Exploring Advantages and Disadvantages of Data Warehouse

Data warehousing is a powerful tool that allows companies to aggregate and organise data spread inside the businesses in a single database. This integration gives the complete picture so companies can unearth trends, know the pattern and exploit insights to make critical decisions. This is from Peter Drucker’s oh-so-famous saying, ‘What gets measured gets managed’. Organisations can better measure and manage data using a data warehouse, driving more effective strategies. However, data warehouses come with some drawbacks: However, they are costly, lack flexibility concerning custom user needs, and may need to be updated over time. Companies must implement and maintain their data warehouses with the greatest care for data warehouses to help.

Understanding Data Warehouse

An organisational Data Warehouse is an organisational system that stores organisational data from different sources using different formats in a single centre. Data, which is separated into data used daily instead of for analysis, decision-making, and reporting, is separated to ensure there is no risk of data being in conflict. The ETL process involves pulling the data from the other business systems, standardising it, and loading it into our warehouse. However, it should be set up, managed correctly, and prepared so that the data warehouse will support business goals and decision-making.

Also Read: Architecture of Data Warehouse

Data warehouses come in all shapes, from simple, straightforward ones that scale, are complex, and meet particular needs. They can differ based on the business’s shape, size, complexity, and needs. Organisations can use their knowledge of the types of data warehouses to select the best one for their current situation. Here are the main types of data warehouses:

1.     Enterprise Data Warehouse (EDW)

An Enterprise Data Warehouse is a centralised storage, using data from one or more sources within the organisation. It is the main mechanism by which a company’s wealth of data is manipulated to analyse and make decisions. This decision-making process is built to handle substantial quantities of structured information, is designed to support complex queries, and is very important for business intelligence. Usually, these warehouses use dimensional modelling to use data in subject-specific markets to make analysis easier and more accessible for different business departments.

2.     Operational Data Store (ODS)

An operational data store is cleared for use as an operational system that integrates real-time transactional data extracted from multiple operative systems of an organisation. ODSs differ from traditional data warehouses, primarily aimed at historical data for analytical purposes, to timely current and near real-time data for operational reporting and decision-making. These high-speed processing systems provide a consistent, up-to-date view of the organisation’s operational data. It is often an ODS as a staging area for data before it goes to the data warehouse for further analysis.

3.     Data Mart

A Data Mart is a section of a bigger data warehouse covering particular business capacities, divisions, or client gatherings. Small and manageable, a data mart hosts tailored data storage and reporting based on a particular business area or user segment’s unique needs instead of an enterprise-class data warehouse. Mostly, they use star or snowflake schema and whether it is dependent (derived from data in an EDW) or independent (standalone; not dependent on an EDW). Data marts are perfect for companies that want their specific departments or teams to be able to access key data for analysis more rapidly.

Advantages of Data Warehouse

• Simplified Data Access for Business Operations: This isn’t for nothing because data warehouses are widely used for a reason—they help you democratically access important data in an organisation. Centralisation of information from several departments renders it easier for businesses to recover and use data in operations that would otherwise require going through many departments.
• Enhanced Application Functionality: Combined operations over a single data structure can help make business applications run more quickly by putting data warehouses in place. It simplifies processes across systems, especially for customer relationship management, to help businesses interact more timely and effectively with customers.
• Accelerated Decision-Making: With real-time data access, decision-making is quicker since panic option action is often required. For example, data warehouses can produce exception reports that highlight variations between predicted goals and realised outcomes, enabling businesses to recognise habits and make reasonable adjustments.
• Support for End-User Data Needs: End users appreciate that data warehouses give them easy access to various data for running decision support applications, such as trend reports. They can also measure product performance over time, helping businesses keep current on progress and establish reasonable goals.
• Increased Operational Value and Planning Support: Data warehouses better serve operations by making data available and analysable, a boon to long-term planning. Warehouses have great historical analysis and data storage capacity that allows them to follow trends over time, allowing companies to build medium- to long-term strategies.
• Improved Interdepartmental Communication: Centralizing data in data warehouses makes it easier for departments to interact better, enhancing their relations with customers and suppliers. Secondly, they always keep everyone in the loop of great and no great outcomes. Eams can act on insights and adjust strategies if necessary.
• Enhanced Business Productivity and Reduced Costs: They reduce the data burden on production systems and allow for data consolidation for accurate planning and streamlined operations, reducing operational costs. It also means that response times for reporting and research are lowered, making business more efficient.
• Simplified Access to Multiple Data Sources: A Data warehouse allows users to access several data sources through a single interface, reducing the time to lookup and improving user convenience. Data warehouses also allow users to analyse different periods and forecast trends, which supports future decision-making by preserving large volumes of historical data.

