Large companies generate a lot of data daily. To facilitate access by other organization departments for analysis, reporting, and decision-making, the data must be kept in a shared capacity.
In a nutshell, data warehousing is combining data from numerous sources into a single repository. As a result, a data warehouse is a repository for all of the data that a company creates and collects from various sources.
Let’s take a closer look at how data is measured before moving on to the phases of data warehousing.
According to Statista, the data generated in 2021 will be 79 zettabytes. If you have no idea what a zettabyte is, the figure is meaningless to you.
A trillion gigabytes is equal to a zettabyte. You’re probably familiar with gigabytes since your phone’s storage capacity is measured in them. An iPhone, for example, may contain 64GB of storage. A gigabyte is 1,000,000,000 bytes.
We’re creating a lot of data globally. Therefore the ‘big data analytics industry is developing at the same pace as data generation and storage, necessitating data warehousing..
The Data Warehousing Process In Stages
Data warehousing is divided into five phases; the first is the definition of business objectives and how they are assessed.
Defining Your Business Goals
Every organization has goals, and the first step in the data warehousing process is to figure out what those goals are. Every firm’s overall goal is to produce a profit, but the business goals that lead to that goal must be more specific.
Aside from that, the company should have an overall aim, such as providing the greatest communications services in the sector. Every department should have goals that support the overall corporate aim.
These goals must be statistically quantifiable for the data warehousing process to succeed. These goals will also help management decide how to run the company.
Information Gathering and Analysis
The information will be collected and analyzed for the data warehouse in the following step. Each business department will provide a summary and analytical reports, including data on numerous business operations.
Data collection might be difficult, but the process becomes more manageable if the information is automated. However, you may need to manually get data from the necessary parties.
Because a data warehouse is a collection of interconnected data structures, it will be essential to migrate data from multiple departments and business processes. Identifying the essential business operations will aid data analysis, which is necessary for data storage.
Data cleansing may be required at this step to ensure that only essential information is maintained in the data warehouse.
Creating A Data Structure
Data must be organized and stored in a timely and effective way at the warehouse. The most effective method is to create a conceptual data model. This will include determining key performance metrics for each business process and the data storage type.
A data framework ensures that the data in your warehouse is accurate. Although the framework takes a long time to build, it makes the data warehousing process relatively simple. The structure may also be difficult to maintain, necessitating reorganization.
You must discover the data sources and arrange the data transformations after knowing the data you want and having a framework to store it.
In other words, you must convert the data into a consolidated data structure based on the company’s present data storage.
Databases and backups are the key sources of information in any organization. Before transferring the data, make sure it is complete from the source or program it to be complete.
The most important thing is to make sure that any reports provided by the data warehouse match the reports supplied by the data source.
It would be ideal if you could additionally schedule the data transmission to minimize the effect on the data’s consumers.
Implementing the Strategy
The plan’s implementation will be the last step. You must verify that the data fits properly into the planned structure or framework once you transfer the data and effectively track its importation into the data warehouse. If the project is substantial, it will require designing data delivery stages and timelines.
Thanks to data warehousing, managing and using Big Data has never been easier than it is today. Data warehousing is a sophisticated process with many moving pieces and things that may go wrong, even for non-techies. However, if you follow the steps mentioned, your company should have a seamless transition with minimum downtime.
Please keep in mind that the stages will differ depending on the company and data. As a result, each organization must identify what they want from their data warehousing project to evaluate activity accurately. You’ll have all you need for decision-making and deciding the business’s future course if you start with the goal in mind.