Business Intelligence Solutions Research Articles

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Leveraging business intelligence solutions for urban parking management

Efficient parking management is essential for enhancing customer experience, mobility, accessibility, and overall quality of life in urban areas. In recent years, parking analytics have emerged as valuable tools for understanding drivers’ behavior and developing data-driven management strategies. However, the application of these tools is often hindered by the complexity of data extraction, transformation, loading and analysis. Additionally, the implementation of these tools can be time-consuming and costly, further limiting their practical use for operators and urban authorities. To address this issue, this paper presents the development of a Business Intelligence tool specifically designed to facilitate parking management through the automated flow of transaction data between collection, processing, and analysis systems. The tool provides easy-to-use analytical capabilities that allow parking managers to analyze parking transaction data, identify trends and patterns, and make informed decisions about parking management quickly and easily. The cost-effective implementation of this tool presents a valuable solution for managing on-street parking in urban areas. This study highlights the potential of Business Intelligence tools for parking management and contributes to improving the effectiveness of parking management.

Business intelligence tools in finance: A review of trends in the USA and Africa

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

Business Intelligence applied to inventory management: case of a small retail company

Business Intelligence (BI) is a set of techniques that assist organizations in making data-driven decisions, enabling interactive access to information, often in real-time. In this way, its application provides precise business analyses, contributing to goal tracking, results projection, identification of opportunities, and anticipation of risks. Therefore, the objective of this work is to create a BI solution using data extracted from an Enterprise Resource Planning (ERP) to assist inventory management in a small-scale retail company specializing in party supplies. Factors such as shelf stockouts and inconsistencies between the system and the real environment have driven the development of this project. Facing ineffective inventory management and limited access to strategic information, a BI solution was developed in the company following steps such as problem analysis, data selection, extraction and processing, database modeling, BI implementation, and testing and evaluation. Thus, two focused dashboards were developed, one for the overall inventory report and another for monitoring safety stock. Through these dashboards, it is possible to assess inventory levels and product movement, devise strategies based on historical analyses, and identify the need to issue new purchase orders by product. The achieved results were positive, highlighting significant improvements in inventory management effectiveness and consolidating decision-making processes through quick and interactive access to strategic information.

How to deeply understand the voice of the customer? A proposal for a synthesis of techniques for analyzing online reviews in the hospitality industry

PurposeThis study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.Design/methodology/approachThis study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.FindingsExperimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.Originality/valueThe novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

A Data Quality Multidimensional Model for Social Media Analysis

AbstractSocial media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.

Bus Operation Safety Business Intelligence Solution: Applying Analytics for Key Performance Indicator, Investigation, and Targeted Actions Analyses with a Centralized Data Warehouse

A business intelligence (BI) system was designed to create a proactive, reactive, and precursor decision tool to improve safety of bus operations at the Massachusetts Bay Transportation Authority (MBTA). The system’s development was motivated in part by recommendations of a safety inspection by the Federal Transportation Administration, and the realization that the MBTA has access to vest amounts of data that are not being used to create information effectively. The developers reduced the complexity of the BI system by incorporating analytics to determine the areas of focus where safety has the most impact. Challenges were posed by the need to integrate internal and external databases with various formats and structures, and the desire to create a self-service BI solution for multiple MBTA users. The resulting BI solution includes applications that support key performance indicator analyses, root cause investigations, and targeted improvement actions. These applications represent an enhanced approach that combines data for a more holistic analysis and eliminates the need to transfer datasets or data analyses by email. The system includes various forms of visualizations to help users navigate the myriad of information, including geospatial maps, interactive pie, line, and bar charts, and word frequency and relationship mapping. The paper details the system’s development and how analytical approaches were used to expose important information that is hidden in the data. Examples of applications are shown using screenshots and a general workflow is presented that could be applicable to agencies with fewer resources.

Business intelligence for Industry 4.0: predictive models for retail and distribution

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

Relationship between Twitter activity and stock performance: evidence from Turkish airline industry

PurposeThe purpose of this paper is to analyze the correlation between the Twitter activity of two airline companies and their stock performance at the Istanbul Stock Exchange (BIST).Design/methodology/approachOverall, 113,018 tweets were divided into 34,152 semantic and 78,866 share tweets. Semantic tweets are tweets mentioning company’s products or services and were labeled manually and with deep learning models. Share tweets were divided into 13,618 relevant and 65,248 irrelevant tweets.FindingsA positive correlation was found between share tweets and stock performance. Semantic tweets did not display a correlation with stock performance. Relevant share tweets displayed as a strong correlation as all share tweets for one company. Also, the manual labeling of 8,000 tweets led to the discovery of many insights related to service provision in the airway industry, management of digital support channels, management of reputation on social media and using Twitter as a customer support platform.Practical implicationsRelevant share tweets comprise only 20% of all share tweets for one company and show the same level of correlation with stock performance. This means that the efficiency of business intelligence solutions created to monitor Twitter activity can be improved five times by saving computational power, network bandwidth and data storage.Originality/valuePrevious research has analyzed all Twitter activity taken together. By dividing tweets into semantic and share tweets, this paper illustrates that it is, in fact, share tweets that are correlated with stock performance and not semantic tweets.