Disadvantages of Data Warehouse

• Limited Flexibility for Unstructured Data: Data warehouses are useful for structuring data based on certain ‘questions’ but only for accessing such data, not assorted unformatted data. Because information must fit the warehouse’s schema, data that does not conform to it can be unusable. Even if relevant data is in the warehouse, it might be stored in an unsuitable format. Moreover, unstructured data (which often can lend valuable insights) is sometimes entirely omitted.
• Inflexibility in New Use Cases: A data warehouse’s structured nature makes it rigid and difficult for businesses to experiment with data. Much time tends to be wasted as companies reformat or adjust data to fit the warehouse’s predefined structure.
• High Initial and Maintenance Costs: Data warehouses are expensive to maintain, and a critical problem is they require enormous upfront investment. Maintenance and upgrades to remain current can add up to large operational costs.
• Lags in Real-Time Decision-Making: Data warehouses don’t allow for real-time decisions. An aspect of data warehouses that most modern technologies are improving on, but they still experience lag times that hinder time-specific decisions.
• Overlap with Operational Systems: Because data warehouses are removed from operational systems, businesses must be very careful in deciding what to perform in the warehouse versus what operational systems should do. Poorly defined roles can result in duplicate data storage or missed opportunities, ultimately increasing costs.
• Difficulty Adding New Data Sources: Whenever there is a need to bring new data sources into a data warehouse, we have to work under some constraints. However, this can be time-consuming, introducing an unnecessary complexity for non-technical users.
• Project Scope Expansion: Over time, the scope creeps, and more requirements are added beyond the original goals. Your business may encounter new regulatory requirements or data consistency issues, which you must rectify for warehouse accuracy.
• Data Standardization Challenges: In data warehouses, data must be of the same format; similar data in different sources must be standardised. Sometimes, this stripping process removes valuable data components unique to the source, which can cause the loss of key data.

Examples of Data Warehousing Solutions

• Teradata: Teradata is known for scalability, powerful analytics, and relatively parallel processing.
• Snowflake: A cloud-native data warehousing solution which is flexible, scalable, and capable of using multiple data types & structures. It’s popular for companies of all sizes because it allows independent computing and storage scaling.
• Amazon Redshift: A cloud-based warehouse with high scalability and low cost, Redshift columnar storage and parallel processing make it suitable for businesses with large data sets requiring a bit of budget.
• Google BigQuery: A go-to platform is a big data database that is server maintenance-free and a generically managed, serverless data warehouse with fast and cost-efficient data processing.
• Microsoft Azure: Synapse Analytics combines big data and data warehousing, discussed in a single service, with tools to capture and prepare the data, perform analytics, build business intelligence, and carry out machine learning.
• Oracle Exadata: In some placements of the Oracle Database ecosystem, Exadata is in transactional and analytical workload max capacity or handling of the Oracle Database, and the particular companies which depend more on Oracle business are involved in making a single exadata.
• IBM Db2 Warehouse: High-speed processing and robust analytics, compatible with IBM’s AI tools, make it an option for large corporations.
• Databricks Lakehouse: Designed for big data and advanced analytics, with data lake and warehouse functionality and built-in support for machine learning and collaboration tools.

Data Warehousing Applications

• Social Media: Data warehouses help manage a huge expanse of data from user activities, interactions, and locations, and they are used on platforms such as Facebook, Twitter, and LinkedIn. They take these data patterns to improve the user experience and target ads more effectively.
• Banking and Financial Services: Banks monitor customer transactions, spending, and credit behaviours through their data warehouses. It lets us detect fraud, serve personalisation, and offer what we want.
• Government: The data warehouses contain upstream tax, census, and social security datasets. By centralising these data into the survey’s repository, the detection of tax fraud, tracking demographic changes, and optimising public services are made easier.
• Healthcare: Data warehouses take healthcare providers’ consolidation of patient records and treatment histories. It makes patient care, treatment effectiveness analysis and proactive health strategies more possible.
• Retail and E-commerce: Data warehouses can tell retailers what’s trending or on sale, how much inventory they need, and how demand will fluctuate over time. Consequently, they can tailor marketing, improve inventory handling and improve the shopping experience.
• Telecommunications: Data warehouses are used by telecom companies to analyse call data, usage patterns and demographics to improve network performance, improve customer service and predict customer retention.
• Logistics and Transportation: Data on shipments, fleet operation, and route efficiency are centralised in logistics companies. It helps to optimise the supply chains, diminish hours of delivery and cut costs.

