ERP system

Microsoft 365 Copilot

Home > Blogs > Unleashing Microsoft 365 Copilot: Revolutionizing Your Productivity

Microsoft 365 Copilot: Revolutionizing Your Productivity

March 20, 2024

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.

Microsoft 365 Copilot

In the ever-evolving landscape of digital productivity tools, Microsoft 365 stands tall as a comprehensive suite that empowers businesses worldwide. Among its many offerings, Microsoft 365 Copilot emerges as a transformative solution, promising to revolutionise the way organisations manage their Microsoft 365 environment.

What is Microsoft 365 Copilot?

Microsoft 365 Copilot is a revolutionary management tool designed to simplify the administration of Microsoft 365 services including those crucial for customer relationship management (Microsoft Dynamics 365 CRM), finance and operations (Microsoft Dynamics 365 F&O), and business central management (Microsoft Dynamics 365 BC).

It comprises three main components: Microsoft 365 apps (like Word, Excel, Teams), Microsoft Graph (incorporating files and data across the M365 environments), and OpenAI models (including ChatGPT-3, ChatGPT-4, DALL-E, Codex, and Embedding), all hosted on Microsoft Azure.

Unlike traditional management methods, Copilot offers a more efficient and streamlined approach, allowing organizations to focus on their core business activities.

M365 Copilot Features:

Effortless Automation: Copilot improves productivity by automating repetitive tasks and workflows, allowing employees to focus on more strategic initiatives.

Reduced Cost and optimizing Resources: It helps organizations save costs and optimize resources by streamlining Microsoft 365 management processes.

AI-powered insights: It leverages AI to unlock valuable insights from your Dynamics 365 sate. Gain real-time customer behavior trends in Dynamics 365 CRM or identity financial optimization opportunities in Dynamics 365 F&O.

Enhanced Security: It empowers businesses to maintain robust security within Dynamics 365. Leverage advanced monitoring and threat detection to keep your data safe.

Streamlined Collaboration: Copilot fosters seamless collaboration within Dynamics 365 applications. Imagine teams working together on sales proposals in Dynamics 365 CRM or project plans in Dynamics 365 Business Central with real-time edits and suggestions.

Microsoft 365 Copilot

How Much Does M365 Copilot Cost?

Microsoft 365 Copilot is available as part of the Microsoft 365 Enterprise subscription, which offers a range of plans tailored to meet the needs of businesses of all sizes. The cost of Copilot varies depending on the specific plan chosen, with pricing starting at $30 per user per month for the basic plan.

Future plans include tailored Copilots for Dynamics 365, Power Platform, security suite, and Windows OS.

How many Modes of Interaction are in Copilot?

-M365 Copilot system offers two main interaction modes: Direct engagement within applications like Word and Teams, and accessibility through Microsoft 365 Chat in Teams

-Within applications, users seamlessly integrate Copilot for tasks like drafting documents and summarizing meetings in real-time.

-The second method of interaction is through Microsoft 365 Chat, functioning as a chatbot within Teams. Microsoft 365 Chat serves as a versatile tool for natural language interactions, enabling users to search across diverse sources.

-Copilot enhances productivity in Word by offering text suggestions, facilitates collaboration in Teams with real-time meeting summaries, and streamlines PowerPoint presentations.

-In addition to automation, Copilot also provides advanced monitoring and reporting capabilities, allowing you to keep track of service health and performance metrics. This information can help you identify potential issues before they escalate, ensuring that your Microsoft 365 environment remains stable and reliable.

Want to try Jet Analytics?

Get Free License for 30 Days

Jet Analytics Hero Section

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

Conclusion

M365 Copilot is a transformative tool that empowers businesses to unlock the full potential of Dynamics 365. With its innovative cutting-edge functionality and user-friendly interface, Copilot is empowering teams to collaborate more effectively and achieve their goals efficiently.

To experience the benefits of M365 Copilot for your business and drive growth, contact us at Global Data 365 today. Our team is ready to help you leverage this powerful tool to take your productivity to new heights.

