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Business Central vs Finance and operations

Home > Blogs > Dynamics 365 Business Central vs Finance and operations

Business Central vs. Finance and operations

April 08, 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.

Business Central vs Finance and operations

Microsoft Dynamics 365 offers a range of powerful business management solutions, each tailored to meet the specific needs of different types of businesses. Two of the most popular offerings in the Dynamics 365 suite are Business Central vs. Finance and Operations.

Microsoft Dynamics 365 Business Central vs. Finance and Operations: Core differences

While both solutions are designed to help businesses manage their operations more effectively, there are some key differences between the functionalities of two ERPs:

Functionality  

D365 Business Central  

 D365 Finance vs Operations  

Microsoft Positioning 

Business management solution for small and medium-sized businesses, offering tools to manage finances, operations, sales, and customer service effectively. 

Solution for medium to large enterprises with complex business processes, offering advanced financial and operational management capabilities 

Type of Companies 

Ideal for small and medium-sized businesses across various industries 

Geared towards medium to large enterprises with complex business processes 

Customization and flexibility  

Flexible and customizable for different industries, enabling operational excellence and digital transformation. 

Suitable for a wide range of industries including manufacturing, retail, distribution, and services, providing tailored solutions for complex business processes. 

Depth of Manufacturing 

Offers robust manufacturing functionality, including production planning, shop floor control, and quality management, suitable for managing manufacturing processes effectively. 

Provides deep manufacturing functionality supporting various modes like make-to-stock, make-to-order, and assemble-to-order, along with advanced features 

Capabilities and Integration 

Provides comprehensive capabilities for financial management, sales, and customer service, integrating seamlessly with other Dynamics 365 components for a unified platform. 

Offers advanced capabilities for managing financials, supply chain, manufacturing, and operations, seamlessly integrating with other Dynamics 365 components 

Minimum Number of Users 

1 

20 

License Cost 

$70 to $100 per user / month 

$115 to $210 per user / month 

Availability 

 Available as a cloud-based solution in 33 countries  

Available as a cloud-based solution in 140 countries  

Localization 

 localized to comply with regulatory requirements of different countries and regions, making it suitable for global operations. 

Localized to comply with the regulatory requirements of different countries and regions, ensuring compliance and ease of use in various markets. 

Business Analytics 

Provides powerful analytics capabilities, offering insights into operations and supporting informed decision-making. 

Offers advanced business analytics capabilities, providing valuable insights into operations and enabling informed decision-making across the Organization. 

Scalability 

Scalable to accommodate business growth, allowing for easy expansion of operations as the business expands. 

Scalable to support business growth, enabling organizations to expand their operations easily as their business grows. 

Deployment 

Offers flexibility in deployment options, including cloud-based deployment for ease of access and management. 

Provides flexibility in deployment options, including cloud-based deployment for improved accessibility and management 

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Conclusion: Business Central vs. Finance and operations

In conclusion, Dynamics 365 Business Central and Dynamics 365 Finance and Operations are both powerful business management solutions, each offering unique functionalities tailored to meet the specific needs of different types of businesses.  

While Dynamics 365 Business Central is ideal for small and medium-sized businesses looking for a comprehensive and cost-effective solution, Dynamics 365 Finance and Operations is more suited for medium to large enterprises with complex business processes and a need for advanced financial and operational management capabilities. Whether you’re a small retail business or a large manufacturing enterprise, Microsoft Dynamics 365 has the right solution to help you streamline your operations, improve efficiency, and drive growth.

Still confused about business central vs. finance and operations?  With Global Data 365 drive growth with the correct solution and experience the benefits of Microsoft Dynamics 365 for your business

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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.

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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.

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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 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.

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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.

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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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Related Resources

How can IT be removed from Financial Reporting

Home > Blogs > How can IT be removed from Financial Reporting?

How can IT be removed from Financial Reporting?

July 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.

How can IT be removed from Financial Reporting

If you are asked to present up-to-the-minute information on cash flow, chances are you would 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 organisations, 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, analysing, 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.

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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

Take into account 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 of 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 that they are allowed to 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.

