Learning CenterLearning Topics
Data Analytics vs. Data Analysis
Quantitative vs. Qualitative Data
What is Behavioral Analytics?
Data Analytics vs. Business Analytics
Data Analytics vs. Data Science
The Difference Between Data Analytics and Statistics
The Difference Between Data Analytics and Data Visualization
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What is Data Analytics?
What is Diagnostic Analytics?
Data Analytics Processes
A top-level guide to data lakes
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
Data Warehouse Architecture
What Is a Data Warehouse?
How to Create and Use Business Intelligence with a Data Warehouse
Best Practices for Accessing Your Data Warehouse
Data Warehouse Best Practices — preparing your data for peak performance
How do Data Warehouses Enhance Data Mining?
Data Warehouses versus Databases: What’s the Difference?
What are the Benefits of a Data Warehouse?
Key Concepts of a Data Warehouse
Data Warehouses versus Data Lakes
Data Warehouses versus Data Marts
Difference Between Big Data and Data Warehouses
How to Move Data in Data Warehouses
What Is Customer Data?
Customer Data Analytics
Customer Data Management
Collecting Customer Data
The Importance of First-Party Customer Data After iOS Updates
Types of Customer Data
What Is a Customer Data Platform?
What is an Identity Graph?
Customer Data Protection
A complete guide to first-party customer data
CDPs vs. DMPs
What is Identity Resolution?
What is Consent Management?
Data Access Control
Data Sharing and Third Parties
What is PII Masking and How Can You Use It?
Data Security Strategies
Data Security Technologies
Data Protection Security Controls
How to Manage Data Retention
How To Handle Your Company’s Sensitive Data
Data Security Best Practices For Companies
What is Persistent Data?
Google Analytics 4 and eCommerce Tracking
What Is Google Analytics 4 and Why Should You Migrate?
GA4 Migration Guide
GA4 vs. Universal Analytics
What are the New Features of Google Analytics 4 (GA4)?
Benefits and Limitations of Google Analytics 4 (GA4)
Understanding Google Analytics 4 Organization Hierarchy
Understanding Data Streams in Google Analytics 4
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What are the Benefits of a Data Warehouse?
A data warehouse (DW) is a software construct that pulls together data from different sources into a single target for business intelligence (BI) analysis and support for strategic decisions. It is sometimes referred to as an enterprise data warehouse (EDW). Successful data-driven businesses require a data warehouse as they strive to understand their history, explore existing trends, and forecast and model future business strategies.
Pulling data from different sources — in various formats — and coalescing it into a standardized format makes business intelligence forensics nimbler, faster, and further reaching. In short, data warehousing saves money, increases opportunities, and confers great competitive advantage to those who live by data-driven decision-making.
Let’s examine the many benefits of implementing a data warehouse.
Data security to protect intellectual property
The data generated from operations, when properly captured, is among the most valuable intellectual properties of a business. Past performance data is the purest expression of how things are being executed; being able to mine this treasure trove provides invaluable insight and competitive advantage. Protecting this data is paramount.
Data warehouses use a wide spectrum of practices to protect company data. These include
- Importing databases via read-only replication
- Encrypting sensitive columns
- Sanitizing data of malicious SQL (to protect against injection attacks)
- Tightening data permissions through row-by-row access control and slim user groups
- Providing a data framework for compliance with GDPR, CCPA, CASL, etc.
The results — cleaner data, safely stored in a secondary source — have the added benefit of faster retrieval than hammering the primary data source.
Higher-quality data to power analytical tools
One vexing aspect of our modern, digital world is that data is stored all over, and in wildly varying formats. Relevant data can range from unstructured (e.g., social media) to structured (spreadsheets, relational databases). JSON, XML, and proprietary API artifacts are all formats that are commonly incorporated into larger datasets.
This data Tower of Babel must be tamed; transformed into a format consumable by business intelligence data analytics tools. Many of the same techniques applicable to data security — like replication and sanitation — convert complex, diverse data into a usable, uniform data system.
