Management Reporter vs Jet Reports

Global Data 365
April 13, 2024

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 vs. 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.

We are going to give you three scenarios where a user should consider switching to Jet Reports or Jet Analytics:

What is 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.

We are going to give you three scenarios where a user should consider switching to Jet Reports or Jet Analytics:

Management Reporter vs. Jet Reports: 7 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 integrated seamlessly with Dynamics GP, but between Management Reports vs. Jet Reports 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.

- 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. However, Jet Reports can refresh data from Dynamics GP at any time, and new accounts will show up, without any hard coding or manually checking for new accounts.

- 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.

- 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 work 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.

In Conclusion

In conclusion, while both Management Reporter and Jet Reports serve essential roles in the realm of reporting tools, but in the comparison of Management Reporter vs. Jet Reports 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: What's The Key Difference?

Global Data 365
December 21, 2023

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 vs 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

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 query able 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.

Find the visual representation and difference between: Data Lake vs Data Warehouse.

Data Lake vs Datawarehouse: Key Differences

Features	Data Lake	Data Warehouse
Purpose	Used for storing vast amounts of diverse data types for future analysis.	Optimized for large-scale analytical queries, storing historical data for reporting and analysis.
Data Type	Stores raw, unprocessed data in its native format.	Stores summarized, aggregated, and historical data.
Data Structure	Schema-on-read, allowing for flexibility in data storage.	Optimized for read-heavy operations (OLAP – Online Analytical Processing).
Users	Primarily used by data engineers, data scientists, and machine learning teams.	Mainly used by business analysts, data scientists, and decision-makers for insights and reporting.
Data Volume	Holds vast amounts of unstructured and structured data.	Handles large volumes of historical data from various sources.
Performance	Performance can vary; optimized for large data ingestion rather than query speed.	High performance for complex queries and large-scale data retrieval for analysis.
Schema Design	Uses a flexible schema design; data is often stored without a predefined schema.	Denormalized schema (e.g., star or snowflake schema) for faster query performance.
Data Processing	Processes a wide variety of data types, including structured, semi-structured, and unstructured data.	Processes complex queries requiring significant data aggregation.
Concurrency	Supports high concurrency for data ingestion and retrieval.	Supports a lower number of users.
Storage Cost	Typically cheaper to store vast amounts of data due to lower storage costs.	Higher storage costs due to large datasets and complex processing requirements.
Example Use Cases	Data exploration, machine learning, real-time analytics.	Business intelligence reporting, trend analysis, forecasting, decision support.
Data Source	Captures data from various sources, including social media, IoT devices, and unstructured data.	Aggregates data from multiple sources, including databases, external systems, and log files.

Data Type:
Data Lake: Stores raw, unprocessed data in its native format.
Data Warehouse: Stores summarized, aggregated, and historical data.
Purpose:
Data Lake: Used for storing vast amounts of diverse data types for future analysis.
Data Warehouse: Optimized for large-scale analytical queries and historical data analysis.
Data Structure:
Data Lake: Schema-on-read, allowing for flexibility in data storage.
Data Warehouse: Optimized for read-heavy operations (OLAP – Online Analytical Processing).
Users:
Data Lake: Primarily used by data engineers, data scientists, and machine learning teams.
Data Warehouse: Mainly used by business analysts, data scientists, and decision-makers for insights and reporting.
Data Volume:
Data Lake: Holds vast amounts of unstructured and structured data.
Data Warehouse: Handles large volumes of historical data from multiple sources.
Performance:
Data Lake: Performance can vary; optimized for large data ingestion rather than query speed.
Data Warehouse: High performance for complex queries and large-scale data retrieval.
Schema Design:
Data Lake: Uses a flexible schema design; data is often stored without a predefined schema.
Data Warehouse: Denormalized schema (e.g., star or snowflake schema) for faster query performance.
Data Processing:
Data Lake: Processes a wide variety of data types, including structured, semi-structured, and unstructured data.
Data Warehouse: Processes complex queries requiring significant data aggregation.
Concurrency:
Data Lake: Supports high concurrency for data ingestion and retrieval.
Data Warehouse: Supports a lower number of users.
Storage Cost:
Data Lake: Typically cheaper to store vast amounts of data due to lower storage costs.
Data Warehouse: Higher storage costs due to large datasets and complex processing.
Data Source:
Data Lake: Captures data from various sources, including social media, IoT devices, and unstructured data.
Data Warehouse: Aggregates data from multiple sources, including databases, external systems, and log files.
Example Use Cases:
Data Lake: Data exploration, machine learning, real-time analytics.
Data Warehouse: Business intelligence reporting, trend analysis, forecasting.

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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Why is Building a BI Strategy Important?

