Stibo Systems - The Master Data Management Company

Hyper-Personalized Customer Experiences Need Multidomain MDM

Master Data Management Blog by Stibo Systems logo
| 8 minute read
November 05 2022

Multidomain master data management gives personalization strategies a better start in life

Customer experience starts with understanding what customers want. Multidomain master data management (Multidomain MDM) is a key discipline to help in the successful development of a personalization strategy. Multidomain master data management can help you build a 360° view of the customer that is needed to fuel personalization and hyper-personalization strategies with the right insights.

 

What is hyper-personalization?

Hyper-personalization is a marketing strategy that involves tailoring content, products or services to the specific needs and preferences of individual customers using data and artificial intelligence (AI) technology to create a highly personalized experience.

This approach goes beyond traditional personalization techniques, which typically involve segmenting customers into broad groups based on demographics or behavior and instead focuses on understanding each customer's unique characteristics and delivering highly customized experiences at every touchpoint.

Hyper-personalization leverages data such as purchase history, browsing behavior, social media activity and other customer interactions to create a comprehensive profile of each individual customer. AI algorithms then use this data to deliver personalized recommendations, offers and content in real-time across multiple channels, such as email, social media, websites and mobile apps.

The goal of hyper-personalization is to create a seamless and engaging customer experience that increases customer loyalty, improves retention and drives revenue growth for businesses.

Definition on hyper-personalized customer experiences: A customer is able to use technologies to find the exact product or service they are looking for, while also having a personalized experience that reflects their specific interests and preferences throughout the buyers journey.

 

How does hyper-personalization differ from traditional personalization?

Hyper-personalization is different from traditional personalization in several key ways:

  • Scope: Traditional personalization typically involves segmenting customers into broad groups based on demographics or behavior, whereas hyper-personalization involves tailoring content, products or services to the specific needs and preferences of individual customers.

  • Data usage: Hyper-personalization leverages a wider range of data sources than traditional personalization, such as purchase history, browsing behavior, social media activity and other customer interactions, to create a comprehensive profile of each individual customer.

  • Real-time delivery: Hyper-personalization uses AI algorithms to deliver personalized recommendations, offers and content in real-time across multiple channels, such as email, social media, websites and mobile apps, whereas traditional personalization may rely on static rules-based approaches that are not as responsive to customer needs.

  • Granularity: Hyper-personalization is more granular than traditional personalization, focusing on delivering highly customized experiences at every touchpoint, whereas traditional personalization may be limited to a few basic personalization elements, such as product recommendations or personalized greetings.

Overall, hyper-personalization is a more sophisticated and targeted approach to personalization that uses data and AI technology to deliver highly personalized experiences at every touchpoint, with the goal of increasing customer loyalty, improving retention, and driving revenue growth for businesses.

Hyper-Personalized Customer Experiences Need Multidomain MDM

What are the elements of a winning hyper-personalization strategy?

A winning hyper-personalization strategy includes several key elements:

  • Data collection: To implement hyper-personalization, businesses need to collect and analyze large amounts of data from various sources, including customer interactions, browsing behavior, social media activity and other relevant data points. This data should be collected ethically and in compliance with data protection regulations.

  • Customer segmentation: Hyper-personalization requires businesses to segment their customers into smaller groups based on shared characteristics or behaviors. This helps create a more personalized experience for each customer and enables businesses to deliver more relevant content, products and services.

  • Personalization algorithms: Advanced machine learning and AI algorithms are needed to process the vast amounts of data collected and deliver personalized recommendations, offers and content in real-time across multiple channels. These algorithms should be regularly refined and optimized to improve accuracy and relevance.

  • Multichannel approach: A winning hyper-personalization strategy involves delivering personalized experiences across multiple touchpoints, such as email, social media, websites and mobile apps. This enables businesses to meet customers where they are and provide a seamless and consistent experience across channels.

  • Testing and optimization: Hyper-personalization is an ongoing process that requires continuous testing and optimization to improve effectiveness and ensure that the strategy is aligned with changing customer needs and preferences. Businesses should regularly analyze data and performance metrics and make adjustments to their strategy accordingly.

