(Newswire.net — November 22, 2021) — The data warehouse is an infrastructure that provides down-the-line users such as data scientists and data analysts with access to data that has been transformed to be able to conform to a business’s particular guidelines and is stored in a formation that is easy to query.
Information gets connected in a data warehouse typically from multiple “source-of-truth” transactional databases within individual business units. In contrast to the information stored in a transactional database and once in the data warehouse, the information gets reformatted for speed.
The data gets stored in a denormalized structure together with pieces that will likely be queried together to increase performance by minimizing the complexity of queries required to get data out of the warehouse.
Now with an understanding of what a data warehouse is, let’s delve into how you and your organization can get the most value from it so that you can quantify the benefits that your company is able to get from using it.
2. Data Enrichment
These days it is a common occurrence for organizations to collect first-party data and third-party data to strengthen their insights and make business choices.
But no matter which industry you are involved with that is gathering data, one of the biggest challenges tends to be with data enrichment.
Data enhancement is the means of enhancing existing datasets with information that is generated from other sources, from product analytics and marketing analytics to sales analytics and billing analytics.
With data enrichment, the aim is to match this customer data to enable cross-analysis and deeper insights to improve data accuracy and establish more customer personalization.
3. Use Cases for Data Enrichment
When it comes to sales data, this would be all of the information about the customer that is captured in the sales pipeline that comes in the forms of:
Billing data: is information that is captured during the payment process.
Product data: is information associated with the customer that is captured through the product.
Marketing data: is the information that is captured in the customer journey.
4. Improving Performance
It isn’t enough to just know how to understand how to structure your data; a data warehouse needs to be optimized to improve performance, such as creating a clustered index on the data in the order it usually gets queried.
Along with a single clustered index, a table can have several non-clustered indexes that won’t affect how the table is physically stored but still create extra copies in memory.
5. Exacting, Transforming, and Loading
The process of shifting data out of its original location is referred to as exacting; performing some form of transformation and then loading it into the data warehouse is an important process.
Database architects should apply a systematic approach that considers best practices for design options, operational difficulties, failure points, and recovery techniques.
The documentation for ETL includes establishing the set of transformation instructions on how to change the structure and content of data in the source system to the structure and content of the target system.
Once the data warehouse is set up, users should be able to easily query data out of the system.
6. Power of Partitioning
Performance enhancement can take place by dividing sizable tables into various smaller portions, which is called partitioning.
The benefit of partitioning, which can be either vertical by splitting up columns or horizontal by splitting up rows, is that by splitting a large table into individual tables of smaller sizes, queries that only require access to a fraction of the data can go through much quicker.
7. Archiving Data
Another great way to get the most out of your data warehouse is by optimizing your organization’s data archiving methodology.
Data that has been archived is important to the organization that is in possession of it and is important to data scientists that set out to conduct regression using historical trends.
Also, data architects should prepare for this by relocating historical data that isn’t actively used into a different storage system with increased latency and strong search capacities.
Sending data over to a less expensive storage tier is one benefit of this approach, along with removing write access from the archived data, which protects it from being modified.
In summary, it is quite clear that using a data warehouse as an internal business information hub is just the beginning of making the most out of data. It still has to be enriched, partitioned, extracted, transformed, and loaded in order to garner results that impact the customer experience with your brand.