The problems faced by decision makers in today’s competitive business environment are complex. By leveraging advanced analytics technique enterprises can derive substantial benefits.

Advanced analytics uses machine learning, pattern matching, semantic analysis, and cluster analysis, for tasks such as making predictions, identifying patterns, generating recommendations, and discovering deep insights in data. Predictive and prescriptive analytics are a large part of the advanced analytics market.

Some of the top advanced analytics use cases for enterprises:

Some of the top advanced analytics use cases for enterprises:

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1. Predictive and Prescriptive Analytics:

As organizations develop more trailblazing big data analytics capabilities, they are beginning to progress from basic descriptive analytics to predictive modeling techniques. Regardless of the industry, predictive analytics represents a huge leap forward for many organizations. Instead of simply presenting the past events to a user, predictive models estimate the likelihood of a future outcomes based on patterns in the historical data. This allows executives to make more informed decisions.

The importance of being one step ahead of events is most clearly seen in health care management, financial sector, supply chain management and marketing. Prediction and prescription go together; by applying powerful optimization technique the models can game out different outcomes based on uncertain conditions. Take the best actions using prescriptive insight.

2. Recommendation Engines for Customer Engagement

Data Driven customer engagement offers industry leaders the opportunity to increase their growth. User- centric recommendation system delivers content to each consumer that is likely to be of interest to that user. The recommendation model studies quantifiable data such as the purchasing patterns, location, click-through rate, watching time, likes, comments, and reposts.

The objectives are achieved using AI algorithms like collaborative filtering model, regression model, factorization machine and deep learning. When the users start being less active in the platform, the intelligent model encourage them to be interactive by delivering extremely customized proposal.

3. Product Development:

For product development, historically companies are relying on intel from surveys which are not always reliable. For one reason, survey participants may not be too keen in letting the business know what changes they would like to see in the product. In such cases, companies have optioned to use of AI to gather such insights.

To understand its consumers, companies are increasingly using AI to oversee and analyze social media content. To know how its products were discussed and shared in social media, companies are setting up ‘Social Network Analytics’. This kind of advanced analytics focuses exclusively on conversations around their product. Supposedly, discussion suggest higher interest in few features, businesses can act upon improvising their product.

4. Conclusion:

While these are the few trending and high-value use cases, there are literally hundreds of other ways to incorporate Advanced Analytics into any business. One such example: TinyML in and its application in IoT technology is massively growing in popularity with every passing year. Tiny ML – Embedded ML is the solution to high power consumption and cloud congestion which is a common problem in Advanced analytics. Furthermore, TinyML facilitates improved response time, less expensive and stable. Even though there are still significant challenges to overcome, IoT analytics and Tiny ML continues to grow. It can offer unprecedented insights that have never been so readily available.

As advanced analytics continues to get easier, less expensive, and stable we can expect to see many economically feasible business cases.