Also Read: Data Warehousing and Data Mining in Detail

Future of Data Warehouse

Emerging trends and changing business demands will shortly make data warehousing more dynamic and adaptable to technology. Here are some key directions in which data warehousing is expected to develop:

• Cloud-Based and Hybrid Models: The shift towards cloud-based and hybrid data warehousing is accelerating. Flexible scaling, reduced infrastructure management, and cost efficiency are available in cloud-based data warehouses, while hybrids can offer a combination of on and in-the-cloud resources for flexibility.
• Real-Time Data Processing: For businesses wanting to make immediate decisions, activities such as these are expected to be done using real-time data analysis. Businesses will soon begin to rely on big data streaming inside data warehouses to process and analyse data as it flows in, creating value in customer personalisation, fraud detection, and operational monitoring.
• Integration with Data Lakes: The ‘lakehouse’ architectures (which merge the best data warehouse features and best data lake features) enable you to store structured and unstructured data. It will allow organisations to have unified analytics across more data types than in the past.

Also Read: Difference Between Data Warehouses and Data Lakes

• Enhanced Machine Learning Capabilities: Future data warehouses will integrate more precisely into AI and machine learning tools, allowing for deeper insights directly from the warehouse. With in-database machine learning, data scientists can write, train and deploy models without moving data into other separate systems.
• Focus on Data Governance and Security: Data warehousing has begun to add governance and compliance features, which will continue to strengthen in response to data privacy and security needs that are becoming more demanding. Data encryption at its advanced level, detailed access control and robust auditing will guarantee data integrity and compliance with regulations.

Challenges in Building a Data Warehouse

The future of data warehousing looks to be more dynamic and flexible as challenges around historical data management and adaptive queries move through current technologies. Here are some key directions in which data warehousing is expected to develop:

• Cloud-Based and Hybrid Models: The journey toward cloud-based and hybrid data warehousing is on a fast track. Data warehouses based in the cloud offer flexible scaling and reduced infrastructure management and are also cheaper. A hybrid model enables us to unite the cloud with the on-premises and the most flexibility.
• Real-Time Data Processing: Now, businesses are racing to make immediate decisions through real-time data analysis. In the future, more and more data we store in data warehouses will be streaming data, and organisations will be able to analyse data as it flows in. This will be useful for customer personalisation, fraud detection, and operational monitoring.
• Integration with Data Lakes: With the successive rise of “lakehouse” architectures, based on synthesising the best data warehouses and data lakes can offer, you can store both structured and unstructured data. That will help organisations provide unified analytics across a larger variety of data.
• Enhanced Machine Learning Capabilities: In future, AI and machine learning tools will become much more integrated with data warehouses, resulting in deeper data insights being provided in the warehouse itself. Data scientists won’t have to move their data to separate systems to create, train and deploy them; instead, in-database machine learning will make it possible.
• Focus on Data Governance and Security: Governance and compliance features for data warehouses will continue improving and are needed in parallel with rapidly growing data privacy and security requirements. The data encryption is advanced, the data access controls are detailed, and the auditing is robust.
• Data Summarization: The raw data from transactional systems must often be bigger to store effectively in a data warehouse. Instead of keeping all the raw data, a common solution is to maintain summary data through aggregation. For example, the warehouse might store total sales by item category or store rather than individual sales transactions. This approach helps reduce storage needs while still supporting effective querying and reporting.

Conclusion

Data warehouses can help companies organise and analyse large amounts of data from various sources. Centralised information means that organisations can act faster by making more informed decisions, be more efficient in their operations, and perform better at strategic growth. However, companies must plan carefully and set up and maintain their data warehouse systems to benefit from these costs. A well-run data warehouse can become a tool for sustained success if used properly. To learn about data and data warehousing professionally, refer to the Accelerator Program in Business Analytics and Data Science With EdX, Aligned with Nasscom and gain expertise.