  • Privacy and security: As hyper-personalization involves collecting and analyzing large amounts of customer data, businesses must prioritize data privacy and security to maintain customer trust and comply with data protection regulations.

Overall, a winning hyper-personalization strategy requires businesses to collect and analyze customer data ethically, segment customers into smaller groups, use advanced algorithms to deliver personalized experiences across multiple channels and continuously test and optimize their strategy.

 

What are the six key benefits of hyper-personalization?

Hyper-personalization offers several key benefits for businesses, including:

1. Improved customer experience

By tailoring content, products and services to the specific needs and preferences of individual customers, hyper-personalization creates a more engaging and personalized experience that can improve customer satisfaction and loyalty.

2. Increased customer retention

Hyper-personalization can help businesses build stronger relationships with customers by delivering relevant and personalized experiences that keep customers coming back.

3. Higher conversion rates

Personalized recommendations, offers and content can help drive conversions by making it easier for customers to find products or services that meet their needs and preferences.

4. Better ROI

By delivering more targeted and relevant experiences, hyper-personalization can help businesses maximize the return on their marketing and advertising investments.

5. Enhanced brand perception

By delivering personalized experiences that meet customers' needs and preferences, businesses can build a stronger brand reputation and differentiate themselves from competitors.

6. Deeper customer insights

Hyper-personalization requires collecting and analyzing large amounts of customer data, which can provide businesses with valuable insights into customer behavior and preferences that can inform future marketing and business strategies.

Overall, hyper-personalization can help businesses improve the customer experience, drive customer loyalty and retention, increase conversions and ROI and gain deeper customer insights that can inform future business decisions.

 

 

What are the six data management challenges that prevents personalization?

Let’s take a closer look at some of the typical data management challenges that prevent you from achieving personalization and what role multidomain master data management can play to alleviate that.

1. There is no business owner for the management of customer data and the development of customer insight, and there are conflicting business processes for the creation and referencing of product data.

Multidomain master data management provides the governance capability to support business ownership and stewardship of the key data elements that are needed for personalization, including customer data, product data, channel data, location data, and more.

2. Data is of insufficient quality

Multidomain master data management can help through its data governance capabilities to ensure that master data such as product and customer data is accurate, complete, and coherent.

3. Customer data does not provide enough insight

Multidomain master data management does not develop data on its own. However, it can harness small data that, when combined with master data, yields new insights. For example, understanding that two people share the same address may reveal a household relationship.

4. Not enough sources of data

The ability to implement a single point of data governance in the organization means that new data sources may be onboarded more easily. For example, census data and social media data, while broad in what content they carry on their own, when combined correctly with master data records, can reveal new insight.

5. Analytics are post-fact rather than being dynamically driven

The ability to drive analytics dynamically at the point of customer interaction that also takes into account the customer behavior is a key personalization capability. Whether this is done by the recommendation engine directly or in conjunction with a customer services agent, the results often yield key decisions – for example, next-best offer or privacy consent – that are immediately relevant to other channels, points of interaction, analytics, and operational systems. Master data management provides an ideal repository for the collection and sharing of these “small data” elements in order to help create a coherent and customer-centric experience.

6. Difficult to collect the data required

This challenge has three different root causes: 1) the customer does not give consent to use the information, 2) the tools to collect the information are lacking or insufficient, and 3) the management discipline and organization required to coordinate data objectives is ill-defined.

Multidomain master data management can help to act as a data brokerage in the support of these three challenges. It brings discipline to data management and provides a safe place to keep the collated data. In so far as asking the customer to share their data, this should be promoted in the development of a win-win scenario where the customer gets clear benefits from doing so without compromising their preferences for privacy. Master data management can ensure that this process is transparent, by recording, governing, and auditing the consent and the consented data.

