Data Management

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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9 Ways you are failing at Business Intelligence

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9 Ways you are failing at Business Intelligence

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.

9 Ways you are failing at Business Intelligence

Business intelligence is critical for making strategic business decisions, but often organizations’ BI efforts are hindered by bad data practices, tactical errors, and other factors. Executives understand the importance of having high-quality data when making business decisions. However, obtaining reliable data in a timely and user-friendly format continues to be difficult. Yes, there is a struggling market of business intelligence (BI) analysts and distributors.

How can you determine ways you are failing at Business Intelligence and it’s time to update or recruit specialist experts? Knowing where others have gone wrong will help you answer these questions.

Doing What Customers Ask, Instead of What a Company Needs

Surely placing customer satisfaction as the top priority leads a company to success. However, when it comes to technology, business users can not always grasp what they are requesting. Apart from that, they try to impose the solution’s technical information.

BI failure is a result of implementing what consumers want rather than what they need. Successful BI projects necessitate the ability to adequately verify BI findings, and the ability to elaborate and manage requirements. One way of understanding what consumers really need is to use the “5 whys” approach, which involves asking why five times about a single problem to gain greater depth.

Using Less Time and Money for Testing

In the marketing world, think about moving fast and break things is a common mantra. And well-established companies need pace. However, in the race to go faster, things that are seen as additional services, such as testing, will suffer. Seeing testing as a waste of time may lead to serious quality problems, particularly if manual testing is used. Instead, look to research and related “ancillary” processes to provide a better BI experience.

Limiting testing, particularly when the only testing performed is manual, results in a high number of errors in user testing, which has an impact on product delivery.

Short-Term Broader Data Integrity is Important

Reading, viewing, and analysing data is a convenience with business intelligence software. But what if the data you’re providing the system is tainted? Or, to put it this way, how can you show an IT analyst that your management decisions are based on high-quality data? If you concentrate solely on the BI tool and its setup, you can overlook this crucial information.

Taking a Defensive Approach to Unsatisfied Customers

Dealing with irritated users is not something any technology expert looks forward to. There will be system errors and annoying points. Your response to these issues will determine whether or not your BI project succeeds.

The two most common mistakes that BI new comers make are concentrating all their attention on delivering requests and failing to include business end-users in the project. What matters is, are you providing your customers with the information they require to make decisions? Do you know what information they require? Is there an alternative to making a new report to solve the problem? It’s preferable to prioritize user complaints based on their relative relevance to your overall plan rather than simply dismissing them.

Conducting Analysis With No Purpose

When you have effective resources at your side, it’s only normal to look for ways to use them. Business intelligence without guidance, on the other hand, is a waste of time. This issue is especially prevalent among young professionals.

Inexperienced and eager business intelligence practitioners risk developing tunnel vision and doing interesting research that isn’t motivated by meaningful questions. The findings often lack a ‘so what’ finding and struggle to offer actionable insights. It takes business knowledge and judgement to avoid this blunder. One way to avoid the “so what” dilemma is to ask yourself, “How does this research apply to the company’s goals?”

Thinking Data is Sufficient

Is it possible that “more data” can solve all of our business problems? Many aspects of business intelligence and analytics are based on this unspoken presumption. It’s not going to be working to just drop data at an executive and hope for the best.

Data is dismissed or trumped by belief if it isn’t interpreted and argued convincingly. The importance of making a strong case and crafting a compelling narrative can never be underestimated. The field analysts may be aware of the implications of data collection. You can’t presume that those who are a few steps away from the data will understand that argument.

Relying only on BI tools

Technologists understand that the right method will make a huge difference. Consider the first time you used a script to automate a time-consuming process. Those early victories motivate you to keep looking for new ways to solve business problems. Unfortunately, putting too much reliance on your business intelligence tool can lead to disappointing results.

Even if the tools are becoming more user-friendly, there are process, cultural, and learning elements that must be addressed to achieve progress.

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Vendor Management is Ineffective

It is possible that your organization doesn’t have a business intelligence department. Working with outside experts makes sense in that situation. You could hire them to act as an outsourced service provider or to help on a particular project. In any case, you must know your vendor and provide oversight, particularly when it comes to subcontractors.

It is your duty to manage the problem and figure out who is working on your behalf if a third party is involved. Otherwise, you might be in for a BI failure.