Global Data 365
March 26, 2023

Many business executives face the ongoing challenge of obtaining straightforward and reliable reporting from their business software systems. While they recognize the vast amount of data at their disposal, the processes of extracting, organizing, and evaluating that data often seem unnecessarily complex. This is where Global Data 365 comes in. We are committed to simplifying the reporting process through a robust BI Strategy, making it more efficient and accessible for everyone in the organization.

Business intelligence is the method of transforming raw data into actionable information. It helps you to gather data from various sources, arrange it, and then analyse it. In recent years, BI has exploded in popularity among small and mid-sized businesses (SMEs), owing to advances in technology that have made powerful analytics accessible to businesses of all sizes.

When you realize the real business advantages that powerful reporting can offer, BI resources provide a very good return on investment. A BI strategy will let you solve all your data problems and requirements, create a coherent structure, and sustain it. What happens if you begin BI implementation without a plan? In other words, if you are focused on making those graphs but no one in the business understands why or how to use them.

There is no one-size-fits-all solution because every other organization is unique. In the end, it’s all about figuring out where the company is now, where you want it to go in the future, and how you will get there.

Why Stock Reporting Tools Are Not Effective

Most businesses already use a variety of monitoring methods, and they may not even be aware of how disorganized their processes are. Consider the different tech platforms that your company employs. Most businesses use an ERP system to manage their financial and operational activities daily. Some companies have their own CRM system that may or may not be integrated with the ERP. Others use a digital marketing automation tool to generate and develop leads, improve customer interaction, and increase brand recognition and reputation.

These structures have their own set of reporting features. Unfortunately, such methods are frequently intended to be effective. They meet the basic reporting criteria, but they are limited in terms of versatility and complexity. Those tools, in many cases, lack the ability to generate custom reports.

Frequently, current reports cannot be modified to benefit the organization’s strategic, complex business processes.

ERP software, in general, is infamous for requiring specialized IT skills for report creation and customization. This entails allocating limited IT resources to the task or hiring costly outside contractors to complete the task. Accounting, inventory, sales, and buying are the only areas where ERP reports can provide details. There is some consumer data there, but it is likely to be minimal.

To some degree, integration can help with the issue of information silos, but it can only go so far. However, some companies have begun to realize that time-consuming workarounds are inevitable disadvantages. The process of running reports through two to three different systems, then exporting the information to Excel, and forming a report takes time and manual errors.

BI systems must address the differences between the software systems that business leaders rely on to control their businesses and to deliver genuinely accurate reporting that offers a unified view of the business. A successful BI platform should deliver data in real-time and be simple enough to use so that everyone in the company can create and change reports without needing specialized skills.

Cloud ERP

As companies move toward Cloud ERP, they must think about the consequences of reporting and analytics. Most tech providers have taken the requisite measures to change the way you access data as they have moved to a cloud-first approach. Businesses that used a cloud ERP framework will no longer render direct database queries using structured query language due to security concerns. This is a technological transition, but it has far-reaching consequences for business intelligence.

As companies move to Microsoft Dynamics 365 Finance & Supply Chain Management from Microsoft Dynamics AX, for example, they will have a variety of options for accessing data and running reports. A better solution for businesses looking for a dependable BI platform with a low total cost of ownership is to look for a robust, validated reporting and analytics product that is optimized for high performance and ease of use.

Building Powerful BI Strategy

In any company, BI can be used to effectively improve operations. Everyone will concentrate on the same goals, collaborating with the same KPIs to push action and develop procedures, when the whole team is working from the same strategy and has access to a common source of reality. Although technology is a part of every discussion about business intelligence, it should not be the baseline. Building an effective BI strategy must address the requirements of the organization’s financial and operational decision-makers. It should meet financial and operational reporting needs with powerful and adaptable tools that enable everyone in the company to create and adjust ad hoc reports without requiring advanced training or IT skills.

At Global Data 365, we provide effective business intelligence solutions to meet your company’s needs. Some major questions that make their BI strategy effectiv e are:

– What are the record systems of your business data?
These are typically the starting point for creating a cohesive approach to reporting, putting together data that provides business leaders with a comprehensive picture, a unified understanding of what’s going on in the company.

– In the company, how does your team use reporting?
Which reports do decision-makers consult on a regular, weekly, or monthly basis? What happens if you need an urgent report?

– Are the financial reporting systems capable of completing period-end closing quickly and efficiently?
Is there a way to make the period-end process more efficient and automated? Do the numbers in the general ledger and sub-ledgers of the ERP method immediately connect to the reconciliations and supporting worksheets?

– When do the stakeholders receive the information?
Have you ever had a situation where you wanted details right away but couldn’t get them? What were the consequences of the delay?

– Is your company thinking of shifting to cloud ERP or cloud CRM system?
If it is so, you must link with a provider who can meet your needs both now and, in the future, when you migrate to the cloud.

