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In today’s world of rising technology, we’re surrounded by data left, right, and center.
But having data alone is not enough to run a business for success.
What businesses need are relevant sources and processes that allow all useful data to be stored, managed, and presented efficiently and effectively.
That’s where data warehousing and data visualization come in. That is, to allow businesses to easily store all useful data, assemble it accordingly, and display it for better and more practical understanding.
Wondering why data is so important that we require unique processes to store and present it?
Well, that is because data-driven decision-making allows businesses to have better customer insights, understand their demands and needs, know the market trends, and draft strategies needed to boost ROI (return on investment).
And data-driven decision-making becomes seamless with the help of data warehousing and data visualization.
Now that we’ve established that they’re necessary to run a business successfully, let’s dive in to understand data warehousing, data visualization and how using them together allows companies to perform better.
But before discussing these two, let’s talk about how to integrate data for effective visualization.
Data integration helps businesses to extract data from multiple sources, such as social media platforms or e-commerce platforms.
It also enables organizations to analyze data better and cleanse it to make it more reliable and beneficial. Best of all is that it enables businesses to combine multiple data to give a holistic overview.
To integrate data more effectively and efficiently, follow these best practices:
🔹 Establish a clear objectives for the project and define the business outcomes you want to achieve.
🔹Identify the data sources you want to integrate and understand their structure, format, and quality.
🔹Use the right data integration tools: choose the right data integration tools that can handle your data volume, velocity, and variety
🔹Minimize the divergence of analytics, automation, and product pipelines
🔹Ensure that your data integration pipelines are secure and comply with data privacy regulations
Just ensure you follow these best practices and your data integration method for data visualization is set for success.
And now it’s time to discuss what is data warehouse and visualization. And how these two can be used together for success.
In simple terms, a data warehouse is a system that collects data from various sources to assemble and store it.
It is a data management system that's designed to enable business intelligence (BI) activities, especially analytics. A data warehouse is also known as the central repository of information that may be analyzed to make more informed decisions.
Data flows into a data warehouse from all sources, such as transactional systems, relational databases, etc., typically on a regular cadence.
Data warehousing helps to:
👉Improve business intelligence and efficiency
👉Save time and enhance decision-making speed
👉Enhance data quality and consistency
👉Generate a high Return on Investment (ROI)
👉Provide competitive advantage
👉Store large amounts of historical data
Statistics reveal that the global data warehousing market size is projected to reach $51.18 billion by 2028. Statistics for data warehouse reveal that 52% of IT managers and executives refer to faster analytics processing as most important for data warehousing.
Moreover, poor data quality is estimated to cost $3.1 trillion dollars to the U.S. economy per year.
Data warehousing makes data visualization much easier and more effective as it’s quite convenient to look for data (to be represented) in one centralized place containing a single source of truth from all sources.
Together, data warehousing and data visualization can help speed up data-driven decision-making in companies and drive them to success.
To better understand how, let’s discuss the role of data visualization and learn how it helps in understanding data better.
On the other hand, data visualization refers to the representation of data through the use of common graphics, such as charts, infographics, and even animations.
The importance of data visualization is that it helps people view, interact with, and understand data. Whether it is simple or complex, visualization brings everyone on the same page.
Moreover, data visualization decreases cognitive load by making data comprehensive and helps in understanding complex information much more easily and effectively. Also, you’d be surprised to know that 90% of information transmitted to our brains is visual.
Some common types of data visualizations are:
️➡️Table:
Table: Displays a table in the form of rows and columns. This can be easily created in a word document or Excel spreadsheet.
➡️Geospatial visualization:
Data is shown in the form of a map with shapes and colors to illustrate the relationship between specific locations. Such as a heat map or choropleth.
➡️Dashboard:
Data and visualizations are displayed, usually for business purposes.
➡️Infographics:
Both visuals and words are used to represent and demonstrate data.
➡️Bar charts:
A type of chart or graph that presents categorical data that uses rectangular bars with heights proportional to the values they represent.
➡️Pie chart:
A circular or round statistical graphic that's divided into segments to illustrate numerical proportions.
However, it is important to note that one must choose a kind of data visualization technique that aligns best with the information. That’s because some visualization techniques suit a specific type of information better than others.
For instance, if you’re a clothing brand and want to detect geographic locations where your brand performed the best, then a heat map will be ideal. Contrarily, if you’re someone from the SaaS industry and want to analyze the trends and fluctuations in your content, then a line graph is ideal to highlight the peaks and downfalls.
To pick the ideal visualization technique, you must understand the amount of variables that you want to analyze and the purpose for analyzing that data.
If you want to take your data visualization game a step ahead, you can opt for operational data visualization.
Operational data visualization uses data visualization techniques to monitor and analyze operational data in real-time. Basically, organizations use it to track key performance indicators (KPIs) and other metrics that are critical to the organization’s success.
Operational data visualization allows businesses to quickly identify trends, patterns, and anomalies.
Data visualization tools can be used to monitor everything from website traffic to manufacturing processes to financial transactions.
You can leverage renowned operational data visualization tools to get real-time insight into data for more apt data-driven decision-making.
Got familiar with the terminologies? Let’s hop onto their mutual benefits.
Data visualization can not operate on its own. To display and demonstrate vast amounts of useful data and information, you need a system that collects, aligns and segregates information or data according to its nature.
Simply said, data visualization needs data warehousing for better information sharing and to enhance business operations through data-driven decision-making.
When used together, data warehousing and data visualization enable organizations to:
In a nutshell, together they enable organizations to identify patterns, get better customer insight and improve services.
Time to discuss some real life examples of how data warehouse resulted in success.
Our words will start to make even more sense to you if we tell you that they’re backed by the world’s leading brands.
You guessed it right; we’re about to unveil to you how some of the world's best-performing branding uses data warehouses and why.
Amazon is an e-commerce platform and a name that needs no introduction.
To store its large amounts of data, Amazon requires a data warehouse. This data warehouse is needed to collect and analyze data related to customer behavior, purchase history, etc key metrics such as trends.
They did not have any analytics dashboard to monitor the health of the business in terms of parcel deliveries, cash collected, and rider and vendor performance until Data Pilot offered them an ETL pipeline and Data Warehousing.
This enables them to fetch data from their operational data store.
As a result, Data Pilot’s data warehousing and visualization services allowed them to have real-time visibility of operational efficiency for the business.
Together data warehousing and data visualization can set your business for success.
If you want these processes to be seamless and efficient for your business, leverage Data Pilot’s data visualization and data warehousing services to collect, store, manage, and present data.
Written by: Rida Ali Khan
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