DATA CONSULTING SERVICES
Do you want to maximize the return of investment of your data?
AtLongLast Analytics is ready to guide you on your data journey. We empower organizations to build an impactful data capability by addressing culture, strategy, technology selection and implementation challenges head-on. In the fast evolving world of data, staying ahead can feel overwhelming but we can reduce the burden by bringing specialist knowledge to supercharge your data teams.
Core Services
Data Engineering & Analytics Services
We help establish and grow data strategies that align with business goals.
Data Strategy Support
We develop novel capability, enhance existing systems, and deploy operational solutions.
Microsoft Azure Solutions
We can develop cloud solutions, deploy new infrastructure, and audit existing environments.
We specialize in operational data platforms and Azure-native services. We can provide hands-on support and advice in the planning and configuration of cloud infrastructure for data and AI workloads.
We are passionate about improving business processes by using data. Our approach is to unify data strategy, data maturity and data culture, which together can increase your return-on-investment on data.
We implement technical solutions combining data engineering, analytics and machine learning to unlock the value of your data. Our solutions are developed with an emphasis on maintainability, reliability, and usability.
Please inquire about other services our team can offer to help maximize the value of your data. We look forward to hearing from you!
Examples projects include (but not limited to):
Ingesting data from source systems using Azure Data Factory.
Content creation using multimedia AI services (text, image & audio).
Deploying shared access solutions in Azure Machine Learning.
Implementing user management and role-based access.
Auditing existing environments to cut running costs.
We can help to improve how you:
Identify and prioritize use cases.
Break down siloes between data and business teams.
Structure your teams, define roles and delineate responsibilities.
Govern and manage your data (and information).
Manage data and software through their lifecycles.
Examples projects include (but not limited to):
Creating data ingestion (ETL/ELT) and data quality pipelines.
Constructing modern Data Warehouses, Data Lakes, and Lakehouses.
Developing proof-of-concept generative AI solutions.
Deploying low-code applications for non-technical users.
Writing automation scripts for repetitive processes.
