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Making BI Better with Data Engineering

Intellicus > Business Intelligence  > Making BI Better with Data Engineering

Making BI Better with Data Engineering

Businesses today amass data in a variety of ways. From excel files to complex data architectures, data is stored in different formats and structures dynamically. Now, when businesses need to analyse their ever-growing data together, the first thing they need is a mechanism to discover and transform this data and make it analytics ready. Data engineering helps them to do just that and more. By making data uniform and usable as a single source of truth, data engineering bolsters the performance and outcomes of business intelligence. Let us look at how data engineering is making business intelligence and analytics more powerful, instant and business user friendly.

Know your data

The first step towards business intelligence is knowing your data. With siloed departments and processes, enterprises struggle to get full visibility into how much data they have and where. Data engineering enables enterprises to connect the dots and discover their data from multiple applications, locations, people and processes. Not just this, data engineering also enables businesses to identify which data is crucial for analytics to get the most valuable insights. It enables them to collate, sort and categorize their structured or unstructured data and prepare it for further transformation.

Build Automated Data Workflows 

Data engineering enables enterprises to create lean and scalable data architectures for their business intelligence initiatives. Enterprises can build smart data pipelines and bring more value to all data-driven processes. Data workflows can be automated to ensure seamless ingestion into a multipurpose data warehouse. This ensures that the most current and accurate data is available for analytics.

Create Multipurpose Data Warehouse

Once the data workflows are set, the data is collated into data marts or data lakes and is pushed to a multipurpose enterprise warehouse or OLAP systems. OLAP can pre-aggregate data and add speed to data analytics. You can bring out insights across multiple dimensions and measures. Also, it becomes much easier to spot patterns and correlations in this uniform data. This data could also be extracted and consumed for reporting. Mutiple actions can be performed on the data available in the data warehouse, as per the analytics need of the business, without disturbing the existing transactional systems or operational processes flows.

Expedite Delivery of Insights

Data engineering expedites the whole BI process for an enterprise. With automation and smart workflows, data is processed at lightning fast speed and is readily available in high volumes to run data science and machine learning algorithms. Enterprises can derive patterns from historical and live data and unravel trends and deeper insights that could be immensely valuable for the business. They can create data stories, generate actionable insights, and transform everyday work with data-driven decision making.

Data engineering brings speed and agility to creation of robust data workflows and adding new data sources as we go. It enables all stakeholders to access rich variety of data and derive powerful insights. This lays the foundation of a scalable, successful business intelligence initiative for any enterprise.

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