Data Warehousing Archives - Page 2 of 2

Anamika Sharma
October 25, 2021
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8 Insanely Useful Ways How Data Warehouse Can Improve Your Business Reporting

These days, multiple startups and companies make use of different software applications to run their business finance and reporting effectively and efficiently. From buzzword-heavy projects to advanced analytics, companies dive hard to get ahead of their competitors. But they often forget to realize the value data can bring to their organization, in terms of effective storage and strategic approach. Data warehousing is a strong foundation for Business Intelligence programs. A warehouse works as the central location and fastens the lifelong storage space for different data sources, which can be further used for integral analysis, reporting, justification, data mining, and more. This article takes you through a step-by-step guide on how a data warehouse can improve your business performance. What Is a Data Warehouse? In simple words, a data warehouse collects and stores data, very similar to how we save our documents and photos on the cloud. Having a location to store or keep data makes it extremely easy to use for all, providing insights and reporting on a large scale. The database collects constructed data from the entire organization, pulls it together from different sources, and organizes and aggregates data for effective comparison and analytics. Data Warehouse maintains a strict processing ETL (Extract, Transform, Load), a process to load data in batches and transfer it into an appropriate structure. Ways How Data Warehouse Improves Business Reporting Organizations use tons of different software applications, for example, ERP, CRM, and Finance frameworks to maintain their business smoothly. These applications produce information that, whenever accessed, provides important understanding into business execution. However, accessing information and reporting from frequently complex frameworks, like Microsoft Dynamics, Sage, Salesforce, and SAP Business One, can be troublesome and time-consuming. At times, to such an extent that it isn’t even sought in any way. Here are the key points on how a data warehouse can improve financial and operational reporting. 1. Report From Structured Data The characteristics of a good data warehouse signify that an organization can store its data in a constructed format, a structure that can change itself in different formats, especially for reporting and analytics. Prime data warehouse gears are known for their ELT performance tuning, that is Extract, Load and Transform processes. The job of ELT data warehouses is taking or extracting the data from a particular source (extract), turning or transforming it into a format that is effective (transform), and then saving or adding it in the warehouse (load). ETL processing is also considered as it uses metadata from the primary transactional database. Metadata is what communicates to a particular person working on data about what the data is regarding, making it easier to locate and understand. Acknowledging data and reporting it becomes much more comforting when it has been transformed in a defined structure and language, using metadata. 2. Report From More Than One Source at the Same Time Businesses these days do not rely on one application in order to solve their numerous objectives. While one data source collects information on its own, like ERP or CRM, other lines of business applications like Excel, CSV, etc., are created by users based on specific formats of exchanging information. Utilizing the ETL process to convert different data sources, that are structured or framed in discrete ways, into one familiar constructed format allows to draft reports from multiple data sources. For example, now it is possible to create one unified report that includes marketing analytics to online sales records. 3. Enables Historical Reporting and Trend Analysis Comparing historical data with the latest trends that change over tweets is impossible to keep up with. While a data warehouse stores historic data it becomes completely effortless to carry pace with trend reporting. With data warehouses businesses can step up their analytics and reporting game with an all-time overview as they are now free from, only a current view option, making it easier to show how the data has evolved over time. 4. Saves Time & Reduces Errors ETL data orchestration makes it an easy job to draft or produce business reports swiftly. With manual tasks being erased from different steps, reports produced with data warehouses present almost zero human errors. Moreover, they save time in finding and accessing data from different systems. With a data warehouse, businesses can update and refresh the data from all business systems regularly leading to scheduled and improvised reports with accurate numbers. 5. Enhanced Business Intelligence Data warehouse provides access to different information from multiple sources on one particular platform, managers, executives, and decision-makers no longer need to depend on limited data or their intuition for making business decisions. Also, data warehouses can be easily applied to various business processes, from financial management to inventory organizing and market segmentation. 6. Generates Return on Investment Data is the new diamond, referring to the value of data in today’s world. Accurate data can lead businesses to significant revenue gains and building quality data in the most structured format with a data warehouse can lead to better work decisions. Turning these data-led decisions to create strategies can further result in a higher return on investments across different sectors of business strengthening the organization. In simple words, data warehousing is an investment rather than a closing cost on maintenance. 7. Data Security Numerous advances have enhanced the security benefits in the data warehouse. With creating and storing data from different wide sources for business growth, maintaining its safe security was one great concern. Advance improvements in techniques that block malicious SQL code and encrypted columns have led to improved security in maintaining confidential data. 8. Higher Query Performance & Insight Continuous business intelligence queries have become a routine part of businesses in today’s world, putting indefinite strain on analytics foundations from databases to data marts. A good data warehouse can efficiently manage queries eliminating several pressures from the system. How Data Warehouse Benefits Business To begin with, data mining is one principal factor for most modern businesses today. Constructive data not only provides effective planning

Kavika Roy
July 22, 2021
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How to Choose the Right Data Warehouse Storage?

Do you know that 2.5 quintillion bytes of data are generated each day? It has been found that less than 0.5% of this data is used and the rest of it is just there, scattered around in enterprises. Even with more and more enterprises adopting data-driven technology, not all of them can make most of the data they have. A primary reason for this is the lack of proper data storage arrangements. Huge volumes of data have to be stored, cleaned, processed and analyzed to derive insights that help SMEs make correct decisions. But where and how should you store such vast amounts of data? Ordinary storage systems are no longer effective. That’s where data warehousing has begun. It is hardly a new concept but is gaining more popularity as enterprises are moving towards streamlined business systems. Read further to learn how to choose a data warehouse storage that would be apt for your business requirements. What is Data Warehouse? Simply put, a data warehouse is a place to store historical and real-time data, which is processed and analyzed to help the sales, marketing, customer service teams, and other departments make better decisions. The data warehouse is not the same as an operational database. It is more expansive and is not updated as frequently as the operational database. A data warehouse provides a long-range view of data from the past and present, and hence the analytics run on this data delivers more insights. It can be either an in-house storage system or a cloud storage system. So how do we pick the right data warehouse for the business? We’ll evaluate all the necessary factors in this post. But before we see more about these factors, let us read a little more about data warehouses. Reasons to Choose Data Warehouse What makes a data warehouse a necessary service for today’s enterprises? How does data warehousing help streamline business operations? How is Data Warehouse Used Within an Organization? How to Choose a Data Warehouse (4 Steps) Investing in a data warehouse doesn’t directly guarantee results unless you choose the right data warehouse for your business requirements. Whether it is choosing between the types of data warehouses or the service providers, you will first need to understand the business requirements. Hiring offshore data warehousing services from data analytics companies will help you get a complete picture of how to plan, adapt, and implement data warehousing in your organization. 1. Business Systems The first step is to understand your business systems. If you have a specific data administrator, you will need to choose a data warehouse that is compatible and can be integrated with it. Read the use cases shared by other companies. Ask the consulting agencies to analyze your business system and suggest the best suitable data warehouse. 2. Technical Specifications Data warehouses are usually designed to suit the varying needs of different SMEs across industries. However, you still need to ensure that the data retrieval speed, data storage speed, and flexibility you require can be provided in a data warehouse. 3. Billing Structure and Resources This point is important when you opt for cloud data warehousing. Each cloud provider follows a different billing structure. The cost of investment in both the short and long terms must be considered. 4. Security Specifications While all data warehouses promise data security, the actual security levels and encryption methods depend on the individual service providers. Does what they offer to match your security requirements? Evaluation Criteria Once you are fully aware of your business systems and what you need from the data warehouse, it’s time to consider the different factors that help you choose the right data warehouse for your enterprise. 1. Cloud vs. On-Premises We have been talking cloud data warehouse for a while now. It has been more popular in recent times when compared to on-premises data warehousing. However, that doesn’t mean cloud services are suitable for every business. For example, if majority of your data is stored in on-premises systems that are not fully compatible with cloud platforms, you will find it easier to invest in an in-house data warehouse. Of course, you can still migrate the entire business system to the cloud and upgrade your IT infrastructure. Companies like Oracle, Microsoft, and IBM offer on-premises data warehousing services. Microsoft has both on-premises and cloud data warehouses. 2. Type of Data What type of data do you plan to store in the data warehouse? Will it be structured or unstructured? Based on the type of data, you can choose between a relational database and a non-relational database. A relational database is suitable for structured data arranged neatly in the rows and columns of a spreadsheet. A non-relational database is ideal for large semi-structured data. Semi-structured data consists of emails, social media posts, demographic and geographical data, audios, videos, etc. What if you have unstructured data? In that case, a data lake might be an effective choice as it has been designed for the same. A data lake is a relatively new concept that promises to offer much more than a data warehouse. An in-depth comparison between a data warehouse and a data lake will give you a better idea about which one is the best for your organization. 3. Cost and Time Factors It can be quite a task to compare the costs of data warehousing services offered by different companies. The calculations are unique to each service provider, and unless you make a detailed comparison of what they offer and what they don’t, it can be hard to decide just by looking at the numbers. Remember that the cost here should also include the cost of implementation. If you hire data analytics companies to assist, you will need to pay them as well. Generally speaking, the cost of data warehousing depends on the storage, size of the warehouse, the resources required to run and maintain it, and the number of queries you run. If more than one team will access the

Yashica Chopra
June 29, 2021
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Data Lake vs Data Warehouse: Which is Best For You?

Data is a salient factor for every business. While it has always been a necessity, nothing in the past compares to the need for big data we see today. No matter if it is a startup or a multinational enterprise, data from the past and present are collected, processed, analyzed, and presented to help make better decisions. Business intelligence and data analytics are an imperative part of many enterprises now. But where does all this data go? It sure needs to be stored somewhere secure, private, and easy to access, right? Many of you might have heard of the terms data lake and data warehouse. These are data storage architectures that allow you to store a huge amount of data in one place. While their main purpose is the same, the two have nothing much in common. Do you know that 95% of businesses face a problem due to unstructured data? However, several SMEs and organizations tend to get confused between a data lake vs data warehouse. And without knowing what they are, there’s no way an enterprise can choose the right one for their requirements. What is a Data Warehouse? A data warehouse is a depository that stores data in one place before it is analyzed and presented using various BI tools. It is one of the first things you need to work on when revamping the business processes. All business intelligence applications require a data warehouse to deliver meaningful insights. The data warehouse combines components and technologies where raw data is structured and processed to derive information. A data warehouse is more of a traditional data storage system tried and tested by many businesses. Does that mean it’s the best, or does it mean it’s an older version and not as useful? It’s neither. The data warehouse has its advantages and disadvantages. Advantages: Faster Data Retrieval The role of data warehouse in business intelligence is a lot more intricate than you would expect. Whether you want to retrieve data in less time or find a crucial piece of information without searching all over the enterprise, a data warehouse offers a quick and effective solution. Easy Integration The data warehouse can be integrated with numerous other systems so that it becomes easy to translate data and present it in an understandable format. If you want to know more about your customers, all you need to do is connect the data warehouse to your CRM system. Great Performance DWs usually have schema-on-write, SQL servers understand how the system works. That makes it simpler for the data warehouse to deliver good performance whenever its need arises. Identification and Correction of Errors DWs ensure that the data stored in them is not incorrect. It shows the errors that need to be fixed, the duplicates that have to be removed, etc., before proceeding to the next step. However, there is a difference between data warehousing and business intelligence. A data warehouse is not a business intelligence tool. DW deals with data acquisition, data cleansing, management, metadata, data transformation, backup, and more. Proven Storage Solution The data warehouse has been here long enough to easily find resources and tools to use with it. While it can be a little challenging to work with the latest functionalities, DW is a reliable and proven storage option for enterprises. Flexibility Third-party consulting companies offer Data warehousing services to help you build, manage, and upgrade the data warehouse in your enterprise. The advantage of DW is that it can be housed on-premises or can be stored and accessed from the cloud platforms. That said, DW has its share of disadvantages that makes enterprises consider data lakes. Let’s check the cons of data warehousing before reading about data lakes. Disadvantages: Time Taking Process Even though DWs are used to simplify the business processes, it might take a little more time to manually feed raw data to the data warehouse. That is something many enterprises are wary of. Limited Use of Data The confidential nature of data might result in restricted access to the data warehouse. And that can directly translate to limited use of data. Data warehousing might be a little less effective if only certain employees can access data. High Costs of Maintenance Data warehouse delivers its best when it’s upgraded to the latest version. While the process isn’t hard, the cost can be slightly on the higher end. Unless you can invest money to maintain and upgrade the DW, it won’t be as effective. What is a Data Lake? A data lake is a relatively new concept that has gained a lot of attention in recent times. A data lake is different from traditional storage systems as it stores data in its raw format. Of course, it can also hold structured and semi-structured data, including binary data. It is pretty much a single storage location for raw data and transformed data. The data lake architecture is flat, where every element has a label and a corresponding metadata tag for easy identification. The data collected from numerous sources are added in real-time to the DL in its original format. No changes are made to the data at this stage. Advantages: Variety and Volume Data lakes make it an easy job of handling big data, whether it is structured or unstructured. A data lake is schema-on-read, and this lets us read the format only when we read it back out. Fast Processing DLs are easy to update. You don’t require to spend too much time transferring data to the data lakes. It all happens in real-time. Accessibility Any user group can easily find the data they want by looking at the open data copies. Of course, you can control and restrict access to certain groups, but it’s still easy to get hold of what one wants without compromising data security. Cost-Effective Storage While data lake is not cheap, it is a cost-effective option when compared to data warehouses. That allows us to store

