Golden Records in Multidomain MDM

The term golden record is a core concept within Master Data Management (MDM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

GoldIn Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Records

Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record.

In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

Location Golden Records

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

GoldLocation management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance is other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Product Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

GoldWhile third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you are present.

Asset (or Thing) Golden Records

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

Product Something Management

This Disruptive MDM Solutions List encompasses what usually is called solutions for Product Information Management (PIM).

But besides PIM there are some other closely related Three Letter Acronym (TLA) coined solutions in play when it comes to the management of product data and product information. These are:

  • PCM which can stand for both Product Content Management and Product Catalog Management. There are various explanations for the difference between the two PCMs and PIM. The consensus seems to be that both representations of PCM is focussed on channel specific and formatting issues and may be relying on other data management disciplines as for example Master Data Management (MDM) and product data syndication services.
  • PDM being Product Data Management in whatever way that is different from Product Information Management. You might consider PDM techier than PIM.
  • PLM being Product Lifecycle Management. Solutions for PLM typically are different from other data management solutions, but then Stibo Systems recently have combined PLM, PIM and Multidomain MDM in a single platform and PLM has been an element from the start in the SyncForce Product Success Platform.
  • PxM is Product experience Management. In a recent post Gartner analyst Simon Walker explains how PCM and PIM for some vendors have merged into PxM in this piece.

Anyway, providers of all kinds of Product Something Management solutions are welcomed to register on this site.

PxM

Master Data Management Definitions: The A-Z of MDM. Part 3

This guest blog post is written by Justine Aa. Rodian of Stibo SystemsThe post is part 3 in a series of 3. Please find part 1 here and part 2 here.

img_A-Z_post3P

Party data. In relation to Master Data Management, party data is understood in two different ways. First of all, party data can mean data defined by its source. You will typically hear about first, second and third-party data. First-party data being your own data, second-party data being someone else’s first-party data handed over to you, while third-party data is collected by someone with no relation to you—and probably sold to you. However, when talking about party data management, party data refers to master data typically about individuals and organizations with relation to, for example, customer master data. A party can in this context be understood as an attorney or husband of a customer that plays a role in a customer transaction, and party data is then data referring to these parties. Party data management can be part of an MDM setup, and these relations can be organized using hierarchy management.

Learn more about party data here.

PII. Personally Identifiable Information. In Europe often just referred to as personal information. PII is sensitive information that identifies a person, directly (on its own) or indirectly (in combination). Examples of direct PII include name, address, phone number, email address and passport number, while examples of indirect PII include a combination (e.g., workplace and job title or maiden name in combination with date and place of birth).

Product Information Management (PIM). Today sometimes also referred to as Product MDM, Product Data Management (PDM) or Master Data Management for products. No matter the naming, PIM refers to a set of processes used to centrally manage and evaluate, identify, store, share and distribute product data or information about products. PIM is enabled with the implementation of PIM or Product Master Data Management software.

Learn more here.

Product Lifecycle Management (PLM). The process of managing the entire lifecycle of a product from ideation, through design, product development, sourcing and selling. The backbone of PLM is a business system that can efficiently handle the product information full-circle, and significantly increase time to market through streamlined processes and collaboration. That can be a standalone PLM tool or part of a comprehensive MDM platform.

Learn more here.

Pool. A data pool is a centralized repository of data where trading partners (e.g., retailers, distributors or suppliers) can obtain, maintain and exchange information about products in a standard format. Suppliers can, for instance, upload data to a data pool that cooperating retailers can then receive through their data pool.

Platform. A comprehensive technology used as a base upon which other applications, processes or technologies are developed. An example of a software platform is an MDM platform.

Profiling. Data profiling is a technique used to examine data from an existing information source, such as a database, to determine its accuracy and completeness and share those findings through statistics or informative summaries. Conducting a thorough data profiling assessment in the beginning of a Master Data Management implementation is recognized as a vital first step toward gaining control over organizational data as it helps identify and address potential data issues, enabling architects to design a better solution and reduce project risk.