Speak with our BI Expert.

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.

Want to try Jet Analytics?

Get Free License for 30 Days

Jet Analytics Hero Section

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

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.

Speak with our BI Expert.

Data Mining

Home > Blogs > What is Data Mining?

What is Data Mining?

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 Mining

Data mining refers to the process of extracting valuable patterns, information, and knowledge from large datasets. It involves uncovering hidden trends, correlations, and associations within the data, providing organizations with actionable insights for informed decision-making.

How Data Mining Works?

  • Data Collection: This involves gathering relevant data from various sources, such as databases, logs, and external datasets. The richness and diversity of the data contribute to the effectiveness of the mining process.
  • Data Cleaning: Identifying and rectifying errors, inconsistencies, and missing values in the dataset is crucial. Clean data ensures the accuracy and reliability of the mining results.
  • Exploratory Data Analysis: Before diving into the modeling phase, analysts perform exploratory data analysis to understand the structure, relationships, and potential patterns within the dataset. This step guides subsequent modeling decisions.
  • Model Building: Mathematical models or algorithms are created in this step to identify patterns and relationships within the data. This phase requires a deep understanding of the dataset and the goals of the analysis.
  • Pattern Evaluation: The effectiveness of the models is evaluated in terms of their ability to reveal meaningful insights. This step ensures that the patterns identified are relevant and reliable.
  • Knowledge Deployment: Implementing the discovered knowledge is the final step, where insights gained from the analysis are applied to drive decision-making and improve business processes.

Data Mining Techniques

Data mining employs various techniques, including:

  • Classification: This technique categorizes data into predefined classes or groups based on identified patterns. It is often used for tasks such as spam filtering or customer segmentation.
  • Clustering: Grouping similar data points together helps identify inherent structures within the dataset. This technique is valuable for market segmentation and anomaly detection.
  • Regression: Predicting numerical values based on identified relationships within the data. It is widely used in areas such as sales forecasting and risk assessment.
  • Association Rule Mining: This technique discovers relationships and patterns that frequently co-occur in the dataset. It is applied in areas like market basket analysis in retail.

The Process of Data Mining

  1. Data Collection: Gathering relevant data from diverse sources sets the foundation for meaningful analysis. The more comprehensive the dataset, the richer the insights.
  2. Data Preprocessing: Cleaning and transforming the data for analysis is essential for accurate results. This step involves handling missing values, outliers, and ensuring data consistency.
  3. Exploratory Data Analysis: Understanding the characteristics and relationships within the dataset guides subsequent modeling decisions. Visualization tools are often employed to aid in this exploration.
  4. Model Building: Developing algorithms or models to identify patterns requires expertise in both the domain and the intricacies of the data. This step is crucial for accurate and meaningful results.
  5. Validation and Testing: Evaluating the model’s performance on new data ensures its generalizability. Techniques like cross-validation help in assessing the model’s robustness.
  6. Implementation: Deploying the knowledge gained from the analysis for practical use completes the data mining process. This step often involves integrating insights into existing business processes.

Applications of Data Mining in Business Intelligence

The data mining process is fundamental to strengthening business intelligence, offering a range of applications that enhance decision-making and operational efficiency:

  1. Strategic Decision-Making: Leveraging data-driven insights enables organizations to make well-informed decisions, fostering strategic planning and optimizing resource allocation for sustained success.
  2. Customer Segmentation: Identifying and comprehending diverse customer segments are pivotal. Data mining facilitates targeted marketing strategies and cultivates personalized customer experiences, driving customer satisfaction and loyalty. The reporting capabilities of business intelligence tools, such as Jet Analytics, offer a robust solution for creating customer-centric reports. By delving into customer data, organizations can tailor their strategies enhancing overall customer satisfaction.
  3. Fraud Detection: Uncovering anomalies and unusual patterns in financial transactions is a critical aspect of business intelligence. Data mining plays a crucial role in proactively identifying fraudulent activities and safeguarding financial integrity.
  4. Market Analysis: In a dynamic business environment, analyzing market trends and predicting future conditions is indispensable. Data mining empowers businesses to stay competitive by providing insights that aid in adapting to changing market landscapes. Integrated reporting solutions, such as Jet Reports, for visualizing and interpreting market data. Organizations can generate reports that highlight key market trends, enabling them to make proactive decisions and stay ahead in dynamic market scenarios.

