Data Management Solution and Consulting
Data Management (DM)
Data management (DM) is the set of related disciplines that aims to manage the data asset fully, from conception to archiving. While data management and data integrity protocols are composed of the numerous data initiatives within the organisation.
Efforts invested in strengthening quality are critical to ensuring decisions are made based on accurate, timely, and relevant data. The associated work of maintaining high-quality data through appropriate data governance magnifies the return on investment by embedding the importance of data as a strategic asset into the fabric of the organisation’s structures and processes.
Most businesses gather data from a number of different systems and channels. Integrating that data and knowing what to do with it, can often be time consuming and complex.
Would your business benefit from having access to all of its data from one single point? Doing so would allow you to understand your customers to drive more revenue, find out where the bottlenecks are in your operations and reduce costs, or improve your marketing ROI.
If you are currently working on or thinking of progressing your data strategy and don’t know where to begin, Nandini Consulting can help cut through the complexities, and craft a data strategy that won’t gather dust on the shelf, but can be deployed to deliver immediate business value.
Data Governance and Data Quality
Data governance is the overarching policies and processes that govern the management of company data. The purpose of data governance organisation within the company is to identify what information is important, establish the processes to manage it and measure the effectiveness of the effort in achieving business objectives.
It helps to manage and resolve data related issues, and understand and promote the value of data assets. As part of the initiative we help you define policies, standards, procedures, and metrics in order to get consistent and reliable data throughout your organisation. Track, and oversee the delivery of data management projects and services.
Enterprise Data Architecture
Data architecture describes how data is collected, stored, transformed, distributed and consumed. IT includes rules governing structured formats, the systems for connecting data with the business process that consume. Typically, it provides models, policies, rules, or standards that govern which data is collected, and how it is stored, arranged and put to use in a database system and or in an organisation model of information about different systems in the organisation (Trading, Risk, Reference Data, CRM, etc.).
The Approach Our Data Architects Follow Is Split Into Three Distinct Areas
Work with you to define your requirements:
- Initial assessments of your current and to-be states – business vision, goals, platforms and guiding principles
- Identify the gaps from the initial assessment and define high-level data architecture options
- Gather and define strategic requirements for integration and information
- Create conceptual data models to identify how your business entities relate to each other
Work with you to create your data architecture:
- Define the approach and use of Data Definitions and Taxonomies to organise corporate data in a single way and make it useable
- Definition of the Logical and Physical data models linking business requirements and systems needs
- Assessment of the types of data sources and types e.g. structured / non-structured
- Define the approach to data lineage from raw data to Information for analytics
- Work with your Enterprise Architects to understand what capabilities are needed from products and vendors
Work with you to deliver your data architecture:
- Create a deployment plan to deliver the data architecture solution into your business
- Implement the necessary policies, processes, procedures to support your analytics needs
- Deliver a solid data architecture foundation that accommodates growth based on future business requirements
Current Architecture Assessment:
Based on the Data Strategy Roadmap, a data architecture assessment is required to evaluate the organisation's capabilities in several areas, like Data Structure and Databases, Data Governance, Data Storage, Data Architecture, Metadata, Data Quality and Data Security.
Future State Logical Architecture
This segment is primarily focused on how users get to the data and how they will use or share it. Typically the first place to start is by creating or updating the organisation’s conceptual data model. This will help identify the key subject areas of the company and how they interact. In addition to creating the logical data model, it is necessary to identify the key stakeholders for the effort, as they will be critical to moving the project forward for alignment with business objectives.
Future State Physical Architecture:
This would include the physical representation of the defined architecture, to address the key components, Data Structure and Databases, Data Governance, Data Storage, Data Architecture, Metadata, Data Quality and Data Security. This would comprise the physical data model along with the actual setup of the infrastructure and environment.
This would include the detailed implementation roadmap to determine the gap and the costs associated with the implementation of the proposed architecture.
Meta Data Management
Nandini’s Metadata Management Strategy and Planning offering is aimed at positioning the use of metadata management in support of an organisation’s data architecture vision. The effort is driven by the mapping of metadata management initiatives against current and/or planned data management initiatives. The resulting "points of integration" are used to understand respective metadata needs, and strategies (such as, collection, maintenance, and deployment). A plan is created to outline the scope and objectives of metadata for both business and technical metadata projects within the organisation.
- Semantic Data Lineage Facilitation
- Business, Technical and Operational metadata
- Data Dictionary Hierarchy and Definition
- Data Rationalization – Critical Data Elements (CDE)
- Metadata Repository Planning and Configuration
- Business Glossary Planning and Configuration
- Standards Knowledgebase Customization
Master Data Management (MDM)
Master data management (MDM) has become one of the hottest topics in data management. Global companies are struggling to come to terms with collections of corporate systems, that have left them with master data, dispersed in various silos and in multiple versions of the same system. Additionally with the popularity of Data Warehouse and Business Intelligence (BI) there has been heightened awareness of MDM technologies, as it provides a consistent view of the business by analysing the performance of the key master entities of business.
MDM is a data management discipline that allows an organisation to actively manage master data (or reference data) across the enterprise, rather than, maintaining it in each functional system. Integrating master data is sometimes perceived as an IT-related issue with little business involvement. In actuality, MDM is a cross-functional, technically complex, process-oriented discipline, affecting data governance of master entities that requires acceptance and support company-wide with top-level business sponsorship.
MDM can be defined as the set of technologies, processes and governance that:
- Enables distributed, disparate master data consolidation from disparate systems and business lines into a master repository.
- It also includes the cleansing and enrichment of the master data,
- ensures integration and distribution of the master data as a single point of truth for a consistent enterprise view of master business entities and
- Utilises master data to service consuming applications, enterprise business processes and BI applications.
Nandini Consulting’s Master Data Management Services can help in this effort by providing you with robust MDM solutions to achieve your EDM goals - thus taking your business to the next level in the process.
Data Movement and Integration
Data Integration requirements will change depending on the goal of the project, depending on whether it’s Business Intelligence, data consolidation, Big Data or ERP, etc. The real goal of any data integration project is to understand the outcome. Understanding what an organisation wants to achieve helps to support the design and development of the end goal solution. Once the purpose is defined, the right architecture can be selected.
Some of these data challenges include:
- Effectively collecting source data on a regular basis
- Accounting for data structure changes or additions to fields or tables
- Integrating disparate data sources into a centralised database
- Identifying business rules across business units (entities) and making sure that accurate versions of the truth applies to each
- Managing customer expectations related to delivery, access, and security