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Posted 14th October 2022

Basic Guide to the Enterprise Data Warehouse (EDW) For Your Business Intelligence

Data is hugely important for your business. It can inform operation decisions in every area, from marketing campaigns to new product lines. Without that data, you’d be mostly stumbling forward blindly, hoping that you were making good decisions. A massive amount of data is generated every day, 2.5 quintillion bytes of it (that’s 2.5 followed by 18 zeros!)

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basic guide to the enterprise data warehouse (edw) for your business intelligence.


Basic Guide to the Enterprise Data Warehouse (EDW) For Your Business Intelligence

By Pohan Lin – Senior Web Marketing and Localizations Manager

Data is hugely important for your business. It can inform operation decisions in every area, from marketing campaigns to new product lines. Without that data, you’d be mostly stumbling forward blindly, hoping that you were making good decisions. A massive amount of data is generated every day, 2.5 quintillion bytes of it (that’s 2.5 followed by 18 zeros!)

In fact, Tim Berners-Lee (who invented the world wide web) once said “Data is a precious thing and will last longer than the systems themselves.” That may or may not prove to be true but we continue to generate an increasing amount of data every year. So, where do you keep all that data, ensuring that not only is it secure but that it is easily accessible? 

Without comprehensive data and good analytics, some business decisions would be next to impossible. With the growing importance of personalised marketing, you need to be able to access and understand as much data as possible. 

For many organisations, the answer to that question is an Enterprise Data Warehouse (EDW). Just what is an Enterprise Data Warehouse (EDW)? How does it work and where would it be located? And how can you utilise an EDW to ensure your business intelligence (BI) can help you improve your organisation’s results?

What is an Enterprise Data Warehouse (EDW)? 

You already know the role of a conventional warehouse; storing large amounts of goods in a convenient space you can access when needed. An enterprise data warehouse follows the same concept only you are, of course, storing data rather than goods or materials. Just like a conventional warehouse, having your data well-organised is a crucial factor. You can supplement your data stored in the warehouse with a dummy data generator

Of course, not all data is created equal so you want your enterprise data warehouse to be able to securely store different types of data. You also want to consider important factors such as customer segmentation data, something that can be crucial to marketing strategies. There are three main types of data you may want your enterprise data warehouse to be able to store:

  • Historical data: This can be data that dates back many years. It may not be used regularly but can still be important. 
  • Integrated data: This is data that you may have harvested from different sources. 
  • Granular data: This is highly detailed data that you may utilise in a number of ways. 

Just as there are three types of data to think about, so there are three types of enterprise data warehouse that you can choose from:

  • On-premise enterprise data warehouse: With this type of data warehouse, the servers that store the data are owned (and maintained) by you. 
  • Cloud-based enterprise data warehouse: These are fully online and space on virtual servers is usually rented from a specialist company. 
  • Hybrid enterprise data warehouse: These are a mix of the other two types. In most cases, you will find an organisation using the hybrid model while they are in transition from on-premise to cloud-based. 

As you would expect, there are different processes that govern how you store your data as well as how it can be accessed, analysed, and used. The most common process is called OLAP (online analytical processing) which can handle complex data queries in much the same way as a convolutional neural network diagram can work with images. 

You could almost think of OLAP as being a ‘super librarian’. You send a query to your enterprise data warehouse and OLAP swiftly looks through your ‘shelves’ of data to find the information you need. This can help you access the data you need to make crucial decisions such as seasonal strategies for your ecommerce business

What is business intelligence? 

You could see business intelligence (BI) as the bridge between the data stored in your enterprise data warehouse and the actual decisions and actions taken. It’s an essential resource for your agile team. You use BI tools to access and analyse your stored data and then present findings in the form of reports, graphs, summaries, and so on allowing you to see what actions you can take. 

Without that wonderful data stored in your enterprise data warehouse, you would not be able to use BI tools or processes. As OLAP can handle complex queries, it enables you to make essential operation decisions such as a need to change aspects of a supply chain to make it more efficient and/or to reduce relevant costs. 

What are the advantages of using an enterprise data warehouse (EDW)

You may be wondering why you should switch to using an enterprise data warehouse. The answer is simple; given that the amount of data you collect and use on a daily basis continues to increase, utilising an efficient storage solution that not only ensures security but also aids with your business intelligence work makes perfect sense. 

1. Flexible solution

Not only is the amount of data you use going to increase but there is a good chance that it will become more complex too. Not only that, but the ways in which you use your data will also likely change to meet business needs or to suit new tools or models. An EDW offers the flexibility and scalability to meet any change in the volume of data or the models you use. 

2. Standardising data

You will find that data may be currently stored in different formats depending on the OLTP (online transactional processing) databases or systems you use. This can be problematic when it comes to analysing different data sets as you first need to ensure all the data you need is in the same format. 

When you transfer data to an enterprise data warehouse, it is done with what is known as an ETL (extract, transform, load) pipeline. This means that all the data stored in your EDW is in the same, standard format, making it easier for you to analyse when needed. 

3. Connecting to BI tools

To fully understand the data you store, you will normally use a range of BI tools. Depending on the tool, and the EDW, you want to be able to connect your chosen tool(s) easily to your enterprise data warehouse. This then allows you to set your most important KPIs and track and analyse them in real-time so you can monitor your business’s performance. 

Types of enterprise data warehouse

If you are considering moving your data to an EDW, then your provider may talk about the architecture of the enterprise data warehouse services they offer. Choosing an option very much depends on your business needs and how much data you have (or will have). There are three types of EDW architecture you should be aware of.

  1. One tier 

As you can probably guess, this is the most basic form of enterprise data warehouse available and is best suited to businesses that deal with smaller data sets. It is an easy EDW to set up and its reporting tools are connected directly to the EDW. Its simplicity presents an issue as when your reporting tools have to deal with any query, it has to comb through all the data and this can take time when dealing with larger datasets. 

  1. Two tier

The next step up is two-tier enterprise data warehouse architecture. With this version, there is a Data Mart layer between your EDW and your reporting tools/layer. These data marts operate as smaller databases that will contain information specific to domains. All your data stored in the EDW are separated into various data marts as part of your database management

Unlike the one-tier model, reporting tools connect to the data marts rather than the EDW itself. This means that even complex queries are performed faster than with the one-tier version. The two-tier EDW is thus more suitable for larger datasets and for real-life analyses. 

  1. Three tier 

The most complex EDW architecture, the three-tier enterprise data warehouse has an extra layer (OLAP) between your data mart layers and your reporting layer. That OLAP layer has OLAP ‘cubes’ which store all your data in a multidimensional format meaning that any analysis of your data is even faster than with a two-tier EDW.

The Takeaway

There are two incontrovertible facts when it comes to data. Firstly, your data is essential when it comes to making crucial business decisions. Secondly, the amount of data you collect will continue to grow in both volume and complexity. That means there will be an increasing need for data-related solutions such as Databricks Lakehouse for retail.

Modern businesses collect and use a lot of data. That data guides you in any changes or new strategies you may adopt. You also need to consider that the challenges you face are increasing and that in most cases, the market is becoming more competitive. This means that businesses need to keep abreast of innovative developments and new technology. 

Just as business is becoming more challenging, so technology is providing tools and systems to meet those challenges. From solutions such as a workflow platform from Process Bliss to automated inventory management systems, there is a tool for every area of your work. However, an enterprise data warehouse is much more than a simple tool; it’s a solution that not only lets you store massive amounts of data but also analyse that data when you need to. 

Categories: Business News, News


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