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Management Reporter Jet Reports

Home > Blogs > Management Reporter vs. Jet Reports

Management Reporter vs. Jet Reports

Dec 21, 2023

Global Data 365 is composed of highly skilled professionals who specialize in streamlining the data and automate the reporting process through the utilization of various business intelligence tools.

Management Reporter Jet Reports

Importance of Reporting Tools in Business Management

In today’s dynamic business environment, the significance of robust reporting tools cannot be overstated. Efficient financial reporting software and business intelligence reporting play a pivotal role in business management; aiding organizations to make informed decisions, monitor performance, and strategize for the future. Two prominent players in the reporting tools arena are Management Reporter and Jet Reports.

What Is Management Reporter?

Management reports are essential components of business intelligence, providing a comprehensive overview of an organization’s financial performance. Management Reporter, a powerful financial reporting tool, stands out with its versatile report designer, enabling the creation of dynamic reports tailored to specific needs.

What are Jet Reports?

Jet Reports, on the other hand, is a dynamic business intelligence tool that goes beyond conventional financial reporting. With its user-friendly interface and Jet Dashboard Designer, organizations can leverage its capabilities for comprehensive financial reporting and analysis. Jet Reports is known for its adaptability and ease of use, making it an asset for businesses aiming to enhance their reporting processes.

Management Reporter vs. Jet Reports: Key differences

Management Reporter (MR) is a standalone application from Microsoft that pulls data from Dynamics GP, while Jet Reports is an Excel add-in that pulls data into Excel from Dynamics GP. Jet Reports works with Microsoft Dynamics GP 9 (November 2005 release) and later. Both tools were designed specifically for working with Microsoft Dynamics data and integrate seamlessly with Dynamics GP, but there are several key differences.

  • Building reports – MR reports are built in components one-by-one, starting with defining rows, then defining columns and trees, while Jet Reports are built in whole, not in components, cutting in half or more the time it takes to create a report.
  • Previewing reports – You cannot preview reports in MR, unless you assign a row to an actual report. In Jet Reports, you can easily toggle between design mode and report mode to check and see if the formatting and formulas you used are working as desired.
  • Adding new accounts – When a new account is added in Dynamics GP, it must be manually added to Management Reporter. Due to the way MR is structured, not all data changes in Dynamics GP will be tracked on the change tracker and do not update the data mart, which is the tool that syncs MR with Dynamics GP. At times there might be hard coding involved to change how the data mart pulls data from Dynamics GP. On the other hand, Jet Reports has the ability to refresh data from Dynamics GP at any time and new accounts will show up, without any hard coding or manually checking for new accounts.
  • Data extractions – MR connects directly to your instance of Dynamics GP, however you can only create reports in MR using that data – you cannot pull from any other source. In contrast, Jet Reports allows you to pull data from numerous sources and consolidate it together, rather than just pulling data from Dynamics GP for building reports. Jet Reports can extract any data from Dynamics GP into Excel, and when the data refreshes, so does your pivot table.
  • Working in Excel – When building reports in MR, you can link to an Excel spreadsheet as a reference and pull in data from a spreadsheet. MR users often find themselves switching between MR and Excel when building reports to pull in data from different sources that might have been exported into Excel. With Jet Reports, you’re working directly inside Excel and can pull data from a variety of locations in addition to from Dynamics GP. This creates a smoother workflow and reduces the need to switch between applications to find the necessary data for a report or dashboard. Additionally, with designing Jet Reports inside Excel, you can take advantage of formatting options, copying and pasting, and other functionalities you are used to in Excel.
  • Compatibility – The most recent version of MR reporter is Management Reporter 2012, last updated in 2014, and is compatible with Dynamics AX 2009 and 2012, Dynamics GP 2013 to 2018, and Dynamics SL 2011 and 2015. Jet Reports is regularly updated by insightsoftware, and is compatible with all Microsoft ERPs including Dynamics 365 products, Dynamics NAV, Dynamics GP, Dynamics AX, Dynamics CRM, and Dynamics SL.
  • Migrating to a newer ERP system – Dynamics GP is no longer supported by Microsoft and many companies realize they will eventually need to migrate to a newer ERP such as Dynamics 365 or choose a new ERP altogether. MR is not supported by newer Microsoft products, and as such, none of your reports built inside MR will migrate with you to a new system. On the other hand, all worked completed in Jet Reports carries over into any ERP you choose since it is an independent platform and can pull data from any source; all you would have to do is update the data connectors.

