Self-service Business Intelligence Tools: A Roadmap For Computer-assisted Information Scientific Research

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Self-service Business Intelligence Tools: A Roadmap For Computer-assisted Information Scientific Research – All business runs on data – information generated from multiple sources both internal and external to your company. And these data channels serve as a pair of eyes for executives, providing analytical information about what’s happening in the business and the market. Therefore, misconceptions; A lack of accuracy or information can lead to a misunderstanding of market conditions and internal workings – which can then lead to bad decisions.

Making data-driven decisions requires a 360° view of all aspects of your business, even those you might not expect. But how do you turn unstructured pieces of data into something useful? The answer is Business Intelligence.

Self-service Business Intelligence Tools: A Roadmap For Computer-assisted Information Scientific Research

In this article, We’ll discuss the actual steps in bringing business intelligence to your existing business infrastructure. Learn how to build a Business Intelligence strategy and integrate the tools into your company’s workflow.

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Business Intelligence or BI collects raw data; A set of structured and analytical practices to turn it into actionable business insights. BI considers methods and tools that transform unstructured data sets into easy-to-organize reports or dashboards of information. The primary purpose of BI is to support data-driven decision making.

Business Intelligence is a technology-driven process that relies heavily on input. The techniques used in BI can be used not only for data mining to transform unstructured or semi-structured data, but also as front-end tools for working with big data.

. With the help of descriptive and analytical analysis or BI, businesses can study the market conditions of their business and their internal processes. Historical data overview helps to find pain points and development opportunities.

Processing based on data of past and current events. Rather than producing overviews of historical events; Predictive analytics make predictions about future business trends. It also enables scenario simulation and comparison. to make possible A complex data architecture involving advanced ML techniques must be created by a professional data science team.

What Is Business Intelligence?

Therefore, predictive analytics can be considered the next level of business intelligence. At the same time, Fourth, prescriptive analysis aims to find solutions to economic problems and recommend actions to solve them. It is the most advanced type.

Although it is a broad concept that can include the organizational aspect (data governance, policies, standards, etc.), In this article, We will mainly focus on technical infrastructure. Most of them are included.

Now we’ll examine all the infrastructure components individually, but if you want to expand your knowledge of data engineering, check out our article or watch the video below.

To begin A core element of any BI architecture is the data warehouse. A warehouse is a database that stores your information in a predefined format, usually structured; Classifies and removes errors.

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However, If your data is not pre-processed; Neither your BI tool nor your IT department can query it. For this reason, You cannot directly connect your data warehouse to your data sources. Instead, you must use ETL tools.

ETL (Extract, Transform, Load) or data integration tools will pre-process raw data from primary sources and send it to the warehouse in three steps.

Usually ETL tools are provided out-of-the-box with BI tools from vendors (we’ll cover the most popular ones next).

Once you have configured data transmission from selected sources, You must build a repository. In Business Intelligence; Data warehouses are specific types of databases that typically store historical data in tabular formats. Repositories are connected to data sources and ETL systems on the one hand, and reporting tools or dashboard interfaces on the other. It allows information from various systems to be presented through a single interface.

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But a warehouse often contains a large amount of data (100GB+), which makes it easy to understand and respond to queries. In some cases, Data may be stored in an unstructured or semi-structured format, leading to a high error rate when analyzing the data to produce a report. Analytics may need certain types of data grouped in a repository for ease of use. Businesses therefore use additional technologies to access smaller, more contextual pieces of information faster.

Tip: If you don’t have a lot of data. Using a simple SQL repository is sufficient. Additional structural components such as data marts will not provide any value and will cost you a lot.

Data stored in a warehouse is usually two-dimensional, as presented in a spreadsheet format (tables and rows). A warehouse is called A method of storing data.

. Because a database can contain thousands of data types, it takes a lot of time to query a data warehouse. to quickly acquire the needs of analysts; to analyze from different dimensions and group them whenever they are needed; OLAP cubes are used.

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OLAP, or online analytical processing, is a technique that simultaneously analyzes and represents data from multiple dimensions. Building your data into OLAP cubes helps you overcome the limitations of a data warehouse.