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Why is Building a BI Strategy Important

Home > Blogs > Why is Building a BI Strategy Important?

Why is Building a BI Strategy Important?

July 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.

Why is Building a BI Strategy Important

One of the many problems we hear from business executives is that they are still having trouble getting straightforward and reliable reporting from their business software systems. They recognize that they have a large amount of data, but the method of extracting, arranging, and evaluating it still seems to be much more difficult than it should be. That is why Global Data 365 was formed: to make the process easier, more efficient, and available to everyone in the company.

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 enable you to solve all of 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. The majority of 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 are 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 all of the software systems that business leaders rely on to control their businesses 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 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.

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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 effective 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 it? 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.

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Where BI fits into your Data Strategy

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Where BI fits into your Data Strategy?

July 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.

Where BI fits into your Data Strategy

Traditional data techniques have been based on business intelligence (BI), but the advent of predictive and prescriptive analytics platforms, due in part to machine learning and artificial intelligence, is shaking things up. Also, business intelligence is changing, with features that were traditionally only available on business analytics platforms. Analysts and consultants believe that knowing the differences between business intelligence and other analytics tools. The value each brings to the organization is critical to developing an effective data strategy. 

Here, we will look at where business intelligence fits into the current analytics landscape and how business analytics is changing as tools, strategies, and staff requirements change. 

Business Intelligence vs. Business Analytics: What’s the difference?

In the widest sense, analytics refers to any technology-enabled problem-solving activity. Experts classify analytics into four groups on a scale of one to four, with descriptive and diagnostic analytics on the lower end of the scale and predictive and prescriptive analytics on the higher end. When starting an analytics system, most companies start with BI, which is part of the descriptive process. Business intelligence is the method of transforming data into actionable intelligence that helps an organization make strategic and tactical decisions. A good BI strategy, It’s what makes it possible for a company to collect, analyse, and present data. 

It’s all about the data, according to Beverly Wright, executive director of Georgia Tech’s Scheller College of Business’s Business Analytics Centre. It isn’t attempting to do something other than telling a story about what the data is showing us. While some business people can associate BI with analytics, Wright says data professionals differentiate between the two. Some define BI as providing insight into what has occurred, while others describe analytics, especially advanced analytics, as predicting what will occur in various future scenarios. 

Business Intelligence for Business Use

BI uses more organized data from conventional business platforms, such as enterprise resource planning (ERP) or financial software systems, to provide views into previous financial transactions or other past activities in areas like operations and supply chain management. According to analysts, BI’s importance to companies today stems from its ability to provide insight into such areas and business tasks as legal reconciliation. 

According to Wright, BI tools, like many other parts of the business technology stack have developed to become much more intuitive and user-friendly. She describes that in the past, companies used data scientists to use these systems to create dashboards. They’re now completely automated. As a result, companies can more effectively implement data systems that enable non-technical business owners to use BI tools to generate reports. Obtain much of the information they need without involving data professionals in day-to-day operations. Analysts believe that this alone qualifies BI technologies as critical business tools. 

BI as a Gateway to Business Analytics

While reporting solutions and other BI tools have a position in the enterprise, analysts claim they have limited capabilities. Bain & Co., a multinational management consulting company, estimates that more than half of companies use at least three separate analytics providers to produce performance reports in its 2017 study Six IT Design Rules for Digital Transformation. 

BI tools don’t offer the kind of in-depth data analysis that can lead to new market opportunities and development. According to John Myers, a senior analyst in business intelligence, “BI is not driving sales and innovation.” Enterprise Management Associates employs intelligence. Even though Myers reports that 20% of US businesses are already at the BI level, he believes that most companies do not want to stop using analytics, and attempts are being made there. Users can begin by looking at sales data and then want that data is calculated by state or product, according to Myers. Then they’ll like to see their top 10 customers from the previous year, as well as their common characteristics. Forecast which customers will be in the top 10 in the coming year based on that detail. 

