Qrapp is a reliable outsourcing partner for companies that want to get insights from their data while skipping the technicalities. We take care of the data analytics infrastructure and apply advanced data analysis to provide our customers with regular and ad hoc reports, alerts, and predictions, as well as self-service analytics. As a result, the companies that make use of our managed analytics services can prioritize on accurate planning of their activities, ongoing business management, change management, and optimization of their business processes.
For those customers who have tons of raw data to derive insights from but don’t plan to invest into constructing a full-scale analytical solution and supporting it, we offer managed analytics services on a monthly subscription fee basis. The subscription fee covers:
Extra activities and services are covered based on the time-and-materials (T&M) basis.
To those customers who already have a centralized analytical solution but don’t have time or resources to upgrade it to satisfy the specific analytical needs of a particular department, we suggest outsourcing the uncovered part of their data analytics to us. In this case, in addition to the same service as with complete outsourcing, we ensure the integration of our data analytics infrastructure with the customer’s central data warehouse and closely collaborate with the customer’s support team.
At this stage, we analyze the customer needs and as-is situation: existing data and data quality practices, as well as the analytical solution, if any. We examine the company’s business plan and collect input from IT and business departments in order to understand the customer’s analytical needs. Based on the findings of the discovery stage, we plan the service and agree on the service level agreement (SLA) terms with the customer.
We extract the data into a data warehouse, clean it to ensure that the data is of high quality, integrate with the customer’s data warehouse, if any, create OLAP cubes for exploratory analysis and train machine learning models, if advanced data analysis is required. This is the stage where the responsibility transfer takes place.
Responsibility we take is high: we deliver value though analytics having high freedom in process and resources:
We provide access to self-service analytics tools, deliver regular reporting, as well as ad hoc analytics upon the customer’s request, which is the foundation for data-driven insights. If required, we set up alerts for business users that notify if any anomaly is detected in analyzed data or a certain threshold is reached. We can also deliver accurate forecasts that will become the basis for optimizing a company’s internal processes.
We are agile and we adjust to the customer’s changing business needs to provide relevant reporting. For instance, if the need arises, we can add data sources, both internal and external – say, to enrich the internal sales data with the findings of the recent external research on the industry performance data, for example, with the revenue per salesperson. Besides, we constantly work to increase the quality of data analysis. For example, to improve the accuracy of predictions, we retrain the machine learning models based on a larger pool of historical data that has become available.
As a data analysis company, we analyze various types of internal and external data:
