advanced technological assets and progressive statistical based algorithms and models to generate future predictions so that business can focus and spend their money and energies towards more positive and expected outcomes.

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