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BI as the Future

While data professionals continue to play important roles in advanced analytics, such as modeling, Myers says their participation varies depending on the business case. To detect possible credit card fraud, for instance, advanced analytics systems rely on unmonitored models rather than data scientists querying the systems. Organizations generally buy off-the-shelf BI products as well as commercial advanced analytics products, Myers adds, but they tend to have their own data professionals build the machine learning and AI capabilities they need because there’s not a set of packages on the market; the products just aren’t there. 

Many BI tools, according to Brahm, are bringing in more and better data signals to generate more reliable, informative reports that blur the boundaries between BI and more advanced analytics. He believes that these new technologies are assisting users in making better decisions by answering questions about how to maximize and optimize the business, such as who the company can target, what promotions are available, and which ones are available to whom. 

Technology organizations are more advanced in their implementation of advanced analytics capabilities, such as machine learning and AI, and are more likely to have done so already. If you find more about how BI is helping to transform businesses, and where BI fits into your data strategy? Contact us. 

How Global Data 365 can help you?

As a leading provider of Power BI services for effective business intelligence in the Middle East and AfricaOur team of experts help organizations streamline their data management, gain valuable insights and drive better business decisions. With a focus on delivering customized solutions, our services are designed to meet the unique needs of each client and maximize the impact of Power BI on their overall success. Trust Global Data 365  to elevate your business intelligence and drive results in the Middle East and Africa. 

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The Future of AI in the Business World

Home > Blogs > The Future of AI in the Business World

The Future of AI in the Business World

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.

The Future of AI in the Business World

When it comes to the implementation of Artificial Intelligence (AI) in an enterprise, the world is at a crossroads. Although the technology that allows computers to mimic human thinking has advanced steadily over the last half-century, the cost-effectiveness of deployment, convenient access to cloud computing, and realistic business use cases are all positioned to help AI make a major impact in the business world in the coming years.

With the future use cases for AI in the industry on the way, and also the capital investments and speed of progress currently powering AI, one thing is for sure: To realize the benefits flowing to the business world, you’ll have to get your framework in place relatively soon. But how are you going to do it? Business intelligence (BI) tools can help with this. Businesses can plan for the future while still taking full advantage today by laying the groundwork with this readily available, open, and inexpensive software. Businesses are starting to question if it makes sense to move through an expensive deployment that won’t produce meaningful results for two or three years after having unrealistic expectations for AI, which have yet to emerge. Instead, they should concentrate on implementing BI today to get some quick results, then layering AI on top of existing BI data to extract new insights and generate greater value as the technology advances.

So, how can BI apps assist in preparing the company for AI, and what potential use cases can be derived from the combination of AI and BI?

What can BI Software Do for You?

Whichever side of the Artificial Intelligence and Business Intelligence discussion you are on, one aspect is certain: you’ll need data to support both. There is no intelligence in AI or BI without data to operate on. There will be nothing to analyse or to which a learning algorithm can be applied. When it comes to intelligence solutions, data is the cornerstone that must be laid.

Data has never been more accessible in today’s business world due to the wider acceptance of cloud computing and the Internet of Things. However, the massive amounts of data produced every day are posing a new challenge for businesses: What knowledge is crucial? What are the best practices for tagging, sorting, grouping, and analysing data? What concerns are answered by disparate data points? How can data collection through various touch points, from retail to supply chain to a factory, be seamlessly implemented?

Data Warehouse

This is where data warehousing comes in. Data warehouses are a way of optimizing data obtained from various touch points, like point-of-sale, CRM, inventory, and warehouse management systems), structuring it to obtain needed insights, and running research. Enterprise companies cannot thrive without efficient data warehousing; data silos consume capital and resources quickly, and any company still attempting to piece together “business intelligence” from numerous reports and fragmented data will quickly fall behind those with centralized data and reporting. The integrated data warehouse, on the other hand, isn’t just a set of relational databases thrown together; it’s based on modern data storage systems like Online Analytical Processing (or OLAP) cubes.

