The COVID 19 pandemic intensified the consumer shift to digital commerce. A global pet products retailer and a global beauty products leader have built the D2C channel, letting them interact directly with their customers, control their customer data banks, messaging and customer experience.
Customer centricity has emerged as the new paradigm and experience is fast becoming the next threshold for differentiation. Brands are focussing efforts on building customized, personal experiences using artificial intelligence (AI). According to Forbes’ market reports, 89% of digital businesses are investing in personalization. One of the American multinational fast food restaurant franchise’s technology-driven pick-up initiative, global e-commerce marketplace leader’s algorithms recommending products based on purchase history or a digital provider’s openness to customer feedback for developing new features exemplifies the trend. Similarly, a global multi-brand beauty products leader, a leading global sportswear giant and a global home improvement products retailer are using data to tailor content to personalize customer interaction at all touchpoints, enabling consumers to customize products and providing service information specific to a customer’s need. Customer centricity and hyper-personalization imperatives are driving market leaders to digital innovation leading them to incessantly employ data to drive business growth.
Trusted Data is Fast Becoming the Life Blood for Successful Organizations
Understanding the customer is key to hyper-personalization. A single view of customer requires integrating all customer transactions from order management system and loyalty management system, interactions from customer care chats, video, and voice, social media feeds, behavioural data from search histories to customer master data management (MDM) platform to understand their customers’ needs. However, data integrity issues could render these initiatives ineffective, in case of inaccurate data, or if it is not structured and contextualised.
Similarly, integrating supply chain data in sync with marketing and sales targets is essential for proper fulfilment. Quicker track and trace of faulty products by item batch data in production batches is critical for faster product recalls, reducing losing brand grace in the market. This requires an MDM platform with big data architecture.
Emergence of MDM 3.0
Hence, keeping with business trends and digital transformation, master data management is progressively becoming a compulsion. The ERPs, other MDMs that focussed on mastering data governance and ensure data quality, compliance, information rationalization and risk management have restricted agility, flexibility, and ability to use emerging data sources. MDM is emerging as a critical tool for driving business value. The third generation MDM focusses on understanding the business trends and intelligence to power business processes and goals. It provides a clear competitive advantage, by maximizing the power of a data driven organization, enabling outcomes.
Key Aspects of MDM 3.0
Along with rule-based task execution, new methods are being used to progressively automate master data maintenance processes like value proposals, duplicate detection, etc. Artificial intelligence / machine learning (AI / ML) is used to suggest actions to improve the data quality.
Disruptive Cognitive Technologies
Organizations are focussed on getting a 360-degree view of customers, to deliver omnichannel and product experiences. A single view across operations, transaction systems, as well as user generated data coming out of conversational AI-based voice assistants, chatbots or intelligent personal assistants. It not only helps improve accuracy and real-time information availability. But improves efficiency, orders, discovering possible relationships, compliance, and privacy management. This will help product discovery through voice command apps.
Cloud native MDM
Cloud-based business applications are trending. Application-specific masters providing transactional & interactional application context designed for enterprise-wide functions, region, or unit-specific processes, carry complex and robust data models. They provide flexibility, while still having an enterprise-wide identifiability. By that, there’s also a high attention towards data quality in the application master data ability of cloud native MDM to easily integrate with cloud-sourced data and applications, match and model this data, helps consolidate associated databases, bringing in agility, flexible capacity and helps reducing cost. Lack of stewardship can not only corrode the data assets but can drive organizations to costly errors.
Beyond the regular customer and product domains, digital assets, supplier, hierarchy, locational and reference data are fast becoming a growing need for optimizing inventories and a complex supply chain. As against the traditional siloed, single domain approach, which needs to be connected later, a multi-domain approach expands the existing structures based on the broader foundation. A multi-dimensional, multi-domain MDM uncovers new connections between different data entities, revealing new opportunities for optimization, efficiencies, and growth.
AI / ML will Drive Agile, Real-Time Insights
Automation of repetitive, predictable master data procedures, apart from reducing errors are helping manage the increasing variety, volumes, and velocity of data from growing number of channels. AI / ML capabilities in MDM, at its nascency is helping de-duplicating data improving data quality assurance and finding more modern business values. Cloud based, multi-dimensional, multi-domain, omni-channel MDM 3.0, leverages user generated big data with AI/ML driven data governance framework. It is focussed on providing real-time insights, prioritizing agility over compliance, and building a data driven organization.
Having understood the emergence and key aspects of MDM 3.0 it is evident that the technology-backed and robustly governed master data management lays true foundation of the highest quality trusted data. It all begins from choosing a solution that is cloud deployable along with an on-prem deployment, easy to use, and provides a higher ROI. Organizational data in its current form must be analysed and then approached with the appropriate data cleansing strategy ensuring the data to stay clean henceforth as well. The outcome of the prolonged clean data is the highest quality trusted data that drives successful results from AI/ML models. MDM thus helps business executives take informed and relevant business decisions ensuring to stay ahead of the competition and give them data-backed preparedness to pounce on opportunities. As most of us would agree by now that the objective of the MDM 3.0 is to turn data into insights and insights into action, organizations would largely benefit with this approach. According to Gartner, about $8.2 million per year is lost by an average company due to poor data quality. Hence, it becomes paramount to measure the cost of data quality, both good and poor. It can hence be fairly concluded that MDM value is measured only when its impact to business value is measured.
About the Author
Group Practice Head for Retail, e-Commerce and Consumer Business, Tech Mahindra Business Process Services
Joydeep is a management graduate, with 27 years professional experience. He has been a retail and CPG industry practitioner for the first 15 years of his professional career, essaying roles in the areas of CPG trade marketing, sales and distribution, retail operations, planning, merchandising, and sourcing and category management. Over the last 12 years, he has been consulting, business transformation, building business friendly IT solutions, doing consultative sales, and implementing them at global retail and consumer clients.
Please reach out to him at JS00801248@TechMahindra.com.