A Sample Road-map for Building Your Data Warehouse

Data warehousing, a technique of consolidating all of your organisational data into one place for easier access and better analytics, is every business stakeholder’s dream. However, setting up a data warehouse is a significantly complex task, and even before taking your first steps, you should be utterly sure about the answer to these two questions:

    1. Your organisation’s goals


  1. Your detailed roadmap to building a data warehouse

Either of these questions, if left unanswered, can cost your organisation a lot in the long run. It’s a relatively newer technology, and you’re going to create a lot of scope for errors if you’re not aware of your organisation’s specific needs and requirements. These errors can render your warehouse highly inaccurate. What’s worse is that an erroneous data warehouse is worse than not having data at all and an unplanned strategy might end up doing you more bad than good.
Because there are different approaches to developing data warehouses and each depends on the size and needs of organisations, it’s not possible to create a one-shoe-fits-all plan.
Having said that, let’s try to lay out a sample roadmap that’ll help you develop a robust and efficient data warehouse for your organisation:

Setting up a Data Warehouse

Data Warehouse is extremely helpful when organizing large amounts of data to retrieve and analyse efficiently. For the same reason, extreme care should be taken to ensure that the data is rapidly accessible. One approach to designing the system is by using dimensional modelling – a method that allows large volumes of data to be efficiently and quickly queried and examined. Since most of the data present in data warehouses are historical and stable – in a sense, it doesn’t change frequently, there is hardly a need to employ repetitive backup methods. Instead, once any data is added, the entire warehouse can be backed up at once – instead of backing up routinely.

Data warehousing tools can be broadly classified into four categories:

    • Extraction tools,


    • Table management tools,


    • Query management tools, and


  • Data integrity tools.  

Each of these tools come in extremely handy at different stages of development of the Data Warehouse. Research on your part will help you understand more about these tools, and will allow you to can pick the ones which suit your needs.
Key Concepts of Data Warehousing: An Overview

Now, let’s look at a sample roadmap that’ll help you build a more robust and insightful warehouse for your organisation:

Evaluate your objectives

The first step in setting up your organisation’s data warehouse is to evaluate your goals. We’ve mentioned this earlier, but we can’t stress this enough. Most of the organisations lose out on valuable insights just because they lack a clear picture of their company’s objectives, requirements, and goals. For instance, if you’re a company looking for your first significant breakthrough, you might want to engage your customers in building rapport – so, you’ll need to follow a different approach than an organisation that’s well established and now wants to use the data warehouse for improving their operations. Bringing a data warehouse in-house is a big step for any organisation and should be performed only after some due diligence on your part.

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Analyse current technological systems

By asking your customers and business stakeholders pointed questions, you can gather insights on how your current technical system is performing, the challenges it’s facing, and the improvements possible. Further, they can even find out how suitable their current technology stack is – thereby efficiently deciding whether it is to be kept or replaced. Various department of your organisation can contribute to this by providing reports and feedback.
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Information modelling

An information model is a representation of your organisation’s data. It is conceptual and allows you to form ideas of what business processes need to be interrelated and how to get them linked. The data warehouse will ultimately be a collection of correlating structures, so, it’s important to conceptualise the indicators that need to be connected together and create top performance methods – this is what is known as information modelling. The simplest way to design an efficient information model is by gathering key performance indicators into fact tables, and relating them to various dimensions such as customers, employees, products, and such.

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Designing of the warehouse and tracking the data

Once you’ve gathered insights into your organisation and prepared an efficient information model, now comes the time to move your data into the warehouse and track the performance of the same. During the design phase, it is essential to plan how to link all of the data from different databases so that the information can be interconnected when we’re loading it into our data warehouse tables. The ETL tools can be quite time and money consuming and might require experts to implement successfully. So, it’s important to know the right tools at the right time – and pick the most cost-effective option available to you. A data warehouse consumes a significant amount of storage space, so you need to plan how to archive the data as time goes on. One way to do this is by keeping a threefold granularity data storage system (we’ll talk more about that in a while). However, the problem with granularity is that grain of data will defer over a period. So, you should design your system such that the differing granularity is consistent with a specific data structure.

Implement the plan

Now that you’ve developed your plan and linked the pieces of data together, it’s time to implement your strategy. The implementation of Data Warehouse is a grand move, and there is a viable basis for scheduling the project. The project should be broken down into chunks and should be taken up one piece at a time. It’s recommended to define a phase of completion for each chunk of the task and finally collate all the bits upon completion. With such a systematic and thought-out implementation, your Data Warehouse will perform much more efficiently and provide the much-needed information required during the data analytics phase.

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Your data warehouse is set to stand the tests of time and granularity. It has to remain consistent for long stretches of time and at many levels of granularity. In the design phase of the setup, you can opt for various storage plans that tie into the non-repetitive update. For instance, an IT manager can set up a daily, weekly, or monthly grain storage systems. In the daily grain, the data can be stored in the original format in which it was collected can be kept for 2-3 years, after which it has to be summarised and moved to the weekly grain. Now, the data can remain in the weekly grain structure for the next 3-5 years, after which it will be moved to the monthly grain structure.
Following the above-mentioned roadmap will ensure that you’re on the right track for the long race that’s to come. If you had any queries, feel free to drop them in the comments below.

What is a Data Warehouse?

A data warehouse is a sort of data management system designed to facilitate and assist business intelligence and analytics activities.

Data warehouses allow you to execute logical queries, create reliable forecasting models, and spot important trends across your company. v

How long does it take to build a Data Warehouse?

Time is a common gripe concerning data warehousing and business intelligence in the market. Although the numbers are debatable, let’s stick to the traditional understanding that Data Warehousing often needs a long time to see results.

The time investment required to set up analytics is simply too large. The amount of time taken to build a Data warehouse may vary from 12 to 24 months. But, it’s totally worthwhile, as successful data warehouse projects can completely transform an organization's processes and vision. They have the ability to shed light on issues, lead the way to new prospects, and help employees at all levels better their daily work life.

What are some of the most important features of a Data warehouse?

Some of the basic components of a typical Data Warehouse are:

1. Central Database : The cornerstone of your data warehouse is a database. These were conventional relational databases that could be used on-premise or in the cloud. However, in-memory databases are rapidly gaining popularity as a result of Big Data, the necessity for true, real-time speed, and a substantial fall in the cost of RAM.
2. Data Integration : Various data integration technologies, such as ETL(Extract, Transform, Load), real-time data replication, bulk load processing, data transformation, data quality, etc are used to gather data from source systems and modify it so that it is ready for rapid analytical consumption.
3. Metadata : It details the data sets in your data warehouse's source, usage, values, and other characteristics. There’s business metadata, which gives your data meaning, and technical metadata, which explains how to access data, such as where it’s stored and how it’s organized.
4. Data Warehouse access tools : Users can interact with data in your data warehouse using access tools such as Query and Reporting tools, Application Development tools, Data Mining tools, OLAP tools, etc.

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