We have some really excellent best practices in data warehousing, and we carefully study to be sure we find just the right sources. Be very careful with data transformations to make sure that we are not losing any value from the data as we transform it. We make models that are really going to be useful. With those models we really want to have hefty metadata management.
Nowadays there is master data management and other things that help us to document and beef up the semantics around data. I could go on and on. You have heard this before. This is sacrosanct stuff. And you start contradicting these careful steps, there are people who push back and say no, no, no wait, I have built my career on this rather slow moving but high quality process. So you ask, 'how do we speed it up the data delivery process without losing the quality of data, without losing the intelligence of models and so on and so forth' That's one of the really hard parts.
In so much of the work we do in BI or data warehousing, the data modeling has been front-loaded. We have got this planning process, the requirements gathering, and across the board I am seeing a lot of people just pushing real fast to get to some kind of prototype. And once you get to the prototype that's when the iterations kick in. That's when things we associate with agile kick in.
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Some people warn, there is nothing more permanent than a temporary solution. So you have to be careful with prototypes because sometimes people, especially business folks, they go well, it’s close enough, deploy it. So we have to be willing to create a prototype quickly but also understand that a lot of them should die away.
You don’t want to put too much of this front-loaded work into a prototype that’s going to die, you wait until the prototypes have survived, and then that’s when you go back and do all that careful data preparation I talked about and all the really careful accoutrements of fine report design, right.
It's hard for me to have a conversation about BI without bringing up big data analytics recently. But the kind of open-ended discovery analytics we are finding with big data recently is agile inherently. And a lot of it does what I was just talking about. You typically grab a lot of raw source data. You typically have one analyst or a small team of business analysts.
We put together analytic data sets very quickly with little or no concern about preparing the data, and really they are trying to build an early data set that will help given the “Aha” moment or answer some business question. And once they do this, it really looks like poor practices compared to what we usually do in data management.
What should survive from that process, they start doing all the careful stuff to institutionalize it because even if something that’s just freeform is this big data analytics. Even that eventually should have a product of BI that does have all the careful semantics and a really good body of reports to go with it.
It just goes to show us that not only is the business environment dynamic but the technology environment s dynamic too. And it's all the more reason for this sort of collaboration and engagement. The word is not just agile. Maybe it’s just about being faster, and that’s all about engagement. It's about people skills.
Case Study: Modernizing National Railways with Data Warehouse Implementation
National Railways Corporation (NRC) is the primary provider of railway services in the country, managing an extensive network that connects major cities, towns, and rural areas. NRC has been in operation for over a century, providing both passenger and freight services. The corporation is a vital part of the nation's transportation infrastructure, ensuring the mobility of people and goods.
Challenge: NRC faced significant challenges in managing and analyzing vast amounts of data generated from its operations, including ticket sales, train schedules, maintenance records, and customer feedback. The data was scattered across multiple legacy systems, making it difficult to access, analyze, and derive meaningful insights. This fragmentation led to inefficiencies, delays in decision-making, and an inability to respond swiftly to operational issues.
Solution: To address these challenges, NRC decided to implement a data warehouse. The goal was to centralize data from various sources into a unified repository, enabling comprehensive analysis and reporting. After evaluating several solutions, NRC selected a modern data warehouse platform that offered scalability, robust data integration capabilities, and advanced analytics features.
Implementation: The implementation of the data warehouse was executed in several phases:
- Assessment and Planning:
- A thorough assessment was conducted to understand the data landscape, identify critical data sources, and define the key objectives of the data warehouse project. A detailed implementation plan was developed, outlining the project timeline, resource allocation, and milestones.
- Data Integration:
- Data from various legacy systems, including ticketing systems, scheduling platforms, maintenance databases, and customer feedback systems, was extracted, transformed, and loaded (ETL) into the data warehouse. This integration process ensured data consistency and accuracy.
- Data Modeling:
- A comprehensive data model was designed to organize the integrated data in a way that supports efficient querying and analysis. The model included dimensional schemas to facilitate complex analytical queries and reporting.
- Dashboard and Reporting Tools:
- Advanced dashboard and reporting tools were implemented to provide real-time insights into key performance indicators (KPIs) such as train punctuality, ticket sales, maintenance schedules, and customer satisfaction. These tools were tailored to meet the needs of various stakeholders, including operational managers, executives, and analysts.
- Training and Change Management:
- Extensive training sessions were conducted to ensure that employees across different departments were proficient in using the new data warehouse and analytical tools. Change management initiatives were implemented to foster a data-driven culture within the organization.
Results:
- Enhanced Operational Efficiency:
- The data warehouse provided NRC with a unified view of its operations, enabling real-time monitoring of train schedules, maintenance activities, and ticket sales. This visibility led to a 30% improvement in operational efficiency, as issues could be identified and resolved more quickly.
- Improved Decision-Making:
- With access to comprehensive and accurate data, NRC's management was able to make informed decisions based on data-driven insights. This improved decision-making process contributed to a 20% reduction in operational costs and enhanced service reliability.
- Optimized Maintenance Schedules:
- The integration of maintenance data allowed NRC to implement predictive maintenance strategies. By analyzing trends and patterns in equipment performance, NRC could schedule maintenance activities proactively, reducing downtime and extending the lifespan of assets.
- Increased Customer Satisfaction:
- The data warehouse enabled NRC to analyze customer feedback more effectively and identify areas for improvement. This led to targeted initiatives that enhanced the passenger experience, resulting in a 15% increase in customer satisfaction scores.
- Revenue Growth:
- By leveraging data analytics to optimize ticket pricing strategies and identify revenue opportunities, NRC achieved a 10% increase in annual revenue. Additionally, improved service reliability and customer satisfaction contributed to higher ridership and freight business.