The DataOps Orchestrator enables the deployment of AI/ML applications at scale to address your multiple use cases. Tackle three times more use cases in parallel than with any other solution and finally reveal your data business value.
Predictive maintenance allows you to reduce maintenance costs and increase machinery lifetime. By analyzing machinery defects history and using IoT sensors data, it is possible to predict when a failure is supposed to occur. Thus, it becomes easier to plan maintenance operations and to increase equipment uptime.
Forecasting offers organizations the ability to anticipate their business activity in the near future. This requires, for example, to analyze inventory and sales history and generate different hypotheses to adjust offer and demand. Ultimately, the supply chain can be optimized, storage costs reduced, and the organization’s overall performance improved.
The churn rate is a great indicator of customers general satisfaction with a company’s offer and should be kept as low as possible. As it costs 5 to 10 times more to gain new customers than to retain them, the value of performing churn prediction is easy to figure out. Analyze clients’ history with Machine Learning and learn who are the most likely to turn their back on you.
Digital frauds have exploded in the last 10 years, leading organizations to look for innovative and scalable ways to protect their assets. Data analytics and machine learning are some ways to fight this kind of criminality by detecting abnormal patterns and behaviors, which eases the work of the teams in charge of fighting frauds.
Customer Intelligence allows to deeper understand customer behavior and motivations and to better profile it. Building this knowledge requires collecting data from disparate sources like mobile devices, company software or social media. Existing customer-related areas can then be strongly enhanced across the organization: segmentation, 360° view or customer support.
Ensuring security regarding customers and personal data has become a critical matter for organizations, especially since the introduction of the GDPR or CCPA. Implementing proper data governance can guarantee that only data with user consent can be used. It also helps to enhance data quality and trust, and monitor data access, minimizing potential breaches.
Process automation offers organizations to drastically transform their business. This requires analyzing a massive amount of data (phone calls, emails, documents, etc.) and applying specific machine learning techniques including voice recognition or automatic character recognition. Results can then be used in several smart business applications (chatbots, email analyzers, invoice automatic recognition) which helps to enhance efficiency and customer satisfaction.
Advanced reporting is a way to provide deeper insights to business teams. This can be done by collecting structured and unstructured data from disparate sources (internal like ERPs, web analytics, … and external like social networks, open data, …), cleaning it to ensure its quality and defining the right KPIs that will be measured. Data visualization tools can then be implemented to expose this processed data and get a better understanding of business operations.Advanced reporting is the first step for organizations to become fully “data-driven”.