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Conclusion

In conclusion, while both Management Reporter and Jet Reports serve essential roles in the realm of reporting tools, the latter stands out as a superior choice for businesses aiming to elevate their business management with efficient financial reporting and business intelligence capabilities. The decision ultimately hinges on the specific needs and priorities of each organization, but Jet Reports’ user-friendly features and comprehensive functionality position it as a strong contender in the reporting tools landscape.

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data lake vs data warehouse

Home > Blogs > Data Lakes vs. Data Warehouse

Data Lake vs Data Warehouse

Dec 21, 2023

Global Data 365 is composed of highly skilled professionals who specialize in streamlining the data and automate the reporting process through the utilization of various business intelligence tools.

data lake vs data warehouse

With the rise of big data and the explosion of new data sources, traditional data warehousing approaches may not be sufficient to meet the needs of modern data management and analytics, creating confusions between Data lake and Data warehouse. This has led to the development of new approaches, including Data Lake and Data Warehouse. Each approach offers unique benefits and drawbacks, and understanding the differences between them is critical to making informed decisions about data management and analytics.

data lake vs data warehouse

Data Lake

A Data Lake is a centralized repository that allows businesses to store vast amounts of raw, unstructured, or structured data at scale. It provides a flexible storage environment, enabling organizations to ingest diverse data types without the need for upfront structuring. This unrefined data can then be processed and analyzed for valuable insights, making Data Lakes ideal for handling large volumes of real-time and varied data.

Benefits and Use Cases of Data Lake

Data lakes provide scalable and cost-effective storage, accommodating diverse data types such as raw and unstructured data for flexible analysis. With a focus on real-time analytics and advanced capabilities like machine learning, they support innovation in algorithm development. Cost-efficient storage solutions, often leveraging scalable cloud storage, make data lakes economical for managing large datasets.

Use cases range from big data analytics, IoT data management, and ad hoc analysis to long-term data archiving and achieving a 360-degree customer view. In essence, data lakes offer dynamic repositories that empower organizations with flexibility, real-time insights, and comprehensive data management solutions.

Data Warehouse

On the other hand, a Data Warehouse is a structured, organized database optimized for analysis and reporting. It is designed to store structured data from various sources in a format that is easily queryable and supports business intelligence reporting. Data Warehouses are characterized by their schema-on-write approach, requiring data to be structured before entering the system, ensuring a high level of consistency for analytical purposes.

Benefits and Use Cases of Data Warehouse

Data warehouses offer a multitude of benefits, including optimized structured data analysis for improved query performance and efficient reporting. They preserve historical data for time-series analysis and audit trails, enhance business intelligence through data consolidation and dashboard creation, ensure data quality and consistency through cleansing processes, and provide scalability to handle growing data volumes.

Common use cases encompass business performance analysis, customer relationship management, supply chain optimization, financial reporting and compliance, and human resources analytics.

Data Management and Analytics

Data Storage:

Data Lakes excel in accommodating massive volumes of raw and unstructured data, offering a scalable and cost-effective solution. This flexibility enables businesses to store data without the need for immediate structuring, allowing for quick and agile data ingestion. On the other hand, Data Warehouses focus on structured data storage, emphasizing a predefined schema for efficient querying and analysis. The structured approach in Data Warehouses ensures data consistency, making it suitable for organized storage and retrieval in analytical scenarios.

Data Management:

Efficient data management is a common thread in both Data Lakes and Data Warehouses, with different approaches. Data Lakes provide an easier environment, allowing businesses to ingest diverse data types without upfront structuring. This flexibility is ideal for exploratory analysis and discovering hidden patterns in raw data. And, Data Warehouses prioritize structured data management, adhering to a predefined schema. This structured approach simplifies data governance, ensuring consistency and reliability for strategic decision-making and business intelligence reporting.

Big Data:

Data Lakes shine when dealing with the volume, variety, and velocity of big data, offering a scalable repository for diverse and large datasets. Their ability to store raw and unstructured data positions them as a valuable solution for businesses dealing with the complexities of big data. Data Warehouses, while excelling in structured data analysis, may face challenges with the sheer volume and variety of big data. However, the two can complement each other in a hybrid approach, providing a comprehensive solution for businesses dealing with the challenges posed by big data.

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Finding the Right Fit: data lake vs data warehouse

Is there room for both Data Lake and Data Warehouse in your data strategy? Explore the benefits of adopting a hybrid approach, seamlessly integrating the strengths of both solutions for comprehensive data management. Discover the factors to consider when choosing between Data Lake and Data Warehouse solutions. From cost considerations to scalability needs and varying data types and formats, find the perfect fit with Global Data 365 for your business’s unique requirements by contacting us now.

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