Data Analytics Support

Identify the data analytics requirements of the organization by understanding the needs of data analysts, business users, and decision-makers.
Conduct stakeholder interviews and surveys to assess data needs.
Identify key business objectives and decision-making processes that require data support.
Map out current data sources, formats, and storage systems.
Assess data accessibility, security, and integration challenges.
Define key performance indicators (KPIs) and reporting needs.

Align data analytics with the organization's strategic goals and create a roadmap for efficient insights generation

Define a Data Analytics Roadmap that aligns with business goals.
Identify key analytics solutions, including predictive modeling, trend analysis, and business intelligence tools.
Select appropriate data management platforms (e.g., Power BI, Tableau, SQL-based solutions).
Map data analytics solutions to business functions such as finance, operations, marketing, and risk management.
Prioritize data analytics initiatives based on business impact and feasibility.

Align data analytics with business processes and document how data fits into operations.

Conduct process mapping workshops with business units.
Document key workflows, dependencies, and data touchpoints.
Identify inefficiencies and opportunities for automation through analytics.
Standardize data collection and processing procedures across departments.
Ensure business processes are designed to support data-driven decision-making.

Implement a structured data governance framework and reporting tools to centralize data collection and reporting.

Develop a Data Governance Policy to ensure data quality, security, and compliance.
Set up a centralized data repository for structured and unstructured data.
Define access controls and role-based permissions for data handling.
Implement self-managed task status reporting, document management, timesheet reporting, and risk management dashboards.
Automate data collection and consolidation to minimize manual effort.

Develop real-time, interactive dashboards to track key performance indicators (KPIs) for better decision-making.

Identify critical KPIs for business functions such as finance, sales, operations, and customer service.
Select appropriate dashboarding tools (e.g., Power BI, Tableau, QlikView).
Develop interactive dashboards that provide real-time insights.
Ensure mobile and web accessibility for users across different departments.
Implement drill-down capabilities for detailed analysis.
Set up automated alerts for anomalies, risks, and performance deviations.

Develop the skills and expertise of employees in data analytics for better adoption and utilization.

Assess current data literacy and technical skills of employees.
Conduct data analytics training programs (Power BI, SQL, AI-driven analytics, Python, R, etc.).
Provide role-based training for business users, data analysts, and IT teams.
Organize workshops on data-driven decision-making for senior leadership.
Offer certifications and continuous learning programs to enhance proficiency.

Establish a governance structure for monitoring data analytics KPIs and ensure ongoing operational support.

Develop a KPI Governance Model to track analytics performance.
Set up regular data reviews and performance monitoring.
Establishing an Analytics Support Team to provide assistance and troubleshooting.
Implementing data validation and auditing mechanisms to ensure data integrity.
Continuously optimize dashboards, reports, and models based on business feedback.
Providing helpdesk support and documentation for analytics tools.