Data is not just any simple random collection of information anymore, rather, an influential insight that can convert into a monetary form if used properly. As a result of such an influence, many corporations have been managing data warehouses powered by DW-a-a-S (Data Warehouse As a Service) to keep important information coming from multiple sources in one common region termed as a data warehouse. These relevant chunks of information are refined and processed for better use and application. As data delivers value to the organization through data visualization, it significantly influences in;
But the role further transcends when data visualization fuses with data warehousing to provide real-time leverage and value to the enterprises. But before we delve deeper and explain the correlation between data warehousing and the data visualization process, the readers must first know these terms.
Table of Contents
Data warehousing is a specific process that collects data from multiple sources and stores it in one place for use. In the data warehousing process, cleaning, integration, and consolidation of data all happen at one single point.
On the other hand, data visualization is a technique representing data in a visual form for a better understanding of underlying data. The role of visualization is essential to clearly convey what the data means and how it can significantly influence decision-making through such representation.
The older data-driven models included decision support applications that worked with transactional databases rather than data warehouses. It is similar to accessing a data lake but without the benefits of using big data. The lack of a data warehouse led to certain challenges like:
Data warehouses arrived as a solution to the above challenges by creating a vast centralized database with historical and real-time information collected from several sources. From managing transactions to organizing and understanding data, data warehouses allow enterprises to use data efficiently irrespective of the nature and volume of the business.
Data warehouses have become an integral part of data pipelines and business intelligence systems. Business intelligence tools are connected to data warehouses to run analytics and generate data visualization reports in real-time.
Data warehouses approach data using a process called OLAP (Online Analytical Processing). OLAP is used by enterprises to run complex queries for day-to-day operations. The retail, sales, and financial systems are some examples of OLAP.
OLAP requires data from a centralized database. Data has to be ETL (Extract, Transform, Load) processed before it can be used for OLAP. Data warehouses provide the necessary infrastructure for both purposes.
The combination of OLAP and data warehousing makes it easy to run business intelligence analytics and derive actionable insights. The insights are presented as data visualization reports using BI tools. These reports are used by employees and management to make faster and better decisions.
Business intelligence is important for analyzing and influencing the stored data in a much more refined manner for better insights. When data is stored in a proper & sequential format, it speeds up the decision-making process. The business intelligence tools showcase important information from the data and portray the real problems which significantly slow down the business. Such information is stored in the data archives or warehouses and DWaaS providers help in the meaning extraction of such data and provide a true shape to it for information gathering and analysis.
When data warehouse fuses with Business Intelligence, better availability of historical data, data analysis from heterogeneous sources, reporting of queries from multiple sources, and availability of data in the required format happen. But everything remains incomplete until and unless, data visualization comes into the picture. Data warehousing collects all the data and stores it in one place; whereas, data visualization significantly pinpoints the key areas that need focussed attention.
With the help of data visualization, an additional value is added to the already existing collaborative and consolidated data. Such insight can help in predicting sales, anticipating trends, and even manipulating the prices as per the changing market dynamics. To understand how data warehousing is adding value to the Data Visualization Process, you need to understand the use case that simplified flight analytics and improved the business of the airlines.
Data visualization cannot act on its own until and unless there is a large chunk of processed, cleansed, integrated and consolidated data available from which the trends and patterns can be sorted or meticulously picked. To better explain this, you need to take the example of air flight travels and how DW-a-a-S along with the Data Visualization Process can significantly change the overall scenario of flight delays and other problems that lead to a significant loss in the revenue of the airlines and discomfort for the passengers.
Everyone flies once maybe in their lifetime and the worst experience for such fliers would be delayed or canceled flights. The situation might be bearable for someone who flies occasionally like 3 to 4 times a year. But what if someone is flying maybe 5 to 10 times in a month. The delay might be unbearable for them. Not just for the passengers but even for the airport and the airline company. So, these airlines companies are looking for a smart solution that can anticipate delays in flight through the use of technology.
In this pursuit, representation of data that must be presented in a clear and concise manner will make the difference, hence Data Warehousing or DW-a-a-S looks like an amicable solution for the same. But without bringing data visualization in the process, it will be very hard to pinpoint the key problem areas that need the right approach for an amicable solution. Data warehousing or DW-a-a-S can add value to the Data Visualization Process by working on the following areas;
Let’s take an airline industry problem example to understand the benefits of the fusion of data warehousing and data visualization. Flight Delay has always been one of the biggest problems the airline industry faces since its inception. According to one of the reports about on-time performance in 2019 by cirium in the global network category, the best airline LANTAM has 86.67% of flights reaching within 15 minutes of the scheduled time. One can imagine how airlines down in the order must be performing worst on this most important KPI of the Airline Industry.
Delays happen quite often when airlines have multiple destinations to hit on a single day and they have to battle the weather and other man-made problems. But understanding the cause of the delay help in simplifying the experience of the travelers by preventing all the factors that contribute to the delay. The only way to achieve that should be through data warehousing using the data visualization process.
The data warehousing technique or DW-a-a-S would store all the data from ATC, ticket booking, weather department, National Air System, security delays, carrier delay, and other contributing factors. Using the data visualization technique, the DW-a-a-S service provider will identify trends and patterns that contribute to the delay every time the flight is scheduled for takeoff. With such an approach, the delays can be analyzed based on classification, airport, airline, state, and the days of the month.
These things can significantly help in curbing the delays. When the insights using the data visualization process rummage through the data warehouse, they can find out the airlines that operate the most flights with maximum delays, the state, town, and city that has most time lag in giving the flight clearance to airplanes to fly on their scheduled times, and the airports that witness most man-made or created problems that lead to the delay. Every piece of information lies in the data warehouse, but finding the refined and real-time insight happens best when the data visualization process comes into the picture. With such an approach, millions of dollars can be saved and the efficiency of the airlines and even other sectors can improve when they can find out the problem contributing areas that are slowing down the progress.
Data Warehousing As a Service or DW-a-a-S has been changing the way we look at data and when it fuses with the data visualization process, additional value is added to the data to give it a true shape and figure out the areas that can be improved upon for better efficiency. Many industries are using the power of BI, data warehousing and data visualization together to make their operations efficient and profitable with the right use of data. For more information regarding Data visualization as a service contact us.