Business
Analytics

Business Analytics is responsible for why some companies leave others behind. We provide data centric solutions that enable our clients to achieve great success by augmenting their decision-making abilities.

Since the dawn of mankind, humans have generated data whether it be stone tables or markings in the caves. The real up heave came after industrial revolution and humans begun to develop processes and leverage from automation. This was the age of processing and storage. Hence, the processing power and storage capabilities of computers enabled us to write complex workflows and automate processes. As a result Enterprise resource planning software (ERP) from SAP, Microsoft, IBM and Oracle were developed. Similarly, Customer relationship management (CRM) programs followed suite and data accumulation begun. Internal data generated by operational application, marketing and customer data along with social media data and other external sources that complement the business. As a result, business data increased in both volume and variety.

Background

In the early days, management information (MIS) and executive information support (EIS) systems generated data for executive decision making on need basis. This practice resulted in reactive decision making. For proactive guidance to executives, Data warehouse and Business Analytics platforms were developed to equip decision makers with live data, visualisations and dashboards. That’s what we do!

Finally, Business Analytics is composed of technologies and technical processes that enable firms to leverage from that data. This includes Artificial Intelligence and Machine learning algorithms that have enabled businesses to predict behaviour of customers.

Implementation Approach

At DM, we use our tried and tested approach of first understanding business goals and objectives for a BI implementation. Followed by deliberating on existing data sources and prevailing IT environment. Finally, the nature and variety of applications running in the organisation. Taking in to consideration the gathered information, we devise a BI implementation roadmap that highlights the best technology stack / BI platform for business. This leads to interaction with business units and KPI cataloging and selecting the best approach for designing DW. Consequently, business and reporting requirements are gathered and pseudo dashboards are developed and approved. A report on data state is developed that helps the data engineers in cleansing the data. Keeping in to consideration the requirements and observations in data state report ETL is developed. Selected data tables or variables within them are extracted, transformed and loaded in to the DW.

Road to Advanced Analytics

Depending on the selected Business Analytics platform data modelling is completed that leads to development of a presentation layer, reports and self service / adhoc analysis capability on the OLAP cube. Advanced Analytical techniques like What-if analysis, predictive and prescriptive analysis capabilities are developed by either using the in-built functions of the BI platform or other tools / APIs.