OLAP cube is a data structure optimized for quick analysis of data from SQL databases (warehouse). Cubes source data from a data warehouse with a compact representation of it. However, Assumes that the data structure has more than 2 dimensions (row and column format of spreadsheets). Dimensions are important components that shape a report; For example, Maybe for the sales department.

Cubes are a multi-dimensional database that lets you group data in different ways and create reports more quickly. Cubes are used in conjunction with a warehouse and OLAP because they store and process small amounts of data for convenience.

Recommendation: The data warehouse + OLAP cubes architecture can be used by companies of all sizes that require complex multidimensional analysis of data. If you don’t want to bombard your warehouse with queries, consider an OLAP architecture approach.

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The warehouse is the first and largest element of the enterprise intelligence architecture. A smaller representation of warehouse datasets is a data mart that collects information specific to a subject area. With the help of databases, specific departments can get the required data.

Tip: Data warehouse + data marts is the second most popular architecture. It allows users to set up constant reporting or easy access to information without giving permission.

Enterprises may need multiple options for data management. Data marts and cubes are different technologies; But both of them are used to represent small data from the warehouse. Data marts represent a subset of the data warehouse problem, but can be implemented differently. Implementation options include relational databases (warehouse or other SQL database) and multidimensional, which are basically OLAP cubes. So you can use both technology to manage your data and distribute it across the organization’s departments.

Tip: You can use both techniques because they support the same idea, but serve different purposes. Data marts security; It can be implemented as part of a data warehouse for data aggregation or accessibility. Or you can use data marts as a multi-dimensional representation of an OLAP cube. Note, however, that both data marts and OLAP cubes will require separate database setups.

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Now that we’ve covered what BI infrastructure entails, Finally, let’s talk about how to implement this in your organization.

The BI adoption process can be divided into the introduction of business intelligence as a concept to your company’s employees and the actual integration of tools and applications. Let’s take a look at the main steps.

To start using business intelligence in your organization, first explain what BI means to all your stakeholders. How you do this depends on the size of your organization. Employees from different departments will be involved in data processing, so mutual understanding is important here. Therefore, Make sure everyone is on the same page and don’t confuse business intelligence with predictive analytics.

Another objective of this phase is to present the concept of BI to key people involved in data management. You must define the real problem you want to work on and gather the experts you need to start your business intelligence.

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At this stage, It is important to mention that you have to make technical assumptions about the data sources and standards defined to control the data flow. You can validate your assumptions and define your data workflow in later steps. So you must be ready to change your data source channels and your team list.

A big step after setting the vision is defining the problem or group of problems you will solve with the help of business intelligence. Setting goals will help determine more high-level parameters for BI:

With the objectives At this stage, သင်လုပ်ဆောင်ရမည့်တာဝန်ကိုမည်သို့ပြီးမြောက်အောင်မြင်ကြောင်းကြည့်ရန် ဖြစ်နိုင်ခြေရှိသော KPIs နှင့် အကဲဖြတ်မှုမက်ထရစ်များကို စဉ်းစားရပါမည်။ ၎င်းတို့သည် ငွေကြေးဆိုင်ရာ ကန့်သတ်ချက်များ (ဖွံ့ဖြိုးတိုးတက်မှုအတွက် အသုံးပြုသည့် ဘတ်ဂျက်) သို့မဟုတ် မေးမြန်းမှု မြန်နှုန်း သို့မဟုတ် အစီရင်ခံစာ အမှားအယွင်းနှုန်းကဲ့သို့ စွမ်းဆောင်ရည် ညွှန်းကိန်းများ ဖြစ်နိုင်သည်။

ဤအဆင့်၏အဆုံးတွင်၊ သင်သည် အနာဂတ်ထုတ်ကုန်၏ ကနဦးလိုအပ်ချက်များကို ပြင်ဆင်သတ်မှတ်နိုင်ရပါမည်။ ဤသည်မှာ အင်္ဂါရပ်များစာရင်းတစ်ခု ဖြစ်နိုင်သည်။

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