“…most marketers are missing opportunities in using a broader set of customer data to improve personalization efforts…“ - Gartner Magic Quadrant for Personalization Engines, 2020


Multidomain MDM moves you from segmentation to personalization

Personalization typically sits beyond more traditional mass-market segmentation strategies and is often characterized as targeting the individual or a collection of individuals with very similar characteristics, interests, and problems, for example, medical professionals or vehicle owners. Segmentation generally addresses a much wider set of demographic similarities, such as geographical region, or gender.

While macro and micro-segmentation strategies remain important, they tend to rely on a view of the world more from the vendor’s perspective and not necessarily that of the consumer. Invariably this leads to a reactive posture; looking at what a consumer has done rather than looking at what they are doing or anticipating their motives.

Generic-Hyper 2-1

Personalization is intrinsically linked to customer experience

To differentiate in customer experience, personalization is key. Personalization automatically changes the way the service is being delivered, depending on how it is being used and perceived. For example, when visiting the website of the BBC for the latest news, there may also be displayed some local news, in France for example, based on your current location. In another example, eCommerce sites will often display product recommendations based on previous purchase history.

Recommendation engines are tools that adjust the experience on a website according to behavior. These engines will often have learning algorithms that will change search results, predict product interest, and change the order of pages to automate recommendation strategies.

Personalization is a highly data-intensive process. The accuracy, relevance, coherence, timeliness, pertinence, and intimacy of the data being used to support personalization is going to have a direct impact on your customer’s experience. Do you want to see dog food being recommended when you own a cat?

65% of companies consider the management, quality and availability of data as a major inhibitor of personalization. - Boston Consulting Group, 2019

Achieving successful personalization is challenging. Despite some good tools being available, such as dedicated recommendation engine solutions, for many, they are only as good as the data that drives them.

That’s why you need multidomain master data management.

 

 


Multidomain MDM moves you from personalization to hyper-personalization

Beyond single domain governance, multidomain master data governance adds new capabilities that play important roles in the management of the data that specifically supports hyper-personalization. Note that multiple-domain refers to a collection of single domain governance capabilities and is not to be confused with multidomain which refers to a unique, cross-domain governance capability. Multidomain master data management carries zones of insight.

Zones of insight refer to the data management capabilities that are unique to cross-domain governance and that make hyper-personalization effective.

Zones of insight provide supporting data management capabilities for hyper-personalization and drive new business models.

The zones of insight occur in the intersections of data domains, here Customer, Product, Location, and Supplier:

 

Zones of Insight at the intersections of data domains



How does Multidomain MDM help hyper-personalization?

There is no getting around it. Successful hyper-personalization relies on hard-to-get and hard-to-manage data. Multidomain master data management can help by providing data collection and management capabilities.

The types of data that might be considered to be managed within a multidomain master data management solution to support hyper-personalization include:

  • Event-based – the acquisition of real-time information directly impacting the individual, such as local weather, location information, behavior, and device usage.
  • Configuration-based – the settings that an individual configures to adapt their user experience to their needs and aspiration, such as their consent information, product preferences, information alerts, and content appearance.
  • Deduction-based – the results of analytics based on current or prior behavior that may result in the development of new information concerning the individual, such as their householding, likely next best offer, sentiment, eligibility, and propensity.

As the list indicates, a truly 360° view of the customer is comprehensive and encompasses not just customer data, but also the context of the customer.

Multidomain master data management, via its ability to manage zones of insight, can not only help to see how a product is sold but also potentially, help to provide insight into how it is used.

Multidomain MDM supports hyper-personalization

Only multidomain master data management will support hyper-personalization.

Multidomain MDM helps you capture relationships and insights

Multidomain MDM provides governance capabilities that are often used to build a 360° customer view. For hyper-personalization, two types of data sets are particularly useful from this 360° customer view: relationships and insights.

Relationships may be inter- or cross-domain. For example, when defining what constitutes a household, multiple parties and location data must be governed together. A household is not necessarily defined in the same way for all organizations. It might be address-based, or more family member-oriented, for example. Multidomain MDM helps to determine and align the data so that it correctly reflects the policy of household definition.

