Organizations involve data from many directions and multiple sources. Also, they could possibly involve structured and unstructured data. So, how do IT companies make use of all the data? How does the data travel through the organization? How does data warehousing help them to stay organized? Here is a quick look on how data flows through the organization from different sources. Check how data integration, data analysis and reporting, and business intelligence. Learn the process of doing data integration, data analysis, reporting, business intelligence. Take a quick look over the data journey.
Data Journey – Part 1
1) Data Integration – Integrating data from multiple operational systems
Data warehousing is the core functionality of every IT organization to organize data and achieve business intelligence. Integrating data from one or more disparate sources helps to structure every data and put them on data warehouse or data marts for further reporting or analysis. The data flows from multiple operational systems. Also, the raw data involves raw information related to sales, marketing, supply chain, enterprise resources, customer information, other external data, spreadsheets, and other flat files.
The first line of data from operational systems and external sources pass through the data staging area. The integration process follows either of the 2 below approaches:
- The standard extract, transform, load (ETL) process (or)
- A variant of ETL – Extract, Load, Transform (ELT) process
ETL vs ELT
The staging area (or) the landing zone hosts the incoming data temporarily for the purpose of integration. It stores the raw data from each disparate data source. The staging area hosts the data from multiple sources.
First, data extraction takes place, followed by data integration. Next, the data validation process starts. This process ensures the correct data format. Then, data cleansing ensures correct (or removes) the inaccurate records. Finally, the data is ready for the transformation or loading after the data cleaning process.
In the case of ETL: The data sets are first integrated by transforming the data from the landing zone. Usually, the transformations include converting the data into a proper storage format (for querying and analysis). Then, data aggregations help in to preparing the data loading process.
The aggregated data loads into an operational data store (ODS) database or a data warehouse or data marts. This process helps to gain business insights. Also, the hierarchical data groups are present in the access layer. And, this is where the users can retrieve the processed data. Also, the stored data is useful for further uses such as data mining, market research, decision support, and online analytical processing (OLAP).
In the case of ELT: A landing zone is used inside the data warehouse itself. So, the data is straightaway loaded into the data warehouse, before any transformations. Next, the data transformations are also performed inside the data warehouse itself to get the target tables of data. In turn, this can be used for further reporting, analysis, or forecasting.
The Role of Data Integration
Data integration solutions help data structuring and filtering as per the end user requirements. And, a single query engine enables to present the final data once all the data is integrated. It gives a centralized view of data across the organization and the ability to present data anytime. Also, it improves the overall data quality through standardized codes, while fixing bad data. Finally, the perfectly organized data delivers excellent query performance, even after considering complex analytic queries. And, there is no impact to the operational systems.
Data Journey – Part 2
Analysis, Reporting & Visualization
Business intelligence through data is the primary goal of every enterprise. This is the 2nd part of the data journey, where the data is ready for analysis. Here, organizations get the insights to support business decision-making. And, data analysis runs through different data modelling methodologies to analyze the data and discovering useful information. Also, this helps businesses to operate more effectively.
The structured data is ready for analysis after appropriate data collection and data cleansing. Then, the data sets are analyzed to study the data characteristics. Also, it helps to understand the messages in the data. Descriptive statistics methods such as finding average or median, or any other custom logic functions, may be used to understand the data better. Besides, the data analysis may include applying algorithms to the data and identifying relationships among the variables. And, the statistical inference helps data analysts to deduce the properties of the underlying data distribution.
Data visualization is a powerful method to understand the results of a data analysis and communicate them. And, it is a communication tool which is clear and efficient to a wide range of audience. Also, there are many interactive BI solutions which can help weave data into beautiful visualizations. And, the key messages in the data is visible through these visualizations. Besides, it gives a complete story to the data and what has happened or even more. Also, it provides the enterprises actionable intelligence to support business decisions.
Krmac, Evelin. (2011). Intelligent Value Chain Networks: Business Intelligence and Other ICT Tools and Technologies in Supply/Demand Chains. 10.5772/18850.
RTTS (Real-Time Technology Solutions, Inc.).