Dismissing Tools like SQL and Excel

Are you aware that there are Microsoft Excel championships held every year? Take, for example, the Microsoft Office Specialist World Championship, which attracts over 500 thousand participants and offers cash prizes to the winners. That is just one indication of Excel’s growing popularity in the corporate world. SQL has a large following in the technology community but to a lesser extent.

Therefore, identify ways you are failing at Business Intelligence and make a big shift with power of BI in a company with ramifications for employees’ jobs. In leading people through the process, the practice of change management and leadership cannot be overlooked.

If you’re interested in knowing how agile BI solutions can lead your company to success, contact us now.

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What are Data Lakes

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What are Data Lakes?

May 21, 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.

What are Data Lakes

The huge volume of data collected by today’s company has entailed a drastic change in how that data is stored. Data stores have expanded in size and complexity to keep up with the companies they represent, and data processing now needs to stay competitive, from simple databases to data warehouses. As enterprise businesses collect vast amounts of data from every imaginable input through every conceivable business feature, what started as a data stream has developed into a data flow.

A new storage solution has emerged to resolve the influx of data and the demands of enterprise businesses to store, sort, and analyse the data with the data lake.

What is a Data Lake and What Does It Contain?

The foundation of enterprise businesses is a collection of tools and functions that provide useful data but seldom in a structured format. The company’s accounting department may use their chosen billing and invoicing software, but your warehouse uses a different inventory management system. Meanwhile, the marketing team is dependent on the most efficient marketing automation or CRM tools. These systems rarely interact directly with one another, and while they can be pieced together to respond to business processes or interfaces through integrations, the data generated has no standard performance.

Data warehouses are good at standardizing data from different sources so that it can be processed. In reality, by the time data is loaded into a data centre, a decision has already been taken about how the data will be used and how it will be processed. Data lakes, on the other hand, are a larger, more unmanageable system, holding all of the structured, semi-structured, and unstructured data that an enterprise company has access to in its raw format for further discovery and querying. All data sources in your company are pathways to your data lake, which will capture all of your data regardless of shape, purpose, scale, or speed. This is especially useful when capturing event tracking or IoT data, while data lakes can be used in a variety of scenarios.

Data Collection in Data Lake

Companies can search and analyse information gathered in the lake, and also use it as a data source for their data warehouse, after the data has been collected.

Azure Data Lake, for instance, provides all of the features needed to allow developers, data scientists, and analysts to store data of any scale, shape, or speed, as well as perform all kinds of processes and analytics across platforms and languages. Azure Data Lake simplifies data management and governance by eliminating the complications of consuming and storing all of your data and making it easier to get up to speed with the queue, streaming, and interactive analytics. It also integrates with existing IT investments for identity, management, and security.

That being said, storage is just one aspect of a data lake; the ability to analyse structured, unstructured, relational, and non-relational data to find areas of potential or interest is another. The HDInsight analytics service or Azure’s analytics job service can be used to analyse data lake contents.

Analytics Job Service

Data lakes are especially useful in analytical environments when you don’t understand what you don’t know with unfiltered access to raw, pre-transformed data, machine learning algorithms, data scientists, and analysts can process petabytes of data for a variety of workloads like querying, ETL, analytics, machine learning, machine translation, image processing, and sentiment analysis. Additionally, businesses can use Azure’s built-in U-SQL library to write the code once and have it automatically executed in parallel for the scale they require, whether in.NET languages, R or Python.

Microsoft HDInsight

The open-source Hadoop platform continues to be one of the most common options for Big Data analysis. Open-source frameworks such as Hadoop, Spark, Hive, LLAP, Kafka, Storm, HBase, Microsoft ML Server, and more can be applied to your data lakes through pre-configured clusters tailored for various big data scenarios with the Microsoft HDInsight platform.

Learn More About Microsoft HDInsight

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Future-Proof Data

For companies, data lakes reflect a new frontier. Incredible possibilities, perspectives, and optimizations can be uncovered by evaluating the entire amount of information available to an organization in its raw, unfiltered state without expectation. Businesses may be susceptible to data reliability (and organizational confidence in that data) and also protection, regulatory, and compliance risks if their data is ungoverned or uncatalogued. In the worst-case scenario, data lakes will have a large amount of data that is difficult to analyse meaningfully due to inaccurate metadata or cataloguing.

For companies to really profit from data lakes, they will need a clear internal governance framework in place, as well as a data catalogue (like Azure Data Catalogue). The labelling framework in a data catalogue aids in the unification of data by creating and implementing a shared language that includes data and data sets, glossaries, descriptions, reports, metrics, dashboards, algorithms, and models.

