ETL

Data ingestion and processing (ETL): Our team of data analysts will initiate the ingestion and transformation of data based on the requirements collected from your business.

We leverage Microsoft Azure data service for ingestion and transformation process. Data is then surfaced to members of your business for exploration and analysis.

Data Exploration

It is crucial to comprehend the data analytics team's work to determine how the data can meet the business needs. Some of tools used to analyze data include Excel, Power Query, and Apache Spark in Azure Synapse.

Building Prototype

At this stage, we will be conducting dashboard and report prototyping to validate how your business envisions utilizing and interpreting the analysis results. The validation process will encompass incorporating trends and insights revealed during data exploration.

Data Analysis

At this point, we initiate our data analysis process. Our analysis may take the form of descriptive, predictive, prescriptive, or even cognitive, depending on the specific needs. The outcome will address the identified business needs, and further analysis can be conducted to address any additional questions that arise during the initial review. Is your business seeking a one-time analysis or a comprehensive analytical solution? Both options have their respective benefits, and the decision between the two should be made during the requirements gathering phase.

Deployment of the analytics solution

The results of the analytics will be presented to stakeholders in the form of a report or visualization. At this phase, we utilize Power BI to enable people to engage with and apply the insights for decision-making. Data will be available to all authorized users. Having access to timely information will transform your organization into a data-driven entity capable of making informed decisions based on data.

Request and process feedback

Seeking feedback through regular review meetings will facilitate effective improvement of the solution. Furthermore, monitoring usage metrics of a data analytics project will provide us with insights into usage patterns over time and highlight areas of the solution that may require review or are not beneficial for the end user.

Optimize solution

We incorporate the feedback into our analytics solutions, assessing latency in the process. This may involve optimizing the underlying code and refining the visual design to ensure that report visuals render swiftly, as well as ensuring that data refresh occurs within the designated timeframe.

Start fresh.

To ensure that the solution aligns with business needs and addresses potential data communication challenges, collaboration with stakeholders is essential at every step of the analytics process. This process is inherently cyclical; uncovering insights often triggers requests for further analytical solutions, leading to more feedback and perpetuating the loop without cessation.