Why Data Analysis

Data analysis services help businesses get better output. It helps your business to grab new and relevant opportunities for better growth. Flexibility, high-performance, and better decision making is a result of proper data analysis. Providing better services improves customer loyalty. Let us give you more examples of why data analysis service is essential for your business:

1. NO MORE ASSUMPTIONS: Data analysis techniques prevent you from assumptions in deciding for your business. Combining data analysis and in-memory analytics gives you better insights regarding your customers and other aspects of your business in real-time. Thus, providing the freedom to make an informed decision and increase productivity.
2. REDUCE BUSINESS COST: Adopting data analysis services gives your organization a significant cost advantage. Effective decision-making increases efficiency and gives your business a perfect start in this competitive industry.
3. BETTER PRODUCT: Getting a clear insight regarding existing gaps and customer needs, you can produce better products to boost your business in cutting edge competitions.

What We Do For You

Below are the Data Analysis Methods we apply:

•    Data aggregation (traditional reporting, online analytical processing).
•    Data Cleanup and Validation
•    Data Exploration process (Descriptive analysis & Graphical representation)
•    Classification
•    Clustering and Decision Trees
•    Forecasting (based on machine learning, including deep learning).
•    Statistical hypothesis verification.
•    Optimization

How we do what we do

1. Discovery: At this stage, we analyze the customer needs and as-is situation: existing data and data quality practices and the analytical solution, if any. We examine the company’s business plan and collect input from IT and business departments to understand the customer’s analytical needs. Based on the discovery stage findings, we plan the service and decide on the service level agreement (SLA) terms with the customer.
2. Transition: 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. The responsibility we take are high:

1. 1. we deliver value through analytics, having high freedom in process and resources:
   2. We are responsible for setting up processes and managing them. The customer may or may not be involved in approving substantial aspects.
   3. We are responsible for allocating and managing proper resources. The customer may or may not be involved in approving them.
2. Service delivery: We provide access to self-service analytics tools, deliver regular reporting, and 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 them 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.
3. Continuous improvement: 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 recent external research findings on the industry performance data, for example, with the revenue per salesperson. Besides, we continuously 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 available