How do you easily start modern data reporting?

2024 04 24 · 3 min read

Statista‘s recent report on worldwide data analytics investment reveals that nearly 70% of organisations across various industries struggle with ongoing challenges due to poor data quality. Unreliable data stands out as a leading factor hindering business growth opportunities. 

This blog post explores the challenges linked to data reporting legacy solutions, discusses potential solutions for businesses, and highlights the importance of opting for a proof of concept (POC) of the data warehouse solution before committing to full implementation. 

What are the challenges associated with legacy reporting solutions? 

If your organisation deals with various data sources, has diverse data reporting goals, or spends significant to prepare reports, the processes can be improved. Below, we outline the primary challenges businesses face: 

• Complex data management. A poorly designed solution led to complicated data management processes, requiring lengthy communication for changes; 
• Poor performance. The data warehouse suffered from poor performance due to suboptimal dimensional modelling practices; 
• Limited functionality. The outdated on-premise reporting tool lacked advanced features and had vulnerability issues due to a lack of updates; 
• High maintenance costs. Troubleshooting issues and suboptimal licensing increased maintenance expenses for the client. 
• No self-service. Poor data design restricted users to static reports despite the client’s desire for self-service data reporting; 
• Low flexibility and scalability. Adding data, tools, and functionality was complex with old on-premise technology; 
• Limited traceability. Lack of visibility on data processing resulted in prolonged investigations and frustrations for developers; 
• Manual workload. Business users lacked tools for manual data management, relying on IT or third parties for modifications, leading to miscommunications and delays.

When is data analytics essential?    

Companies aim to calculate their return on investment, but assessing data analytics projects can be challenging due to their potential for future opportunities. However, certain criteria can help evaluate their effectiveness. Let us explore them: 

• Data integration challenges. If your reports draw data from multiple systems, combining them can pose difficulties. Problems like complex data joins, many-to-many relationships, and recursive hierarchy structures can arise, affecting data quality; 
• Manual data updating. Spending over an hour daily updating reports often indicates reliance on manual processes, especially when extracting data from multiple systems. This can lead to duplicated data and potential GDPR issues. Data analytics can drastically reduce manual workloads by up to 95%; 
• Inconsistent KPI values. Divergent interpretations of KPIs across departments can result in conflicting values, leading to poor decision-making and miscommunication. Well-structured KPI architectures and comprehensive documentation can mitigate these issues; 
• Workarounds and maintenance. Relying on workarounds to achieve functionality highlights a lack of proper processes and tools. Over time, these makeshift solutions become unwieldy and difficult to maintain, necessitating a redesign. Implementing robust design, processes, and tools is crucial to avoid such complications; 
• Dependency on third parties. Heavy reliance on third-party providers for reporting, especially when internal systems are involved, can lead to lengthy development cycles and high costs. Data analytics solutions offer self-service tools, enabling employees to create and update reports quickly. 

These issues indicate a potential need for a data analytics solution. Even if you already have an advanced analytics solution, persisting challenges may stem from selecting the wrong tools or failing to adhere to best practices. 

Modern reporting solution for smooth data management 

If you struggle with data-related processes, then modern reporting can be the right choice towards easier data governance. You can consider upgrading to modern reporting tools for streamlined operations and enhanced analytics.  

With a data warehouse modernisation, your organisation can benefit in different ways: 

• Streamlining complex data management; 
• Upgrading outdated reporting tools to modern, scalable solutions; 
• Enhancing flexibility and scalability; 
• Implementing best practices for dimensional modelling; 
• Optimising logging and error-handling processes. 

Another option is to create new data analytics from scratch or upgrade existing solutions. This could involve a modernisation and migration project to transition legacy or End-Of-Life databases to an Azure-native platform. Such a solution comprises key components: 

• Azure Data Factory. This ETL tool transfers data from various systems into a data warehouse; 
• Azure SQL Database. Divided into a Staging Area for temporary data storage and a data warehouse for cleaned, transformed, and enriched data; 
• PowerBI. Contains dimensions, measures, reports, and dashboards. 

Additional components can enhance the reporting solution: 

• PowerApps. Used as a user interface for managing data, such as creating hierarchies, modifying dimension attributes, and addressing data discrepancies; 
• LogicApps. Primarily employed for email notifications, such as job completion or data quality issues, and can integrate and automate other Azure processes. 

Case study: Modern reporting for the US client 

According to Precisely‘s report, only 18% of data analytics experts find the overall data landscape comprehensible, while 26% of business managers face limitations in extending data requirements. Below, we present a quick overview of the implemented data warehouse solution, which notably boosted data processing efficiency, enhanced data insights, and reduced the client’s IT-related expenses.  

• Client. Professional Billing, Inc. (PBI) is a privately owned US company that offers tailored medical billing and technology solutions to healthcare providers, including physicians.  
• Problem. The client’s existing DWH solution was inefficient, requiring substantial resources to maintain Tableau and PostgreSQL on-premises. Additionally, the PBI’s infrastructure at that time was too complex, hindering effective reporting and monitoring while lacking flexibility and scalability.  
• Solution. We provided a Microsoft Azure cloud-based DWH and reporting solution, connecting to the Progress Database using Progress OpenEdge ODBC Driver. Data for reporting was extracted from the Progress Database and stored in the Azure SQL Database. Our team utilised Azure Data Factory for incremental updates to the DWH, leveraging change tracking tables.  
• Created value. The new DWH solution reduced costs by optimising resource usage and streamlining ETL processes. It offered flexibility for resource scaling and faster data access through Power BI. Fully caching and partitioning data in Power BI improved processing, querying, and data quality while minimising redundancy.  

This success story highlights one of our implemented solutions for clients. Click to explore our other case studies now. 

Why is it worth choosing the POC of the data warehouse first? 

A data warehouse solution is a crucial system for businesses, helping them analyse and report structured and semi-structured data from various sources like sales transactions, customer management, and marketing tools. However, before implementing the full solution, organisations can opt for a Proof of Concept (POC) to decide on the potential data warehouse solution. Here are a few benefits of having a POC project: 

• Reduced risk. Choosing a POC for the data warehouse solution means you can see actual results within the month with a low initial investment; 
• No disruption of your systems. Expect no major changes to your current solution when implementing the POC project; 
• Continuous development. After the POC, you can expand the system to include other business reports like production, commercial, purchasing, and personnel. 

With the data warehouse POC implementation, you can get a reliable, cost-effective data warehouse solution on Azure cloud. This helps enhance your data analytics and streamline data reporting – all backed by expert consulting on Azure technologies. The POC implementation of the data warehouse solution includes: 

• Infrastructure setup;  
• ETL configuration for up to 50 tables (raw data);  
• Integration of 3 data sources;  
• Simple data transformations and quality assurance;  
• Full historical data import;  
• PowerBI data model’s creation;  
• Development of 3 PowerBI reports;  
• 1 PowerApps for 1 SQL table manipulation.  

The best thing – you can get the MVP of your data warehouse solution in just three weeks. Then, you can test it out and decide on possible development later. Want to learn more? Contact our data analytics team now and get all the questions answered.