This article has multiple issues. Please help improve it or discuss these issues on the talk page . (Learn how and when to remove these messages)
|
A Business Intelligence Competency Center (BICC) is a cross-functional organizational team with defined tasks, roles, responsibilities and processes for supporting and promoting the effective use of business intelligence (BI) across an organization. [1]
Since 2001, the BICC concept has been refined through practical implementations in organizations that have implemented BI and related analytical software.
In practice, the term "BICC" is not well integrated into business or public sector organizations and there are large variations in the organizational design for BICCs. Nevertheless, the popularity of the BICC concept has resulted in the creation of units within organizations that focus on the use of BI software for decision-making, thereby increasing that organizations return on investment (ROI) of BI. [2]
A BICC coordinates the activities and resources to ensure that a fact-based approach to decision making is systematically implemented throughout an organization. It has responsibility for the governance structure for BI and the use of analytical programs, projects, practices, software, and architecture. The BICC is responsible for building the plans, priorities, infrastructure, and competencies that the organization needs to take forward-looking strategic decisions by using the BI and analytical software capabilities.
A BICC’s influence works within a typical business unit, playing a central role in the organisational change and strategic process decision of that unit. Accordingly, the BICC’s overarching purpose will aim to empower an entire organization to coordinate BI from all units. It will similarly ensure that information and best practices are communicated and shared through the entire organization for overall benefit from successes and lessons learned." [3]
The BICC can also play an important organizational role, facilitating interaction among various cultures and units within the organization. Knowledge transfer, enhancement of analytic skills, coaching and training are central to the mandate of the BICC. A BICC should be pivotal in ensuring a high degree of information consumption and a ROI for BI.
Business Intelligence Competency Centers in U.S. Healthcare
Next to the U.S. government, the American healthcare industry generates the second largest amount of information every year. However, despite having complex information management needs, a KLAS report revealed that one-third of healthcare organizations do not have the appropriate business intelligence tools. [4]
Many finance and energy industry companies have successfully implemented BICCs; these centers have produced financial returns on investment and accelerated decision-making speed. With these as examples, the healthcare industry has begun the use of BICCs. [5] This is not without it challenges - creating a business intelligence competency center in healthcare involves prioritizing information needs, creating data governance structures, identifying data stewards to provide data quality assurance, establishing ongoing education programs, and defining predictive modeling, analytics, data warehouse, and cloud storage tools. [6]
Skills Needed
Information technology specialists in structured query language (SQL) design, operation of relational databases, programming, reporting software, and analytics can provide the necessary technical information management skills for successful implementation of BICCs. Data stewards, such as data analysts and scientists, are also needed - they understand the creation, capture, storage, and access processes needed to ensure high quality data for the BICC to work optimally. [7]
In recent years knowledge-oriented shared service centers have emerged in many organizations. Their primary focus has been the offering of analytics and data mining as an internal service across the organization. [8] These centres are often referred to as Analytics Competency Center (ACC), or Analytics Center of Excellence; Analytics Service Center; Big Data CoC; or Big Data Lab.
By the end of 2017 it is observed that approximately 25% of all large firms have a dedicated ACC unit (or equivalent) for data and analytics. [9] In contrast to classic BICC these centers do not place emphasis on reporting, historical analysis and dashboards. ACCs follow the strategic objective of transforming a company towards a data focus, building expertise in data analytics, formulating a data strategy, identifying use cases for data mining, and drive the general adoption of analytics across the organization. [10] BICCs may be transformed into an ACC, but new formations of ACC can also be found in practice.
Customer relationship management (CRM) is a process in which a business or another organization administers its interactions with customers, typically using data analysis to study large amounts of information.
Enterprise resource planning (ERP) is the integrated management of main business processes, often in real time and mediated by software and technology. ERP is usually referred to as a category of business management software—typically a suite of integrated applications—that an organization can use to collect, store, manage and interpret data from many business activities. ERP systems can be local-based or cloud-based. Cloud-based applications have grown in recent years due to the increased efficiencies arising from information being readily available from any location with Internet access.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.
A management information system (MIS) is an information system used for decision-making, and for the coordination, control, analysis, and visualization of information in an organization. The study of the management information systems involves people, processes and technology in an organizational context. In other words, it serves, as the functions of controlling, planning, decision making in the management level setting.
Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making.
SAS is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics. SAS' analytical software is built upon artificial intelligence and utilizes machine learning, deep learning and generative AI to manage and model data. The software is widely used in industries such as finance, insurance, health care and education.
In computer information systems, a dashboard is a type of graphical user interface which often provides at-a-glance views of data relevant to a particular objective or process through a combination of visualizations and summary information. In other usage, "dashboard" is another name for "progress report" or "report" and is considered a form of data visualization.
Operational intelligence (OI) is a category of real-time dynamic, business analytics that delivers visibility and insight into data, streaming events and business operations. OI solutions run queries against streaming data feeds and event data to deliver analytic results as operational instructions. OI provides organizations the ability to make decisions and immediately act on these analytic insights, through manual or automated actions.
Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription.
An integration competency center (ICC), sometimes referred to as an integration center of excellence (COE), is a shared service function providing methodical data integration, system integration, or enterprise application integration within organizations, particularly large corporations and public sector institutions.
Mobile Business Intelligence is defined as “Mobile BI is a system comprising both technical and organizational elements that present historical and/or real-time information to its users for analysis on mobile devices such as smartphones and tablets, to enable effective decision-making and management support, for the overall purpose of increasing firm performance.”. Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes.
TARGIT A/S is a Danish business intelligence (BI) and analytics software developer based in Aalborg, Denmark, with American subsidiary TARGIT US Inc., based in Tampa, as well as international offices.
Collaborative decision-making (CDM) software is a software application or module that helps to coordinate and disseminate data and reach consensus among work groups.
The term is used for two different things:
Saffron Technology, Inc., was a technology company headquartered in Cary, North Carolina, that developed cognitive computing systems. Their systems use incremental learning to understand and unify by entity the connections between an entity and other “things” in data, along with the context of their connections and their raw frequency counts. Saffron learns from all sources of data including structured and unstructured data to support knowledge-based decision making. Its patented technology captures the connections between data points at the entity level and stores these connections in an associative memory. Similarity algorithms and predictive analytics are then combined with the associative index to identify patterns in the data. Saffron’s Natural Intelligence platform was utilized across industries including manufacturing, energy, defense and healthcare, to help decision-makers manage risks, identify opportunities and anticipate future outcomes, thus reducing cost and increasing productivity. Its competitors include IBM Watson and Grok. Intel purchased the company in 2015, then shuttered it less than 3 years later.
Agile business intelligence (ABI) refers to the use of Agile software development for business intelligence (BI) projects. ABI attempts to enable the BI team, business people, or stakeholders to make business decisions more quickly.
Software intelligence is insight into the inner workings and structural condition of software assets produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems in information technology environments. Similarly to business intelligence (BI), software intelligence is produced by a set of software tools and techniques for the mining of data and the software's inner-structure. Results are automatically produced and feed a knowledge base containing technical documentation and blueprints of the innerworking of applications, and make it available to all to be used by business and software stakeholders to make informed decisions, measure the efficiency of software development organizations, communicate about the software health, prevent software catastrophes.
Embedded analytics enables organisations to integrate analytics capabilities into their own, often software as a service, applications, portals, or websites. This differs from embedded software and web analytics.
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper.
ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models" in Multi-Agent Systems. "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business key performance indicators (KPI's). It grants business domain experts the capability to evaluate AI models in production, independent of data scientists.