Business Intelligence Software Case Study – Development of special solutions for integration and presentation of important data; to create systems for analyzing and visualizing multidimensional data.
The dashboard was developed in just 6 months; the customer can now do better business; plans to build more business-critical dashboards.
Business Intelligence Software Case Study
The client is a US-based residential retail company. They presented us with a unique problem – they had collected a large amount of data that they wanted to use to help them make business decisions. Specifically, the client has more than 300 stores in the United States, and each store collects data such as number of deliveries, online/offline deliveries, deliveries by region, and more. Although each store controlled important information, the main problem was in each store. had its own database and the user did not have a unified solution to visually display it. This situation disrupted user growth and did not provide real-time situational assessment.
Bi Unit 4
Initially, the client needed a team of 3 people – 1 on-site business analyst and 2 developers. We had to coordinate our work with the client’s IT department and project managers. So we’ve incorporated a number of Agile principles to inform our work with stakeholders. Regular daily meetings, regular project progress updates, and Q&As were important tools that helped us align our work with the client’s needs.
The team and the client discussed the requirements and analyzed data sets collected from different stores and departments. The data blocks needed some extra customization and we tried several solutions. In the end, we chose MicroStrategy’s Dossier solution, which has several advantages over other solutions we tested, including:
These advantages allowed us to visualize data and maintain data connectivity, which allowed our customers to maintain data consistency and security.
We focused our development efforts on creating dashboards that managers and business analysts can understand intuitively. The dashboard consists of 8 main indicators to track data points in real-time. Indicators such as on-time delivery, number of deliveries, online and in-person deliveries help our customers track the success of each store and provide important information in one place.
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We used several modes to visualize the data. For example, one week’s shipment volume is represented by a line, while a pie chart reflects the daily delivery portion.
We’ve also added another important feature to the dashboard: real-time transaction price changes. This feature tracks price changes for various reasons: lost goods, staff errors, damaged goods, etc. The customer can use this function to understand the situation of such abnormal problem and improve the subsequent management work.
Finally, we’ve added several spreadsheet features to help our customers make sense of the large amounts of data they’ve collected over time. We included:
This way of presenting information allows the client to better understand the timely delivery of all partners with whom they are currently working, and to improve these parameters.
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We took several steps to help work with the client on this project. First, the team shifted their work 2 hours earlier to share working hours with the on-site BA. We also had an on-site bachelor working directly at the client’s headquarters. We were constantly in sync with the client’s development team to keep the project running smoothly.
When it comes to software we develop for clients, we make sure that the dashboard is easy to understand for both technical and non-technical staff.
We’ve also included a number of filters in the user controls to help you refine your search and find what you’re looking for.
We’ve also made some parts of the dashboard interactive – for example, the map showing high delivery volume is highlighted in red, medium delivery volume in orange, and low delivery volume in blue. Every manager can now access these locations for more detailed information.
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The client was happy with our work and they are now planning to have us develop another dashboard and bring three developers to their team.
Shinetech’s experts have previous experience in the automotive and retail industries, having used Microstrategy solutions with a German-based retail brand and an Italian automotive giant.
Luis has 3 years of experience in the retail industry and 2 years in the automotive industry, working on projects for an Italian car giant. He has 5 years of experience in BI development, particularly in MicroStrategy.
Mike has over 5 years of experience in BI report development. He is also proficient in MSTR, Power BI and SQL Server.
What Is A Business Case Study And How To Write With Examples?
Mike has experience in web development and requirements analysis and development. He has strong analytical and problem solving skills.
We will be happy to help! Enter your detailed requirements and contact us using the contact form below. A Shinetech representative will contact you within two business days. All businesses work with data generated from multiple sources, both internal and external to your company. These news channels act as a pair of eyes for executives and provide analytical information on what is happening in the business and the market. As such, any misunderstanding, misinformation or lack of information can lead to a distorted view of market conditions and internal operations, and thus lead to incorrect decisions.
Making data-driven decisions requires a 360° view of all aspects of the business, even if you don’t expect it. But how do you turn chunks of unstructured data into something useful? The answer is business intelligence.
We have already discussed machine learning strategies. In this article, we’ll discuss the practical steps for introducing business intelligence into your existing enterprise infrastructure. You’ll learn how to create a business intelligence strategy and how to integrate the tools into your company’s workflow.
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Let’s start with a definition: Business intelligence, or BI, is a set of practices for collecting, structuring, analyzing, and transforming raw data into actionable business insights. BI deals with methods and tools that transform unstructured data sets and translate them into easy-to-understand reports or dashboards. The primary purpose of BI is to provide actionable business insights and support data-driven decision making.
A big part of BI implementation is using the actual tools that process the data. Different tools and technologies make up the business intelligence infrastructure. In most cases, the infrastructure includes the following technologies: data storage, processing, and reporting.
Business intelligence is a technology-based process that relies heavily on inputs. Technologies used in BI to transform unstructured or semi-structured data can be used not only for data mining but also as a primary tool for working with big data.
. This type of data processing is also called interpretive analytics. With the help of visual analytics, businesses can study the market conditions of their industry as well as internal processes. A review of historical data helps identify business pain points and opportunities.
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Based on the processing of information from past events. Rather than providing an overview of historical events, predictive analytics makes predictions about future business trends. These assumptions are based on the analysis of past events. Therefore, both BI and predictive analytics can use the same techniques to process data. To some extent, predictive analytics can be considered the next phase of business intelligence. Read more about analytics maturity models in our article.
Prescriptive analytics is a third type that looks for solutions to business problems and recommends actions to solve them. Prescriptive analytics are currently available with advanced BI tools, but this area as a whole has not yet developed to a reliable level.
So here’s a key point where we can start talking about actually integrating BI tools into your organization. The whole process of bringing business intelligence to your company’s employees and actually integrating the tools and applications can be broken down. In the following sections, we’ll look at the key points of BI integration in your company and break down some of the pitfalls.
Let’s start with the basics. To start using business intelligence in your organization, first explain the meaning of BI to all your stakeholders. The range of terms may vary depending on the size of your organization. Since different departments are involved in data processing, mutual understanding is very important. So, make sure everyone is on the same page and don’t confuse business intelligence with predictive analytics.
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Another goal of this phase is to communicate BI concepts to key people involved in data management. You’ll need to identify the real problems you want to work on, set KPIs, and organize the expertise you need to launch your business intelligence initiative.
It is important to mention that at this stage you will make technical assumptions about data sources and data flow control standards. In the next step, you will be able to validate your assumptions and define your data workflow. For this reason, you must be prepared to change the channels of information sources and the composition of your team.
After setting your vision, the first big step is to define what problem or group of problems you want to solve with business intelligence. Defining goals will help define further high-level BI parameters, such as:
With objectives, at