Visualization of Business Intelligence Insights into Aviation Accidents

Despite the recent tragic loss activity, flying is often said to be the safest form of transport, and this is at least true in terms of fatalities per distance travelled. The Civil Aviation Authority reports that the death rate per billion kilometers travelled by aircraft is 0.003, which is much lower than the rates of 0.27 for train travel and 2.57 for vehicle travel. Despite the fact that safety has been the aviation industry's top focus for the last century and a half, accidents involving aircraft continue to be a source of horror even in the present day. Hence, the aim of this project is to identify the major causes and reasons that led to accidents in the aviation industry and to carry out research, finding, design, build and suggest a Business Intelligence (BI) solution to the problem. Throughout the project, it will discover problems, both elementary and critical which needs to be corrected or changed in order to prevent major negative happenings and improve the current situation in a positive way. Tableau will be the primary BI tool used in this process. Data visualization is the graphic depiction of information and data. Data visualization tools offer an easy approach to data analysis that observe and find patterns, outliers, and patterns in data by employing visual elements like charts, graphs, and maps. The project will also cover the initial to building and deployment stage of the BI solution to improve and prevent further accidents.

Дослідження даних у промислових data-mining проєктах в епоху генерації великих даних

Introduction. The review material is based mainly on business intelligence (BI) solutions designed for tasks with corporate data. But all the main aspects of working with data discussed in the work are also used on data processing platforms (Data Science Platform). Many BI vendors have expanded the capabilities of their systems to perform more advanced analytics, including Data Science. They added the phrase “Data Science” to their marketing research, and the term “advanced analytics” lost some popularity in relation to corporate data. The Data Science Platform provides a comprehensive set of tools for use by advanced users who traditionally work with data. Capabilities that allow you to connect to multi-structured data across different types of storage platforms, both on-premises and in the cloud, and the infrastructure architecture of a modern BI analytics platform enable high-performance workloads, including business intelligence. It uses distributed architecture, massively parallel processing, data virtualization, in-memory computing, etc. The combination of traditional relational data processing with calculations on the well-known Apache Hadoop software infrastructure, which integrates a number of components of the Hadoop ecosystem (Apache Hive, HBase, Spark, Solr, etc.) with the necessary target functions, allows you to create a fully functional platform for storing and processing structured and non-structures data. Purpose. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. Methods. The paper describes the use of Data Mining methods in big data processing tasks, as well as methods of business, recommendation and predictive analytics. Result. The study suggests that machine learning-enhanced master data management (MDM), data quality, data preparation, and data catalogs will converge into a single, modern Enterprise Information Management (EIM) platform applicable to most new analytics projects. The results of the analysis of the process of identifying useful data can be useful to researchers and developers of modern platforms for processing and researching data in various spheres of society. Conclusion. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. It is shown that a high-quality solution to the problems of working with first-level data indicated in this review will be provided by data research in modern analytical platforms. Successful penetration into their essence at the level of obtaining knowledge using machine learning and artificial intelligence algorithms will make it possible to predict future results in managed objects (processes) and make informed decisions.

ETL Maturity Model for Data Warehouse Systems: A CMMI Compliant燜ramework

The effectiveness of the Business Intelligence (BI) system mainly depends on the quality of knowledge it produces. The decision-making process is hindered, and the user’s trust is lost, if the knowledge offered is undesired or of poor quality. A Data Warehouse (DW) is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions. The Extract, Transform, and Load (ETL) process is the backbone of a DW system, and it is responsible for moving data from source systems into the DW system. The more mature the ETL process the more reliable the DW system. In this paper, we propose the ETL Maturity Model (EMM) that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced. The EMM is made up of five levels of maturity i.e., Chaotic, Acceptable, Stable, Efficient and Reliable. Each level of maturity contains Key Process Areas (KPAs) that have been endorsed by industry experts and include all critical features of a good ETL system. Quality Objectives (QOs) are defined procedures that, when implemented, resulted in a high-quality ETL process. Each KPA has its own set of QOs, the execution of which meets the requirements of that KPA. Multiple brainstorming sessions with relevant industry experts helped to enhance the model. EMM was deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim. This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system. This model can also provide high-quality information to assist users in making better decisions and gaining their trust.

