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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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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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