Top Data Warehouse Best Practices to consider in 2022
Today’s business leaders understand enterprises able to leverage data and analytics as a basis for decision-making will outperform peers that don’t. According to research, highly data-driven companies are three times more likely to achieve better strategic business results — which include greater profitability and enhanced productivity.
But wanting to utilize data to answer pressing, business-relevant questions and doing so consistently, affordably, and quickly are two very different things. Data warehouses are among the data management and storage architectures developed to support data-driven decision-making at an enterprise scale.
What is a Data Warehouse?
A data warehouse is a processing and information collection system designed to power decision support solutions and Business Intelligence. Data warehouses integrate data from numerous, disparate sources into a centralized repository to serve as a single source of truth for the organization. They may comprise numerous technologies and components, but all are designed to transform data into something used for reporting, analysis, and strategic decision-making.
Data warehouses were built to resolve the critical issues encountered by the organizations. The data flows into the warehouse from numerous operational sources. There, it’s cleaned, prepared, and stored so that it’s ready to fuel the analyses, reports, and dashboards that modern BI engines generate. Data warehouses can contain multiple databases and data ingestion and transformation tools, but their primary purpose is always to support data analysis.
When Do You Need a Data Warehouse?
The prime driver for most businesses building data warehouses is supporting analytics platforms such Tableau or Qlik. These platforms execute complex and frequent queries that lay a lot of stress on a database, and on the other side, it’s risky to subject operational databases to those stresses. Instead, data warehouses are explicitly modeled for consumption by Business Intelligence engines, and they are up to the mark.
Modern cloud data warehouses are more pliable and easier to manage than DW hosted on legacy on-prem hardware. They can easily be integrated with cloud data lakes, which offer more general-purpose storage, holding huge amounts of semi-structured or unstructured data before it is prepared for analytic utilization in the DW. Many companies that start by building a data lake in the cloud need a data warehouse to support more complex analytical capabilities.
Top Data Warehouse Best Practices
Begin with robust Master Data management practices.
Master Data Management focuses on building a controlled process through which consistent, correct, and validated master data is devised and established as the system of record for the organizations. The primary issue in MDM is making sure that accurate and reliable master data is feeding the DW. You’ll be required to ascertain that the quality of data is maintained across all the data sources, that no records are being deleted or lost as they are moved into the warehouse, and that you’re tracing data source anomalies. This helps in removing much of the transformation effort involved in populating your warehouse.
Invest time and effort into data standardization.
Imagine that your enterprise uses five different claims processing systems across other business units. You’ll require to aggregate and normalize the data from all of them to integrate them into your data warehouse. It needs engineering effort to harmonize disparate systems so that you can consistently report on them, but it’s a worthwhile endeavor. Creating a standard data format will allow you to eliminate glitches in data formats, structures, and schemas so that your data can be analyzed reliably and consistently.
Build stable ETL or ELT pipelines.
ELT/ETL pipelines are like a plumbing system for your data warehouse. The traditional ETL process involved collecting event or transactional data volumes in a staging area, standardizing and cleaning the data, and eventually loading it into the DW. ELT is the latest process that relies on the capabilities of modern cloud data warehouses. They can transform the data once it comes inside the target system. Whichever process they utilize, most organizations now rely on purpose-built tools to automate the extraction and make sure that data flows are efficient and reliable.
Plan for how you’ll define permissions and access controls.
When you’re aggregating data from many sources, it’s critical to review the security needs of every one of them. The sources might’ve enforced numerous types of field-level security: how will users maintain similar controls within the DW? And how can users ensure that data security compliance requirements and best practices will continue to be met? Think via privilege management and access control strategy with care.
Figure out how you’ll maintain observability in the DW
Data warehouses are inherently complex entities. They involve a humongous amount of external sources, and something can go wrong with any one of them. Establish monitoring, logging, and alerting infrastructure that will let users know when it does. Observability is a significant concern with data warehouses, so you’ll need to build a system that enables you to see what’s failing, what happened, and what didn’t happen at any given time.
Final Thoughts
Today’s cloud data warehouse solutions bring all the advantages of cloud — scalability, elasticity, high availability — to DWs. They are secure, dependable, and rapid to query. Cloud offerings integrate seamlessly with a wide array of commonly used business applications, offer easy self-service abilities for users, and terminate hardware costs and administrative burdens. Most involve integrated query engines and pipeline tools, and some can connect with data stored anywhere in the provider’s infrastructure.
To grasp more how Polestar Solutions can help you prepare your business to harness the power of its data and become more data-led, Book a consultation today!