Built your BI Infrastructure

The data lake will remain a crystal-clear source of information for your company for several years if you set it up with additional tools that allow for better organization and analysis, such as Jet Analytics.

At  Global Data 365, you can contact our team to find out more information on how to effectively organize your data or executing big data systems seamlessly.

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Why Is Good Data Management Essential For Data Analytics

Home > BlogsWhy Is Good Data Management Essential For Data Analytics?

Why Is Good Data Management Essential For Data Analytics?

May 21, 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 Good Data Management Essential For Data Analytics

Today, Businesses have more data at their disposal than ever before. Over time, businesses that can efficiently use data as a strategic advantage can eventually achieve a competitive edge and outperform their rivals. Business administrators, on the other hand, must add order to the chaotic world of various data sources and data models to do this. Data management is the general term for this method. Data management is becoming an essential component in successful business management as the amount of available data grows.

On the other hand, a lack of effective data protection can lead to incompatible or unreliable data sources, as well as data quality issues. These challenges can hinder an organization’s ability to derive value from data-driven insights, recognize patterns, and spot problems before they become major issues. Worse, bad data management can lead to managers making decisions based on incorrect assumptions.

Availability of Data

The emergence of systems, such as ERP, CRM, e-commerce, or specialized industry-specific applications, is causing such problems. When you add web analytics, digital marketing automation, and social media to the mix, the data volume skyrockets. When you add in external data from vendors and service providers, it becomes unmanageable.

Many businesses understand the importance of using externally sourced third-party data to supplement and extend the context of knowledge they already have. However, it’s difficult to imagine taking that step without first having a grasp on the organization’s current data. Bringing all of this uncertainty under control is a key first step in implementing a strategic data analytics program. That is a two-step method from a high-level perspective. To begin, you must collect all of the data and store it in a centralized location. This includes filtering, transforming, and harmonizing data so that it blends to form a coherent whole.

Secondly, the data must be available to users around the enterprise so that you can put it to good use and add value to the company. In other words, you must implement processes that allow users within the organization to access the information easily, efficiently, and with enough versatility that they can evaluate and innovate without extensive IT training. To ensure efficiency, you must identify and implement these two aspects of data management individually. Flexibility and usability result from a pre-built data management process and interface; the quicker you assemble and clean up the data, the easier the data will start producing value for the business.

Multiple Systems

When a company runs several processes, data processing becomes a problem. As previously stated, this may include ERP, CRM, e-commerce, or any other software framework. It’s also usual for many companies to use several systems to accomplish the same job. Different ERPs may be used by different divisions or corporate agencies operating under the same corporate name. This is especially true when it comes to mergers and acquisitions.

Many businesses would like to perform reports against historical data stored in a defunct database. Since migrating accurate transactional data to a new ERP system is not always feasible, many companies use a workaround or simply go without, leaving important legacy data out of their existing reporting systems. Multiple data models are invariably present when multiple software systems are involved. A clear report detailing all of the company’s customers becomes a little more complex. If one ERP system has different tables for clients and vendors, while the other merges them into a single table (using a single field to classify them as customers, vendors, or both). Before loading data into a centralized repository with a uniform approach of the customer, you’ll need to extract and transform data from those two ERP systems. The process must include a type of translation in which data structures and semantic models are aligned.

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Extracting, Transforming, and Loading Data

The term “ETL” refers to the method of processing, converting, and loading data into a central repository. ETL is one of the most important aspects of a data warehouse, and it’s necessary for businesses who want to provide dependable, scalable, and reliable reporting. A data warehouse that embraces a complete view of data from across the enterprise, irrespective of which system it came from, is the end product of a very well ETL process.

This procedure often connects records that are spread through different systems. It is normal, for example, to designate master records with unique identifiers that aren’t always consistent across two or more systems. The central repository must link those two documents and classify them as the same individual to create reports that provide a full image of that customer.

Diverse Options

You’ll be confused if you search “BI solutions”, attend a related tradeshow, or read quite a lot of BI reports. There are several options available. But how do you know which approach to business intelligence is right for you?

The solution is to avoid putting the cart before the horse. First, assess the requirements. Evaluate them from a market and a technological standpoint, and then use the results of that exercise to guide the quest for approaches and solution providers.

