Master Data Management Trends to Get Ahead With
MDM technology has advanced leaps and bounds in the last five to 10 years to keep up with business trends and digital transformation. The best practices many businesses centered their ERPs and customer data systems on now severely limit their agility and ability to use emerging data sources for competitive advantage. Organizations that think mastering governance in legacy MDM systems must precede any leap to using machine learning and AI in more modern MDM platforms are falling behind the curve.
- First-generation MDMs: Focus heavily on master data governance to ensure data quality. These are the original ERPs and other MDMs that united consumer and product data
- Second-generation MDMs: Incorporate truly multi-domain and omnichannel strategies for real-time insights that use machine learning and AI for data governance.
As Forrester puts it in a 2019 report on master data management, second-generation MDMs prioritize agility over compliance. If your MDM strategy stops at data governance, your business is destined to fall even further behind the next set of master data management trends in 2021. Third generation MDMs are fast approaching—some argue they’ve arrived—and understanding the business trends ushering them can help you create a better master data management strategy and power your business processes and goals.
6 Business Trends Influencing MDM
MDM trends emerge in response to digital innovation and more importantly, business needs. Your business goals should base your MDM strategy, and market leaders continuously use data in new ways to power business growth. Below, we’ve identified six business trends influencing MDM in 2021.
#1 Customer-Centricity
Focusing on products rather than solving consumer problems has been the death knell for companies from Blockbuster to Kodak. Companies competing and thriving in the experience economy understand how their goods and services affect their customers’ lives. They create products and services that solve problems and organize their businesses to consider the customer’s needs from every angle.
Examples:
- Subway’s incredibly efficient, technology-driven curbside pickup
- Amazon’s algorithms that suggest products based on purchase history
- Slack’s openness to customer feedback to add new features.
MDM’s Role: Incorporating all transactions and interactions, unstructured data from social media, help chats, video, and audio to create a single view of customer overhauls a company’s ability to understand their customers’ needs.
#2 Hyper-Personalization
Hyper-personalization uses data to tailor content, product, and service information specific to a customer’s preferences. Entire brands are being built off of customized, personal experiences using AI, and according to Forrester, 89 percent of digital businesses are investing in personalization.
Examples:
- L’oreal is disrupting the beauty industry by letting consumers customize the products.
- CarMax hyper-personalized the customer interaction at all touchpoints.
MDM’s Role: Integrating loyalty programs, digital ad data, revenue segments, and search histories (among other data) into an MDM platform powered by AI and machine learning form the connected customer views necessary for hyper-personalization.
#3 Omnichannel and Direct-to-Consumer
The consumer shift to eCommerce intensified in 2020 as COVID-19 pushed multiple segments to online shopping ahead of projections. Selling online rather than, or in addition to, retail outlets lets brands interact directly with their customers and retain control over their messaging, customer experience, and consumer data.
Examples: Numerous retail brands like Petco and L’Oreal were able to make a quick shift to D2C during the pandemic because they were already in process of building the D2C channel while other traditional retail outlets are still trying to catch up.
MDM’s Role: Aggregating data on customer interactions and personalizing customer experiences is key in D2C. Integrating supply chain data for proper fulfillment and to inform marketing targets is also essential. Both require an MDM platform with Big Data architecture.
#4 IoT
The Internet of Things promises massive swaths of data from physical objects embedded with sensors and software. Most current IoT data use centers around customer experience, but IoT has enormous potential for creating operational efficiency and new business lines.
Examples:
- Apple Watch and Apple TV provide particular information on movement and viewing habits.
- Amazon’s Echo and Alexa are key drivers for hyper-personalized algorithms.
MDM’s Role: The amount of data generated by IoT is staggering and far beyond almost every MDM’s capacity at present. Integrating IoT into MDM could provide invaluable insight into managing product attributes, creating supply chain visibility, and gaining even more customer insights.
#5 Remote Work
During COVID-19, 42 percent of the U.S. labor force was working from home full-time, and experts anticipate the trend to continue in force in 2021. Accessibility and security of data are major concerns for many businesses, and so is employee satisfaction and the threat of future disruptions.
Examples:
- The luxury retailers had to empower their employees with customer data so that they can continue serving their customers from home
- Fast serve restaurants had to pivot to the new business model of takeout.
MDM’s Role: Agility and scalability that come with the flexible data model is the need of the hour to pivot with a changing market. This needs MDM built on an elastic architecture to adjust based on the business needs.
#6 Regulation and Privacy
Many data-driven companies still struggle to meet the burdens set by GDPR, CCPA, and other privacy laws or limit their data use because of regulatory concerns. New technologies providing access to even more consumer data will likely spur a new wave of regulations and privacy specifications. Lax security and privacy breaches create huge PR and legal liabilities.