Data Mining Uses

Data mining finds applications across various industries, including healthcare, finance, retail, and manufacturing. It is utilized for:

  • Healthcare: In healthcare, data mining is instrumental in predicting disease outbreaks and optimizing patient care. By analyzing vast datasets, it contributes to improved public health initiatives, early detection of health trends, and personalized treatment strategies.
  • Finance: Data mining plays a crucial role in the financial sector by identifying fraudulent transactions and predicting market trends. These insights aid in effective risk management, fraud detection, and the formulation of sound investment strategies, contributing to the stability of financial systems.
  • Retail: In the retail industry, data mining is employed to analyze customer behavior and optimize inventory management. Understanding consumer preferences and purchasing patterns enhances the overall retail experience, enabling businesses to tailor their offerings and improve customer satisfaction. This can be further visualized with Power BI Dashboard that can be custom made for your preference.
  • Manufacturing: For manufacturing, data mining is utilized to improve production processes and predict equipment failures. By analyzing data related to machinery performance, production workflows, and quality control, manufacturers can enhance efficiency, reduce downtime, and make informed decisions to optimize operations.

Pros and Cons of Data Mining

Pros:

  • Informed Decision-Making: The insights gained from data mining empower organizations to make informed decisions, leading to strategic advantages. This results in a more agile and adaptive approach to changing market conditions.
  • Efficiency: By optimizing processes and identifying areas for improvement, data mining contributes to increased operational efficiency. Streamlining workflows and resource allocation enhances overall business productivity.
  • Predictive Analysis: The ability to predict future trends and behaviors enables proactive decision-making and planning. Businesses can anticipate market shifts, customer preferences, and potential challenges, staying ahead of the curve.
  • Innovation Catalyst: Data mining often sparks innovation by revealing hidden patterns and opportunities. Organizations can uncover novel ideas and strategies that drive product development and business growth.

Cons:

  • Privacy Concerns: The use of personal data raises ethical and privacy concerns, necessitating careful handling and compliance with regulations. Striking a balance between data utilization and privacy protection is an ongoing challenge.
  • Complexity: Implementing data mining processes can be complex, requiring skilled professionals and significant resources. The intricacies of algorithmic models and the need for specialized expertise may pose challenges for some organizations.
  • Data Accuracy: The accuracy of results is highly dependent on the quality and precision of the input data. Ensuring data accuracy remains a perpetual challenge, as inaccuracies in the input can lead to misleading insights and flawed decision-making.
  • Integration Challenges: Integrating data mining into existing systems and workflows can be challenging. The process may disrupt established routines, requiring careful planning and effective change management to mitigate potential disruptions.

Want to try Jet Analytics?

Get Free License for 30 Days

Jet Analytics Hero Section

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

Conclusion

In conclusion, data mining is a dynamic process that transforms raw data into actionable intelligence, driving informed decision-making in various industries. While offering numerous benefits, careful consideration of privacy and data accuracy is essential. As businesses continue to leverage data mining for strategic advantage, a balanced approach that addresses both the advantages and challenges will be crucial for success in the data-driven landscape.

Speak with our BI Expert.

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.

Want to try Jet Analytics?

Get Free License for 30 Days

Jet Analytics Hero Section

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

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.

Speak with our BI Expert.

Related Resources

Jet Analytics Data Warehouse as a Future-Proof Business Solution

Home > Blogs > Jet Analytics Data Warehouse as a Future-Proof Business Solution

Jet Analytics Data Warehouse

March 26, 2021

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.