Self-Service Reporting and Data Visualization

The second important aspect of good data management is to make information readily available to users across the enterprise. Provide them with resources that allow them to innovate and add value to the company. In fact, data visualization tools are becoming a strong tool for informing, aligning, and encouraging leaders across entire organizations. Data visualization tools are now simpler to deploy, maintain, and use than ever before.

Until recently, installing and maintaining a data warehouse facilitated a significant investment in highly specialized technical services. A reliable computing infrastructure capable of handling the necessary workloads. Legacy tools necessitated a thorough understanding of the source data as well as meticulous preparation ahead of time to decide how to use the resulting data. Modern data visualization tools are extremely efficient and adaptable, requiring far less advanced IT knowledge. Many of the tasks associated with designing dashboards, graphs, and other visualizations can now be performed by frontline users who communicate with the data daily.

Data Management with Jet Analytics

Both aspects of the data management process as described here, are provided by Jet Analytics from Global Data 365. For starters, it offers a robust framework for constructing a data warehouse. With developing and managing the ETL method, bringing data from various fragmented systems under one roof for simple, relevant reporting and analysis. Along with that, Jet Analytics provides a robust reporting package that allows practically everyone in the company to create powerful visual dashboards, analyses, and ad hoc analysis.

To find out more about how Jet Analytics can help your company manage the complexity of multiple data sources, contact us.

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Why-Power-BI-Is-a-Better-Choice-than-Excel-for-Analytics

Home > Blogs > Why Power BI is a Better Choice than Excel for Analytics

Why Power BI is a Better Choice than Excel for Analytics

April 21, 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-Power-BI-Is-a-Better-Choice-than-Excel-for-Analytics

Modern businesses depend on data, and we’re producing more of it than ever before. However, accumulating volumes of digital data is useless unless companies can make use of it. Business intelligence tools can help with this. Are you planning to introduce a platform to assist you in extracting valuable, actionable insight from your data? You have arrived at the right place.

We will go over the fundamentals of Microsoft’s flagship BI app, Power BI, in this article, like what it can do, what it costs, and what changes it can provide to your company.

Characteristics of Power BI Desktop

– Can link to several different data sources. With the Auto-Refresh option, you can keep this data up to date.

– It aids in the rapid modelling of data.

– Using the drag and drop map, it is possible to generate interactive reports.

Characteristics of Power BI Service

– It is a web portal that allows users to monitor and view reports generated with Power BI Desktop.

– Reduce the amount of time spent moving and sharing information.

– Data can be imported from a variety of on premise sources (Excel, DB, CSV, etc.) or directly documented from a variety of cloud web services, including Azure, MailChimp, Zendesk, and Salesforce.

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Why should you Choose Power BI over Excel?

Power BI has many benefits to offer than Excel. Listed below are some of the benefits.

Convenience and Data Size

Power BI can handle massive amounts of raw data as well as several data tables. The analytical tool is capable of loading and processing large amounts of data into a single PBIX file. Multiple tables can be configured and, if necessary, combined based on common fields. In terms of user interface and ease of use, the Power Query Editor and Data Modeling parts are easier to use.

Data Connectivity and Auto Refresh

One of the key reasons to use Power BI is that it can link to a broad range of data sources, including databases, online sites like Facebook, and Salesforce reports, among others. When compared to the previous data, the data is automatically inserted into the Power BI Workbook. Excel’s ODBC Driver takes up a lot of time.

Power BI has a great choice for keeping data in alignment with the source called Auto Refresh. To have all the reports updated, Power BI Desktop has a Refresh option, and Power BI Service has a Refresh Now, as well as a Scheduled Refresh option. When you choose Refresh, the data in the file’s model is replaced with the most recent information from the original data source. This form of a refresh, which takes place entirely inside the Power BI Desktop program, varies from Power BI’s Refresh Now and Scheduled Refresh solutions.

Power BI uses the information in the database to link to the data sources identified for it, search for updated data, and then upload the updated data into the dataset when you refresh data in a dataset, whether using Refresh Now or setting up a refresh schedule. In Power BI, unlike Excel, the dashboard can be refreshed.

Reporting and Cross-Filtering

Power BI reporting is much more advanced and engaging than Excel reports, and a single graph can provide numerous perspectives. In Excel, cross-filtering is not possible, but it is possible in Power BI. This has an impact on how users want the filtering for data with table relationships to move.

Alerts and Emails

To submit a mail and a reminder in Excel, a user has to use the VBA Editor to generate a macro. In Power BI, creating a warning and sending an email when a condition (such as a threshold value) is met has never been easier. This will keep users updated when on the move, and they will be able to view the report at any time and from any place.

