How Data Engineering Kicks Your BI Into High Gear
The objective of this blog
Building reliable intelligence at the speed of business can be a challenging task. A well-designed data engineering strategy ensures that your analytics resources are spent on uncovering insights rather than laying foundations. In this post we’ll explore some of the benefits and the general steps of forming a data engineering strategy. These benefits include:
Faster and more reliable BI project results
Better use of analytics resouces
Time saved on structuring complex and quickly changing data
How Does Data Engineering Achieve Faster Results?
When we ask our analysts to extract data from various sources, find a way to integrate them, keep these integrations up to date and deliver intelligence solutions, we risk the chance to act on insights that could have been delivered when the time was right. Data engineering lays the foundations so analysts using tools like Power BI can do their best work and launch projects at blazing speeds. Let’s look at some specific processes that go into data engineering and how they help make business intelligence easier.
Sourcing Data
Unless you’ve already implemented some solid data practices, your business insights aren’t likely to be in one place. You may have customer data that’s only accessible via an API, CSV files provided from a third-party vendor and an internal database that records customer transactions. Tools like Power BI have powerful data integration features, but not all analysts will follow best practices using these tools. This can lead to performance bottlenecks, delays caused by the need to completely redesign the nuts and bolts of a dashboard and high maintenance costs caused by stretching tools beyond their capacity.
Good data engineering provides the expertise necessary to provide an integration solution that’s reliable and easy to maintain. There are many tools in the data engineering space for integration and picking the best one for your business use case is beyond the scope of this post, but it’s important to keep future data needs and costs in mind.
Transforming and Scheduling Data Processing
Once you’ve called your API’s for data, transferred your CSV’s into your integration tool and queried your databases, you’ll need to ensure that this data is correctly formatted and organized before it lands in a data warehouse analysts can easily query. It’s also important to correctly sequence the refreshing of all your data sources; if your third party CSV’s are refreshed before your internal transactional database for example, you could end up with an incomplete or inaccurate big business picture. Many data integration tools also contain a scheduling component to ensure that data sources are refreshed only once prerequisite dependencies have been fulfilled.
Data Warehousing
Somewhere between ensuring correct data formats and loading data into its destination, data engineering requires putting some thought into modeling data in a way that both reflects your business accurately and makes for easy analysis. Data warehousing tools like Azure Synapse, AWS Redshift, Snowflake and even Power BI dataflows provide the means to store your newly integrated, shaped and scheduled data in a format that lends itself perfectly to measuring inventory, forecasting future profits and even running machine learning models on massive amounts of historical data to predict consumer behavior.
An important part of warehousing data is taking the time to design a data model that mirrors the reality of your business. “But isn’t a data warehouse just another database? My business already has a database, can’t we just use that?”, you might be asking? While your website, CRM or ERP’s database may be queried, it wasn’t designed with analysis in mind. These databases are designed to make it very easy to record large amounts of business activity while keeping the database lean. Data warehouses on the other hand, consist of models that are designed to be easy to extract insights from. They often have very different physical and conceptual properties.
The End Result
A well-designed data engineering strategy results in a neatly packaged analytical endpoint. Your business intelligence professional fires up Power BI, connects to your data warehouse and is immediately off to the races. No excel worksheets are downloaded, no connections to SharePoint folders are made and no queries are written or updated. Projects are completed in record time and your business catches the profit boosting trend well in advance of the next quarter. The next time you embark on a new BI venture, ask yourself if your data is truly ready for that upcoming project. You’ll be glad you did!
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