Vaishali
February 22, 2020
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A Complete Guide To Data Warehousing – What Is Data Warehousing, Its Architecture, Characteristics & More!

With the aid of an in-depth and qualified review, the study extensively analyses the most crucial details of the global data warehousing industry. The study also provides a complete overview of the market based on the factors that are expected to have a substantial and measurable impact over the forecast period on the market’s growth prospects. Specific geographical regions such as North America, Latin America, Asia-Pacific, Africa, and India were evaluated based on their supply base, efficiency, and profit margin. This research report was examined based on various practical case studies from different industry experts and policy-makers. It makes use of various interactive design tools such as tables, maps, diagrams, images, and flowcharts for readers to understand quickly and more comfortably. Global Data Warehousing Market Report contains highly detailed data, including recent trends, market demands, supply, and delivery chain management approaches that will help identify the Global Data Warehousing Customer Industry’s workflow. This Report provides essential and comprehensive statistics for research and development estimates, row inventory forecasts, labor costs, and other funds for investment plans. This sector is enormous enough to build a sustainable enterprise, so this Report lets you recognize opportunities for each area in the global data warehousing market. What is Data Warehousing? Data Warehousing (DW) is a process for collecting and managing data from diverse sources to provide meaningful insights into the business. A Data Warehouse is typically used to connect and analyze heterogeneous sources of business data. The data warehouse is the centerpiece of the BI system built for data analysis and reporting. It is a mixture of technologies and components which helps to use data strategically. Instead of transaction processing, it is the automated collection of a vast amount of information by a company that is configured for demand and review. It’s a process of transforming data into information and making it available for users to make a difference in a timely way. The archive of decision support (Data Warehouse) is managed independently from the operating infrastructure of the organization. The data warehouse, however, is not a product but rather an environment. It is an organizational framework of an information system that provides consumers with knowledge regarding current and historical decision help that is difficult to access or present in the conventional operating data store. Characteristics of data warehousing Here is the list of some of the characteristics of data warehousing: 1. Subject oriented A data warehouse is subject-oriented, as it provides information on a topic rather than the ongoing operations of organizations. Such issues may be inventory, promotion, storage, etc. Never does a data warehouse concentrate on the current processes. Instead, it emphasized modeling and analyzing decision-making data. It also provides a simple and succinct description of the particular subject by excluding details that would not be useful in helping the decision process. 2. Integrated Integration in Data Warehouse means establishing a standard unit of measurement from the different databases for all the similar data. The data must also get stored in a simple and universally acceptable manner within the Data Warehouse. Through combining data from various sources such as a mainframe, relational databases, flat files, etc., a data warehouse is created. It must also keep the naming conventions, format, and coding consistent. Such an application assists in robust data analysis. Consistency must be maintained in naming conventions, measurements of characteristics, specification of encoding, etc. 3. Time-variant Compared to operating systems, the time horizon for the data warehouse is quite extensive. The data collected in a data warehouse is acknowledged over a given period and provides historical information. It contains a temporal element, either explicitly or implicitly. One such location in the record key system where Data Warehouse data shows time variation is. Each primary key contained with the DW should have an element of time either implicitly or explicitly. Just like the day, the month of the week, etc. 4. Non-volatile Also, the data warehouse is non-volatile, meaning that prior data will not be erased when new data are entered into it. Data is read-only, only updated regularly. It also assists in analyzing historical data and in understanding what and when it happened. The transaction process, recovery, and competitiveness control mechanisms are not required. In the Data Warehouse environment, activities such as deleting, updating, and inserting that are performed in an operational application environment are omitted. What are the Basic Elements of Data Warehousing? The following are some of the basic elements of data warehousing that should be considered by the data engineering team. ETL Toolkit with Screens ETL is to extract, transform, and load data to the DW. Quality screens are not always used as they are an additional requirement. But these screens process and validate data and the relationship between different data columns or sets. External Parameters Table Using an external parameters table will make it easy to add/ delete/ modify the parameters without affecting the configuration table in the data warehouse or changing the code. Team Roles and Responsibilities The team includes builders, maintainers, miners, analysts, and others who take care of data cleansing, data integrity, metadata creation, and data transportation. Warehouse administration, loading and refreshing data, information extraction, etc., are some functions performed by the team. Data Connectors The data connectors need to be updated and linked to external data sources. Legacy systems may not work with the latest software. Every connection and integration has to be checked and updated regularly. Architecture Between Environments The development environment, production environment, and testing environment should be in sync and align with each other. Differences in this could lead to defective results and loss of time and money for the enterprise. DDL Repository Having a backup is considered essential, at least during the initial phase. However, it is important to carefully consider the structure of the DDL (Data Definition Language) repository for the long term. Tests Building a test environment in advance will help in running a test, even before the data warehouse is fully functional. This helps catch errors and