Insights are arguably the golden data elements of hyper-personalization. They help to make the user experience unique. Many of the data elements can be considered as being small data, such as determining location-based preferences, sentimental analysis, or a life event.

Main points of how to achieve hyper-personalization with Multidomain MDM

  1. For effective hyper-personalization, a governance strategy is required that provides data transparency (quality, coherence, relevance, auditability, etc.) across many data sources, from analytical to transactional to real-time event-based data. Multidomain master data management provides the governance strategy for transparency.
  2. Actively managing the complex relationships between data domains adds the ability to reveal new types of information that can directly impact customer experience. Multidomain master data management governs this new information in “zones of insight” making it reliable, and as a result, actionable for hyper-personalization.


Master Data Management Blog by Stibo Systems logo

During the past 20 years, Darren has been advising companies on selecting and implementing software tools that support their data governance strategy. Darren helps business leaders understand and quantify the positive impact that good data governance and data management, can have on their organization.

Discover Blogs by Topic

  • MDM strategy
  • Data governance
  • Customer and party data
  • See more
  • Retail and distribution
  • Manufacturing
  • Data quality
  • Supplier data
  • Product data and PIM
  • AI and machine learning
  • CPG
  • Financial services
  • GDPR
  • Sustainability
  • Location data
  • PDX Syndication

Master Data Management Roles and Responsibilities

5/20/24

8 Best Practices for Customer Master Data Management

5/16/24

What Is Master Data Governance – And Why Do You Need It?

5/12/24

4 Common Master Data Management Implementation Styles

5/10/24

Guide: Deliver flawless rich content experiences with master data governance

4/11/24

Risks of Using LLMs in Your Business – What Does OWASP Have to Say?

4/10/24

Guide: How to comply with industry standards using master data governance

4/9/24

Digital Product Passports - A Data Management Challenge

4/8/24

Guide: Get enterprise data enrichment right with master data governance

4/2/24

Guide: Getting enterprise data modelling right with master data governance

4/2/24

Guide: Improving your data quality with master data governance

4/2/24

Data Governance Trends 2024

1/30/24

NRF 2024 Recap: In the AI era, better data can make all the difference

1/19/24

Building Supply Chain Resilience: Strategies & Examples

12/19/23

How Master Data Management Can Enhance Your ERP Solution

12/14/23

Shedding Light on Climate Accountability and Traceability in Retail

11/29/23

What is Smart Manufacturing and Why Does it Matter?

10/11/23

Future Proof Your Retail Business with Composable Commerce

10/9/23

5 Common Reasons Why Manufacturers Fail at Digital Transformation

10/5/23

How to Digitally Transform a Restaurant Chain

9/29/23

Three Benefits of Moving to Headless Commerce and the Role of a Modern PIM

9/14/23

12 Steps to a Successful Omnichannel and Unified Commerce

7/6/23

CGF Global Summit 2023: Unlock Sustainable Growth With Collaboration and Innovation

7/5/23

Navigating the Current Challenges of Supply Chain Management

6/28/23

Responsible AI relies on data governance

5/11/23

Product Data Management during Mergers and Acquisitions

4/6/23

Master Data Management Definitions: The Complete A-Z of MDM

3/14/23

4 Ways to Reduce Ecommerce Returns

3/8/23

Asset Data Governance is Central for Asset Management

3/1/23

How to Leverage Internet of Things with Master Data Management

2/14/23

Manufacturing Trends and Insights in 2023-2025

2/14/23

Sustainability in Retail Needs Governed Data

2/13/23

What is Augmented Data Management?

2/9/23

NRF 2023: Retail Turns to AI and Automation to Increase Efficiencies

1/20/23

What is the difference between CPG and FMCG?

1/18/23

5 Key Manufacturing Challenges in 2023

1/16/23

What is a Golden Customer Record in Master Data Management?

