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.
  • 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.

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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.

 

The List is Growing

The Disruptive Master Data Management Solutions List was launched in autumn 2017 and it is now time for a short status.

The list is now running at just below 1,000 monthly visits. There are just below 400 followers if you, as WordPress does, counts the Twitter followers as well. There are 12 registered solutions including the founding solution Product Data Lake.

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The number of high quality guest blog posts by featured vendors has increased dramatically during the last month and more are expected to join.

If you represent an end user organization or someone who consults such an organization, you can use the list for making a vendor longlist and/or shortlist for an upcoming Master Data Management (MDM) / Product Information Management (PIM) implementation.

If you represent a vendor not on The Disruptive MDM List, you can register here.

Cloud multi-domain MDM as the foundation for Digital Transformation

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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).

Master Data Management Definitions: The A-Z of MDM. Part 2

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

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Enterprise Asset Management (EAM). The management of the assets of an organization (e.g., equipment and facilities).

Enterprise Resource Planning (ERP). Refers to enterprise systems and software used to manage day-to-day business activities, such as accounting, procurement, project management, inventory, sales, etc. Many businesses have several ERP systems, each managing data about products, locations or assets, for example. A comprehensive MDM solution complements an ERP by ensuring that the data from each of the data domains used by the ERP is accurate, up-to-date and synchronized across the multiple ERP instances.

Enrichment. Data enrichment refers to processes used to enhance, refine or otherwise improve raw data. In the world of MDM, enriching your master data can happen by including third-party data to get a more complete view, for example, such as adding social data to your customer master data. MDM eliminates manual product enrichment processes and replaces them with custom workflows, business rules and automation.

Entity. A classification of objects of interest to the enterprise (e.g., people, places, things, concepts and events).

ETL. Extract, Transform and Load. A process in data warehousing, responsible for pulling data out of source systems and placing it into a data warehouse.

G

Golden Record. In the MDM world, also sometimes referred to as “the single version of the truth.” This is the state you want your master data to be in and what every MDM solution is working toward creating: the most pure, complete, trustable data record possible.

Governance. Data Governance is a collection of practices and processes aiming to create and maintain a healthy organizational data framework, by establishing processes that ensure that data is formally managed throughout the enterprise. It can include creating policies and processes around version control, approvals, etc., to maintain the accuracy and accountability of the organizational information. Data governance is as such not a technical discipline but an indispensable discipline of a modern organization—and a fundamental supplement to any data management initiative.

GS1. Global Standards One. The GS1 standards are unique identification codes used by more than one million companies worldwide. The standards aim to create a common foundation for businesses when identifying and sharing vital information about products, locations, assets and more. The most recognizable GS1 standards are the barcode and the radio-frequency identification (RFID) tags. An MDM solution will support and integrate the GS1 standards across industries.

H

Hierarchy Management. An essential aspect of MDM that allows users to productively manage complex hierarchies spread over one or more domains and change them into a formal structure that can be used throughout the enterprise. Products, customers and organizational structures are all examples of domains where a hierarchy structure can be beneficial (e.g., in defining the hierarchical structure of a household in relation to a customer data record).

Hub. A data hub or an enterprise data hub (EDH) is a database that is populated with data from one or more sources and from which data is taken to one or more destinations. An MDM system is an example of a data hub, and therefore sometimes goes under the name Master Data Management hub.

I

Identity resolution. A data management process where an individual is identified from disparate data sets and databases to resolve their identity. This process relates to Customer Master Data Management.

Information. Information is the output of data that has been analyzed and/or processed in any manner.

Learn more about the difference between data and information here.

Integration. One of the biggest advantages of an MDM solution is its ability to integrate with various systems and link all of the data held in each of them to each other. A system integrator will often be brought on board to provide the implementation services.

Internet of Things (IoT). Internet of Things is the network of physical devices embedded with connectivity technology that enables these “things” to connect and exchange data. IoT technology represents a huge opportunity—and challenge—for organizations across industries as they can access new levels of data. A Master Data Management solution supports IoT initiatives by, for example, linking trusted master data to IoT-generated data as well as supporting a data governance framework for IoT data.

Learn more about the link between IoT and MDM here.

L

Lake. A data lake is a place to store your data, usually in its raw form without changing it. The idea of the data lake is to provide a place for the unaltered data in its native format until it’s needed. Why? Certain business disciplines such as advanced analytics depend on detailed source data. A data lake is the opposite of a data warehouse, but often the data lake will be an addition to a data warehouse.

Location data. Data about locations. Solutions that add location data management to the mix, such as Location Master Data Management, are on the rise. Effectively linking location data to other master data such as product data, supplier data, asset data or customer data can give you a more complete picture and enhance processes and customer experiences.

M

Maintenance. In order for any data management investment to continue delivering value, you need to maintain every aspect of a data record, including hierarchy, structure, validations, approvals and versioning, as well as master data attributes, descriptions, documentation and other related data components. Master data maintenance is often enabled by automated workflows, such as pushing out notifications to data stewards when there’s a need for a manual action. Maintenance is an unavoidable and ongoing process of any MDM implementation.

Modelling. Modelling in Master Data Management is a process in the beginning of an MDM implementation where you accurately map and define the relationship between the core enterprise entities (e.g., your products and their attributes). Based on that you create the optimal master data model that best fits your organizational setup.

Matching (and linking and merging). Key functionalities in a Customer Master Data Management solution with the purpose of identifying and handling duplicates to achieve a Golden Record. The matching algorithm constantly analyzes or matches the source records to determine which represent the same individual or organization. While the linking functionality persists all the source records and link them to the Golden Record, the merging functionality selects a survivor and non-survivor. The Golden Record is based only on the survivor. The non-survivor is deleted from the system.

Multidomain. A multidomain Master Data Management solution masters the data of several enterprise domains, such as product and supplier domain, or customer and product domain or any combination handling more than one domain.

Metadata management. The management of data about data. Metadata Management helps an organization understand the what, where, why, when and how of its data: where is it coming from and what meaning does it have? Key functionalities of Metadata Management solutions are metadata capture and storage, metadata integration and publication as well as metadata management and governance. While Metadata Management and Master Data Management systems intersect, they provide two different frameworks for solving data problems such as data quality and data governance.

N

New Product Development (NPD). A discipline in Product Lifecycle Management (PLM) that aims to support the management of introducing a new product line or assortment, from idea to launch, including its ideation, research, creation, testing, updating and marketing.

O

Omnichannel. A term mostly used in retail to describe the creation of integrated, seamless customer experiences across all customer touchpoints. If you offer an omnichannel customer experience, your customers will meet the same service, offers, product information and more, no matter where they interact with your brand (e.g., in-store, on social media, via email, customer service, etc.). The term stems from the Latin word omni, meaning everything or everywhere, and it has surpassed similar terms such as multi-channel and cross-channel that do not necessarily comprise all channels.

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.