Data Engineering and Business Intelligence
Data transformation through assimilation, extraction, and governance
Companies spend considerable time and effort to make data ready for analysis. According to a report by Gartner, more than 50% of the effort in any data science or reporting project is consumed by data engineering. This is because data is stored in different areas such as legacy systems, local machines, or on social media support platforms.
Data engineering initiatives focus on data extraction, cleansing, integration, quality, and governance. Careful choice of IT tools and technologies, as well as the right human skills, are needed to perform data engineering tasks.
At HGS Digital, we have a dedicated global data science and analytics practice with the right people, processes, and technology know-how to help work on data and build meaningful reports. Our pre-built assets help accelerate data the transformation journey at lower cost and faster time-to-market.
Data challenges everywhere
CDOs are tasked with managing data assets throughout the enterprises so that data is readily available for downstream reporting and analysis. They face several challenges in their efforts to be the data supplier for the organization. While some of the challenges can be solved with adequate staffing, others are difficult to surmount as their solution lies in the prevailing data culture in the organization. Here are a few challenges faced by organisations:
Data quality
About 88% of companies see a direct effect of inaccurate data on their bottom line, losing an average of 12% of their revenue according to a report by Experian. A good part of data engineering efforts focuses on data quality. Data quality suffers for many reasons:
- Obsolete and duplicate data
- Compliance and security issues
- Merger and acquisition activities
Data architecture
The success of data engineering efforts depends on the right data architecture. Factors to consider when designing a data architecture include:
- Choice of technology solutions
- Scalability and future-proofing
- Current data structure and processing capabilities
- Advanced analytics capabilities
Business intelligence
Many organizations lack a holistic, enterprise-wide approach to investing and maintaining their investments in business intelligence (BI). There are several challenges that need to be addressed:
- Multiple BI tools used
- Need for a self-service model
- Data governance for trust in data
- Real-time reporting capabilites
Here’s how we can help
Data engineering
- We design a future-proof, scalable, cloud-based data architecture and developing data hygiene protocols to ensure high quality of data
Business intelligence dashboarding
- We help create the appropriate data environment for self-service reporting, designing and developing executive, department, cross-department, and mission-specific dashboards
Want to kickstart your data transformation?
In our data science center of excellence (COE) workshop, you can learn how to:
- Identify business cases for setting up a COE
- Create a strategy and roadmap
- Define timelines and arrive at an execution plan
Launching a data-led transformation initiative?
Our workshops can jump start your transformation initiatives. We offer a Data Engineering & Cloud Data Strategy workshop.
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quality for your business?
Set it right the first time with the right data hygiene strategy, and make data-backed decisions. Learn from our experts at one of our workshops.
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