Jet Analytics Data Warehouse as a Future-Proof Business Solution

To remain competitive, a business needs to implement a jet Data Warehouse system that can keep with future requirements. Preparing to implement an ERP system can be a challenging task. Data storage technology’s future will be characterized by speed, convenience, and efficiency. Many of Microsoft’s legacy GP, NAV, and SL customers are likely to be considering a shift to the new platform now that the company’s latest ERP software version for small and midsize businesses has been released.

Microsoft Dynamics 365 (D365 BC) is the next version of the Microsoft Dynamics NAV code base. It is expanding it to a more cloud-friendly platform and incorporating it more deeply than before with the remainder of the Microsoft stack. The process of data transfer is never an easy task, no matter which ERP system you are moving from. Some particular problems related to data transfer are there. Surprisingly, many of them can be easily handled using a data warehouse.

Although data warehouses have been built for a different reason (i.e. to store data for big data analytics), they can provide tremendous value during an ERP transfer. That is because an entire data warehouse solution can pay itself from the savings generated by the process of migration itself. Enterprises have access to an increasing amount of data from all departments of their sector. Controlling how data travels through the enterprise becomes increasingly important as a company gathers data in different formats.

Data warehouse Obstacles during Data Transfer

Data warehouse technology has not changed much. However, the rise of Big Data and an excess demand for data has uncovered technical vulnerabilities that some legacy warehouses are not equipped to manage. One of the first questions asked from a project team when it comes to data transfer is which data is going to be moved from the old system to the new one? Firstly, all the data is going to be transferred. Secondly, many businesses have collected a large amount of historical data. Exporting data, filtering it, cleaning it, and reformatting it for the new system costs time and money.

Then the challenge arises of matching transactions. Bringing the list of the history of customer payments and invoices is a separate thing from recreating the history, providing the details of the payments made to certain invoices in certain amounts. On the other hand, the cloud model distinguishes storage from computing, resulting in a new level of cost and performance efficiency. Enabling IT to:

– Pay for only what is used.
– Gain total cost/performance leverage.
– Reduce duplication of data.
– Eliminating loading of data.
– Multiple platforms can access the same data.

If the company continues to retain the old system intact, it will cost them time and money. If only a single person knows how to operate the system leaves, or if the system update conflicts with the old ERP software. It will cost your company time on support and maintenance.

Jet analytics Data Warehouse as a Solution

Most ERP system manager fails to think of the alternative; a data warehouse solution that contains data from your old ERP system. It contains all of the data you require for historical reference. With a data warehouse, there is no need to handle transactions on the old system.

In comparison to the cost of maintenance of an old ERP system vs. a data warehouse, the data warehouse comes out on top every time. It not only solves the issue of historical ERP records but also serves an ongoing function as well. It significantly reduces security risks. The cloud has changed the way companies handle and store data for the better. To satisfy your existing and future business needs, cloud computing will help you create a new modern data infrastructure. Your organization now has the opportunity to harness its data’s potential, delivering unmatched productivity and ROI. You are finally ready to turn your data to reveal the deeper insights that will help you make better business decisions and produce higher-quality results.

Data Warehouse as a Migration Tool

By creating a standard data model for your old and new ERP systems within the data warehouse. You can utilize the data warehouse as a migration tool itself. You may proactively use the data harmonization process among the two platforms to clean and normalize data from the old system and prepare it for transfer to the new system.

Since most people think of a data warehouse primarily as a staging area for reports. This is a creative solution to the issue of data migration that is rarely suggested.

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

Want to try Jet Analytics?

Get Free License
for 30 Days

Jet Analytics Hero Section

ERP Migration without Reformatting Reports

The future is hard to foresee, but one certain thing is that the most productive data warehouses are those that can use their data efficiently to optimize operations, anticipate market shifts, and boost availability. Similarly, Jet Analytics is a reporting platform from Global Data 365 that deals with the entire Microsoft Dynamics products, which include Microsoft Dynamics CRM, AX (Axapta), NAV (Navision), GP (Great Plains), SL (Solomon), BC, and Microsoft Dynamics 365 Finance and Supply Chain Management product.

