According to a new study, the compound annual growth rate of global data production and replication will reach 23% by 2025.
Advanced analytics and cost-effective data warehousing are driving the current data explosion. However, gathering this information in one place where it can be quickly verified is challenging.
Teams struggle to get accurate and consistent data from various analytics and ETL tools when data is scattered across different systems in an organization.
Fortunately, companies can collect, organize, and analyze research data with a data warehouse.
The Role of the Data Warehouse
Big data from various sources is consolidated and optimized using data warehouses to enable business-enhancing analytics, decision-making, and competitive advantage.
A data warehouse is not a database in the sense defined here.
While both are relational data systems, a database stores current transactions through electronic transaction processing (OLTP) and provides quick access to transactions related to the current business operation.
On the other hand, data warehouses provide fast and sophisticated search across all data through online analytical processing (OLAP) and store large amounts of historical data.
This article discusses the benefits of a data warehouse and the application scenarios in which this system can be helpful for your organization.
Benefits Of a Data Warehouse
A well-managed data warehouse benefits your organization in many ways. Data warehouses help users analyze the entire data set and extract valuable insights.
1. Synchronism
Usually including the conversion of data from several sources and formats into one standard format, data warehousing helps users to analyze and provide insights on the whole data collection.
More consistent data means that different departments within an organization, such as marketing, sales, and finance, can use the same data source to conduct surveys and reports and get results that match those of other departments.
2 Centralization
Most organizations need to combine data from different subsystems distributed across separate platforms to get helpful business intelligence. Data warehouses solve this problem by consolidating data into a single data warehouse, allowing all data to be available in a centralized location.
3. Onboarding
Onboarding improves end-user access to a wide range of business data.
Business users and decision-makers often have to log into each departmental system and collect data manually or request reports from the IT team to access the data they need. With a data warehouse, users can create reports and queries themselves.
Instead of having to connect to multiple systems, users can access all company data through a single interface. Easy access to data means more time to analyze data and less time to mine it.
4. Capacity Control
The purpose of a data warehouse is to ensure that data is accurate, up-to-date, and available; this is the purpose of control.
By controlling the functions and tasks involved in extracting data from source systems and transferring it to the data warehouse, the data warehouse helps ensure data integrity.
Data warehouse security measures ensure that users have read-only access to data.
5. Data Cleansing
Due to missing, duplicate, or redundant data, integrating data from different systems can lead to inconsistencies. If data is not cleaned or corrected, these errors can appear in search queries and reports and lead to erroneous conclusions.
The data warehouse is cleansed of poor-quality information using a purge mechanism. Records are overwritten, modified, or deleted in this approach to ensure data integrity and consistency once duplicate, corrupted, or erroneous records are discovered.
Data Warehouse Usage Examples
These examples show how you can use a data warehouse in your organization.
1. Effectiveness Of Marketing/Sales Campaigns
Customer relationship management and sales systems are just two of many systems in an organization where marketing data can be siloed. When teams assemble this disparate data into spreadsheets to calculate key performance indicators, it may already be outdated.
A marketing data warehouse creates a centralized source of information that the marketing team can work with. In addition, information from internal and external systems such as CRM systems, advertising channels, and web analytics tools can be combined.
With a data warehouse, everyone in the marketing department can access the same consistent data, allowing them to complete projects faster and more efficiently. Teams get more accurate information and can better track performance metrics such as customer acquisition cost, lead performance, and return on investment (ROI).
Data warehouses enable real-time data processing, helping marketers create campaigns based on the most relevant data to generate more leads and business opportunities.
2. Analyzing Team Performance
Data warehouses allow you to evaluate the performance of teams in an organization. Users can create customized dashboards or reports that show team performance based on specific criteria by viewing team data.
Customer service, sales, and marketing teams can be evaluated based on metrics contained in the data warehouse: usage patterns, customer lifetime value, and acquisition sources, and each of these metrics can be linked together.
In addition, aggregated datasets from other departments can show the teams’ contributions to business goals and overall performance.
3. IoT Data Aggregation
Networked devices such as smartwatches, home appliances, and security systems – Internet of Things (IoT) devices – generate large amounts of data that can be analyzed to improve systems and processes.
This information must be collected and stored in relational formats to enable real-time and historical analysis. It can then be directly searched across millions of events or devices to identify real-time anomalies or predict trends based on past data.
Analyzing IoT data requires an advanced and readily available highly versatile platform. With a data warehouse, this information may be compiled and filtered into fact tables to produce time-trended analyses and other measures.
4. Combining Data from Legacy Systems
Accessing and processing legacy data and information stored in legacy systems or formats can be challenging. Despite technological advances in platforms, architectures, and tools, many organizations rely on mainframe environments and other legacy application systems.
One problem is that, over time, knowledge and business rules have become ingrained in these systems, making it challenging for them to migrate to newer platforms and applications. On the other hand, data stored in legacy systems can be an excellent asset for analytical systems.
Since these systems were designed for specific purposes, they had no plans to analyze the data. Therefore, organizations running their core operations on mainframes or other legacy systems cannot access centralized transactional data for timely, real-time information.
Solving business problems and identifying trends that cannot be detected with more recent data depends on accessing data stored in legacy systems.
5. Analyzing A Massive Data Stream
Massive data stream is the real-time processing of large amounts of data to find useful information and trends. A constant stream of unstructured data is examined before it is stored on disk; if not processed immediately, the value of the data may be diminished.
Processing is done in real-time on a cluster of high-speed servers. Once transmitted, the data cannot be re-analyzed.
Significant data streams are constantly generated from many sources. Data can range from a mobile device or web app registration to player activity in a game, social media information, and online purchases. Analytical uses of processed data include aggregation, filtering, correlation, and sampling.
Big data analytics enables companies to understand business and consumer activity better, including service usage, website clicks, device geolocation, and server activity.
Significant data streams can be aggregated into a data warehouse to produce summary statistics. For example, a delivery company can collect data on delivery events per session to create aggregated statistics.
The above application scenarios demonstrate the numerous benefits of implementing a data warehouse, such as
- easier information sharing
- Increased consistency and quality of data
- Improved BI
- Competitive advantage
- Better decision making
And Finally
Organizations that value data can better cope with changing customer expectations and market conditions. Thus, data warehouses can be of great benefit to a business in terms of collecting data and creating more consistent data that is more easily accessible to users across the organization.
Data warehouses can also be helpful in various business applications such as marketing campaigns, IoT data integration, and big data analytics.
A data warehouse helps simplify complex data requirements. You can save time by optimizing your data by connecting all your data to a centralized data warehouse using modern data warehousing solutions.
