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Dive Into the New Age of Accelerated Analytics with new Data Strategy

Our Services

Enterprise Data assets assessment and review

  • ​Data Inventory: Start by conducting a comprehensive inventory of the company's data sources, including data systems, databases, and data storage locations. This inventory should identify the types of data the company collects, how the data is stored, who has access to the data, and the quality and completeness of the data.

  • Data Quality Analysis: Analyze the quality of the company's data by examining factors such as accuracy, completeness, consistency, and timeliness. This analysis should identify common errors and inconsistencies in the data, and evaluate the impact of these errors on business operations and decision-making.

  • Data Security and Privacy Analysis: Evaluate the company's data security and privacy practices to ensure that sensitive data is protected from unauthorized access, theft, and breaches. This analysis should assess the company's policies, procedures, and technical safeguards, and identify areas for improvement.

  • Data Governance Analysis: Evaluate the company's data governance practices, including the roles, responsibilities, and processes for managing and governing data. This analysis should identify areas for improvement in the data governance framework, including the definition of data ownership, data stewardship, and data quality management.

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Generative AI Assessment and  Strategy

  • Assessment of current (If any) AI initiatives: Conduct an assessment of the company's current ML and AI initiatives (if any), including the number of projects, their size and scope, their level of complexity, and their level of success. This assessment should identify the company's strengths and weaknesses in ML and AI, and help determine the company's current level of ML/AI maturity.

  • Review of AI and data processes: Review the company's ML and AI processes, including the processes for acquiring and preparing data, training models, deploying models, and monitoring performance. This review should identify areas for improvement and best practices that the company can adopt to enhance its ML and AI capabilities.

  • Recommend AI infrastructure: Propose ML and AI infrastructure, including the hardware, software, and cloud resources that are needed to support ML and AI projects. This analysis should evaluate the company's capacity for scaling ML and AI initiatives and identify any limitations that could impede the company's ability to achieve its goals.

  • Pilot Machine Learning and AI initiative​​

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  • Develop a prototype: Develop a prototype of the AI solution that addresses the identified business problem. This prototype should be designed with the end-user in mind, and it should be flexible enough to accommodate changes based on feedback.

  • Pilot testing: Conduct a pilot test of the prototype in a controlled environment. This will help identify any technical or operational issues that need to be addressed. The pilot test should also involve stakeholders who will use the AI solution in production.

  • Evaluate results: Evaluate the results of the pilot test and gather feedback from stakeholders. This feedback should be used to make any necessary changes to the prototype.

  • Identify business problem and opportunity: Start by identifying the business problem that the AI initiative aims to solve. This will help determine the value proposition of the project and its potential impact.

Advisory

Comprehensive service designed to help businesses acquire the right data to inform their decisions and optimize their operations. The service is led by experts in data acquisition and machine leaning (ML) and AI who work closely with clients to understand their specific needs and requirements.

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RFI and RFQ support

  • The process begins with a consultation where we gather information about the client's data requirements, format, and timeline for delivery. Based on this information, we will create a customized Request for Information (RFI) or Request for Quotation (RFQ) document that outlines the client's data specifications.

  • The RFI or RFQ is then sent out to potential data providers (we can suggest a list of vendors but we are not associated with anybody), providing the client access to a competitive market of providers offering their best data solutions. The client is then able to choose the best data provider that meets their needs and budget.

  • By utilizing this service, clients are able to get the right data they need in a fast and efficient manner. With the right data in hand, they are able to make informed decisions and optimize their operations, leading to increased efficiency, reduced costs, and improved overall performance.

 

 

 

 

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