Artificial Intelligence Training in Trivandrum

Benefits From Artificial Intelligence & Machine Learning

• Big data leads to bigger assets. For many organisations though, the reliability of these assets is still a challenge. Business teams have lost patience with the speed and efficiency in which they are able to get reliable, relevant, and actionable data; and have invested in their own self-service data preparation, visualisation and analytics tools, while others have even employed specialised data scientists.
• The common refrain is that data first has to be made reliable, and connected with the rest of the enterprise, so that it can be trusted for use in critical business initiatives. Isolated initiatives such as MDM and Hadoop-powered data lakes have not been very successful, and have raised doubts about their value.
• Information augmentation is the logical first step to getting data organised in a manner so that it can be reconciled, refined and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance.
• Organising data across any data type or source, with ongoing contribution and collaboration on limitless attributes, describes a state of continuous IA that organisations should achieve before they can consider AI or ML as a potential next step.

• Data capital requires new computing infrastructure and a deep understanding of creating applications that analyse data and use the information effectively. Organising data for the benefit of AI, ML or other initiatives results in clean, reliable data that is connected and forms a trusted foundation.
• While being data-driven continues to be in vogue, companies have achieved surprisingly little in the way of measurable, quantifiable outcomes for their investments in technologies and tools. Some of the total cost of ownership (TCO) metrics such as savings realised from switching to cloud vs. on-premises are evident, but there has not been a clear direct correlation between data management, BI, or analytics and the current wave of AI and ML investments.
• What’s missing is a proven system of capturing a historical baseline and comparing it to improvements in data quality, generated insights, and resulting outcomes stemming from the actions taken.
• Much of this gap can be attributed to the continued disconnect between analytical environments such as data warehouses, data lakes and the like, where insights are generated; and operational applications where business execution actually takes place.
• Self-learning data management technology can power data-driven applications that are both analytical and operational, delivering contextual, goal-based insights and actions, which are specific and measurable, allowing outcomes to be correlated, leading to that return on investment (ROI), the Holy Grail, and forming a foundation for ML to drive continuous improvement.
• Acting as technology portfolio managers for large and small companies that want to focus on nimble and agile business execution, self-learning data platforms are also multi-cloud, keeping up with the best components and services that solve business problems.
• As an added bonus, multi-tenant platforms in the cloud will also begin to provide industry comparisons, so that companies can finally understand how they rank compared to their peers.

• Data analytics has become an indispensable feature of successful companies across all industries. This reality dictates that companies invest heavily in data analytics to remain competitive and profitable.
• An open ecosystem that allows you to choose and partner with the technologies and domain experts of your choice is critical to getting the most out of a still young and evolving AI/ML landscape.
• Getting locked into a single vendor, delivering siloed disparate data management and advanced analytics tools may further complicate an already fragmented data management strategy.
• Give yourself the openness and flexibility of your partners of choice to meet your business needs, so that you can quickly harvest the benefits of the latest AI and ML technologies.

• Enterprise data-driven applications are built to infuse data from multiple enterprise sources and associate all the relevant and related transactions around the product or customer business entity.
• Marketing, e-commerce, customer service and sales teams access a consumer-grade interface with out-of-the-box analytics and reports to generate insights about top customers, top products, and top channels to make business decisions faster.
• ML and predictive analytics incorporated into such data-driven applications can suggest next-best-actions for sending relevant and timely information to customers, and finding opportunities for up-sell and cross-sell. Insights like churn propensity, lifetime value, preferences and abandonment rates can be delivered to relevant teams, along with recommended actions to capitalise on this information.
• Analytics and guided suggestions, when delivered within operational applications, are much more powerful and actionable than those presented in isolated, disconnected reports or dashboards.