1/9/23

The Future of Master Data Management: Trends in 2023-2025

1/8/23

Innovation in Retail

1/4/23

5 CPG Industry Trends and Opportunities for 2023-2025

12/5/22

Life Cycle Assessment Scoring for Food Products

11/21/22

Retail of the Future

11/14/22

Omnichannel Strategies for Retail

11/7/22

Hyper-Personalized Customer Experiences Need Multidomain MDM

11/5/22

What is Omnichannel Retailing and What is the Role of Data Management?

10/25/22

Most Common ISO Standards in the Manufacturing Industry

10/18/22

How to Get Started with Master Data Management: 5 Steps to Consider

10/17/22

What is Supply Chain Analytics and Why It's Important

10/12/22

What is Data Quality and Why It's Important

10/12/22

A Data Monetization Strategy - Get More Value from Your Master Data

10/11/22

An Introductory Guide: What is Data Intelligence?

10/1/22

Revolutionizing Manufacturing: 5 Must-Have SaaS Systems for Success

9/15/22

An Introductory Guide to Supplier Compliance

9/7/22

What is Application Data Management and How Does It Differ From MDM?

8/29/22

Digital Transformation in the Manufacturing Industry

8/25/22

Master Data Management Framework: Get Set for Success

8/17/22

Discover the Value of Your Data: Master Data Management KPIs & Metrics

8/15/22

Supplier Self-Service: Everything You Need to Know

6/15/22

Omnichannel vs. Multichannel: What’s the Difference?

6/14/22

Digital Transformation in the CPG Industry

6/14/22

Create a Culture of Data Transparency - Begin with a Solid Foundation

6/10/22

The 5 Biggest Retail Trends for 2023-2025

5/31/22

What is a Location Intelligence?

5/31/22

Omnichannel Customer Experience: The Ultimate Guide

5/30/22

Location Analytics – All You Need to Know

5/26/22

Omnichannel Commerce: Creating a Seamless Shopping Experience

5/24/22

Top 4 Data Management Trends in the Insurance Industry

5/11/22

What is Supply Chain Visibility and Why It's Important

5/1/22

6 Features of an Effective Master Data Management Solution

4/30/22

What is Digital Asset Management?

4/23/22

The Ultimate Guide to Data Transparency

4/21/22

How Manufacturers Can Shift to Product-as-a-Service Offerings

4/20/22

How to Check Your Enterprise Data Foundation

4/16/22

An Introductory Guide to Manufacturing Compliance

4/14/22

Multidomain MDM vs. Multiple Domain MDM

3/31/22

Making Master Data Accessible: What is Data as a Service (DaaS)?

3/29/22

How to Build a Successful Data Governance Strategy

3/23/22

What is Unified Commerce? Key Advantages & Best Practices

3/22/22

How to Choose the Right Data Quality Tool?

3/22/22

What is a Data Domain?

3/21/22

6 Best Practices for Data Governance

3/17/22

5 Advantages of a Master Data Management System

3/16/22

A Unified Customer View: What Is It and Why You Need It

3/9/22

Supply Chain Challenges in the CPG Industry

2/24/22

Data Migration to SAP S/4HANA ERP - The Fast and Safe Approach with MDM

2/17/22

The Best Data Governance Tools You Need to Know About

2/17/22

Top 5 Most Common Data Quality Issues

2/14/22

What Is Synthetic Data and Why It Needs Master Data Management

2/10/22

What is Cloud Master Data Management?

2/8/22

How to Implement Data Governance

2/7/22

Build vs. Buy Master Data Management Software

1/28/22

Why is Data Governance Important?

1/27/22

Five Reasons Your Data Governance Initiative Could Fail

1/24/22

How to Turn Your Data Silos Into Zones of Insight

1/21/22

How to Improve Supplier Experience Management

1/16/22

​​How to Improve Supplier Onboarding

1/16/22

How to Enable a Single Source of Truth with Master Data Management

1/13/22

What is a Data Quality Framework?

1/11/22

How to Measure the ROI of Master Data Management

1/11/22

What is Manufacturing-as-a-Service (MaaS)?