Some other features include:

Natural Language Query (NLQ)

By asking a common person question in Power BI Service, everyone can get a fast response from the current insights. It’s helpful when someone isn’t familiar with the data model but needs fast answers to questions about the insights. Furthermore, this saves a lot of time.

Deeper Insights

The backend program, which is driven by intelligence and algorithms, can generate interactive insights at the touch of a button. It will help you save time and interpret data more quickly.

Dashboards and Customized Reports

The reports that are produced can be modified in every way to achieve the desired outcomes. On the dashboard, the report tiles can be rearranged and relocated as desired.

Sharing Reports and Access

The reports and dashboards may be shared with the general public or only a small group of associates.

Downloading and Exporting Dashboards

The Power BI Service allows you to download and transfer dashboards in a variety of formats. The dashboards can be submitted as a .PBIX file or exported as a PowerPoint presentation, PDF, or event print.

Conclusion

In today’s data-driven environment, a fast and efficient data analytics tool is needed. Power BI makes use of business intelligence to ensure that all reports are produced efficiently and provide a wealth of information. Changes in time and technology necessitate the use of a versatile tool like Power BI, which makes work easier and saves time while delivering the best performance. Get a suitable training for your needs or for more information, contact us today!

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Difference between database and data warehouse

Home > Blogs > The Difference between Database & Data Warehouse

The Difference between Database and Data Warehouse

May 21, 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.

Difference between database and data warehouse

For corporations of all sizes and sectors, the world of Big Data keeps expanding. The performance and profitability of any business rely mainly on the volume, consistency, and reports of the information they gather and how well the companies will analyse, gain input from, and take action on the data they have collected. It is not easy to transform the raw data collected into valuable insights.

It requires organizations to learn the practice of corporate data management so that workers can effectively produce, archive, view, handle and interpret the data they need to succeed at their work. So, when it comes to gathering, storing, and analysing data, what could prove to be the right decision for your company? The most common types of data storage in enterprise data management are databases and data warehouses. So what is the difference between a database and a data warehouse, and which one is the right choice for your company?

What is a Database?

By definition, a database is a systematic collection of data gathered in a way that makes common sense and makes data search, storage, manipulation, and analysis easier. Typically, databases contain data assembled in rows, columns, and tables, arranged primarily for easy insight and the collection of various events. The most common type of organizing databases is SQL (relational), NoSQL (non-relational), CRM systems, and Excel spreadsheets.

Databases contain multiple tables, each of which consists of columns and rows. Every column is appointed to an element, and a single record is held in every row. To browse through a relational database, users type questions in Structured Query Language (SQL), a domain-specific language for database communication.

It is possible to store databases either on a local server or in the cloud and access them for reporting in various ways through the system’s limited native tools that are integrated with the data collection itself to Excel exports or different options for direct connectivity. Using SQL to write queries can be a huge benefit for productivity and easy use, but in terms of data hierarchy, relational databases are often less versatile and more static.

What is a Data Warehouse?

Data Warehouse can be defined as a system that collects and stores data from several diverse resources within an enterprise. In comparison to a database, a data warehouse’s infrastructure is designed to get the data out, and not just by technical tools, but for regular users like finance professionals, executives, management, and other workers.

The objective of a data warehouse is specifically business-oriented: it is intended to promote decision-making by enabling end-users to consolidate and interpret data from multiple sources. Being the basis for BI and analytics, it takes out information from existing databases, defines a series of rules to covert the data, and then transferring it into a single central repository to view and manage easily.

A data warehouse stores information of the transfer level and supports the larger reporting and analytical needs of an organization, providing one basis of reality for building semantic models or the provision of organized, simplified, and aligned data for tools, such as Excel, Power BI, or even SSRS. Companies that have a higher level of data or analytical needs tend to use a data warehouse. Regular data transactions like standard costing, currency conversions, unit of measure conversions, and other business approved and permitted calculations are all integrated into the data warehouse by making sure that reports reflect the desirable data. The only drawback to a data warehouse is that it is complicated, time-consuming, and costly to construct and maintain.

Key Differences between Database and Data Warehouse

With more volume and complexity of data used in the organizations, they want to receive more analytical insight, which is why data warehouses are receiving more visibility for database reporting and analytics. The key distinction is that databases contain accumulated data that are organized. Whereas data warehouses are data systems constructed from various information sources, as they are used to analyse information.

