Infometry helps SaaS customers build Cloud Data Integration (CDI) platforms and OEM analytics solutions by OEM’ing industry leader solutions like Informatica Cloud, Dell Boomi, Tableau, and Looker. Our team work closely with product marketing, product management, solution architects and the support team to build reusable components, security implementation, seamless deployment, and support process.
OEM Analyze, Report and Predict Solutions
Analyze, report, and predict solutions are a collection of AI and analytic-powered technologies required to effectively merge and extract value from any data source and present the results in easy-to-consume visualizations and dashboards. The solutions transform structured and unstructured data from its raw form into actionable insights and forecasts.
Enable Data-Driven Insight
The overwhelming majority of users want access to analytics within the applications they are already using. However, they still switch between systems to access the insights they need for a given process. OEM Analytics can provide a range of capabilities to augment solution features, from self-service analysis, reporting, and dashboards.
Leverage Unstructured Data
Unstructured data, such as emails, contracts and invoices, accounts for most of the data available for analysis in customer organizations, but only half leverage unstructured data analytics in their processes. With AI and ML, sentiment analysis and text mining, customers can transform this unstructured content into business value.
Accelerate Time to Action
Data-driven organizations report higher profits and productivity than those still operating on intuition. Providing predictive and prescriptive analytic capabilities—like data discovery, business intelligence and reporting and text mining—helps customers evaluate situations and act more quickly in times of need.
Open New Revenue Streams
Today’s fastest-growing companies use data for more than just decision-making; they use it to augment existing offerings or launch entirely new business models. Help customers thrive in the digital era by monetizing the zettabytes of data generated from manufacturing, financial systems, business applications, social media, Iot Sensors, etc
Why Choose Infometry?
Infometry is committed to your victory and decreasing your risk. We know every organization is unique, so we customize our solutions to support the most diverse business requirements. Our offerings can help you accelerate the deployment time, and our qualified technical teams accelerate you through the OEM Analytics Strategy process.
- Partner with our training, consulting, and OEM Data Analytics experts.
- Offer a full range of augmented analytics, including AI-generated evaluations and insights, automated creation and data prep, search and natural language interaction and ML and predictive analytics.
- Allow your users to use data more efficiently.
- Leverage the experience of a dedicated OEM strategy team.
- The Infometry Data Analytics platform supports using DevOps tooling and APIs to customize and automate an OEM's customer onboarding and provisioning tasks.
Connect with Us to Discuss Your Project Now
Frequently Asked Questions
An Original Equipment Manufacturer (OEM) strategy refers to a company’s approach to producing and distributing products that are branded and sold by other companies. The OEM manufacturer has the effects according to the specifications of the brand owner and then sells them to the brand owner, who then sells them to the end customer under their brand. This arrangement is often used in the technology and manufacturing industries, where companies may outsource the production of specific components or products to specialized manufacturers. The OEM manufacturer typically focuses on production and may not be directly involved in the marketing or sales of the branded products.
OEM analytics refers to analyzing data related to original equipment manufacturing. This could include data on production processes, supply chain management, quality control, and other aspects of the OEM business. OEM analytics may be used to identify areas for improvement, optimize production and supply chain operations, and improve the efficiency and effectiveness of the OEM’s procedures.
Various tools and techniques can be used for OEM analytics, including data visualization, statistical analysis, machine learning, and other advanced analytics methods. By analyzing data from different sources, OEMs can gain insights into how their operations are performing and make informed decisions about how to improve them. For example, OEM analytics might be used to identify bottlenecks in the production process, improve forecasting and inventory management, or optimize the use of resources. OEM analytics can help OEMs to increase their competitiveness, reduce costs, and improve the quality of their products and services.
OEM data is generated or collected during original equipment manufacturing (OEM) operations. This data can come from various sources, including production processes, supply chain management, quality control, and other aspects of the OEM business. OEM data can include structured data, such as that captured in databases or spreadsheets, and unstructured data, such as text, images, and audio or video files.
OEM data can be used for various purposes, including OEM analytics, which involves the analysis of OEM data to identify trends, patterns, and opportunities for improvement.
OEM integration refers to integrating original equipment manufacturer (OEM) products or components into a more extensive system or solution. This can involve integrating the OEM’s products into the systems or products of the company that is purchasing them or integrating the OEM’s products into a third-party system or solution.
OEM integration can be a complex process, as it often involves coordinating the integration of different components or systems, ensuring compatibility and interoperability, and testing and verifying the performance of the integrated system. OEM integration may also require customizing the OEM’s products to meet the specific needs and requirements of the company integrating them.
OEM integration can provide benefits such as increased efficiency and effectiveness and the ability to offer a more comprehensive and integrated solution to customers. However, it can also pose challenges, such as the need to manage and coordinate the integration process and the potential for technical issues or other difficulties to arise.
Original Equipment Manufacturer (OEM) refers to a company that produces products or components sold and branded by another company. The OEM manufacturer has the products according to the specifications of the brand owner and then sells them to the brand owner, who then sells them to the end customer under their brand. This arrangement is often used in the technology and manufacturing industries, where companies may outsource the production of specific components or products to specialized manufacturers.
The OEM manufacturer typically focuses on production and may not be directly involved in the marketing or sales of the branded products. They may also provide technical support and other services to the brand owner. On the other hand, the brand owner is responsible for marketing and selling the products to the end customers and may provide customer support and other services.
OEM arrangements can benefit both the OEM manufacturer and the brand owner. The OEM manufacturer can focus on production and leverage their expertise and resources to produce high-quality products efficiently. At the same time, the brand owner can offer a broader range of products under their brand without investing in the production infrastructure and capabilities themselves.
There are several benefits of an OEM data analytics strategy:
Improved efficiency: By analyzing data from different sources, OEMs can identify bottlenecks and inefficiencies in their operations and take steps to address them. This can help to reduce costs and improve the efficiency of their operations.
Increased competitiveness: OEMs can use data analytics to understand their market and customers better and develop and deliver products and services that are more closely aligned with customer needs and preferences. This can help to increase competitiveness and drive growth.
Enhanced decision-making: Data analytics can provide OEMs with insights and information that can inform decision-making at all levels of the organization. This can help to ensure that decisions are based on data-driven insights rather than gut instincts or assumptions.
Improved quality and reliability: OEMs can use data analytics to monitor and optimize production processes and supply chain management, which can help to improve the quality and reliability of their products and services.
Enhanced risk management: OEMs can use data analytics to identify potential risks and vulnerabilities and develop strategies to mitigate or manage these risks. This can help to reduce the impact of disruptions and improve the overall resilience of the organization.