Business Intelligence As Well As Huge Information Developer – Business Intelligence (BI) is a very complex area of science/industry, which is very difficult to understand and very easy to misinterpret – especially for those who are not domain experts in the field. The purpose of BI is to convert complex data into information and knowledge to help knowledge workers and managers better understand, analyze, and develop competitive business strategy. People working in this field are very familiar with the difficulty in translating BI business requirements to the technical side and explaining why some technical implementations are so complex, requiring large amounts of resources to meet specific business requirements. it occurs.
In every BI relevant project, it is important to identify the components and aspects of the BI environment that will be affected by the project and that may need to be modified. This is a very complex task, because it is difficult to initially identify all the elements that make up a BI environment and identify the relationships and dependencies between them.
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To support the process of identifying elements of the BI environment that could be influenced or modified by the project in question, I developed a Holistic Framework for Business Intelligence (HBIF) as part of a project at Staffordshire University. HBIF is the result of more than 12 months of work and involving more than 130 business intelligence and data warehousing experts from 27 different countries.
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Its aim was to explain the complexity of BI in one picture, as well as to enable an immediate understanding of the BI environment for those working outside the field.
Figure 1 presents the HBIF, which consists of two views: layers and perspectives. In the vertical view, HBIF is divided into three layers: source layer, data warehouse layer, and presentation layer. Vertical data-based separation of layers is a well-established approach based on the theoretical foundations of BI (Inmon, Kimball, etc.). The three-tier approach enables us to identify components and aspects at a specific data level when working with a BI environment. For example, it enables identification of relevant concepts, applications, hardware, types of data, and users at the data source level. It follows the typical data journey in a BI environment.
I have extended this traditional vertical and data-based three-tier separation with a horizontal presentation of the BI environment/ecosystem. This enables us to view the layers in the broader context of the BI environment. For example, the resource manager for a BI project needs to understand the project’s hardware, applications, and user requirements to be able to plan. Each perspective must be clearly defined to support optimal acquisition and delivery. The framework enables an overview of the resources required at different stages, for example implementation at the storage layer or presentation layer. The framework structure can support users with different needs. For example, IT management may be interested only in the high-level view while implementation teams, and in particular, teams dealing with hardware infrastructure and providing applications may use the framework to focus only on Are their areas of interests and expertise in the project.
The framework can be used as a stand-alone representation of a general BI environment and also provides a basis for exploring the broader BI environment.
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To learn more about the process of developing HBIF, each perspective, layer or element, please check
Despite the fact that the development of the framework was entirely my own work, the role of data engineer has existed in some form or another for the past decades. Since the beginning of the relational database, it has existed to support the development and implementation of the infrastructure as well as create the pipelines used to collect and transform data. Although initially there was a lot of variation in the tasks it performed, over time it became consistent with what was expected from Business Intelligence engineers. We will try to analyze the aspects in which the roles are similar, as well as mention the challenges faced by BI engineers, how data engineering can support their operations and what the future looks like in the era of real-time big data. Is.
Since the beginning of data warehousing, BI engineers have been tasked with understanding data and extracting insights from it. The most important aspect of the BI role is to be able to understand data from a domain knowledge perspective to create KPIs that allow measuring the success of a given operation. They are tasked with preparing simple descriptions of the business for stakeholders to improve efficiency and smooth operations by automating repetitive tasks. These overviews are usually dashboards with carefully spaced KPIs that are aggregations that should provide the most important information at a glance. They also have the responsibility to find insights from the data that allow them to be informed in a timely manner if something is not working properly, or to find out if there are new business opportunities that can be explored. BI teams create dashboards for tasks such as budget reporting, sales and stock forecasting, as well as payroll and other functions. These dashboards are then used by various teams as a source of truth providing them a way to be self-reliant when it comes to making business decisions, optimizing operations, and using the data needed to support critical decisions. Let’s use the KPIs shown for.
In the past, it took a long time for BI engineers to be able to create new visualizations and integrate new data. Earlier data warehouses had a rigid schema structure that did not allow simple changes and there was no easy way to visualize the data. BI teams had to spend a lot of time creating new sources of truth and ensuring that data matched before launching new dashboards.
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Recently there have been advances in Operational Intelligence, a trend that attempts to apply the same successful ways of working developed in BI to daily tasks. The main difference between them is that Operational Intelligence seeks to provide insights from real-time data to support immediate decisions, compared to BI which is generally used to support medium to long-term decisions. This trend pushes the boundaries of the methods used to create BI dashboards. Dashboards that were previously built entirely on data warehouses required lengthy overnight processes to provide daily updates and took weeks, if not months, to properly test and design. These new dashboards are designed to aggregate data from streaming sources like pub/sub systems, consume real-time analytics from IoT devices stored as small log files in multiple cloud data buckets, and perform stateful computation on data streams with technologies like Spark or Apache. Requires implementation. Blink. This real-time data is collected to create KPIs that accurately reflect the context in which operations are taking place in minutes rather than days.
Data engineering plays a major role to support these operations as the architecture and tools selected to build the pipelines that fuel these dashboards are of paramount importance to properly support BI teams facing these challenges.
The future will be full of examples of companies that are actively working to create data-driven cultures that seek to support all functions with clear and real-time information. The changing and ever-growing nature of data presents a challenge as BI and data engineers need to work together to create reliable pipelines that can handle these new requirements. Data engineers can leverage the use of data lakes, transactional programming to overcome gaps in tooling, and containerized microservices API vision to more easily distribute information through the organization and provide easier ways to consume data. Can adopt. Furthermore, regulation and the implementation of data governance practices hold these developments to a high standard of quality. Data engineers should always remember that they have a role behind the informed decisions of a given organization, and they should keep this in mind when designing data pipelines in the future.
What data engineering has learned from DevOps, the need for version control and accountability also applies to the way we handle data.
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