Q

Quality. As in data quality, also sometimes just shortened into DQ. An undeniable part of any MDM vendor’s vocabulary as a high level of data quality is what a Master Data Management solution is constantly seeking to achieve and maintain. Data quality can be defined as a given data set’s ability to serve its intended purpose. In other words, if you have data quality, your data is capable of delivering the insight you require. Data quality is characterized by, for example, data accuracy, validity, reliability, completeness, granularity, consistency and availability.

R

Reference data. Data that define values relevant to cross-functional organizational transactions. Reference data management aims to effectively define data fields, such as units of measurements, fixed conversion rates and calendar structures, to “translate” these values into a common language in order to categorize data in a consistent way and secure data quality. Reference Data Management (RDM) systems can be the solution for some organizations, while others manage reference data as part of a comprehensive Master Data Management setup.

S

SaaS. Software as a Service. A software licensing and delivery model in which software is licensed on a subscription basis and is centrally hosted. SaaS is on the rise, due to change in consumer behavior and based on the higher demand for a more flat-rate pricing model, since these solutions are typically paid on a monthly or quarterly basis. SaaS is typically used in cloud MDM, for instance.

Supply Chain Management (SCM). The management of material and information flow in an organization—everything from product development, sourcing, production and logistics, as well as the information systems—to provide the highest degree of customer satisfaction, on time and at the lowest possible cost. A PLM solution or PLM MDM solution can be a critical factor for driving effective supply chain management.

Silos. When navigating the MDM landscape you will often come across the term data silos. A term describing when crucial data or information, such as master data, is held separately whether by individuals, departments, regions or systems. MDMs’ finest purpose is to “break down data silos.”

Stock Keeping Unit (SKU). A SKU represents an individual item, product or service manifested in a code, uniquely identifying that item, product or service. SKU codes are used in business to track inventory. It’s often a machine-readable bar code, providing an additional layer of uniqueness and identification.

Stack. The collection of software or technology that forms an organization’s operational infrastructure. The term stack is used in reference to software (software stack), technology (technology stack) or simply solution (solution stack) and refers to the underlying systems that make your business run smoothly. For instance, an MDM solution can—in combination with other solutions—be a crucial part of your software stack.

Stewardship. Data stewardship is the management and oversight of an organization’s data assets to help provide business users with high-quality data that is easily accessible in a consistent manner. Data stewards will often be the ones in an organization responsible for the day-to-day data governance.

Strategy. As with all major business initiatives, MDM needs a thorough, coherent, well-communicated business strategy in order to be as successful as possible.

Supplier data. Data about suppliers. One of the domains on which MDM can be beneficial. May be included in an MDM setup in combination with other domains, such as product data.

Learn more about supplier data here.

Synchronization. The operation or activity of two or more things at the same time or rate. Applied to data management, data synchronization is the process of establishing data consistency from one endpoint to another and continuously harmonizing the data over time. MDM can be the key enabler for global or local data synchronization.

Syndication. Data syndication is basically the onboarding of data provided from external sources, such as suppliers. An MDM solution will typically automate the process of receiving external data while making sure that high-quality criteria are met.

Swamp. A data swamp is a deteriorated data lake, that is inaccessible to its intended users and provides little value.

T

Training. No, not the type that goes on in a gym. Employee training, that is. MDM is not just about software. It’s about the people using the software, hence they need to know how to use it best in order to maximize the Return on Investment (ROI). MDM users will have to receive training from either the MDM vendor, consultants or from your employees who already have experience with the solution.

U

User Interface (UI). The part of the machine that handles the human–machine interaction. In an MDM solution—and in all other software solutions—users have an “entrance,” an interface from where they are interacting with and operating the solution. As is the case for all UIs, the UI in an MDM solution needs to be user-friendly and intuitive.

V

Vendor. There are many Master Data Management vendors on the market. How do you choose the right one? It all depends on your business needs, as each vendor is often specialized in some areas of MDM more than others. However, there are some things you generally should be aware of, such as scalability (Is the system expandable in order to grow with your business?), proven success (Does the vendor have solid references confirming the business value?) and integration (Does the solution integrate with the systems you need it to?).