When you answer these questions, it can help you figure out what you want to do with BI in the coming years, as well as the kinds of benefits your company will reap in the short and long run.

Usually, when companies are implementing a BI plan, the finance department’s specific reporting requirements are ignored, but with Global Data 365 onboard that won’t happen, as we prioritize customer satisfaction.

Talk to Our BI Experts!

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How can IT be removed from Financial Reporting?

Global Data 365
February 26, 2023

Financial reporting plays a crucial role in providing real-time insights, especially in crisis scenarios. If you are asked to present up-to-the-minute information on cash flow, chances are you will create a worst-case scenario based on some new assumptions, such as a 20% decrease in sales and a 15-day delay in collections. Even though you’ve generated hundreds of cash flow reports, you’ve never been asked for this exact version before. It requires some additional details, such as a sales pipeline review and improvements to the aging study. Almost anyone who has ever worked in finance or accounting has encountered a situation like this at some stage. With the coronavirus outbreak, business leaders couldn’t bear to have inefficiencies slowing their access to the information as they tried to evaluate the situation and react quickly.

Eradicate the Chokepoints

To produce or change system reports, the finance and accounting department has mainly focused on IT experts or costly outside consultants. Many accounting and ERP frameworks provide report design tools that are inflexible and require a steep learning curve. This raises a variety of difficulties. For starters, it establishes a reliance on a third-party department. Cross-team dependencies are common in most organizations, but when one department’s goals vary from those of others, conflict or chokepoints occur, preventing work from being accomplished quickly and efficiently.

When a particular department is overburdened with conflicting interests, problems may occur. Many IT divisions were preoccupied with tasks related to the enablement of remote staff as the coronavirus crisis unfolded, for example. This occurred at precisely the moment when C-level executives required the most urgent access to financial data and analysis. Businesses can try to remove these forms of dependencies as much as possible as a long-term strategy, so those cross-functional collaboration strategies will concentrate on areas where teamwork and diverse viewpoints add real value.

Enhance Flexibility

The second issue with conventional reporting tools is that they often lack the versatility that finance and accounting users need. Because of its immense strength and versatility, most F&O users tend to work in Excel. Excel is an excellent tool for manipulating, analyzing, and visualizing data. Almost every finance expert knows how to make good use of it. The finance and accounting department will kill two birds with one stone by allowing real-time data from various software systems to be accessed directly inside Excel.

For starters, they may reduce their reliance on technology. Second, they will enable finance and accounting professionals to create reports and conduct analyses using a single, strong, and familiar tool. Many businesses have found interest in implementing web-based dashboards so that leaders around the enterprise can have real-time access to a shared collection of business metrics. A few of these web-based dashboard platforms follow a common user-empowerment philosophy, allowing the finance department to set the agenda and implement a strategy for carrying out corporate dashboards without relying on the IT department in the long run.

Eliminate Errors

When finance takes over the reporting role, it will be able to address another issue that most F&O teams are familiar with. Traditionally, manually copying and pasting data from ERP, CRM, and other internal systems into Excel was needed for reporting and analysis. It is a time-consuming procedure that is prone to introducing errors into the reporting process. As data source formats alter (for example, when a new row is added to a General Ledger report), data may be pasted incorrectly into a pre-defined Excel template. Expert spreadsheet users will also incorporate error-checking algorithms or workarounds to avoid incorrect results in this case, but such methods are far from foolproof.

Another major disadvantage of the copy/paste process is that it is time-consuming. It generates reports that are based on out-of-date data. Data extracted from a source system, such as ERP, no longer provides an accurate and up-to-date picture of what’s happening in the industry. Data must be updated, and then copy/paste procedure must be repeated to review reports. Building a reporting strategy focused on real-time data access is a safer option.

An Alternative Approach

Consider this alternative strategy, in which data is made accessible in real-time by connecting to multiple source networks within the enterprise. Because when the finance department can create reports directly in a familiar method like Excel instead of having to copy and paste data from other systems, it can concentrate on what it does best: compiling and evaluating data to make better business decisions.

The need to refresh content won’t arise since this system enables real-time access to information. Direct links to source systems such as ERP or CRM may be used to automatically refresh data. With less effort, less cross-team dependency, and a lower risk of mistakes, everybody gets a real-time view of what’s going on in the company.

All these advantages are open to companies using several off-the-shelf ERP, CRM, and other software systems due to Global Data 365‘s powerful reporting tools. Reports can be created, updated, and distributed securely within the organization. Users only have access to information they can see due to built-in data protection. The year 2021 will see a renewed emphasis on software automation tools. Automation of reporting and related tasks is a reasonable first step for companies looking to improve efficiencies by doing more with less, and remote workers aim to collaborate easily and efficiently with other team members.