Organizations may struggle with getting the full value of their data without knowing it. Their data science team uses data warehouses to power business intelligence solutions to create reports, dashboards, and other data visualizations. However, the time it takes for this information to reach teams makes it difficult to use for daily decision-making.

Operationalizing data warehouses sends these insights directly into daily operations systems, thereby allowing for immediate access. Use this seven-step process to get more out of the organization’s data using ETL and Reverse ETL

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Table of Contents

  1. Talk With Stakeholders About the Data They Need to Make Decisions 
  2. Create Data Governance Policies for Operationalized Data Warehouses
  3. Select the Data Sources 
  4. Extract Data at Scale
  5. Transform and Cleanse the Data 
  6. Load the Data Into a Data Warehouse
  7. Extract the Data Into Systems of Record 
  8. Set Up Your Operationalized Data Warehouse with Xplenty

1. Talk With Stakeholders About the Data They Need to Make Decisions

Get feedback from stakeholders before making any decisions on how to operationalize a data warehouse. Understanding the decisions they make daily and what data could help them improve this process acts as a guide for the right solution. 

Look for stakeholders across all business units and roles, as the data requirements can change drastically. Ask them about collaborative opportunities they currently cannot take advantage of because of data silos, slow data insights, and other challenges. 

Keep track of the systems used by each business unit, as organizations need to choose solutions that integrate properly with these applications. 

Organizations can also use these stakeholder discussions to get buy-in for the data warehouse operationalization project. Explain the benefits of having analytical insights directly in the software stakeholders use every day, rather than needing to go to a business intelligence tool or rely on reports from analysts. 

The change management requirements should be relatively small since this information is being pulled into systems that the stakeholders use every day for their job duties. 

2. Create Data Governance Policies for Operationalized Data Warehouses

Establish the data governance policies needed to operationalize the data without running into compliance or security issues. Since stakeholders have already explained what information they need to make better decisions, you can build the data governance policies around these data types. 

Organizations need to pay special attention to data that is sensitive or protected under data privacy regulations. Confirm that this information is actually needed for decision-making and establish how it will be kept secure. Strictly controlling access to protected data types is essential to avoid data breaches. 

One common method for leveraging personally identifiable information for decision-making without compromising its privacy is pseudonymization. This practice replaces the sensitive data with similar values. The analytics process can use real-world data that doesn’t risk compromising someone’s personal information. 

3. Select the Data Sources 

Designate which data sources should be pulled into the data warehouse, how often they should be extracted, and what security measures need to be in place to keep them safe. Determine whether this data needs to be enhanced with third-party sources to provide even more information for decision-making processes. 

4. Extract Data at Scale

Manual data extraction is unsustainable. It also makes it impossible to keep up-to-date information flowing to and from applications. An Extract, Transform, Load solution eliminates this roadblock. ETL tools automate data pipelines and connect with a wide range of applications and databases. When selecting an ETL tool for the organization, look for options that have built-in integration with most, if not all, of the relevant applications. To cover other applications, look for ETL tools with a robust API that makes that process straightforward. 

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How fast does the data need to move from the applications to the data warehouse? Streaming and batch extraction options are available. These both allow businesses to customize the schedule and frequency of data updates. 

5. Transform and Cleanse the Data 

Data comes in many forms, so it may not be ready to move directly to a data warehouse. The ETL tool provides many types of transformations to prepare the data for its eventual use. This process joins together different sources, standardizes them into appropriate formats, filters, and sorts.

If sensitive data is part of the set being ingested, the ETL tool can remove it from the pipeline, mask it for better protection, or take other actions needed for regulatory compliance. 

The transformation step is also an excellent opportunity to improve data quality. Cleansing the data eliminates duplicated data, error-filled records, incomplete information, and more. By removing this data before it’s used for operationalization, the generated insights are based on high-quality data. 

6. Load the Data Into a Data Warehouse

Once the data is transformed, it loads into the data warehouse. Batch ingestions are processed based on a designated schedule, which works best for predictable data that doesn’t need to be updated on a real-time basis. Streaming ingestion is ideal for powering decisions that need up-to-the-minute data. 

Business intelligence and analytics tools can work directly with this data, as they’re built for that purpose. However, the tools used for daily business operations cannot do this. The data pipeline needs to go through one more process to make the data warehouse operationalized: reverse ETL

7. Extract the Data Into Systems of Record 

Reverse ETL takes the prepared data in the data warehouse and moves it back into the applications. This process delivers high-quality data that is centralized from all the systems of record in the organization. Stakeholders gain a more complete view of what they are doing through this data, whether they are helping customers or trying to reach their sales goals. 

The data warehouse acts as a single version of the truth. This is valuable because it allows the organization to work with a consistent data set. Collaboration between business units is easier, as everyone is looking at the same data. 

A reverse ETL tool, much like standard ETL, connects with applications through built-in integration. This functionality makes it simple to set up and change connections as the organization’s data requirements change. As the organization introduces new data types, and business units develop the ways they use data, the reverse ETL tool helps the data warehouse keep up. 

Set Up Your Operationalized Data Warehouse with Xplenty

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Xplenty delivers a cloud-based, modernized data pipeline builder that offers robust ETL and Reverse ETL features. Setting up data pipelines is as simple as dragging and dropping the components in place, enabling non-technical users to get the data they need. Advanced users enjoy many powerful features for creating complex data pipelines and custom connections. Contact us to learn more about Xplenty’s platform and set up a 14-day demo. 2wgv3ihO