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Data Analytics & AI BANT - Coggle Diagram
Data Analytics & AI BANT
1. Before the Call - Preparation
1.1 Who is the organization and what is their primary business/es?
1.2 What is the revenue/TurnOver?
1.3 How do they(customer) make their money and what do you think they would want to monitor in their business processes?
Business process1
Business process 2
Business Process3
1.4 decide on the Case Studies/Stories that can be shared relevant to their business
1.5. What are the data challenges which would make them to buy our services?
1.6. What do we know about the account? - if existing Rxt customer comsumption details and services used.
2. During the call
2.1. General Questions (Start here for all calls)
2.1.1 What are your primary business objectives for the next 12 months?
The objective of this question is to understand customer goals can help identify how data and AI can align with their business strategy.
Mandatory
2.1.2 Are you currently using data analytics or AI in your operations or decision-making processes?
This question assesses their current level of engagement with data and AI technologies.
2.1.3 What challenges or pain points are you experiencing in your business?
Identifying their challenges can help determine how data and AI may offer solutions
Mandatory
2.1.4 Have you allocated a budget for data and AI initiatives, and if so, what is the range?
Understanding their budget constraints can help tailor solutions accordingly.
2.1.5 What specific use cases or projects are you considering for data and AI?
Identifying their specific needs or projects can guide your service offerings.
Mandatory
2.1.6 What is your timeframe for implementing data and AI solutions?
Knowing their timeline can help align your services with their schedule
Mandatory
2.1.7 How do you envision the role of data and AI in your organization's growth or efficiency improvement?
This question helps identify their expectations and strategic vision
2.1.8 Do you have concerns about data privacy, security, or compliance when using AI solutions?
Addressing potential concerns is essential to building trust.
2.1.9 Who are the key stakeholders or decision-makers in your organization for data and AI projects?
Identifying decision-makers helps you understand the decision-making process and tailor your communication accordingly.
Important but not mandatory
2.1.10 Have you worked with any other service providers or vendors in the data and AI space previously? [Optional question]
Knowing about their prior experiences can provide insights into their preferences and expectations.
2.1.11 Are there any specific regulations or industry standards that you need to comply with regarding data and AI?
Compliance requirements are important to consider in your proposed solutions.
2.1.12 Explain our data solutions porfolio
post explanation please find out the area of interest from the customer accordingly you follow these steps
Identify of the area of Interest
If area of Interest is
Assessment
identify the service
DB Modernization assessment
1 more item...
Data warehouse or Hadoop assessment
1 more item...
Well architected review for data assessment
1 more item...
Cloud data management capabilities assessment
1 more item...
Data Design Labs
1 more item...
Cloud Data Migration
identify the service
DB modernization
1 more item...
Data warehouse Migration
1 more item...
Data lake Migration
1 more item...
BI Modernization
1 more item...
Cloud Data Platform
Identify the service
Cloud native data platform
1 more item...
Data driven apps
1 more item...
Operating Model and Governance
1 more item...
AI & Gen AI
Identify the service
Cloud Native Analytics
1 more item...
Demand Forecasting and Anomaly Detection
1 more item...
Computer Vision
1 more item...
Conversational AI and Intelligent Document Processing
1 more item...
Managed Service
Identify the service
DataOps
1 more item...
MLOps
1 more item...
DB Ops
1 more item...
2.2 Data Solutions Portfolio
2.2.1 Data & AI Strategy and roadmap
2.2.1.1 DB modernization
What databases and data management systems are you currently using in your organization?
Are you experiencing any performance issues or scalability challenges with your current databases?
What are your data storage and retrieval requirements?
Are you considering a shift to cloud-based databases, and if so, which cloud providers are you interested in?
Have you encountered any data security or compliance issues with your current database solutions?
What are your database modernization goals, both in terms of performance and cost optimization?
2.2.1.2 DWH/Hadoop Assessment
Are you currently using data warehousing or Hadoop-based solutions for data storage and analysis?
What types of data do you store and analyze within your data warehouse or Hadoop clusters?
Are you facing challenges related to data integration, ETL processes, or query performance in your data warehouse?
What specific analytics or reporting needs do you have that could benefit from data warehouse or Hadoop capabilities?