Parindsheel Dhillon
February 07, 2020
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Data Warehousing for Business Intelligence: Full Guide

Some would say data’s value is like that of petroleum or water, but in actuality, data is much more precious than water or petroleum because these are depleting or depreciating assets; on the contrary, data will be an appreciating resource continuously adding to its value in the near future. So, if the tech pundits and think tanks from the IT domain say that Big Data is going to get bigger and better with time, they surely have a reason to put up such a bold statement. Those premonitions or rumors that you have heard that data warehousing is going to be dead must kiss the dust as such is not what is going to happen even 30 to 40 years down the line. But just statements don’t make people believe in the larger scheme of things, so you need to read further to know whether DW-a-a-S or Data Warehousing-As-a-Service will see some bright days ahead or it will just settle for no good at all. Future of Data Warehousing for Business Intelligence Data warehousing will turn redundant when the old BI or Business Intelligence techniques cannot easily create valuable queries for a large pool of data, but the pressing question is that is it even possible. Numerous use cases when referred to do not give up a concrete viable solution using the old BI technique. To further demonstrate this claim, we need to look at one of the cases that happened with an employee who joined a Tier 1 Investment Bank in London as a Data Warehouse Architect. His job was to process the Query on a multi-terabyte Oracle Warehouse system querying micro-batch data loading and the end-user performance. But doing this using the old BI systems made life tough. Let’s look at the challenges he faced while using the old BI techniques. Key Challenges Querying Large Chunks of Data Using Old Business Intelligence Dashboard Maximizing Query Performance Data miners or analysts need a solution that can minimize the latency and maximize the query per second. With the old BI systems, the end-user query performance does not perform maximum outcomes. As a result, the analytical query demands have been turning high and unaccounted for. Maximization of Throughput ETL or Extraction of Data must be done in a faster and much more rigorous manner. Such a demand would maximize and utilize the complete potential of the machine. All these things would require high maintenance of the CPU and faster technology solution that can instantly capture queries and deliver efficient query optimization plans. It is quite unlike for an old BI system to perform up to the true potential of the requirement. Hence, the need for a much more scalable and agile data analytics system arises that can instantly resolve this problem. Maximum Utilization of Machine When you have to analyze and process a large chunk of data, it should begin with analyzing and processing 100% of the CPU capacity. The old BI systems do fail to utilize 100% of the machine performance. But the new systems are equipped to utilize the machine at its full potential. Therefore, you tend to get the full volume or true potential of the machine. ETL Process The old BI systems completely overrun the true potential of the machine. When the machines are forced to perform beyond their processing levels, they either give botched or inefficient results or at times, they completely heat up and fail to deliver any results at all. At such times, the need for a fully functional data warehousing architecture is required that can cope with the existing tech infrastructure and deliver the results that are expected of it. How to Overcome These Challenges? When experts and IT think tanks raise questions on the existence and sustainability of DWaaS (Data Warehousing as a Service) or Data Warehousing in the near future, here are a few key arguments to support that it will go strong without any signs of giving up