Where BI fits into your Data Strategy

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Where BI fits into your Data Strategy?

July 26, 2021

Global Data 365 is composed of highly skilled professionals who specialize in streamlining the data and automate the reporting process through the utilization of various business intelligence tools.

Where BI fits into your Data Strategy

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

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

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

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

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

Business Intelligence for Business Use

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

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

BI as a Gateway to Business Analytics

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

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

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BI as the Future

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

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

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

How Global Data 365 can help you?

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

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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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