Have you considered cloud-based data warehousing solutions?
What are your scalability and data governance requirements for these platforms?
2.2.1.3 Well-Architected Review
What is your current data architecture and infrastructure?
Are you concerned about the scalability, reliability, or security of your data systems?
Do you have specific data compliance or governance requirements?
Are you looking to optimize your data systems for cost efficiency?
What data analytics, machine learning, or AI projects are you planning, and how do they fit into your data architecture?
Are you interested in best practices and recommendations to ensure a well-architected data environment?
2.2.1.4 Cloud Data Management Capabilities
Which cloud providers are you currently using or considering for your data management needs?
What are your objectives in migrating data and services to the cloud?
Do you have specific data security or compliance requirements for cloud data storage and processing?
Are you looking to optimize data costs in the cloud?
How do you plan to manage data integration, transformation, and analytics in a cloud environment?
Are you interested in assessing the capabilities and limitations of various cloud data management services?
2.2.1.5 Data Design labs (ideation workshop)
What are the specific data-related projects you are planning or considering?
Do you have data architecture, modeling, or schema design needs for these projects?
What are your data visualization and dashboard requirements?
Are you looking for assistance in designing data pipelines or ETL processes?
Do you need support in designing data structures and databases for optimal performance and scalability?
Are there any particular data design challenges or opportunities you'd like to discuss in detail?
2.2.2 Cloud Data Migrations
2.2.2.1Database Mod
What databases are you currently using, and what are the specific challenges or limitations you're experiencing with them?
Are you looking to migrate your databases to a different platform or modernize them in-place?
What are your performance, scalability, and data security requirements?
Are there specific compliance or regulatory considerations for your data that need to be addressed during modernization?
What are your goals for database modernization in terms of performance improvement and cost optimization?
Are you open to cloud-based database solutions, and if so, which cloud providers are you considering?
2.2.2.2 Data warehouse Migration
What data warehousing solutions are you currently using, and what are the key drivers for migrating them?
Are you planning to move to a different data warehousing platform or to the cloud?
What types of data do you store and analyze in your data warehouse, and what are your data integration needs?
What specific analytics and reporting requirements do you have for the new data warehouse?
Have you established a migration timeline or budget for this project?
What data governance and compliance considerations are relevant to your data warehousing needs?
2.2.2.3 Data Lake Migration
What data lake solutions are you currently using, and what are the reasons for considering migration?
Are you planning to move to a different data lake platform, a data warehouse, or the cloud?
What types of data are stored in your data lake, and how do you manage data ingestion and transformation?
Are you interested in optimizing data lake storage and query performance?
Do you have data governance, security, or compliance requirements that need to be addressed during migration?
What is your desired outcome in terms of data lake migration, such as cost savings, improved data accessibility, or enhanced analytics capabilities?
2.2.2.4 BI Modernization
What BI tools and solutions are currently in use, and what challenges or limitations are you facing?
Are you looking to upgrade or replace your existing BI tools, or are you seeking to modernize your BI infrastructure?
What specific analytics, reporting, and dashboarding needs do you have for your organization?
Do you have plans for self-service BI and data democratization?
Are you interested in cloud-based BI solutions, and if so, which cloud providers are you considering?
What are your goals for BI modernization in terms of improving decision-making and data-driven insights?
2.2.3 Cloud Data Platform
2.2.3.1 Cloud Native Platform
What is your current data architecture and infrastructure, and what are the limitations or challenges you're facing?
Are you interested in migrating your data to a cloud-native platform, building a new platform from scratch, or optimizing an existing one?
What types of data do you work with, and what are your data storage, processing, and analytics requirements?
Do you have data security and compliance considerations that need to be addressed in a cloud-native environment?
What are your scalability, high availability, and disaster recovery requirements?
Are you open to cloud providers, and if so, do you have a preference for a specific cloud platform?
2.2.3.2 Data Driven Apps
What types of applications are you developing or planning to develop, and how do they utilize data?
What are your specific data integration, real-time data processing, and data visualization needs for these apps?
Are you interested in incorporating machine learning or AI capabilities into your data-driven apps?
Do you have requirements for data analytics, reporting, and dashboards within your apps?
What is the intended user base for these data-driven apps, and what are their requirements and expectations?