Cubes are multi-dimensional data sets designed for analytical processing applications like AI or BI. Cubes are superior to tables in that they can connect and sort data across several dimensions, enabling non-technical users to access a wider wide range of role-specific and highly contextual data points to discover new insights and make real-time adjustments to strategies and decisions. Most non-technical sales agents and buying associates will struggle to link several tables along with a standard report, but with Business Intelligence cubes, all they have to do is drag and drop the metrics and dimensions that apply to their own customized dashboard.

So, how do you get the data? SQL is a language for manipulating and extracting data from cubes. SQL was created as a common language for interacting with databases, irrespective of the type of database being used, and is ultimately the tool for extracting, retrieving, deleting, modifying, and handling data in a table.

Other Methods to Address Data Demands

Aside from data warehousing and OLAP cubes that provide the technological base, there are a few other components that can assist enterprise businesses in meeting their data needs:

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Data Modelling

Data modelling is a technique for sorting out individual data sources within an organization and deciding how they should communicate to obtain the most useful business insights. Data Modelling can be done at the conceptual (high-level, linked to business objectives), logical (mapping to each business function), and physical (how the actual measurements, metrics, and structures are related inside a data cube).

Analytics and Reporting

The ability to capture, structure and store data is essential, but the ability to analyse and report on it is the ultimate objective. End users can find the valuable insights they need with little technological expertise due to business intelligence solutions that provide easy, open analytics and reporting functions. It also facilitates business processes in avoiding unnecessary data bottlenecks by providing them with immediate access to the data they need.

Data Visualization and Dashboards

Business intelligence relies heavily on analytics and reports, but you are not alone if you have ever spent hours sifting through a table of values trying to find out what the data means. Important insights are presented in vivid graphical representations that are much easier for the user to understand using data visualization tools. According to Aberdeen Group research, businesses that use data visualization software are 28 percent more likely to find accurate information than those that depend entirely on controlled reporting; the same research also found that 48 percent of business intelligence users at companies with visual data exploration will find the information they need without the assistance of IT personnel. Dashboards can quickly compile visualizations and reports into customized displays for each end-user or business activities, by providing individuals with immediate access to KPIs that help drive improved business results from the ground up.

Protection, usability, and efficiency are three major benefits that business intelligence solutions help to drive, as well as three key indicators of enterprise business performance. Protection, usability, and efficiency are three major benefits that business intelligence solutions help to drive, as well as three key indicators of enterprise business success.

The Future of AI

Soon, AI algorithms will be expected to be efficiently implemented in your current data stores, providing you with even more insight. AI applications in line with business should fall into three categories:

Automated Processes

The most common use of AI in the business right now is to automate systems and business processes. Although previous iterations of automation focused on sharing data between systems, AI can take this skill to the next level by interacting with data as if it were a person, either inputting or consuming as required. AI robots are now capable of analysing legal contracts and extracting specific clauses, updating customer records through several networks, and automating customer outreach in response to changing circumstances. Businesses will be able to automate even more processes as these algorithms become smarter.

Meaningful Insight

Cognitive insight is the ability to derive meaning and distinguish patterns from large amounts of data using AI algorithms. Although BI software and data stores will certainly include the “diet” for cognitive insight algorithms, as they learn, they will be ready to access those learnings to larger data sets, respond to new data in real-time, and recognize possible data matches across multiple platforms.

Cognitive Engagement

The human-interfacing aspect of AI is referred to as cognitive interaction. Consider chat-bots, knowledge bases, and product recommendation engines, among other things. Externally (for customers) or internally (for employees), cognitive engagement technologies can be used to simplify the connection between users and systems. Since companies are still wary of the relatively new technologies, most existing applications concentrate mainly on internal engagement. However, as AI growth and implementations progress, objections are likely to fade away as companies discover new ways to use existing data to drive practical automated interactions with humans all over the world.

Takeaway

In the future, artificial intelligence will eventually start to live up to its potential. We have been reading a lot about the excitement in the business world. Computers can assist in ushering in a new age. For cutting-edge businesses, a new age of growth and profitability awaits, but only if you have already laid the groundwork, which begins with business intelligence.