Below are some more differences that further distinguishes database and data warehouse from each other.

– Databases use OLTP Solutions, whereas data warehouses are better suited for OLAP solutions.

– Databases are designed to manage thousands of users at a time. Due to their complex structure, data warehouses can only manage a small amount of data users.

– For small, atomic transfers databases are more useful. Data warehouses are equipped for larger queries that need greater analysis.

– Downtime of databases can be costly, as they need to function all the time. Data warehouses are not compromised by downtime.

– For CRUD operations, databases are configured to be quick in creating, reading, updating, and deleting data. Data Warehouses are configured for a limited number of complex queries over several large data stores.

– Databases are organized as effectively as required, with multiple tables without duplicate data. Usually, data warehouses denormalize their information, valuing reading operations over-writing operations.

– Usually, databases store only the updated data, which makes it impossible for old queries. Data Warehouses have been constructed solely for reporting and analysis.

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

Importance of Databases and Data Warehouses for Businesses

Companies can reap the benefits of both databases and data warehouses for reporting and analysis in different ways. Let’s see why:

Data Quality and Accuracy

Data warehouse includes transferring information from different sources, standardising it, naming it, arranging it, and making sure the uniform restrictions are sorted and labelled. This ensures better confidence in the information being displayed, minimizes organizational errors, and gives better possibilities for partnership as independent business sectors like sales, marketing, and finance all depend on similar reporting from the data repository.

Power Business Intelligence

One of the greatest advantages of data warehousing is the rising scope and efficiency of data storage. By optimising access to the data of your organization, you are strengthening the leadership’s willingness to adopt a smarter plan centred on a more complete and effective solution. Data warehouse-powered business intelligence provides deeper insight into sales operation, financial stability, and much more.

Increased ROI

The use of data warehousing helps organizations to save money on their analytics, and as a result, a larger amount of profit is generated. As the expense of data warehousing reduces, this effect grows exponentially, and by using BI software and data warehousing in coordination to effectively democratise data and slash headcount in reporting and analytics operations, companies can generate a return on investment faster.

Improved Efficiency

Data warehouses are designed for speed, in particular to providing large businesses quick access to retrieval and analysis of data. Instead of devoting useful numerical data, data warehouses are all about the ability to edit and maintain specific data records. By making sure that the data can be obtained, collated, and processed as easily as possible, the process of making important business decisions in an instant becomes easier.

Best Way to Build a Data Warehouse

It is popularly known that there are as many ways to create data warehouses as there are companies to develop them. Every data warehouse is special, as it adheres to the requirements of business users in numerous functional areas in which firms face diverse market environments and competitive forces.

Creating the Staging Area

Before analysis of the data, it goes through the process of retrieval, conversion, and loading of data. As the warehouse is as strong as the data stored within it, for the success of your company it needs to match department requirements and objectives.

Building an Environment

Usually, data warehouses have three main physical settings: development, testing, and manufacturing. And these three settings will exist on entirely different physical services.

Data Modelling

Data Modelling is the process of visualizing the distribution of data in your data warehouse. Before constructing a data warehouse, it is important to know where and why data goes. This is why data modelling is used.

Choosing Your Extract, Transfer, Load Solution

ETL Solution is the process you will use to extract data from your existing storage solution and place it in your warehouse. That is why it is pertinent to carefully choose the right ETL solution for your warehouse.

Create Front-End

It is important to have front-end visualization, so users can instantly comprehend and utilize the results of data queries. BI tools like Power BI work best for visualization, and you can also customize your own solution.

Queries Optimized

Having your queries optimized is a complicated process that answers your required needs. Make sure that your manufacturing, testing, and development setting have similar resources to prevent lagging.

Conclusion

Database and data warehouse serve different functions in practice. If you are contemplating about building your own data warehouse or database, then it is one indication that the organization is dedicated to the practice of effective corporate data management.

Every company has different needs to build a data warehouse, which is why Global Data 365 designed a reporting and BI solution that provides the user with a pre-built data warehouse and cubes set ready to be used. With a wide dashboard library and report templates, Jet Analytics is built to provide you useful insight day one into your results. In the years to come, the accuracy, durability, and usability of data will be the key differentiator for firms of all types. That is why organizations would want to make sure that they are placing themselves up for sustainable growth by selecting the best infrastructure and storage.

To know more about data warehouse and how you can implement it in your business, contact us now.

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