Higher-speed analytic throughput
Business intelligence data analytics tools require not only a source of data in a standard format but also that the information be up to date and available immediately for rapid data mining. Data warehouses have the computing power to reformat incoming data and the transmission throughput to reliably feed analytics tools. In turn, well-supplied analytics tools provide the bedrock for trend and forecast model inspection, along with inputs to customer relationship management tools, quarterly and annual reports, and all types of sales and marketing needs.
Elevated human and machine efficiencies
Employees are arguably the most costly business resource. The considered application of specialized knowledge requires education, training, and experience. Tasks and processes that can be automated should be, as they act as force multipliers and enable cogitation without unnecessary delay — as well as ironing out inefficiencies and execution errors
Data warehouses maximize labor’s performance by automating the tasks of repeatedly retrieving data from multiple sources and cleaning and sanitizing that data.
Having this cleaned data already assembled and centrally located boosts machine efficiency by unclogging networks between the user and the various data sources. Being able to model and forecast at full speed on local data makes reports more timely and maximizes the efficiency of important, skilled employees. Heightened centralization and standardization also prepare for automations that are capable of fully replacing certain analytical roles. By preparing your data system for improvements in AI, you can improve the flow and velocity of data-driven insights.
Future-proof business growth
Over time, the quantity of raw data generated keeps growing, and so does the number of models and forecasts that need to be run. It was only a few years ago that gigabytes of data seemed unthinkable; exabytes are now on our horizon. A weekly workload of a handful of reports has now been replaced with real-time dashboards and hourly data examination.
Whether the data sources are on-premises or part of a simple, hybrid, or multi-cloud topology, modern data warehouses can handle the accompanying variations in network speed, assembling consolidated data reliably.
Because data warehouses can provide the answers to frequently-repeated queries, they protect legacy systems — the primary computing resources and data stores — from strain. Businesses can leverage existing investments for much longer — a major cost saving.
Data warehouses, especially those in the cloud, scale well to explosive growth trends. Bottlenecks in the ability to analyze, model, forecast, and report directly impact both a business’ agility and the bottom line.
Historical insight to fortify intuition
To use an analogy from the restaurant world, a chef de cuisine keeps short-term data — meal order tickets — to keep the kitchen workers on track and to ensure courses come out in an orderly fashion. They also keep inventory calendars, capturing the quantities of the most-ordered dishes to have proper supplies ordered and delivered appropriately. In this way they back their domain knowledge up with historical insight. Successful chefs are prepared for the seasonal spikes in demand for pumpkin soup or roast turkey.
Similarly, a company’s institutional knowledge, often held in the recollections of senior employees, provides invaluable insight through experience and intuition. This intuition can be expanded and enhanced by data-driven decision-making — driven by data captured over time, from a variety of sources. Numbers from past sales pitches, inventory counts, and generated revenue are precisely the types of historical data that data warehouses deliver for key performance analysis, modeling, and forecasting.
Predictions to drive revenue
Just-in-time manufacturing and parts delivery are an integral part of modern business. Being able to predict what’s needed ahead saves the costs of procuring and acquiring resources, be they pumpkins and turkey or server space and network bandwidth. Buying things at the very last minute, after the need comes to light, is both expensive and prone to break business processes.
Data warehouses power the predictions that can translate to revenue gains. Seen in this light, a data warehouse can be seen as not a cost, but an investment. Data warehouses improve business decision-making, drive targeted products, lessen costs of acquiring resources, lessen response times, and enable a business to more effectively strategize and execute against competitors. Data warehouses are key to competitive advantage.
Data Warehouse: add depth to your data systems
As demands on data grow to become more complex, the tools for handling that data must likewise mature. Data warehouses are a powerful asset for projects with diverse data types or sources. By efficiently stitching together a quilt of information, data warehouses generate deep insights, advancing observation and modeling resolution while improving response time. Data warehouses are a key competitive advantage for a modern business to have because they:
- Help democratize data by aggregating all the data a business might generate into a central repository that is both secure and scalable.
- Offer the ability to join data from various systems together to provide a true customer 360 view.
- Protect production systems from unnecessary strain from analytical or ad-hoc extraction workloads and offer the ability to give as much or as little access to data as a given user or group may require.
- Enable a host of modern predictive technologies, from regression modeling to machine learning and AI, to be developed in a quick and cost-effective way.