The COVID-19 pandemic has caused enterprises to reassess their data management capabilities. This post will explain Next Generation Data Management and provide a schedule for implementation.
Master data management (MDM) has recently become a widely utilised data management discipline. For key cross-functional business activities, consensus-based business entity definitions and consistent execution throughout an organisation are critical success criteria. For these and other reasons, several companies have installed first- or second-generation MDM systems. The current concern is passing the torch.
For example, some MDM systems just focus on customers and must extend to items, finances, partners, employees, and locations. However, MDM is intended to share common definitions and reference data across several programmes, not just one (typically ERP or BI). While most MDM hubs provide offline aggregation and standardisation of reference data, they should also support advanced features including identity resolution, two-way data sync, real-time operation, and master data approval processes.
AI and MDM
Businesses now face enormous data explosions. The variety of data from fresh sources such as sensors and connected devices connected to the Internet of Things demands immediate attention (IoT). The rise of cloud technologies has also redirected technology investment away from hardware and infrastructure purchases and toward maximising business data assets.
These features make it difficult for enterprises to stay committed to their old data management systems, preventing them from effectively using their data assets. Enterprises must become ‘Data Agile’ to efficiently respond to evolving global data management demands.
According to a credible analyst report, incorrect master data leads in a 27% revenue loss. Businesses will continue to use better data management systems as they embrace AI and ML technologies to stay relevant in a highly competitive business.
As business ecosystems become more digital, businesses are bombarded with data on their products, customers, suppliers, employees, and stakeholders. This data handling capability is crucial for firms to thrive and gain a competitive edge.
⦁ As the amount of master data and the number of sources increases, discovering master data and determining the domain type gets increasingly difficult.
No amount of manual data inspection and evaluation across millions of sources can ever keep up. A combination of machine learning methods like as clustering, data similarity, and semantic tagging may automate master data discovery and domain identification, improving scalability and efficiency.
Entity discovery uses machine learning to understand how users combine different data fields in master data stewardship activities to generate master data entities throughout the business data environment.
⦁ AI for master data management -The AI engine indexes master data sources and their domain types, as well as how master data transfers between sources and applications. Using machine learning–based connection finding, we can automate lineage mapping. This lineage map may be layered with properties and business processes.
For example, Know Your Customer (KYC) in financial services and product tracking and traceability in life sciences need this sort of lineage. It records ingredients, suppliers, manufacturers, and distributors in order to swiftly identify problems, trace them, and recall them.
⦁ Many digital transformation activities need master data modelling, including application modernization, cloud data warehousing, and data lakes. By centralising master data management, applications and analytical data stores may employ a single source of truth to simplify and expand MDM. The hub must maintain master data models with common properties and hierarchies across all sources.
⦁ AI can assist in identifying mappings between attributes or sets of characteristics in semantically connected master data models. Using Bayesian probabilistic approaches, the discovery process can match all characteristics across master data models. Based on the schema matchings, the algorithm may propose basic characteristics and hierarchy architectures for data models.
⦁ AI for master data quality- AI can help you assess your master data’s correctness, completeness, and consistency. Master data profiling, purification, and standardisation may be automated using NLP and mixed machine learning approaches (e.g., deterministic, heuristic, and probabilistic). They can also aid with scaling and productivity.
⦁ AI match and merge for master data management– Incremental backups of entries inside and across applications is another typical master data management task. The AI engine can automatically discover duplicate master data records and suggest ways to merge them.
Modern, multi-cloud, multi-hybrid setups need AI-based capabilities to optimise corporate master data management. In a world where master data sources, consumers, and use cases multiply, AI automation is essential.
And moreover, to remain competitive, the bulk of future strategic business goals will need the deployment of Next Generation Data Management (NextGen DM). NextGen DM, in our opinion, is the natural extension of traditional MDM methodologies to a data-driven business model operating in a dispersed environment:
The Value of Data: Effective data management may be just as useful as the insights gained from data analysis.
Automation of data processing processes may significantly cut process costs and increase the quality of master data.
External Factors: Adaptation of data management operations to new needs imposed by external factors such as organisational structures, cultural norms, and legal constraints.
Analytics & Data Science: Applying sophisticated analytics to master data allows the realisation of optimization opportunities and may identify value chain synergies.
User Experience: Increased user involvement as a result of a positive user experience may help enhance overall data quality and increase user happiness.
End-to-end capabilities help in facilitating management in the “new world” – Benefits
Organizations will instantly begin to realise the advantages of modernization as they transition from conventional data management to next-generation capabilities. These advantages permeate several facets of the organisation. However, they also fulfil three critical strategic goals of the contemporary digital business:
Providing the means for digital change-
You cannot opt out of digital change; you are either driving it or responding to it. The digital enterprise understands the strategic value of all data, regardless of origin, format, or content. However, with so many data sources, the volume of documents that enterprises must handle is easily in the billions. Businesses want a data management system that can handle this volume and velocity while maintaining data rigour, correctness, and security.
Business users may self-serve-
A user interface that is intuitive and provides a business-friendly user experience is a significant benefit, not only because it is simple to use, but also because it fulfils an expectation. Today’s app-savvy, mobile-enabled workforce is highly self-motivated and unaccustomed to requesting favours from IT. Millennials, in particular, desire to fix their own issues utilising purpose-built self-service solutions. Next-generation MDM enables business users to discover and analyse data in order to address everyday difficulties.
Agility is critical. To get it, enterprises want simple-to-use solutions that do not require coding, scripting, or weeks of wait time for IT assistance. Additionally, they want increased deployment flexibility and modularity, as well as the ability to swiftly fire up data management capabilities in the cloud. Businesses may install next-generation MDM in the cloud, on-premises, or in a hybrid environment.
The fundamental mission of MDM – to eliminate mistakes and duplication from data repositories — remains intact in the entire solution. However, increasing business requirements need the development of new capabilities, beginning with identifying and accessing data, enhancing it in several ways, and lastly distributing it fluidly throughout the company in a safe, contextual, and controlled manner. Thus, an MDM system that does not have these end-to-end features cannot be considered next-generation.