DEV Community: Pimcore

How Can Organizations Ensure the Success of Their Customer Master Data Management Initiatives?

Pimcore — Thu, 27 Feb 2020 05:48:05 +0000

Many business applications, irrespective of whether they are operational or analytical, rely heavily on data to serve their intended purpose. From managing customer account information to delivering personalized recommendations, the quality of the available data determines the accuracy and effectiveness of all these applications. That is where Master Data Management (MDM) comes into the picture. It helps eliminate data inconsistencies and create a unified view of the customer, product, vendor, and material data. Implemented properly, a Master Data Management solution can help organizations strengthen enterprise architecture, support business intelligence requirements, increase operational efficiency, and enhance experiences across all touchpoints.

However, despite the obvious benefits of master data management, many organizations struggle to justify the value of their MDM initiatives. One of the primary reasons for this is the traditional time-taking approach to MDM implementation. It is not uncommon for traditional MDM projects to take as long as 12 to 24 months. Data on the other hand increases at an exponential rate within any organization. As a result, by the time the MDM implementation is completed, data either become outdated or gets altered. This particularly holds true in the case of customer data.

As the business requirements, as well as the customer data, is always changing and growing, creating a golden record cannot be a one-time activity. Furthermore, different business users may need to consume master customer data in different ways. As such, unless the survivorship rules (that define which values are selected to populate a single master record) are in sync with evolving business requirements, it can become very difficult to address customer expectations in an omnichannel environment.

So, what is the solution? How can organizations ensure the success of their customer master data management initiatives?

Agile is the way forward

Adopting an agile approach to customer master data management can resolve all the challenges associated with a traditional implementation. Instead of working as a project-based silo, the agile MDM approach follows the practices of agile software development and evolves with the business requirements. Providing business users access to composite and accurate master data view designed to address well-defined business objectives is always at the front and center of agile implementation.

Here are 5 reasons why adopting agile MDM is the best way to consolidate your customer master data:

Better collaboration

Agile MDM strategy makes it possible for all the teams and applications that use the customer data to refer to a single source. This helps ensure that data stays consistent and coherent, and all updates take place against this unique copy of master data. From opening or closing accounts across businesses to provisioning accounts and deriving customer insights for marketing, all teams are easily able to access one master record of customer data. All the teams always have access to the latest data, universal changes get updated automatically, and employees can work in collaboration with each other without worrying about information latency.

Enhanced consistency

In an agile approach, master data management is business-focused and aligns with clearly defined use cases. Being usage-driven, the data corresponds well with how various business users work with or use that data. For instance, in the case of eCommerce, customer profiles that are managed as part of master customer data can be utilized by various parts of their ecosystem like eCommerce platform, online marketing, payment gateway, shipping software, accounting software, inventory management system, etc. This ensures that data is always used in a consistent manner by all the stakeholders.

Improved ROI

The success of an agile MDM approach is determined based on actual value delivered to business users. Is the master customer data able to support business applications or not? How easy is it for business users to manipulate and consume data? All such parameters are taken into consideration. This association with direct business deliverables makes it easier to showcase the ROI of MDM initiatives. Also, improved quality of customer master data has a direct correlation with the ROI of MDM implementation as well. Through agile customer MDM, organizations can support marketing and sales teams in targeting customers in the best possible way and keep adjusting to their expectations, hence maximizing the ROI.

Reduced risks

Agile records have a higher success rate compared to traditional data management. Since it is driven by agile or evolutionary data modeling, which is carried out in an iterative and incremental manner, it lays special emphasis on data quality. Various aspects related to data consistency, accuracy, and usability are often tested with respect to different business use cases. Better quality data, in turn, leads to business risk mitigation. For instance, by de-duplication of customer records, chances of a financial loss occurring due to defaulting on a line of credit or loan (because of multiple profiles of a customer) drops tremendously.

Lean data governance

Unlike a command and control strategy that overtly strives to manage and direct development teams, an agile MDM strategy involves data professionals as active contributors in development teams. In such a scenario, the data management team is no longer a bottleneck which the development team is always trying to bypass. Instead, they start working together as one team. Lean data governance is all about allowing teams to select their way of working and educating everyone across the affected teams and departments. In the case of customer data, agile data governance becomes a key contributor to improved quality, better productivity, enhanced time to value, and greater customer satisfaction.

Agile MDM also gives you the ability to expand to new data domains at speed. This ability to quickly ingest new data sources coupled with the ease of model evolution for different business needs lets you scale at will.

Implemented properly, agile customer master data management holds the potential to unlock limitless possibilities for your business. With access to near real-time, always accurate, and consistent customer data across all applications, your customer service, sales, marketing, and core business operations teams can always stay a step ahead of customer expectations.

Pimcore’s Master Data Management platform supports agile MDM approach. With Pimcore MDM platform, enterprises can manage any data such as product data, customer data, employee data, digital assets data, partner or vendor data, location data, reference data, and IOT or ‘things’ data. It is the leading open-source MDM platform in the market today.

Do you want to know more about Pimcore MDM platform? Get a Free demo!