W

Warehouse. A data warehouse—or EDW (Enterprise Data Warehouse)—is a central repository for corporate information and data derived from operational systems and external data sources, used to generate analytics and insight. In contrast to the data lake, a data warehouse stores vast amounts of typically structured data that is predefined before entering the data warehouse. The data warehouse is not a replacement for Master Data Management, as MDM can support the EDW by feeding reliable, high-quality data into the system. Once the data leaves the warehouse, it is often used to fuel Business Intelligence.

Workflow automation. An essential functionality in an MDM solution is the ability to set up workflows, a series of automated actions for steps in a business process. Preconfigured workflows in an MDM solution generate tasks, which are presented to the relevant business users. For instance, a workflow automation is able to notify the data steward of data errors and guide him through fixing the problem.

Y

Yottabyte. Largest data storage unit (i.e., 1,000,000,000,000,000,000,000,000 bytes). No Master Data Management solution, or any other data storage solution, can handle this amount yet. But, scalability should be a considerable factor for which MDM solution you choose.

Z

ZZZZZ… With a Master Data Management solution placed at the heart of your organization you get to sleep well at night, knowing your data processes are supported and your information can be trusted.

If you’d like the whole A-Z e-book in a downloadable format, please find it here.

Justine Aagaard Rodian is a marketing specialist at Stibo Systems with a background as a journalist. Five years in the data management industry has armed Justine with unique insights and she is now using her storytelling and digital skills to spread valuable business knowledge about Master Data Management and related topics.

 

Cloud multi-domain MDM as the foundation for Digital Transformation

Upen-200x300

Upen Varanasi

This guest blog post is an interview by Katie Fabiszak with CEO & Founder Upen Varanasi on the new MDM release from Riversand Technologies.

Riversand is pleased to announce the newest release of our cloud-native suite of Master Data Management solutions. Helping organizations create a data and analytics foundation to drive successful digital transformation was the driving factor behind Riversand’s decision to completely rethink and rebuild a master data platform that would meet the future needs of the digital era. According to Riversand CEO & Founder Upen Varanasi, “We knew we had to make an aggressive move to envision a new kind of data platform that was capable of handling the forces shaping our industry.” Katie Fabiszak, Riversand Global Vice President of Marketing, sat down with Upen to talk about Riversand’s vision of the future and how the bold decisions he had made several years ago led to the company’s own transformational journey and a new MDM solution:

KF:        Where do you think the market is heading?

UV:       Well, we are truly in the digital era. The markets are rewarding companies that are leaders in this digital landscape. This is forcing all companies to truly embrace digital transformation. A key aspect of this transformation is the role of data. Data is the new “fuel” of digital transformation. What matters is how enterprises can leverage data and unlock its power to create better and faster business outcomes. Turning data into insights is a key factor. Another important aspect is to understand the complexities of the information supply chain. Simplifying the flow of data is critical to drive better and faster business outcomes. Let’s take an example from the retail/CPG space: Enterprises want to better understand their customers (utilizing customer insights through AI), create/sell products and services that meet and exceed customer expectations (product positioning through AI), meet their customers across all touch points (channel management), secure their customer’s information (privacy), and interact with trading partners to creating cutting edge product differentiation (cloud and AI).

KF:        Why did Riversand decide to shift gears and embark on what became a 2+ year transformational journey for the company?

UV:       At Riversand, we understood the need to envision a new kind of platform to handle the trends occurring in our industry. We focused on creating a data-driven multi-domain Master Data Management System that has the following properties:

  • A contextual master data modeling environment.
  • A platform that can handle scale, velocity and variety of data, built to handle master data as well as transaction and interaction data.
  • Embedded AI to drive better insights and outcomes.
  • Built on cloud, with the ability to integrate data and processes across hybrid clouds.
  • A highly business-friendly user experience.
  • Apps built on the platform that can solve the “final mile” problem for business users. PIM and customer domain solutions are each apps built on this same platform.

