Data Analytics, Data Science and Machine Learning: The Building Blocks of Enterprise AI

The present burst of technologies playing a key role in shaping the digital landscape and the app universe focuses mainly on predicting the future, deciphering the accurate trends that are in the making and delivering data driven insights into the present state of things.

Enterprise AI or the enterprise specific exploration of the Artificial Intelligence (AI) technology can be traced back to data analytics, data science and machine learning technology. These have been the key building blocks of Enterprise AI so far.

No matter how far the use cases of Enterprise AI go, these building blocks remain unchanged and they continue to play a major role in transforming the digital landscape of businesses across niches. In our times, when you hire an app developer for your enterprise, you may look for AI or machine learning expertise besides other skills. Here we are going to explain these constituent technologies one by one.

Data analytics

In the present day business world, massive amounts of data is generated across diverse sources. The rapidly growing and escalating data in all enterprises create never before opportunities to draw data driven insights for various decision making and strategic purposes. This is how data analytics tools became the part and parcel of business decision making processes and strategies.

The intent to gather understanding about the past, present and future of an enterprise creates unique analytical approaches corresponding to the business data. Since, no business cannot fully understand the future trends and the way things are going to be shaped through the years, at least they can have a better understanding of the future business contexts across different scenarios.

Since several years in the recent past, this capability of gathering and analysing business data has become better. Thanks to more sophisticated approaches about gathering more relevant business data and subjecting them to more powerful analytics, the field has experienced more boost in efficiency and analytical output than ever before.

Data Science

Data science refers to the comprehensive discipline referring to the tools, techniques and practices corresponding to data analytics. Data science is all about figuring out the application, testing and evaluating the data analytics tools and techniques and exploring new data analytics opportunities. In a way, it is the cutting-edge technical expertise corresponding to data analytics.

Since increasing number of businesses are embracing data analytics to incorporate data driven insights into their business processes, exploring new techniques and practices for utilising data to the best of business advantages has become more important than ever before. This is why, data scientists are experiencing huge demand across multitude of enterprises from all niches.

In this respect we also should consider the increasing importance of data engineering. This increasingly important field basically looks after structuring and restructuring data for more precise analytical output. Data engineering helps convert the structured, unstructured and semi-structured data originated from various sources to help utilise the data more appropriately and to draw more business-specific value from the data.

Machine learning and deep learning

Machine learning refers to a subset technology of AI that mainly focuses on learning from the data and improves this data-driven learning over time. Thanks to this learning, the algorithms can further fine-tune their data processing rules and mechanisms to generate more precise insights. Thanks to Machine Learning a computer by learning from the data can actually determine setting the rules of generating data driven insights.

The biggest benefit of Machine Learning technology is that it can deal with a variety of data types ranging from totally unstructured data to semi-structured and fully structured data. With the help of Machine Learning the computer can get hold of precise insights leading to system-generated decisions and actions across different contexts.

Machine learning technology also continued to evolve over the years. Earlier, it was only about detecting the common features and qualities across different data sets. The machine simply by detecting the common attributes and features could help detection of certain patterns more precisely. Over time, this capability further gets better as the machines continue to get more exposure increasing volumes of data.

Deep learning as another subset of the machine learning technology more or less follows the same rules for exploring the data driven insights and patterns that machines can learn over time. But Deep Learning created by using an in-depth

neural network interconnected hierarchically with different layers is more capable to explore larger volumes of data compared to the classical machine learning.

How do all these together shape the enterprise AI benefits?

The advantages of artificial intelligence (AI) are too evident in our lives. AI has already penetrated deeper into digital applications and solutions we use in every walk of life. AI exploring the intelligence imbued in the machines and data is already making the decision making process easier for countless enterprises across all niches. While enterprises will continue to garner the benefits of AI for competitive gains, the key building blocks of enterprise AI that are explained above will continue to enjoy more importance.

Both machine learning and deep learning technologies now help bringing together and weaving data from diverse sources in a more meaningful and business-optimised manner. The latest machine led data analytics and data driven insights helped with the decision making process across multitude of industries in a never-before manner.

Thanks to the robust scope of using data driven insights into decision making processes created by machine learning and deep learning technologies, business processes across all verticals ranging from manufacturing to sales to inventory management to supply chain management to support have been tremendously benefited.

Conclusion

The growing importance of AI and the all encompassing role of AI and other analytics technologies and tools left little scope for the enterprises to opt out of AI in their crucial decision making procedures and practices. AI and data driven insights are already part and parcels of all business processes and applications. The use cases of AI and Machine Learning are only going to expand across more and more decision making facets and expertise areas. In the years to come these building blocks of enterprise AI will continue to leave bigger influence on business strategies, decision making and management practices.

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