Student Mobility Management System and Business Intelligence Solution for Higher Education Institutions

Erasmus+ is a form of mobility supported by the European Union that provides education and internship opportunities at another contracted university abroad, subject to the agreements that university students and even faculty must adhere to. This way, international education opportunities are provided, and in addition to cultural exchange and integration, the goal is to increase international cooperation. This mobility is critical to its students' career development. While managing student mobility is straightforward for universities with a small number of programs, it can become complicated and time consuming for universities with a large number of faculties and institutes, and thus students. Our study proposes an information system for managing this complexity effectively. Prior to modeling, the developed information system was successfully run, with the goal of managing the process effectively. The study shares user experiences, critical points in the needs analysis process, developed processes and user roles, as well as screenshots of developed systems and their tasks. The findings indicated that administrators in universities welcomed such practices, and that effective management was provided for universities with intensive programs, particularly those with difficult-to-coordinate and dispersed campuses. By incorporating business intelligence technology into the developed system, it is ensured that educational technologies used in higher education institutions are up to date. No study has been identified in the literature that comprehensively evaluates the learning mobility process and connects the subject to current technologies.

Association Rules Mining Regarding the Value of Business Intelligence Solutions

The paper investigates the importance of business intelligence solutions in modern enterprises using association rule mining techniques. The research is based on a questionnaire addressed to different employee target groups regarding their age interval, their employment status, their domain of employment, their experience or inexperience with business intelligence tools and their positive or negative aspect regarding the importance of business intelligence in modern companies. 90 responses have been received and used for dataset formulation. Using the association rule induction standard procedure, the most popular rules with respect to different antecedent item combinations and business intelligence value as consequent item have been inferred setting as minimum confidence 50% and minimum support 0,1. The collected data have been prepared in common separated values format and the association rules have been inferred using the R- Package. In general, among other rules, a strong relation between BI experience and positive BI aspect can be reported which is also confirmed via simple Pearson X2 statistical test in R. An investigation paradox which has been spotted is the negative opinion regarding the BI usefulness stemming from a minority of respondents familiar with BI tools.

Logistics Coordination Based on Inventory Management and Transportation Planning by Third-Party Logistics (3PL)

Currently, there is still a strong trend in research and in the market connected with the role of 3PL (third-party logistics) companies and the trend of developing and creating coordination in logistics networks. The most important issue for the following paper is the examination of the 3PL enterprise, which is able to create the demand forecasts to gain the functions of logistics coordination in the fields of inventory management and transportation planning. This research paper focuses on showing the demand forecasting tool results for 29 chosen distribution networks (in this paper, the traditional forecasting methods were used based on time series exponential smoothing, ARIMA, machine learning and neural-network-based methods, created in the R programming environment). In the next steps, the forecasting results were compared in the conditions of transportation planning and inventory management (in the conditions of “future” dynamic ABC analysis). The forecasting function supports the inventory management and transportation planning activity of 3PL as a key element of logistics coordination in distribution networks. The proper way to transfer the results of forecasting to an ABC analysis and transportation planning concept is to create a cloud-based system supported by data from the WMS (warehouse management system), while providing the possibility of results visualization by using some BI (business intelligence) solutions or different tools to create managerial information dashboards. Currently, one of the most efficient models connected with logistics coordination is the centralized network with 3PL responsible for planning and executing logistics processes with the creation of additional value. As such, 3PL is able to create a similar forecast for different types of DN and with different aggregations (per SKU or per recipient). It could support coordination in DN from the point of view of inventory management and transportation planning.

Methodology for Business Intelligence Solutions in Internet Banking Companies

Business intelligence in the banking industry has been studied extensively in the last decade; however, business executives still do not perceive efficiency in the decision-making process since the management and treatment of information are very time-consuming for the deliverer, generating costs in the process. On the other hand, there is no formal methodology for developing business intelligence solutions in this sector. This work aims to optimize decision-making in a business unit that works with internet banking companies, reducing the time, the number of people, and the costs involved in decision-making. To meet the objective, basic and applied research was conducted. The basic research allowed the construction of a new methodology from a study of critical success factors and approaches from the business intelligence literature. The applied research involved the implementation of a business intelligence solution applying the new methodology in a pre-experimental study. Thirty decision-making processes were analyzed using pre-test and post-test data. Tools such as a stopwatch and observation were used to collect and record data on time spent, the number of people, and the decision-making costs. This information was processed in the specialized Minitab18 statistical software, which allowed the observation and confirmation of relevant results regarding time reduction, the number of people, and the costs generated. Therefore, it was concluded that the business intelligence solution, applying the new methodology, optimized decision making in the business unit that works with internet banking for companies.

