Data Analytics and AI Machine Learning: Know the Difference

Data analytics and machine learning: it is crucial to understand the differences between the technologies to decide which best suits your business needs.

    Difference Between Data Analytics and Machine Learning
    Published By - Kelsey Taylor

    AI has been dominating the market and news lately. AI has already been adopted by various companies and industries and has transformed the way the organizations’ function. The popularity has been increasing ever since.

    Product developers are now able to create products and services which until now were not within reach of the average marketing budget.

    But before plunging into the decision to implement AI, it is necessary to understand the differences between data analytics and AI machine learning. It is necessary to know which the best solution for your organization is.

    Data Analytics

    Data analytics includes processing of datasets on certain defined parameters to draw conclusions about the required information.

    It empowers the decision-makers by providing all the information they require at their finger-tips.

    This includes newsletter conversation rates, tracking of user behavior on apps and websites, click-through rates of online advertising, and much more.

    Marketers are more comfortable dealing with data presented in the form of dashboards. Data mining offers access to vast quantities of data, generally unstructured.

    Marketers, however, require data which offers them the features to deduce ratios, percentages and averages.

    The basic requirement is an aggregation of data to find relationships between specific variables, report a result, search for patterns in the report, etc.

    Data analysis does not predict the effect of change in a variable on the ecosystem. It is descriptive since it is based on past events.

    AI Machine Learning

    Before directly jumping machine learning, let us get to know a bit about predictive analysis.

    Predictive analysis, as the name suggests, predicts the behavior and trends making use of both historical and new data.

    Machine learning is an extension of predictive learning with one difference. It is a branch of AI (artificial intelligence) which is capable of making assumptions and can test and learn autonomously without human interference.

    Machine learning (ML) is one of the most implemented and utilized technique under the AI umbrella.

    ML is capable of making assumptions, reassess models and accordingly evaluate the data, all without human intervention.

    Machine learning does not require humans to code for each action or reaction by the system to a certain event.

    Machine learning is able to predict every possible combination in the systems at a speed that no human could attain.

    Complex analysis is attained automatically and instantaneously by the machine learning systems. This simultaneously teaches or trains the system for future analysis.

    This training can prepare the system for micro-target insights that no human would be able to achieve. These results and predictions can be used to define crucial business strategies.

    Which to Choose?

    Between data analytics and machine learning; it important that business owners understand the benefits and limitations of both.

    Data analytics allows finding patterns from the data from past events. Whereas, AI machine learning allows analyzing data and learning from the current process to provide predictions at the depth and scale that humans cannot attain.

    Marketers are often required to make decisions which have significant technology implications. However, it is crucial to understand the differences and benefits of the technologies to decide which best suits your business needs.

    You May Also Like to read:
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    Kelsey manages Marketing and Operations at HiTechNectar since 2010. She holds a Master’s degree in Business Administration and Management. A tech fanatic and an author at HiTechNectar, Kelsey covers a wide array of topics including the latest IT trends, events and more. Cloud computing, marketing, data analytics and IoT are some of the subjects that she likes to write about.

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