Are you looking to deploy these apps on a specific cloud platform or on-premises?
2.2.3.3 Operating Model & Governance
What is your current data governance and operating model, and what challenges are you encountering?
Are you looking to establish or refine data governance policies, data quality standards, and data management processes?
What data compliance requirements, such as GDPR or HIPAA, are relevant to your organization?
Do you have a clear data ownership structure and data stewardship in place?
Are you interested in implementing data catalogs, metadata management, or data lineage tracking for improved governance?
What are your objectives in terms of aligning your data operating model with your overall business strategy and objectives?
2.2.4 Analytics and applied AI
2.2.4.1 Cloud Native Analytics
What types of analytics are you currently performing, and what are your specific objectives for cloud-native analytics?
Are you looking to migrate your analytics workloads to a cloud-native platform, build new analytics solutions from scratch, or optimize existing ones?
What data sources do you use for your analytics, and what are your data processing and storage requirements?
Do you have concerns related to data security, compliance, and data governance that need to be addressed in a cloud-native environment?
Are you interested in real-time analytics, machine learning, or AI capabilities within your cloud-native analytics solutions?
Are you open to cloud providers, and if so, do you have a preference for a specific cloud platform?
2.2.4.2 Demand forecasting and anomaly detection
What are your specific business areas where demand forecasting and anomaly detection are important?
What types of data do you have access to for forecasting and anomaly detection, and how frequently does it change?
What are your historical data and real-time data processing needs for these purposes?
Do you have data quality, data cleansing, or data preparation requirements for accurate forecasting?
Are you looking to implement machine learning or AI models to improve the accuracy of your forecasting and anomaly detection?
What key performance indicators (KPIs) are relevant for assessing the success of these initiatives?
2.2.4.3 Computer Vision
What specific tasks or challenges are you aiming to address with computer vision, such as image classification, object detection, or video analysis?
What are your data collection and annotation needs for training computer vision models?
Do you have preferences for deploying computer vision solutions, such as on edge devices or in the cloud?
Are there regulatory or ethical considerations for your computer vision projects, like privacy or data protection?
What are your expectations in terms of accuracy, real-time processing, and scalability for computer vision solutions?
2.2.4.4 Conversational AI and Intelligent Document Processing
In what areas of your organization do you see potential for implementing conversational AI and document processing solutions?
What are your specific use cases, such as chatbots, virtual assistants, or automated document processing?
What types of data sources and formats are relevant to your conversational AI and document processing needs?
Do you have data security and compliance requirements, especially when dealing with sensitive documents or personal data?
Are you interested in enhancing these solutions with natural language understanding (NLU) or machine learning capabilities?
How do you envision measuring the success of your conversational AI and intelligent document processing projects, such as improved customer service or reduced manual data entry?
2.2.5 Managed Services
2.2.5.1 DB Ops
What databases and data management systems are you currently using, and what are the challenges or limitations you're experiencing with them?
Are you looking to optimize and automate database management and maintenance processes using DB Ops practices?
What types of data do you store, and what are your data security and data compliance requirements?
Do you have scalability, high availability, and disaster recovery needs for your databases?
Are you interested in cloud-based database solutions or on-premises database management?
What are your objectives for DB Ops in terms of improving database performance, reliability, and cost efficiency?
2.2.5.2 DataOps
What is your current data pipeline and data integration process like, and what challenges are you experiencing?
Are you looking to optimize data integration, data quality, and data delivery processes using DataOps principles?
What are your specific data governance and data compliance requirements?
Do you have data security considerations for sensitive data during the data pipeline?
Are you interested in automating and orchestrating your data workflows for efficiency and scalability?
What are your objectives for DataOps in terms of improving data delivery, collaboration, and agility within your organization?
2.2.5.3 ML Ops
Are you currently developing or deploying machine learning models, and what are your specific challenges in this process?
Are you looking to improve model development, deployment, and monitoring through MLOps practices?
What is the nature of your data sources and data preprocessing requirements for machine learning?
Do you have data privacy, compliance, and regulatory considerations for your machine learning models?
Are you interested in automating the end-to-end machine learning pipeline, including model training, validation, and deployment?
What are your goals for MLOps in terms of improving model accuracy, deployment efficiency, and model governance?