anytime soon. As the Business Intelligence is transcending with an advanced time loop for managing key data analytics, the need for agile and advanced warehousing solutions has been felt more than ever. DWaaS or maintaining a Data warehousing architecture is so essential and it will remain that way 10 to 20 years down the line because; Agility is The Future Trend Agility will be the new language that most enterprises would love to speak in the upcoming feature and DW-a-a-S will empower businesses by helping them take a collaborative approach to problem-solving. With advanced DWaaS solutions, enterprises need not have to maintain separate departments, teams and setups for data mining and analysis. When the new data warehousing architecture will help adopt a new model that helps in cross functioning of different teams to support the continuous evolution and improvement, enterprises can better deal with data extraction in a much more fascinated and smart manner. The Dawn of Cloud Systems The needs of the enterprises will change from MYOS or Maintain-Your-Own-Server to cloud-based movement or shift. Cloud-based DW-a-a-S improve the sources from where the data can be gathered and analyzed for future business intelligence. There will be fewer chances of data duplicity when massive data movement is involved using the DWaaS. These trends completely paint a rosy picture of DWaaS as the game-changer in the near future when enterprises and businesses are in need of the right BI dashboard that can perform multiple business operations. Do We Still Need Data Warehouse? One question many people in the industry ask is where we still need a data warehouse. Is it relevant in today’s world? The short answer is yes. Though data warehouses are becoming older models, and some enterprises are replacing them with data lakes, the data warehouse is still a part of the business intelligence infrastructure. There are many reasons for this: Data warehouses can integrate data from multiple sources. A data lake can store them in one place but not integrate structured, unstructured, and semi-structured data the way a data warehouse

Parindsheel Dhillon
January 24, 2020
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Data Warehousing and Data Visualization for Massive Growth!

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 choosing and 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; 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. What is Data Warehousing or DWaaS and Data Visualization? Data warehousing is a specific process that collects data from multiple sources and stores it in one place for use. Data warehousing services include data cleaning, data integration, and data consolidation. 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. Data Warehousing for Business Intelligence 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: 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. Why use Data Warehouses of OLAP? 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. How Business Intelligence Relies on Data Warehousing & How Data Visualization Adds Value to It? 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 focused attention. With the help of data visualization services, 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 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. Use Case to Demonstrate How Data Warehousing is Adding Value to Data Visualization Process 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 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 airline companies are looking for a smart solution that can anticipate delays in flights 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 into 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; Specifying the Reasons for Flight Delays Let’s take an airline industry problem

Parindsheel Dhillon
January 22, 2020
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Data Warehousing as a Service (DWaaS): Faster Business Decision Making