Examples: Numerous companies are fined because they didn’t meet the customer consent. Gartner says 7 out of 10 privacy executives want a strategy supporting digital transformation and lack confidence in their existing plans.
MDM’s Role: Including consent and privacy preferences as part of the profile will eliminate this risk. This means a push to a connected and more holistic MDM platform.
Master Data Management Trends
Turning these business trends into a competitive advantage requires a modern master data management solution. Below, we’ve outlined six major trends you need in your MDM platform to get ahead in the experience economy.
#1 A flexible, customizable structure
The first wave of MDM systems catered to specific industries using built-in parameters. Ostensibly, those parameters made structuring the MDM system easier, but they’ve proven very limiting.
Modern MDM platforms focus more on flexibility and capability. The on-cloud consumption-based model allows customers to scale up and down the capacity-on-demand based on the workloads. For example, it can handle the spike in usage during initial data load or seasonal spike in business demand and save during the downturn by scaling down the resources. You don’t have to worry about investing too much or too little in the infrastructure as in the case of the on-premise solutions. Secondly, you can also add or delete the attributes on the fly based on business needs.
#2 Multi-domain
Truly multi-domain MDM platforms deploy customer, organization, employee, asset, location, and product data to make connections across silos and provide insight-ready data and power operations. The reason: agility.
Optimizing a marketing campaign in real-time at scale requires a multi-domain MDM. D2C business models require data integration for customers, products, supply chain, and assets. Integrating selective data sources or powering partial business functions with the MDM platform will not provide a holistic end-to-end business view. It is also important that any data update on MDM is reflected across all channels in real-time. For example, if the customer sees a location-based promotion at the store that if they sign up on the app for an offer they will receive a discount of 20%, it should immediately reflect on the POS when the customer goes for checkout. Any delay in this information can result in a bad experience which means losing to your competitor.
#3 Using unstructured data
Getting a 360-view of customers, becoming truly customer-centric, and delivering a hyper-personalized, omnichannel customer experience requires integrating data from social media sites, help chats, customer emails, and phone calls. Voice technology like chatbot and AI personal assistants have completely changed customer expectations about how they interact with your brand.
To get a fully connected customer 360 view, modern MDM platforms aggregate all customer data, including unstructured sources. This kind of innovative MDM solution helps companies dig deep into profile information for segmentation and targeted product development and marketing. You need to get more from your data in order for your business to thrive from the benefits of master data management.
#4 The shift to cloud
Every business trend points toward the need for a cloud-native MDM platform. In addition to the known advantage of get up and running with lower effort, there is a bigger benefit from the flexible microservices architecture for easy upgrades, seamless scaling, as well as quick portability.
#5 Machine learning and AI take over data governance
AI far outstrips human capability in error-free, repetitive, predictable tasks. Big Data sets—particularly IoT-level data sets—demand machine learning and AI for data governance; they’re simply too big for humans to process. Machine learning also improves at a steady rate, meaning that your master data quality increases with quantity.
Gartner had predicted that more than 40 percent of data-based tasks would be automated by 2020, and there is no end in sight to this trend. Much like cloud-native deployment, machine learning, and AI will support every business trend involving data because they’re essential to keeping up with volume, velocity, and variety.
#6 Graph technology connects data
Insight-ready data comes from finding connections and relationships in data. Customer-centricity and hyper-personalization demand graph technology to analyze data and find unanticipated relationships. Coupling graph technology with AI and machine learning can rationalize massive data sets and create connected customer views at break-neck speed. It conveys those connections that users never thought of, which is the key to breaking into new markets. Legacy MDM systems not using graph technology don’t provide the insight-ready data businesses need to power real-time operations at scale.
Get Ahead With Reltio’s Connected Customer 360
Reltio built a Connected Data Platform to specifically satisfy the business trends we outlined and anticipate the next wave of competitive business needs. Our platform was purpose-built to be multi-domain, with a user interface that makes MDM governance intuitive and adheres to master data management best practices. Machine learning with user verification allows the platform to continually improve matching and make more accurate predictions. We believe in the power of data and the right MDM platform’s ability to power your business transformation.
Sources:
- https://www.reltio.com/resources/the-forrester-wave-master-data-management-q1-2019/
- https://go.forrester.com/blogs/transform-your-personalization-strategy-at-forresters-consumer-marking-forum/
- https://www.cbinsights.com/research/direct-to-consumer-retail-strategies/
- https://news.stanford.edu/2020/06/29/snapshot-new-working-home-economy/
- https://globalworkplaceanalytics.com/work-at-home-after-covid-19-our-forecast
- https://www.shrm.org/hr-today/news/hr-news/pages/covid19-a-few-large-companies-prolong-work-from-home-to-september-and-beyond.aspx
- https://www.csoonline.com/article/3410278/the-biggest-data-breach-fines-penalties-and-settlements-so-far.html