Our goal was to create a platform and solutions that can be quick to implement, provide insights and recommended actions to business users, and automates many of the more mundane data management and stewardship functions. We believe we can provide Enterprises with the critical capabilities from the Master Data Management and Product Information Management space to differentiate themselves in the marketplace.

KF:        What are the core values of Riversand?

UV:       Our core values are innovation, commitment & integrity. We have consistently strived to bring leading edge innovation to the market. We have dared to step back at times in order to take a bigger leap forward. What helps is that we have had a long-term view of our company and the industry we play in. We have been in business for 18 years and have been committed to this space and to our customers all along the way. Integrity in everything we do is key in dealing with our stakeholders – employees, customers and partners – and has been our focus since inception. We might not get things right all the time to begin with, but we will always make it right with our stakeholders over time.

KF:        What do you see as Riversand’s biggest strength?

UV:       Our people, our passion for this space and our long-term thinking give us an incredible edge against our competition. We are blessed to have a team who have shaped this company over many years and who are invested deeply in the company and this market space.

Riversand picKF:        What would you say sets Riversand apart?  How is our technology and approach different than the other MDM and PIM vendors in the market?

UV:       The path we have chosen provides clear benefits for our customers with respect to our competition. Some key points of differentiation are:

  • We provide a single platform to help implement MDM and PIM initiatives. No need to create further silos of data and processes.
  • We future-proof both business users and IT users with a platform that is flexible and future enabled.
  • We are built for the cloud and SaaS eras: Upgrades are easy and done by us, and customers can scale with pay as you go models.
  • Our AI engine is built on a big data stack to drive insights and actions for better business outcomes.
  • With our big data technology stack, enterprises have the ability to scale with data and handle both its variety and velocity.
  • A completely new and extremely business friendly user interface and experience that people love to use!
  • The app building toolkit (SDK) to help customers and partners build their own apps so that they can solve their final mile problems: This is in addition to the core apps we are actively building.

KF:        Where is Riversand today along this journey that we began over 2 years ago?

UV:       Our new solutions were introduced as a kind of soft launch with a select few customers over the past year. The feedback and insights we received during this year were extremely useful in becoming enterprise-ready. We are now in a position to launch our platform to the broader market. Over the next two quarters, we will be further enhancing our offerings including: the analytics/AI platform, app SDK for partners and customers, launching additional SaaS appsnd entering additional vertical markets. We also look forward to creating robust partner ecosystems for these vertical markets.

KF:        What’s next for Riversand – what do you envision for the future?

UV:       We are really excited about the potential of our new master data platform and we look forward to working with our current customers as well as new customers to help them with their digital transformation journey. We are disruptors in our space and we will continue to establish ourselves as a larger, bolder and leading global brand.

We hope you enjoyed the interview!  You can check out additional information by reading the press announcement here.

Katie Fabiszak oversees and directs global marketing efforts at Riversand.  She is an accomplished executive with more than 20 years of success in global marketing for high tech companies. She is responsible for leading the strategic evolution of the company’s branding and marketing strategy.  With proven success developing effective marketing strategies to drive revenue, Katie’s extensive career includes marketing leadership positions at Informatica, StrikeIron and DataFlux (a SAS company).

Why next generation MDM and PIM solutions must be in the Cloud

In this guest blog post Shamanth Shankar of Riversand explains why the next generation of Master Data Management (MDM) and Product Information Management (PIM) solutions must be deployed as cloud solutions.

As retailers grow, managing data across their application landscape becomes crucial. Business users and Information Technology (IT) leaders want to choose data management solutions that provide insights to them and provide relevant information to their customers along their purchasing life-cycle. Accommodating scale, speed and different data types requires that data management solutions evolve.

How are enterprises coping with scale and flexibility?

Enterprise software solutions are going through a revolution to accommodate scale and flexibility. Looking at the growth of AWS and Azure, one can imagine how aggressively enterprises are embracing cloud. AWS grew 54.9% year on year and Microsoft’s Azure grew  triple digits over the last five quarters. Cloud is growing at a 22% Compound Annual Growth Rate (CAGR), four times the rate of software spending growth. Flexibility in operating models (Operational Expense vs. Capital Expense), ease of implementation and cost savings lure business leaders towards the cloud. To strike a balance with the legacy of on premise solutions and cloud-based business friendly applications, enterprises will pursue a hybrid cloud model.

