Previously, Data Analytics was strictly a domain reserved for seasoned business analysts and statisticians, making it somewhat inaccessible. However, a lot of details can be left out in the eyes of experts. Apart from that, to get an idea of the gains, for example, the data would have to stack up an entire year just to provide an even less coherent annual balance sheet.
There are 3 levels of analysis in the Data Analytics process:
- Predictive Analytics
- Prescriptive Analytics
- Descriptive Analytics
Often, the data to be processed are as sensitive as health diagnostics, finances, and sales which makes Data Analytics one of the most demanding processes in terms of execution. With its internal difficulties, Data Analytics also inherited the above domains, namely the very changing trends, the complex processing of data before discovering some patterns, not to mention the errors that unintentionally slip with other incidents.
But today, with Big Data on track, deductions are made more reliable and at the same time faster. Data can be transmitted continuously in high-performance analytical systems. These are not only the cases of the occupational categories mentioned above, but can now be well managed by other business and technical entities operating in the field of use case.
With the increasing amount of data coming in, with Big Data, these 4Vs are more needed than ever:
- Velocity: performant hardware coupled with for a small wait time and low computational cost.
- Variety: a collection of related data from different sources.
- Volume: the amount of data involved in the solution.
- Veracity: the accuracy and reliability of datasets.
Such a breakthrough cannot be achieved without the integration of Artificial Intelligence into the data analysis process. This can be demonstrated by the important role of Machine Learning and its subset Deep Learning, which is to provide more and more useful information to address the most complex use cases of Data Analytics. This is, for example, the role of machine learning in the scientific field of genetics and genomics, in which the supervised ML helps physicians successfully detect viral signatures hidden in DNA sequences, a useful knowledge to cut off the road to the spread of the COViD-19 virus.
Let us look in the few apps that follow, how Machine Learning has contributed to unprecedented progress in Data analytics.
- Machine Learning in Financial Analytics
- Machine Learning in the Manufacturing domain
- Machine Learning in the demographic growth forecasts and election outcomes
- Machine Learning in Healthcare
- Machine Learning in Sales and Marketing
#1 Machine Learning in Financial Analytics
Finance, as an area in which money is involved, has always been a very delicate area that requires expertise. In this area, the use of ML enables the identification and neutralization of fraudulent transfers. This can be done using pattern recognition to identify subtle irregularities in the data set of financial records. This is a key asset that businesses need to avoid bankruptcy. Here is an article about what we need to know about AI in finances.
#2 Machine Learning in the Manufacturing domain
Manufacturing comprises several industrial stages ranging from design to delivery: Concept and Development, Ordering Process, Production Scheduling, Manufacturing, Transportation.
Preventive maintenance and quality controls are among the most important uses in manufacturing.
As described in this article, there are many ways to render manufacturing as efficient as possible. ML can intervene in each of these steps to automate replicable processes and optimize the quality, flow, and accuracy of highly non-linear thermomechanical processes such as additive material shaping and injection molding, among others. As we already know, these processes use a large amount of heating and cooling. Besides, the use of deep learning to control them is very practical in terms of energy, smart network management, and water and waste management. Moreover, with embedded systems and the Internet of Things as key players in industries, advanced computing and cybersecurity need to be strengthened, using powerful ML algorithms.
#3 Machine Learning in the demographic growth forecasts and election outcomes
It has been difficult to conduct censuses in large countries, especially when they are rocked by crises. Governments can find real benefits by adopting ML to forecast population growth to understand what measures to take in sustainable resource management, for example. ML also helps to understand the results of election campaigns in advance by following social media trends that reflect public opinion.
#4 Machine Learning in Healthcare
Preventive, proactive, and curative, measures can all be undertaken with the help of ML in Data Analytics, medical diagnostics, and health monitoring. This interesting article provides some illustrative examples of AI in healthcare.
#5 Machine Learning in Sales and Marketing
Modern companies have an advantage in including ML in their marketing tools. Whether to stimulate sales with their referral system, or to ensure the launch of a product for the appropriate public, ML in the data analytics allows all this to be done. Through clustering, reinforcement learning, and customers classifying, ML in Data Analytics is sure to provide the commercial teams of the right weapons to win the battle for the best part of the market.