August 23, 2021

Data Warehouse And Its Ultimate Outcome!

In the time of digital marketing, data has been a key source for understanding client behaviors. The data warehouse has been the go-to solution to store this information and help us better understand our clients. However, as we have more and more data collected on a daily basis, there needs to be a new system in place so that it can be properly managed. In this blog post, we will explore how a data warehouse is not only an essential tool for marketers but also what its ultimate outcome if used correctly.

As a marketer or business analyst, you are well aware that data is critical to your success. And the way you arrange and store your data will either make or break your job. You can store data in a variety of methods, one of which is data warehousing. This is a great alternative for companies that need to analyze a huge volume of data from a variety of sources. Let's take a look at what a data warehouse is and how it can assist you in data analysis today.

What Is A Data Warehouse? 

A data warehouse is an essential tool or a repository that provides information in the form of reports, analyses, and summaries. Its purpose is to support business intelligence (BI). Data warehouses are centralized repositories of data. They consolidate data from disparate sources into a single source for reporting purposes. Businesses use them to gain more accurate insights about their customers, products, and services, to make better decisions, to reduce costs, to increase speed, and so on.

They are not designed to provide real-time operational data. They are not designed for online transaction processing (OLTP). These warehouses are not used in the transaction of goods and services (although they do allow businesses to make better decisions about what goods and services to offer their customers, how much to charge, etc).

Data warehouses are not made for disaster recovery. Businesses need disaster recovery plans that separate OLTP from BI systems. Otherwise, it is difficult or impossible to meet regulatory requirements like the Payment Card Industry (PCI) standard which requires a mandatory backup of credit card information within seconds.

Data warehouses are not constructed to scale indefinitely. When a data warehouse is implemented, its intended lifespan exists indefinitely. That's what makes it a low-cost alternative to data marts and other BI tools. However, the size of the repository of historical information typically outgrows what any one person or group can usefully analyze within a reasonable time frame. This creates two problems. Firstly, the speed of business analysis diminishes as analysts wait longer for answers. Secondly, what one analyst finds useful may conflict with what another analyst finds useful, which leads to multiple reporting structures that don't share the same reporting data. 

To solve this problem businesses need a Big Data strategy that supports on-demand querying of big chunks of information available from multiple sources. Let us now know a bit more about the outcome of the data warehouse. 

What is the ultimate outcome of a data warehouse? 

To know exactly about - what is the ultimate outcome of a data warehouse? Let us summarize it as the proficiency to streamline and structurally simplify inspection processes over time and provide easily accessible and secure storage and retrieval of what would otherwise be very complex and convoluted information. Data warehouse projects have been so successful that many major corporations have fully invested in what is known as the "data warehouse industrial complex".

The outcome of a data warehouse is what's known as BI 2.0 or what Gartner refers to as the Enterprise App Store. The publication, Strategic Blueprint for Actionable Business Intelligence with Big Data in Small- and Medium-Sized Enterprises, provides an extended discussion of what this means for businesses struggling to make sense of the information coming from both inside and outside their organizations.

The key takeaway is that while data warehousing has served businesses well, its usefulness is limited by what it assumes about what organizations know when they want answers. This assumption no longer holds in an era where IT delivers what researchers at GigaOM refer to as Big Data's 4 V's: Volume, Velocity, Variety, and Veracity.

Data warehouses cannot solve what Gartner refers to as the problem of 'too much data, not enough insight'. Enterprises need a strategy for business intelligence that supports on-demand querying from multiple sources. That is what the enterprise app store is all about. 

1. Historical data: The ability to look at a big volume of historical data across time is one of the ultimate outcomes of data warehouses. You may consolidate a big amount of data from multiple sources using a data warehouse to better influence your business decisions. You can study trends over time and strategize more effectively if you look at previous data. Historical data allows you to derive future expectations.

2. Data from multiple sources: Furthermore, a data warehouse will collect data from numerous sources, giving you a more complete picture when it comes time to examine the data. Data marts, on the other hand, are designed to process and organize data from various sources, whereas data warehouses are designed to process and organize data from numerous sources. The outcome of data warehouses is what is known as BI 2.0 or what Gartner refers to as the Enterprise App Store - an app store for business intelligence tools that can process big chunks of data coming from multiple sources, both internal and external. 

The enterprise app store allows you to create apps specific to your own needs by combining what-ifs with what exists now, what used to exist but no longer does, and what might be implemented in the future - all in one place where you can study what it means for businesses growing more dependent on information sharing across organizational boundaries. It helps in knowing what is the ultimate outcome of a data warehouse.

3. Stability: Businesses need stability when they are implementing a data warehouse because it can be costly and time-consuming. Implementation of a data warehouse takes a lot of planning, expertise, and useful investment. Houses are also more reliable data sources that may be used to analyze data at both a high and granular level. This provides you the freedom to examine data in detail and run queries rapidly. Because it comes from numerous sources, a data warehouse will have high-quality data that is more consistent and reliable.

Final Word

Data can be gathered from a variety of sources, including relational databases and transactional systems, in a data warehouse. Analysts will use business intelligence tools to evaluate, data mine, create visualizations, and report on the data. Data is becoming increasingly important for organizations to stay competitive as it evolves. A data warehouse's ultimate goal is to extract insights, track performance, and improve decision-making. Analysts have all the tools they need to make the best judgments by employing reports, dashboards, and visualizations. Hence, it becomes easier to know what is the ultimate outcome of a data warehouse!

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Tara McWhite

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