In the process of Business Discovery, understanding stakeholder expectations and defining dashboard objectives are paramount. It involves delving into the business context and constraints while carefully selecting tools and technology to meet requirements. Additionally, identifying internal and external data sources plays a crucial role in envisioning the desired outputs and dashboard design, ensuring alignment with organizational goals and maximizing value delivery.
During the Analytics Design phase, thorough exploratory data analysis guides the selection of industry-standard KPIs and best practices. These chosen KPIs, tailored to address specific business problems, undergo stakeholder scrutiny for alignment with organizational goals. Decisions on refresh frequency are then made to ensure data currency and relevance for effective decision-making.
In the realm of Data Processing, the journey begins with a deep dive into the business and application landscape, understanding its intricacies and requirements. Following this, meticulous mapping of data sources to the target database ensues, ensuring seamless integration and alignment. Subsequent stages involve rigorous data cleansing and consistency checks to maintain data integrity, followed by transformative processes to construct key performance indicators (KPIs). Finally, the processed data is loaded into the target database, ready to fuel insights and drive informed decision-making.
In the dashboard development process, we start with creating mock-ups and wireframes to visualize the design. Through iterative revisions with stakeholders, we understand their wishlist and requirements thoroughly. We then proceed to build a prototype incorporating key performance indicators (KPIs) and metrics. After ingesting and transforming data, we conduct User Acceptance Testing to ensure functionality and usability. Finally, we release the dashboard and establish a schedule for regular updates to maintain relevance and effectiveness.
A structured, iterative approach of drilling the data on dimensions of relevance to discover patterns of interest through both visual and tabular analysis.
Quick identification of problems, verify compliance and reduce risk of data migration
Implement advance automation techniques which utilizes metadata to drive efficiency, accuracy, and scalability.