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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

Home > Blogs > Where BI fits into your data strategy

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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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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Signs that you are using bad data for decision making

Home > Blogs > When You Are Using Bad Data for Decision Making?

When You Are Using Bad Data for Decision Making?

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.

Signs that you are using bad data for decision making

Every business wants to unravel the power of big data. But is your data ready for prime time? We live in a big data environment, which is primarily due to the widespread use of computers and technology in businesses. Are you making decisions by relying on bad data? It is difficult to answer that question because you are mostly unaware that you are using bad data until it is too late.

A study conducted by Gartner reports that nearly 40% of enterprise data is unreliable, incomplete, or inaccessible. Bad data quality costs an average of $15 million a year in various forms. Such as financial loss, lost opportunities, and high-risk decision-making. What is the explanation for this? Because poor analytics is a result of bad data.

In today’s environment, the more data you gather, the better. However, with vast amounts of data from various sources covering several geographic areas, data has become increasingly complex. Although technology investment in managing business processes and collecting data has increased. It has greatly outpaced the time and money devoted to data management and governance.

So how can you be aware that the information you obtain and evaluate meets those criteria? To begin, you must first understand what qualifies as bad data.

Signs of Using Bad Data for decision making

The data that most executives are provided with almost once a month is used to make major decisions. When you have low confidence in the data you depend on, it impacts how you work. After working with several Microsoft Dynamics ERP clients who have struggled with bad data, we have compiled a list of signs that you are using bad data for everyday decision-making.

Information Silos

There are different types of reports that exist on the servers, local machines, and networks, resulting in information silos.

Incorrect Records and Manual Errors

Your financial team is forced to manually rummage through spreadsheet after spreadsheet, searching for inaccuracies and human mistakes because your month-end numbers don’t add up. Businesses that are just getting started often ignore the value of inventory management, assuming that their production isn’t high enough to justify it.

Limited Resources

The resources are stretched thin with the extra calculations and machine workarounds placed to try and interpret the data the system is generating.

Delay in Approvals

Since report and budget approvals are continually delayed, you’re having trouble getting executive buy-in.

Fixing Bugs

You devote considerable time to fixing problems and putting out fires than you do analyze and improving your data.

If these signs appear familiar, you might be unknowingly relying on inaccurate data. To get true insights into your market, you need the right tools and processes. Optimize the value of data and analytics in your company. Data is often inaccurate and unfinished, but with smart data management, efficient data governance, and centralized data storage. You can get a long way toward being a truly data-driven company.

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Using Master Data Management (MDM) to Reduce the Risk of Bad Data

Since Microsoft Dynamics isn’t designed to handle data, we suggest integrating Master Data Management (MDM) into your business intelligence. MDM works by creating a clear, trustworthy view within an enterprise, organizing numerous data objects. It gives you total control over your data. It can be used to find the most up-to-date version of the reality in your data. Ensure accuracy and transparency in data governance, and prepare data for analytics.

After you’ve developed your data governance and Master Data Management strategy, you’ll need to put it into action with centralized data storage. Allowing you to transfer and incorporate data from a variety of sources. You can produce reliable reports and dashboards that are consistent across the enterprise with a single view of your data. Enabling you to make super smart and useful business decisions.

Have a clear idea about what needs to be done to enhance the accuracy of your data and prepare it for reliable analysis. Contact Us to learn more about the effective ways to simplify data management. Find the benefit that a data warehouse designed particularly for your Dynamics approach will add to your decision-making processes.

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