Data analytics plays a vital role in the success of any organization today. It enables businesses to analyze and gain priceless, actionable insights from historical data by providing each department with multiple angles of information.
However, without a proper way of filtering through the important data and storing it in an easily retrievable manner, everything risks falling apart. It takes longer to run queries, and the forecasting models are hardly ever as reliable in identifying crucial trends in the business.
This is where data warehousing comes into play. When done right, it helps your institution keep up with the ever-increasing volumes of data by automating the interoperability between your various data sources in a cost-effective, time-saving fashion.
What is a Data Warehouse?
In a nutshell, a data warehouse is an infrastructure that facilitates access to data that is selectively shaped according to the organization’s unique rules and goals. The data often comes from varied systems within the business – like CRM, sales stack, and marketing stack.
It is usually stored such that it enables interested parties/ departments, including data scientists, to run queries and receive actionable insights.
Benefits: Why Does Your Business Need a Data Warehouse?
When evaluating your need for DWH, consider the following ways in which you stand to benefit:
Organize Your Large Volumes of Data
More data equals more problems if you don’t have a way of organizing it. Consider a bank. There’s the core department for handling day-to-day processes, such as transactions and loans. Then, there are the loan operations, private banking, lending, and credit/ market risk departments, among others. Also, there are technologies in place for recording all the data generated from these processes. Seems like too much? Well, this is why most businesses consider seeking data warehouse consulting services. You can also try some high-performance solutions like Azure Cloud to store your huge data files. Learn more about Azure Migration Services and Azure Desktop as a Service.
Any business that has embraced technology generates so much data every day. If all of it were organized and stored in a single system, it would be a lot easier to run queries and retrieve information. On the contrary, trying to understand the big, complex data across different systems is time-consuming. Yes, it can be done, but there’ll be a lot of avoidable heavy lifting.
Measure the Level of Success and Determine Causes of Failure
Understanding which of your strategies are succeeding and areas that are doing well is crucial to the success of your business -just as much as being able to account for failures.
Data warehousing provides you with neat, up-to-date historical data and a way of getting to it without having to go through many systems. It also helps you analyze trends within your organization in real-time and identify areas that need improvement.
Simplified KYC (Know Your Customer)
One of the primary reasons for collecting data is to leverage it and gain a competitive edge. This involves understanding your customers. You have to know not only what they want today but also what they’ll need in the future, even when they’re not aware yet.
With the right data set and systems in place for making sense of it, you can get a 360-degree view of all your customer bases. You can make informed decisions based on 100% accurate data, allowing you to acquire new customers faster and retain your old ones more.
Secure Your Data
The arrangement of your data in the warehouse will be purposefully categorical, providing many levels of access to various departments. Every department will only be able to query the data that concerns them. This is especially important when you consider that all the information is being stored in a single location.
Steps to Follow When Designing a Data Warehouse
Define Your Objectives
Since your data warehouse affects almost every aspect of your business, you’ll have to get all the company heads together and determine what defines your success. Remember, you will need to drive insights from varied data sets to use in decision-making.
For instance, the sales data has to relate to the marketing data; else, most of your query results will be inaccurate.
Besides, all the departments have to understand the goals of designing the data warehouse and how they stand to benefit. That way, you can ensure that the department goals are aligned with the overall project.
The project also has to concur with your future needs. Besides, there should be a plan for recovery in case of a disaster. Each security layer has to be taken under consideration – like detection and mitigation of threats, control of identities, risk reduction, and monitoring of activities in and out of the warehouse.
Constructing the Data Model
Data modeling involves visualizing the distribution of data within your warehouse. You can look at it as a blueprint of your warehouse, illustrating all the entities, their properties, and where they go in the warehouse.
Here, you want to create a diagram showing the entities, their relationships, and objectives. You also want to identify the dimensions relating to the facts, pinpoint your KPIs (key performance indicators) for every business process under consideration and determine what format the facts will take.
Be sure to categorize each department’s data in the right data marts – areas within the data warehouse where you store data related to specific functions within the organization. For instance, your legal team will be accessing the data warehouse in different ways from the sales team, so each of them will need unique data sets.
Data warehousing structures take time to construct. However, you’ll have a very efficient data warehouse and lesser restructuring to deal with in the future when you pay adequate attention to the modeling phase.
Choosing Your ETL (Extract, Transfer, Load) Solution
Your organization probably already has data storage solutions in place. ETL is the process used to retrieve data from the existing systems and load it into the warehouse.
The ETL process has to be strong and efficient for you to end up with a functional warehouse that will provide value to every layer of the business.
Building/ Purchasing a Front-End Reporting Tool
You’ll need a reporting tool for your data warehouse that will allow users to visualize, comprehend and apply the insights offered by data queries.
This can range from Microsoft office’s Excel Pivot Table Service, purchasing an OLAP (online analytical processing) tool to custom building your own. Using a third-party tool is more economical while building your own provides opportunities for more tailored solutions.
Optimizing Your Queries
The process of optimizing your queries has to be entirely customized to your unique needs. Third-party online services often don’t suffice here. However, there are a few rules that cut across. For one, you want to create mirrored resources in your production environments so that your server does no hang when you transfer projects between environments.
On-Premise vs. Cloud Data Warehousing
Cloud data warehousing is often more attractive to most businesses because of reduced costs (not that you can call it cheap). But On-Premise warehousing, too, has its strengths.
On-Premise Data Warehousing Strengths
- You have complete control of the stack
- Easy compliance with government and other regulatory authorities.
- Speed and performance are unaffected by external factors.
- Increased security because you don’t share your system with many other parties.
Cloud Data Warehousing Strengths
- On-demand scalability so long as your vendor provides the required services
- Bundled capabilities. For example, analytics and IAM.
Data warehousing is a much more comprehensive process that requires high levels of expertise to design. Click https://diceus.com/industry/banking/data-warehouse-development-for-integrated-banking-data/ for more on DWH services.