The role of data is changing significantly with advanced methodologies used for upgrading sales and integrating business intelligence in the organizational schema for better outcomes. But in the absence of proper tools and techniques to manage and organize such a large pool of data, results might appear too far-fetched. So, what is the way out to handle bundles of unstructured data and harness its true potential for catalyzing growth? Data warehousing is the solution that enterprises seek to support in decision making and grabbing potential markets using real-time insights provided by it. Data analytics helps in reporting, analyzing, and creating numerous use cases that revolve around solving your business problems. Today, several industries around the globe are partnering with data warehousing consultants or data warehousing companies to streamline their business processes. What is Data Warehousing & How It is Helping Enterprises Grow Rapidly Through DWaaS Data warehouse is an advanced system designed to help enterprises perform data analysis and reporting. The organization has multiple sources to collect data; therefore, it requires an advanced approach that structures all collected data and harnesses the value from it to speeds up the decision-making process and contribute to the faster growth of the organization. But modernizing the use of such a huge chunk of unstructured data requires an innovative approach. Therefore, enterprises trust DW-a-a-S for this purpose where data usage by integrating the organization’s operational system (ERP, Historian, PI System) can be simplified under one hood termed as data warehouse and refining process can work better to fit in the use cases that directly contributes to the growth and market capitalization. What is the Purpose of Data Warehousing? A data warehouse is a repository of digital data stored by the enterprise. The primary purpose of data warehousing is to allow companies to access the huge amounts of data stored in the centralized database. The data is cleaned, formatted, and analyzed to provide actionable insights. The right data warehouse is used as a data management system where business intelligence and data analytics can be performed to understand patterns in the data and derive actionable insights for decision-making purposes. The data warehouse is used to run queries to find the information the business wants and can seamlessly deal with large datasets, unlike database systems. Data warehousing is not a new concept in the industry. With data becoming increasingly available from several sources, enterprises need to find a way to store this data and use it for analytics. A data warehouse is a comprehensive solution as it can store huge volumes of data in its multi-tier structure. It can be a physical storage unit located on-site or a cloud storage platform accessed through the internet. Depending on the size of your business and the data collected, you can opt for either of the data warehouse architecture: A data warehouse is important to run business intelligence tools without spending too much money on querying. It helps define the data flow within the enterprise and makes it easy to access and share data among the departments and teams. Working of Data Warehousing, Data Lake & Benefit They Can Bring For the Enterprise Data warehouse works like a big data lake of information that is designed to store and preserve a large chunk of information. Business requires real-time insights that support trend analysis by setting up an efficient system that improvises the processing and sorting of details; thus preventing data duplication and creating a better usable data history that yields results. Such an approach or working of data warehouse helps in maintaining a complete data history even if it has been purged from different source transaction systems. When the data gets assembled from a data lake is one place working like dashboards, the resulting outcome is different business applications working efficiently to deliver quality as they have just one data source from where they can extract information and refine the data for different purposes in the organization. Why Enterprises/Corporations Prefer Data Warehousing as a service (DWaaS) for Business Intelligence? Bettering Business Intelligence Decisions are important in business and they can be perfect when fuelled by insights from real-time data collected. While framing the business strategy or setting up a specific operational module as a standard operating procedure (SOP), corporations have to rely on data-driven facts that can help them take concrete decisions. Real-time insights from unstructured or parsed data that establish a better data visualization can increase the efficiency in market segmentation, inventory management, financial management and lastly sales. In this way, the business can grow up to become much more competitive and market-ready to withstand any disruptions occurring at the technology, trends, or consumer behavior. Setting Up High Performance Data Analysis Data warehouse is not just a godown storing information rather a place that expedites the data retrieval process and organizes data engineering and analysis. When you need to make quicker decisions, the presence of a system that can query the stored data in a structured manner and provide real-time feedback simply makes the process swift. The high-performance data analysis helps in understanding trends and adapting as per the change so that better decisions can be made in due time that delivers results for the corporations. Faster Data Access Data warehouses create one single dashboard that stores KPI or Key Performance Indicators. Some of the data indeed are KPIs that influence decision making and when they are available in a flash, it saves a lot of time for the corporation. When one dynamic dashboard works in the organization, the higher management does not have to rely on the IT team for collecting data. When you have access to one single dashboard that performs multiple functions, decision-making becomes faster as you do not have to spend much time on gathering data, rather, you can focus more on the analysis that can influence the outcomes. Quality Enhancement Quality enhancement is necessary for creating a frugal data visualization that has the potential to influence decision-making with long-term objectives. In data warehousing,

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