Self-service Business Intelligence Tools For Association Rules Techniques – Every business process creates information that is used to make decisions and create strategic plans. So you could say that data drives business decisions. A good and well-constructed data strategy helps ensure that data is the driver of the right decisions. Without a data strategy and management plan, data is lost, access to data is hindered, and companies cannot use the full potential of their data. This can also be a very costly problem in the long run because without proper access to information and management information can be misused and exploited by managers and employees to suit their assumptions and decisions. This can create an unreliable and inconsistent environment – in the worst case – little or no visibility of departmental data at the administrative level.
Information strategy is necessary for determining the scope and purpose of the information management system within the enterprise. Data strategy includes a set of decisions that create a high-level framework that facilitates extracting maximum value from data. It helps in the introduction of information, from defining the initial requirements to using them for the desired effect. The core of the information strategy at the company level is the elimination of information silos (single access and authority of information stored and not connected), redundant information and other deficiencies that require the flow of information within the company. Other important features of the data strategy are structured data management programs, data sharing with traceability and provenance frameworks supported by metadata formats and accepted standards (embedded in Data Catalogs), together with data management systems that respect the FAIR guidelines. These principles help facilitate knowledge discovery by making information searchable, accessible, interoperable, and reusable—thus helping humans and machines discover, access, integrate, and analyze.
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The journey of data through the company is also referred to as the data life cycle or, at De Hyve, we refer to it as the data value life cycle. When we evaluate a customer’s data cycle, our main goal is to extract the maximum value from the data asset as it progresses through a sequence of steps, from the initial requirements and creation of the data to the final deletion or storage of ‘ e data. of his useful life. I have defined a high-level modular visualization framework for data lifecycle assessment that will provide a holistic view of a company’s data environment, existing software, data, files and processes. This framework shows what is needed and what is missing for information to travel across departments while serving the needs of different users and systems. Any module of the framework that is missing within your company’s information environment means that the information is not used to its full potential, causing a loss of resources (time, money and effort).
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The basic point of any digital transformation project, siled data, refers to when the data is created for a specific purpose in mind, there is no planned reuse or in scope, the data is on an isolated hard drive and there are no or insufficient metadata. In most cases, siled information has only one access and authority, so it can be easily misused and has a particularly short life. Generally, siled data is shared on USB drives, cloud/network drives (NAS), share points and/or even email. It has no or very low potential for sharing and reuse in most cases. Machines cannot use this information themselves due to the lack of (enough) metadata, so AI (artificial intelligence) programs generally do not collect and process this information. Well-known examples of silos are departments within hospitals and academic institutions, the plant, animal and food industries as well as many pharmaceutical companies. About three-quarters of the departments that generate data are at this stage worldwide. The reasons for the existence and sustainability of information silos are structural (customized software that works on specific data sets), political (ownership between data owners), cultural (lack of knowledge and unwillingness to change) and bureaucratic (vendor lock-in) .
To move information out of silos, the first step is to inventory the applications in the business that create, operate, consume, process, store and store information. The next step is to inventory the data itself. A good data catalog can simplify data discovery at scale and is the foundation of a good data management program. Selecting and cataloging information can be a challenge in itself.
As part of our data landscape survey, De Hyve may perform such data and application inventories. These can be used to fill in optional items. The information landscape is a representation of the information assets of the organization, the system for creating, analyzing, processing and storing information, and other applications that exist in the information environment of the enterprise. For this service, our team collects information about all data sources (databases, data warehouses, data warehouses) within the organization, all systems (research, clinical, commercial), data types (high-level data sets, experimental images, text files . ), as well as current data ( system extracts, data item dumps). Then these elements can be ranked, ranked and mapped into concepts or semantic models using the algorithms we have. This model is populated and then visualized in the form of a dashboard, a static or dynamic map and, for complete management and flexibility, a knowledge graph is created. This allows business leaders and researchers to answer questions such as:
This flyer is an example of a usage we implemented for a top 10 pharma client that resulted in a Knowledge Graph. This chart is regularly consulted to answer questions from both research and business departments. For this project, The Hyve inventoried and ranked data systems from an original list of thousands of sources, narrowing them down to a dozen critical systems that work with the highest impact data. Then we develop one conceptual model of the data landscape. We have collected data for the data landscape in visits to different locations and departments, interviews with about fifty stakeholders who work across the pharmaceutical departments around the world.
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This phase of the project includes the actual tracking of information between departments and departments, between systems and storage facilities, between producers and consumers – all from the origin, its use, storage and / or deletion. Many open source tools are available to implement and visualize the data journey.
Information management is defined as the exercise of authority and control (planning, compliance and enforcement) in the management of information assets by DAMA International (International Information Management Association)
Information sharing is the fifth phase of the information value life cycle. When releasing data, it is clear that access must be controlled by relevant agreements against the need to limit the availability of classified, proprietary and business sensitive information. Data catalogs provide a structured channel for data discovery and evaluation by the user. They also help in automatic metadata management. Additional custom parameters such as data quality, FAIRness score, current impact, etc. can be attached to the data within the catalog itself as a metadata attribute. These parameters enable an additional layer of filtering on the data set. The connection between data assets and the system operating on them can be represented using tools such as dashboards, static and dynamic maps and knowledge tables. We define a knowledge graph as a way to visualize a collection of interconnected descriptions in a specified knowledge domain consisting of entities and relationships, interactions and information flows. Modern data catalogs can create semantic relationships between data and metadata in an automatic format that is primarily a graph of knowledge.
Modern data catalog, with integrated metadata management and overview of data assets, is an important step to find data and thus make it accessible to people. With accepted metadata standards (FAIR) and interfaces (ontologies, vocabularies) in place, data is still accessible to machines (systems, software that work on data) and thus AI-ready. Some data catalogs have a higher potential to be fair compared to others as mentioned in the Data Catalog Fairness Measurement Test. In the complex environment of pharmaceutical research and development, automated machine access to high-volume, diverse, and high-velocity data is essential for AI to perform. A prerequisite for an AI-centric project is to respect the guiding principles of fairness and integrate them into the business information strategy. Many AI-centric projects fail due to lack of data and related access, no or fuzzy metadata and no or low semantic interoperability. Data sharing and access are not only important for AI, but also ensure long-term sustainable access to data while reducing costs to a higher order.
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As we discussed above, the process of moving from a closed, silo approach to an open, distributed and federated infrastructure requires significant changes in tools, methods and mindsets. Information strategy is more than collecting, annotating and storing the organization’s information assets; It has the higher purpose of ‘long-term care’ for the company’s valuable digital assets, allowing them to be discovered and reused in an easily accessible and consistent way for different data users, groups and business leaders.
Senior executives generally view data as an important asset that must be managed, nurtured, enhanced and delivered in a timely manner to employees, customers,
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