Knowledge Is Power: The Potential Of Business Intelligence And Data Engineering.

Fast and intelligent decision-making is inhibited when there isn’t a consolidated view of key business information. If data sits in different places – documents, spreadsheets, internal and external databases – pulling it together to draw comparisons and find a single answer is like trying to fit square pegs in round holes. For business leaders that can be the difference between capitalising on an opportunity and missing out.

One of the most important solutions business intelligence offers is the data warehouse – it is a platform where disparate data can be brought together by implementing a data-driven architecture. A good data warehouse build will consolidate the data and present it in a user-friendly manner. For example, a company with a large customer base will have a huge CRM database with a lot of unique customer IDs. They might have a separate database with billing information and another system with account management details. Bringing these three sources together in a data warehouse will provide a “360-degree customer view” and allow end-users to see potentials within their customer base that they otherwise would not be aware of.

When distributed data is even more complex, and involves unstructured and structured data alike, a data lake might be a more suitable solution for providing a single source of truth than a warehouse. This type of solution opens a more complex set of analytical capabilities, so users can gather insights and comparisons that derives reasoning from sources beyond just strictly formatted datasets.

Kickstarting your Business Intelligence solution

It doesn’t have to take an army of developers to deliver a business intelligence solution. Businesses can start with a “minimal viable product” approach and take a portion of their data sources to consolidate at first. Cloud-based data warehousing tools are built for scalability, so delivering a proof of concept that works with a smaller dataset gives the opportunity to trial the potential of business intelligence for the wider business.

Taking an agile approach to business intelligence also helps delivery move forward faster – tackling the work feature-by-feature to break down the overall project. Adhering to sprints will help the team prioritise tasks and will promote discussion of the solution in a wider business context during retrospectives and sprint planning sessions. If an existing development team is in place, working closely with them is a win-win because it promotes knowledge sharing – implementing good development practices will result in a better business intelligence product, and a data-driven mindset helps developers build products with customer use in mind.

Data Engineering at Godel

Godel’s Data Engineering division has been delivering business intelligence solutions to UK clients for over a decade. Recent client engagements have involved the management of tonnes of terabytes of datasets in AWS and Azure, advanced data analytics, building cost-effective data pipelines for newly-built data lakes, deployment of data ingestion environments and creation of CI/CD pipelines for better delivery. Overall, Godel’s data engineering team has helped clients realise benefits such as:

• Drill-down capabilities providing end-users with in-depth insights.
• Ability to perform user segmentation analysis, calculation of Customer Lifetime Value and vastly improved detection of anomalies across transactions.
• Saving high factors of time by delivering simplified reporting solutions for complex datasets.
• Integrating data-driven approaches into wider business departments – development, marketing, operations, HR etc.
• Enabling predictive analytics within the financial services sector – using data to identify customer opportunities.
• Helping development teams gain a better understanding of their software products through analysis of customer data.

If you’d like to learn more about Godel’s Data Engineering division, feel free to  reach out for a conversation.