The relation between Jet Analytics and the various products of Microsoft Dynamics operates independently. Users can extract data from the ERP system, which is integrated inside the data warehouse to a harmonized data structure. The customer records of both Microsoft Dynamics GP and Microsoft Dynamics Business Central may appear the same in the data warehouse. Invoice records from various systems will also appear similar.

If you are thinking about transferring data from one system to another, particularly from Microsoft’s legacy ERP products to D365 BC, you can save time and money by implementing these approaches. Jet Analytics data warehouse can offer the following benefits:

– You can automate the removal and transfer of data that you intend to migrate by linking the Jet Analytics product to your old system, storing them in the data warehouse for import to the new system.

– You can tackle the issue of historical data by using Jet Analytics to provide unlimited access to data that is too difficult or costly to transfer. This decreases the probability of security breaches, saves recourses, and improves efficiency.

– If you have used Jet Analytics to develop reports for your previous Microsoft Dynamics ERP system. You can continue using the reports for data with little to no change from your new Microsoft Dynamics ERP system. This saves considerable time and money on implementation.

– You will have the most sturdy BI and reporting platform on the market after the migration, which will remove any potential reporting inefficacies.

A Detailed View over Time

The Jet Analytics data warehouse approach enables you to view both old and new data together as a single whole. Jet Analytics helps businesses to run comparative reports that look back through different years. Information is structure and interpreted by the data warehouse as if it originated from a single system.

Any level of compliance is involved in most ERP system implementations. One such compliance is the necessity of a complete break from the past. This particular problem is tackled effectively by the Jet Analytics data warehouse approach.

Jet Analytics Data warehouse as a Solution

For the success of any business, the present and future warehouse management systems need to embrace the incorporation of BI software solutions and visualization of insight. You can get started with Jet Analytics whether you have upgraded to the latest versions of Microsoft’s ERP system. There are many advantages to implementing a Jet Analytics data warehouse system.

– Jet Analytics can be deployed ahead of an ERP framework update to give you a head start, reduce risk, and lighten the overall implementation workload. When you finally introduce a new ERP system, report creation on Jet Analytics will continue to pay off.

– By acquainting yourself and your staff with the data warehouse setting, you can obtain an understanding of the benefits of implementing the Jet Analytics data warehouse system.

It is time to unlock the potential of your data to drive your business ahead. To learn more about how Jet Analytics can benefit your company or learn to improvise with Jet Analytics training.

Contact us to get more information.

Speak with our BI Expert.

Dynamics AX reporting

Home > Blogs > Reporting Challenges in Microsoft Dynamics AX 2012

Reporting Challenges in Microsoft Dynamics AX 2012

March 26, 2021

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.

Dynamics AX reporting

Introducing a new ERP system requires a huge amount of resources; time, money, and the company’s overall energy. Most times, the business’s essential day-to-day operational needs, such as monitoring and general data, are fulfilled by the legacy ERP system, which has developed with years. Despite these challenges, businesses can opt to replace legacy ERP systems with a more modern, versatile ERP system to adapt to evolving business needs, take advantage of technological advances, and help propel the company forward.

Similarly, Reporting in Dynamics AX can be difficult, particularly after the release of AX 2012 introduced a more complex underlying data structure, increasing the table count from an average of 1800 to 6400. The feedback gathered from hundreds of implementing partners and users around the world about reporting and analytics in Dynamics AX 2012 is that it takes time, money, and unreliability at best. SSRS is the most popular native tool for reporting from Dynamics AX 2012. As the reset tool, all regular out-of-the-box operational reports need the use of a technical resource who is familiar with Visual Studio, SSRS, report design, and the Dynamics AX database structure.  If you’ve been using Dynamics AX 2012 for some period of time in your daily operations activities, you’re probably familiar with the process of generating and exchanging reports. If you’ve been using Dynamics AX 2012 for some period of time in your daily operations activities, you’re probably familiar with the process of generating and exchanging reports.