SOA enabled ELTA: approach in designing business intelligence solutions in Era of Big Data

The current work presents a new approach for designing business intelligence solutions. In the Era of Big Data, former and robust analytical concepts and utilities need to adapt themselves to the changed market circumstances. The main focus of this work is to address the acceleration of building process of a “data-centric” Business Intelligence (BI) solution besides preparing BI solutions for Big Data utilization. This research addresses the following goals: reducing the time spent during business intelligence solution’s design phase; achieving flexibility of BI solution by adding new data sources; and preparing BI solution for utilizing Big Data concepts. This research proposes an extension of the existing Extract, Load and Transform (ELT) approach to the new one Extract, Load, Transform and Analyze (ELTA) supported by service-orientation concept. Additionally, the proposed model incorporates Service-Oriented Architecture concept as a mediator for the transformation phase. On one side, such incorporation brings flexibility to the BI solution and on the other side; it reduces the complexity of the whole system by moving some responsibilities to external authorities.

Business continuity in South Africa during Covid-19 pandemic: digital technology perspective

South Africa, like many other countries globally, implemented a lockdown in an attempt to contain the spread of the coronavirus which has had an adverse effect on business operations and sustainability. Minimal information is available concerning the impact of digital technology utilisation on business continuity during such a pandemic. Therefore, this study aimed to determine the impact of digital technology on business continuity during this period at a South African Testing, Inspection and Certification (TIC) company. A cross-sectional study was conducted among 59 employees and data were collected using an online anonymous questionnaire for the different types of digital technology used pre-pandemic and during the pandemic, as well as its impact on interactions and work expectations. Results revealed that the company used virtual meetings, cloud storage, electronic job systems, telecommunications, messaging applications, business intelligence solutions, big data analytics, and online training. There were moderate positive relationships found between the types of technology and business continuity during the pandemic. Areas for continual improvement were reported as network connectivity, communication, and training. It is recommended that post-COVID-19, companies need to continually evaluate the use of digital technology to further reduce the time spent by employees on manual processes thereby improving productivity to provide better customer service and enhance competitive advantage.

The method for a deeper understanding of user experience using customer rating analytics: A study in tourism sector

The level of competition between businesses in the post-COVID period will be fierce, especially those in the tourism sector according to a survey from the World Economic Forum, wherein the tourism industry is the industry o suffer heavy losses during the COVID-19 pandemic. Businesses are actively applying technology and digital transformation to increase revenue, cut costs, and optimize resources. In general, in a business, there will be many factors that need to be taken care of and one of them is customers because customers are the ones who directly generate revenue for the business, helping the business to maintain its existence and develop. Therefore, understanding customer experience is an incredibly important step in business. From this, businesses can evaluate the effectiveness of the business model. This research approaches a new Net Promoter Score (NPS) to measure how willing customers are to recommend a company's products or services to others. This score contributes to helping enterprises reassess their business models and indirectly reflecting the growth ability of enterprises. The novelty of this research is the application of Business Intelligence (BI) solutions to support multidimensional analysis, thereby solving the weakness in the traditional NPS, which is based on only one factor. Also from multidimensional analysis, businesses will visualize customers more clearly, thereby taking appropriate and timely actions. This research has three main objectives, including (1) Collect data and measure NPS automatically; (2) Design the Dashboard system to solve complex problems in retrieving, exploiting and synthesizing information to measure NPS, which supports businesses to make timely and more effective decisions; (3) Experiment the solution with data crawled from Agoda. Customers' NPS remarks can be investigated further. Enterprise will discover what customers are lacking in their product, which features they like, which features they don't comprehend, and what additions they should build in the future.

Approach for Data-driven Controlling of Construction Projects using the example of Claim Management

Current methods for controlling of construction projects are characterized by manual processes, which are failure-prone, tedious and for that time-consuming and cost-intensive. Furthermore, an additional challenge is caused by the absent dynamic in conventional controlling processes, which is of high importance to avoid damages and ensure a proactive management of a construction project. As construction projects are highly complex due to various influences which imply an enormous amount of information and are designated by dynamic processes, conventional controlling methods are insufficient and not able to meet these requirements. Therefore, current data-driven developments which can deal with an enormous amount of information are indispensable for a sufficient controlling of complex and dynamic projects. Accordingly, within this paper an approach for a data-driven controlling solution is presented, in which common data environments, databases and business intelligence applications are combined to ensure an efficient and proactive controlling. To investigate the advantages of business intelligence solutions for data-driven controlling the exemplary scenario of claim management controlling is used. It is shown that the user is able to speed up the processes and secure a proactive controlling by using a consistent database throughout the project. To achieve these targets an automatic data orchestration and visualization of the claim offers is exploited. Consequently, the claim manager is able to predict developments, identify risk potential, investigate KPIs or take actions to minimize negative effects e.g., time effort and costs. The requirements for these purposes are considered in a recommendation for action differentiated regarding the individual elements of a socio-technical system (human, organization, technology). The outlook includes an overview of potential technologies to ensure real-time analytics to shorten reaction and damage times related to the claims by immediately initiating countermeasures.