Cloud and Retailers

Compared to other verticals, retail has a lower barrier to adopting cloud. Cloud is expected to grow five-fold in retailing. Additional data points supporting this continued accelerated growth of cloud-based solutions, include;

  • Digital commerce platforms are growing at 15% CAGR, driven by SaaS revenues.
  • Non-Store retailers reported 12% year on year growth per the US Department of Commerce.

Retailers invest in various hybrid cloud-based solutions throughout their enterprise to providetheir consumers with personalized attention, constant engagement and better experiences.The central focus of this effort is to provide business users with a better user experience along with consistent data (especially product data) all with the goal of creating great experiences for consumers.

Product Information Management Solution

Synchronization of product data across applications and channels is critical to enterprises for improved efficiencies, faster new product introduction and higher sales. A Product Information Management (PIM) solution provides a single source of truth, high quality product content, global product taxonomies, aggregation and syndication with internal and external sources.

PIM solutions improve sales by providing the right product for the right customer at the right time, improve efficiencies by accelerating new product introduction, reduce supply chain costs and identify bottlenecks through better reporting. By connecting with data pools, vendor data, marketplaces, e-commerce platforms and Digital Asset Management solutions, PIM solutions connect to the entire application ecosystem and keep all the business users on the same page with “Trusted Product Data”. The foundation of this “Trusted Product Data” drives an enterprise towards data-driven and outcome based operations. In such an ecosystem, data models are defined, product data is mapped to marketplace structures, outcomes are measured and changes are accommodated on a periodic basis.

Transformation in Product Information Management

As retailers grow (organically or through acquisition), data models need to be changed, infrastructure needs to be expanded and global variations need to be consolidated into one solution. In addition to strategic topline growth, changing consumer choices, interactions and sentiments force business transformations.

Business leaders and IT leaders want to gain insights from their PIM solution and ensure they are providing relevant information to their customers. They expect a PIM solution to help them solve the following:

Assortment and Product Intelligence

Can they match the relevant assortment and products to the respective consumer segments, and understand  particular consumer’s sentiments?

Channel and Operational Intelligence

 How are the products performing on various channels and what impact does product data quality have on supply chain costs?

Competitive Intelligence

Does the competition differ by merchandising category?
How is the merchandise performing compared to the competition?

Both business and IT users are looking to accommodate this inward-looking information into their application ecosystem.

IT teams are finding it challenging to correlate external data with their internal data management practices. They are looking for cost effective technologies that can provide insights into underperforming product segments.

Merchandising teams curating product content have limited to nonexistent abilities to map or correlate product content to consumer segments, competitors, or sentiment via social channels.

Customer service teams are trying to analyze and draw insights from consumer and market segment analysis and marry them with category specific context.

Next Generation Solution

Current data management solutions need to evolve to manage increasing master data  and related expanding data pools at scale. These solutions will have to be based on hybrid technology frameworks involving SQL, NoSQL, Graph and persist both structured and unstructured data. To provide higher Return on Investment and lower Total Cost of Ownership to retailers, such solutions will need to be able to scale in or out with a pay-as-you-go model. Considering that enterprises will continue supporting on premise, private cloud and public cloud models, PIM solutions must provide all these capabilities both on premise and in the cloud.

Next Generation data management will bring global business complexities together into a single solution that is web-scale, dynamically configurable and offers the best user experience for both business and IT teams.

Shamanth Shankar supports business operations at Riversand. Till recently Shamanth led Marketing at Riversand. Prior to Riversand Shamanth consulted clients on Data & Analytics products, led S&OP at a manufacturing company and managed a Product Line at a public company. Shamanth holds degrees from Rice, Texa A&M and IIT Madras.

industry and Internet of Things concept. woman working in factory and wireless communication network. Industry4.0.