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How to Setup Your Enterprise Data Warehouse in 6 Easy Ways

The enterprise data warehouse market will grow into a $30 billion industry by 2025. Startups, SMBs, and large-scale enterprises rely on data warehousing to power their operational intelligence. This necessitates a streamlined aggregation of data from multiple sources, including internal and external sources. The list includes data from internal data lakes, real-time APIs, and third-party data warehouses.

Thus, businesses require high-performing data warehouse tools to operationalize their Business Intelligence (BI) data and orchestrate a responsive ETL pipeline.

Data warehouses help with the bi-directional logistics of your data. So, for any enterprise, BI serves as the cornerstone for competitive advantage in the market. Modern, cloud-based warehouses utilize automation for faster, streamlined data aggregation, reporting, CRUD, and BI analytics. 

Notably, entrepreneurs and data specialists must learn the basics of data warehousing and their use cases before they can start integrating connectors and consolidate different business data sources under one roof.


What is Enterprise Data Warehouse?

enterprise data warehouse architecture

An enterprise data warehouse is a repository for structured and unstructured data. The warehouse facilitates the data it aggregates for your business intelligence tools to perform various data-oriented tasks, such as CRM data entry, performance benchmarking, retail conversions, revenue generation, and many more. 

Thus, your data warehouses manage massive volumes of data extraction, transaction, and loading (ETL) for various business processes, at any given point. Consequently, every business requires customized solutions for their ETL data warehouse, as it helps regulate tasks on a data type, source, or destination basis. 

Thus, data warehouses need to be flexible, secure, and scalable when compared with traditional data repositories to manage large datasets regularly. 

Why Do Many Enterprises Need a Data Warehouse?

There is no denying the industrial reliance on data-driven, analytical insights and performance metrics for guiding operations and strategies. Enterprise data warehouses need to archive various data types, including discrete and non-discrete data forms. Conclusively, a warehouse’s usefulness lies in how it improves revenue and decision-making. 

Here are some use cases for data warehouses:

  • Integration with diverse data sources
  • Harboring different data types for ETL
  • Asynchronous and real-time Big Data analysis and graphical inferences
  • AI automation and ML innovation for intelligent custom reports and analytics
  • Data mining and sciences

With Artificial Intelligence & Machine Learning in tow, data operations, and their automation can generate dynamic analytical insights from their data warehousing tools. You can scale your enterprise data warehouse vertically or horizontally based on your enterprise’s requirements. You can even maneuver it to help meet your business SLAs better.

Enterprise Data Warehouse Setup Process

Setting up a data warehouse requires a few careful considerations. Firstly, businesses should decide on a compatible data warehousing provider; the budget and operation scales help choose whether to opt for on-premise or cloud-based solutions.

The following steps further illustrate the action plan:

1. Determine Business Objectives

enterprise data warehouse

Start by prioritizing the business processes. Collect and analyze information on every process so you can also select the correct type of database solution. Some steps include identifying the metric indicators for each category, delegating roles to data specialists for each department, amongst many others. 

Set the goals you have for analyzing your data and the data model that they will follow. Dimensional data modeling offers businesses excellent data model controls right from the granular level. Choosing such a model conserves time as it is replicated for every process.

2. Create Data Governance Policies

Enterprise Data Warehouse Governance

Your data governance policies dictate the kind of business data operations your enterprise data warehouse can expedite. The policies decide the sensitivity and discretion level of various data types.

Hence, it is practical for most businesses to adopt signature data nomenclature conventions to help identify data. These policies also decide the apt security level required for a given data type.

3. Select Data Sources

Enterprise data sources

You can segregate your business data sources into three types:

  • Internal operations data
  • External applications
  • Business product/service data

This is a stage where your enterprise data warehouse integrates with your data sources. The data cleansing protocols you set impact automation efficiency. Selecting the right indicators and metrics in the first step can make or break the efficacy of the data in your enterprise data warehouse.

4. Select Data Extraction Processes

It is unfeasible to manually sift high volume data at SME scales, let alone enterprise-scale. Modern data warehousing services and cloud ETL platforms supply tools for ETL pipeline automation.

Some of the services mentioned below can fluidly integrate with any business data model to extract data. Streaming and batch extractions are easy to implement with such tools.

5. Transform and Cleanse Your Data

You can rely on data transformation tooling platforms offered by AWS Redshift or even Kloudio for initiating sophisticated data transformations. Data transformations help standardize your structured and unstructured data into a singular, legible format. Cleansing your data also rids your data pipeline of obsolete data

It is imperative for businesses to take data scrubbing into account before their business scales and data amasses.

6. Load Your Data Into the Warehouse

enterprise data warehouse loading

Post-transformation, you can begin loading your data into its destination. This process is called ingestion. Streaming and batch ingestions are responsible for minute-by-minute analysis of real-time data. Your data warehouse architecture must provide for your routine data-loading schedules to facilitate the seamless transition of your data ingestion process. 

In some cases, the process continues with reverse-ETL that loads standardized, analyzed data back into your data applications.   

How Can Kloudio Help With Data Integration?

Kloudio is a SaaS/PaaS organization that meets the urgency of scalable data warehousing services and offers an unparalleled list of data processing tools for clean and consistent analytics. Their enterprise data warehousing techniques augment your products, strategies, and campaigns to yield better Returns on Investment (ROI). Its customizable and straightforward data warehouse setup goes a long way in improving analytical standards, business intelligence accuracy, and overall data operation efficiency.

Kloudio, as a data-centric organization, puts a wide array of third-party SaaS integrations at your disposal. It also helps render ETL and reverse-ETL data warehousing and eliminates the security concerns with managing decentralized enterprise data.

To truly exercise the powers of data management, you can sign up for Kloudio 2.0’s tour to get a glimpse into their products and services.

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