You may have noticed that your organization is not getting all of the information it requires when it comes to reporting. So what challenges arise when it comes to Microsoft Dynamics AX 2012 implementation?

– Complex underlying AX 2012 data.
– No skilled AX developers.
– Dependency on consultants and cost of facilities.
– No simple way to integrate additional data sources.
– Inability to get a single, accurate, real-time view of your data.
– Lack of available out-of-the-box cubes.

These challenges lead to business leaders thinking about whether they need a new reporting solution, a huge ERP upgrade, but an ERP system upgrade won’t fix the reporting and data visibility obstacles. Listed below are the four main areas in Dynamics AX 2012 reporting and analytics that take time, money, and effort. We will start by breaking down the problems and then presenting a solution to help save Dynamics AX users’ time and money.

Interact Live
with Dashboards

Increase efficiency and deliver success now with Microsoft Power BI. Enjoy a 20% discount on all Power BI services.

Interact Live
with Dashboards

Increase efficiency and deliver success now with Microsoft Power BI. Enjoy a 20% discount on all Power BI services.

SSRS Programming

The problem in programming in the SSRS setting is that it is slow and expensive. Because of the large volume of data in the system and the requisite linking properties, conventional programming (SSRS) can be slow, bottlenecked, and costly when it comes to reporting in Dynamics AX 2012.

So what is the solution? With an easy-to-use, faster front-end reporting platform designed for Dynamics AX, data is better organized. A data warehouse that automates and optimizes data from Dynamics AX along with a user-based front-end reporting tool, such as Jet Analytics from Global Data 365, enables non-technical users to generate and distribute reports and dashboards quickly and easily.

Building OLAP Cubes

The problem is time consuming and expensive. Because of the complex tools and resources needed to use them, generating or changing data cubes in Dynamics AX 2012 is time-consuming and costly. You can’t write a new query if you lack resources to understand both SSAS data cubes and Dynamics AX 2012.

So what is the solution? Easy backend cube management system. The Jet Data Manager, which automates the building of SQL code and data movement monitoring for you, makes handling cubes in Dynamics AX 2012 much simpler and faster. Any database administrator on your team can change data cubes using Jet Analytics’ drag-and-drop or point-and-click features, enabling you to be more effective in getting the data you need, whenever you need it, without the need for costly experts or delays.

Managing Ungoverned Data in Excel

The problem is fragmented and inaccurate data. Microsoft Excel is the most common and widely used reporting tool, but it is not regulated or safe. Without hold over Microsoft Excel, each employee in a company has their own spreadsheets, each of which has been compiled with data in their own specific way and only resides on their PC; as a result, everyone has their own version of the facts.

So what is the solution? Controlling Microsoft Excel. Implementation of MDM and a data warehouse to organize your data will secure it and act as one operational place for your reporting needs. Jet Analytics helps you to use Excel for all of your reporting needs while still handling the delivery, security, and run-time of these reports through a single source and data management framework.

Single Source View in Power BI

The problem is that it is hard to use and organize. Power BI is a common visualization tool, but it’s difficult to use and maintain in Dynamics AX 2012 without the correct underlying data solution. Because of all the data, combining and mapping needed to construct a single view, putting together views is difficult. It also does not allow data segmentation.

So what is the solution? Implementation of an underlying data solution. Jet Analytics provides the right framework to reap the benefits of Power BI‘s powerful visualization tools by planning the data first with easy-to-access, structured data cubes. It eliminates the need for joins or locks by storing all of your data in one location and instead relies on links. With Jet Analytics, a process that would usually take two days takes just half an hour, and any regular user can do it.

Takeaway

With a reporting and analytics platform built for ease and reliability, you can help the Dynamics AX users become more self-sufficient. Implementing Jet Reports for Dynamics AX is a simple, versatile self-service reporting tool that allows any employee in your company to generate and exchange financial and operational reports directly from inside Excel.

Schedule a demo with our Power BI experts

Scroll to Top