Data modelling is a process implemented to define and analyse data requirements which are used to support the business processes of institutions, large or small, embracing the corresponding information systems of those organisations. Data models assist in the clarification, improvement in understanding and in the distribution of information, clearly resolving many aspects of a wide range of business issues and problems which assist institutions to gain in depth insights not previously observed. Data modelling techniques and methodologies are used to model data in a standard, consistent and predictable manner in order to manage data as a valuable resource asset.
The most important aspect of a data model is its ability to share and present information to technical and development teams, which has been gathered and agreed with business users. There are three layers which facilitate this transition: Conceptual, Logical and Physical. Simplity’s approach is based on a comprehensive business understanding which is captured by the Business Information Model (BIM). It should be stressed that a data model is a dynamic concept which is continuously evolving and therefore requires continual updates.
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Data Warehouses may be constructed in many different ways, depending on the specific requirements of the business. The primary challenge in designing a data warehouse is the creation of the ability to define the right architecture which facilitates the quick and efficient consolidation of data. This data will be cleansed and may be integrated from multiple disparate databases, which may run on different technical platforms, in different geographical locations.
Different data warehousing systems may have different architectures. Some systems may have an ODS (operational data store), whilst others may have multiple data marts. Some systems may have a small number of data sources, whilst others may have a multitude of data sources. However, the primary feature which most well-designed data warehouses have in common is a tiered architecture: acquisition, integration, and access layer. Simplity has a wealth of experience in designing and implementing solutions which meet such challenging requirements, which are also cost-efficient.
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Managing data intensive solutions, not only data warehouses, but also core banking systems, is an immense challenge. The magnitude and complexity of the implementation of projects may cause communication challenges and result in unnecessary delays. Additionally, a lack of data harmonization may lead to inconsistent reporting, which in turn could endanger the value of the data to business users.
Business Information Modelling (BIM) methodology is business driven; it provides simple guidance whilst being comprehensive throughout the entire implementation process, leading to improved time to market and reduced costs. BIM ensures that all involved parties, such as business and IT departments, have the same common understanding of the requirements, using a common language.
Simplity’s Accurity Glossary tool assists in maintaining this comprehensive business information model by providing structured representation of data requirements, common language for business and IT, as well as business and technical metadata. Furthermore, it allows capturing mapping to technical data models, sophisticated search, data lineage, and integrated approval workflows.
Simplity believes that its specific IT solutions can provide the key advantage over its competitors. Business Information Modelling keeps business goals at the centre of all activities. Business teams are involved throughout the lifecycle, and the Simplity methodology is maintaining its leading role in defining mutual objectives throughout the entirety of the project.
Business Analysis is the practice of empowering change in an organizational context, through defining requirements and designing solutions which deliver value to stakeholders. Business analysis may also be described as a type of internal consultancy role which has the responsibility for building the defining bridge between business and IT Units.
Simplity’s business analysis approach is centred on our proven Business Information Modelling methodology, successfully used by our clients to collect and collate structured business and related data requirements in a formal way. This approach not only helps to avoid costly misunderstanding, but also accelerates the subsequent implementation steps by providing formalized requirements in a commonly agreed, organization-specific language.
Master Data Management (MDM) is a comprehensive methodology for enabling an institution to link all of its critical data to a common point of reference. MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, and quality-assuring, and distributing such data throughout an institution to ensure consistency and control throughout the ongoing maintenance and application use of this information.
Customer and other master data scattered across various systems not only engages institutions in significant efforts to minimise data duplication and synchronization, but also gives rise to unnecessarily high infrastructure costs. However, consistent master data is paramount for reviewing the past – product activity, interaction history, recent product reviews, and campaign activity – in order to understand better the present – active /dormant customers – to influence the future – customer churn, cross-sell opportunity, and next campaign message.
Simplity has successfully delivered numerous MDM-related projects based on leading solutions such as Informatica MDM Hub, as well as tailor-made implementations.
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Data governance encompasses the people, processes, and information technology required to create a consistent handling of an institution’s data across the business enterprise. The primary goal of data governance is to set up processes and to define the responsibilities which ensure that important data assets are formally managed throughout an institution.
Data governance ensures that data may be trusted; moreover, individuals could be assigned and made accountable for particular events which occur due to adverse data quality. Data governance highlights the importance of assigning responsibility to individuals and groups, who will ensure prevention and control of data deficiencies in order to facilitate the enterprise improving efficiency. Data governance is about managing data, and data requirements, across the organizational, architectural, and political silos in your institution today and in the future.
Simplity’s consulting services, data warehouse migration, and analytics, coupled with our Accurity product family, will ensure data quality, availability, integrity, security, and usability within your institution.
The ability of companies to manage their data efficiently has become the key to success in this complex world. Low quality data is a major problem, often resulting in the inability to utilise the data as well as giving rise to poor quality decisions based on that data. Data are often imprecise, incomplete, irrelevant, obsolete, or duplicated, because our technical world is increasingly more influenced by information technology.
Various aspects such as digitalization and datafication result in larger data volume as well as heightened complexity of the data processed. All of the aforementioned factors place new demands on the correct settings for systems producing and administering data.
At Simplity, we have the ability to define the methodology for setting the data governance model – required roles, responsibilities and principles of data ownership, in addition to defining the required levels of data quality as well as the measures needed to meet those requirements. In addition to defining and measuring data quality indicators, Simplity is also experienced in leading Data Quality improvement initiatives, as well as developing operative and managerial Data Quality reports and dashboards, supported by Simplity’s Accurity Quality module.
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In many institutions, IT and business projects are handled separately, by different teams at different times and in different locations. However, both project categories aim to address and utilise the same data. We at Simplity work with, and assist, our clients to examine their data from a wider perspective to see the bigger picture. This enables clients to identify and understand their business priorities, and to align the institution with the right technology, thus ensuring efficiency whilst aiming to maximise the best business value from the manufactured data.
Simplity’s Data Integration Roadmap Service brings together client and Simplity professionals to evaluate and recommend technology solutions which directly facilitate the evaluation and prioritisation of business priorities. It is a collaborative Business and IT framework, which defines the efficacy of different business capabilities and capacities within the data warehouse. It drives the most efficient and effective sequence of events which rationalizes IT efforts with defined business expectations and corresponding value. It serves as an effective communication tool to align Business, IT and Senior Management, and ensures that investment in a data warehouse is held accountable on mutually agreed success criteria.