At Global Data 365, we understand that business intelligence and data management are essential components of every enterprise artificial intelligence strategy. To get the most out of artificial intelligence, you need to start with good reporting and analytics. So make sure you are laying the groundwork for your company’s future success today!

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Overcome Your Fear of Advanced Analytics

Home > Blogs > Overcome Your Fear of Advanced Analytics

Overcome Your Fear of Advanced Analytics

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.

Overcome Your Fear of Advanced Analytics

We have all read that information is nothing unless it can be turned into meaningful data. It can be daunting in business to have access to so much data. How do you know where to begin when you’re bombarded with information It’s possible to get lost in the midst of it all, overwhelmed by the flood of data and uncertain where to start looking for ways to improve business processes. That is where you can utilize advanced analytics.

Advanced analytics is the automated or semi-autonomous analysis of data using materials and tools that go beyond traditional business intelligence (BI). It’s a catch-all word for a number of analytics sub-fields that collaborate using predictive capabilities. High-level approaches and software forecast future patterns, activities, and events, allowing businesses to create “what-if” models and estimates to future-proof their operations.

Advanced analytics involves data mining, big data, and predictive data analytics, which allows you to mine your data for deeper, more analytical, and eventually actionable insights. Whereas traditional analytical methods show you where you’ve been, advanced analytics focuses on where you’re going next, providing insight into what could happen based on a variety of potential opportunity scenarios.

Advanced analytics includes newer technology such as machine learning and artificial intelligence, as well as visualizations. In reality, advanced analytics encompasses so many areas and has such a wide range of applications that it has a variety of applications, including marketing, inventory and warehousing, and manufacturing. Keeping this in mind, it appears that any company should be interested in using advanced analytics to solve critical business problems.

Assumptions Regarding Advanced Analytics

Many CFOs still have assumptions about advanced analytics:

– They cost money.
– They take time to deploy.
– They are complex to understand.

Advanced Analytics is Costly

You might be wasting money if you’re sitting on a heap of useful data and aren’t analysing it because you’re afraid of investing in advanced analytic tools. Your data is your most valuable resource for uncovering answers to your potential questions by properly processing your history.

Many enterprise resource planning (ERP) systems quickly integrate with external services that are both dependable and cost-effective. When you are likely to afford less time doing the hard work and more time understanding the report findings, the rewards can quickly outweigh the costs.

Deployment takes Time

Yes, some technology takes a long time to get up and running. Running out the first ERP or switching to a new one is a lengthy process that can take months to complete, and that’s assuming everything goes smoothly.

This is not the case for advanced analytics. Many of these systems can be set up in a matter of hours, if not minutes, and begin crunching the data right away.

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Complex to Understand

Although advanced analytics was developed to use complex formulas and equations, they are used to provide the end-user with data that is simple to understand. In reality, several advanced analytics user interfaces are built to help people from all walks of life use data to search for information.

Enterprise solutions can also assist users in learning techniques by assisting them in selecting and processing appropriate data from a variety of sources. The end-user’s experience will be simple to navigate, regardless of the technicality of what advanced analytics might be doing in the context.

Still Having Doubts?

If you’re still having doubts about using advanced analytics even after clearing out the assumptions, we are here to clear them out.

Begin by gathering the information you’ll need to examine. For your business, this may have been a time-consuming process in the past. Data is stored in an ERP (or two) as well as other diverse relational databases that don’t always get along. As a result, putting together the data could have required many data backups and several hours of manipulation to get it into the format you need. If the data changes during this phase, for example, if you receive a late invoice payment that still counts against the month you’re working on, you’ll have to restart the whole process.

Using Global Data 365’s finest reporting systems, you can connect data from more than 140 ERPs and EPMs, as well as other relational databases, into one automatic report that lives inside the framework of Excel or displays in a readily available web-based dashboard. Dynamic links are used in our solutions to extract real-time data from your ERP, connect it to other sets of data, and deliver the accurate reports you require based on safe and simple-to-assemble parameters.

With one click, you can check your reports. Return information for a single account, a collection, or a wildcard quest. It’s all designed to offer users quick and easy access to their data so they can spend less time figuring out what is going on and more time predicting the future.

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