Source: Pimcore Blog

Why Flexible Data Modeling Is Key for Your Product Information Management Strategy

Pimcore — Wed, 12 Feb 2020 07:26:52 +0000

Organizations’ growing focus on digitalization has intensified the proliferation of new channels and touchpoints. On the other side, customers improved experience day in and day out. This has made effective management of key demand product data imperative. Now, organizations cannot ignore the importance of Product Information Management (PIM) platforms to ensure consistent, shareable, accurate, and unified product data.

Your PIM Strategy: More Than a Plug-N-Play Game

Even if you are taking all the measures for controlled scalability, a growing business has to account for dynamic system requirements, unforeseen operational workflows, increased staffing, and so on. In such a scenario, without a dedicated system in place, it is very difficult to avoid product data from getting scattered, flawed, duplicated, or siloed. Over time, information like item numbers, references, catalogs, SKUs, images, videos, translations, localizations, and attributes become increasingly difficult to manage. This leads to discrepancies, higher workloads, slower processes, and ultimately reduced conversion rates, especially in organizations with large, complex product catalogs that need to be updated frequently, or in real-time across multiple channels.

Consider a situation when you are introducing a new product, and the launch process itself has 30 steps. The workflow might involve as many as 15 employees, and the attributes of the new product can reach up to 100 across the value chain. With such an arrangement, delaying the launch can result in a significant fallback. You need fast implementation and an agile model to steward all the data into one unified platform. It is nearly futile to install any solution without structure, schema, and/or architecture to support it. After all, you cannot always halt the operations to structure the database into one relational schema that can be integrated with a product information management system.

Having a dedicated PIM system in place does mitigate all the aforementioned troubles in a viable and user-friendly manner. It enables you to maintain all your product catalogs and product information in an accurate, unified, and consolidated form. It provides the required flexibility for eCommerce when handling large volumes of products, frequently updated catalogs across multiple channels, markets, or geographies, including:

1: Facilitates unification of product data across the eCommerce ecosystem for B2C and B2B organizations
2: Assures semantic consistency of product data throughout the supply chain
3: Adheres to regulatory compliance (a boon for industries like FMCG, F&B, Pharma, etc.)
5: Improves the use/reuse of product data across entities like manufacturers, distributors, and retailers
6: Provides agility and accelerates time-to-market of new products
Lowers risks and reduces the cost

However, implementing a PIM system is just one half of the solution. Where organizations bank on PIM for effective and flexible support for their product master data, they cannot harbor its true potential without suitable data modeling.

Data Modeling Flexibility: A Requirement of the Digital Age

Data modeling provides a generalized, user-defined view of data. When you create a data model, you define the data, its attributes, its interrelationships, and its constraints. This helps comprehend how to address data needs and design the database most suitable for the organization. It also allows the user to easily handle multiple data scenarios, attributes, locations, and relationships. For example, you can create a data model for a product where the vendor attribute links to vendor data stored in a separate spreadsheet.

When this model is made flexible, it further enables data editing and translation of interfaces for the effective manipulation of product data. Such a model facilitates you to access, view, and manipulate complex business requirements without having to reconfigure the system for performance optimization.

Flexible data modeling in a PIM software makes it easier to cover all the structured and unstructured data (or metadata) and, if needed, modify them later on. It primarily addresses the need for continually upgrading data sets as the business grows and requirements change. The whole setup allows you to add another data source or make changes to PIM interfacing. Consequently, the PIM integration, modification, and restructuring at any stage in the business become fast, non-iterative, and of course, flexible!

Benefits of Flexible Data Modeling

. Manage product data with uniformity— Reduce time-to-market and simplify all editorial processes. Edit data one time in one place and the changes reflect across the value chain.

. Classify standards— Handle text, media, static attributes, and relationships in a single view. Classify and structure product data based on industry-specific classification systems such as eCl@ss, ETIM, or GS1.

. Meet organizational structure— Ranging from small and centralized to global and distributed teams, every organization has a different structure. The usage and focus of product master data, ranging across use cases for design (information architecture), implementation (building the system), operations (running the business), and analytics (reporting on the business) demands agility and compatibility in data modeling capability. This can be easily done via a model that is flexible enough.

. Manage multi-lingual product data— Address the growing concern of product data in native/colloquial languages as the business grows beyond geographic boundaries. Translate and edit product data interfaces with flexible modeling to boost productivity.

. Resolve complexity— From simple to elaborate use cases – deploy PIM effectively in different scenarios with better levels of data governance, risk management, and control.

Though, a PIM system presents a trusted, uniform version of product master data that optimizes business value chains and drives the economic activity of product-centric organizations. However, a reliable PIM system inherently needs the support of a flexible data model that allows seamless updating and governance of master data. It must have a flexible workflow that can be easily fine-tuned with the business process while maintaining data governance. This means that the adaptive user interface (UI) must adjust to underlying data models automatically without any additional coding or implementation efforts.

To sum it up, diving head-first in the PIM implementation is not the answer. Businesses must carefully evaluate how they can amplify the benefits of PIM with flexible data modeling.

To know more about Pimcore Product Information Management, Digital Experience Platform, Master Data Management and Digital Asset Management. Follow Pimcore.