1/7/22

The Ultimate Guide to Building a Data Governance Framework

1/4/22

Introducing the Master Data Management Maturity Model

1/3/22

Master Data Management Tools - and Why You Need Them

12/20/21

The Dynamic Duo of Data Security and Data Governance

12/20/21

How to Choose the Right Supplier Management Solution

12/20/21

How Data Transparency Enables Sustainable Retailing

12/6/21

What is Supplier Performance Management?

12/1/21

What is Party Data? All You Need to Know About Party Data Management

11/28/21

What is Data Compliance? An Introductory Guide

11/18/21

How to Create a Marketing Center of Excellence

11/14/21

The Complete Guide: How to Get a 360° Customer View

11/7/21

What is the Difference Between Master Data and Metadata?

11/1/21

How Location Data Adds Value to Master Data Projects

10/29/21

How Marketers Should Prepare for the 2023 Holiday Shopping Season

10/26/21

What is Supplier Lifecycle Management?

10/19/21

What is a Data Mesh? A Simple Introduction

10/15/21

How to Build a Master Data Management Strategy

9/26/21

10 Signs You Need a Master Data Management Platform

9/2/21

What Vendor Data Is and Why It Matters to Manufacturers

8/31/21

3 Reasons High-Quality Supplier Data Can Benefit Any Organization

8/25/21

4 Trends in the Automotive Industry

8/11/21

What is Reference Data and Reference Data Management?

8/9/21

What Obstacles Are Impacting the Global Retail Recovery?

8/2/21

GDPR as a Catalyst for Effective Data Governance

7/25/21

All You Need to Know About Supplier Information Management

7/21/21

5 Tips for Driving a Centralized Data Management Strategy

7/3/21

Data Governance and Data Protection, a Match Made in Heaven?

6/29/21

Welcome to the Decade of Transparency

5/26/21

How to Become a Customer-Obsessed Brand

5/12/21

How to Create a Master Data Management Roadmap in Five Steps

4/27/21

What is a Data Catalog? Definition and Benefits

4/13/21

How to Improve the Retail Customer Experience with Data Management

4/8/21

How to Improve Your Data Management

3/31/21

How to Choose the Right Master Data Management Solution

3/29/21

Business Intelligence and Analytics: What's the Difference?

3/25/21

Spending too much on Big Data? Try Small Data and MDM

3/24/21

What is a Data Lake? Everything You Need to Know

3/21/21

How to Extract More Value from Your Data

3/17/21

Are you making decisions based on bad HCO/HCP information?

2/24/21

Why Master Data Cleansing is Important to CPG Brands

1/20/21

CRM 2.0 – It All Starts With Master Data Management

12/19/20

5 Trends in Telecom that Rely on Transparency of Master Data

12/15/20

10 Data Management Trends in Financial Services

11/19/20

Seasonal Marketing Campaigns: What Is It and Why Is It Important?

11/8/20

What Is a Data Fabric and Why Do You Need It?

10/29/20

Transparent Product Information in Pharmaceutical Manufacturing

10/14/20

How to Improve Back-End Systems Using Master Data Management

9/19/20

8 Benefits of Transparent Product Information for Medical Devices

9/1/20

How Retailers Can Increase Online Sales in 2023

8/23/20

Master Data Management (MDM) & Big Data

8/14/20

Key Benefits of Knowing Your Customers

8/9/20

Women in Master Data: Kelly Amavisca, Ferguson

8/5/20

Customer Data in Corporate Banking Reveal New Opportunities

7/21/20

How to Analyze Customer Data With Customer Master Data Management

7/21/20

How to Improve Your 2023 Black Friday Sales in 5 Steps

7/18/20

4 Ways Product Information Management (PIM) Improves the Customer Experience

7/18/20

How to Estimate the ROI of Your Customer Data

7/1/20

Women in Master Data: Rebecca Chamberlain, M&S

6/24/20

How to Personalise Insurance Solutions with MDM

6/17/20

How to Democratize Your Data

6/3/20

How to Get Buy-In for a Master Data Management Solution

5/25/20

How CPG Brands Manage the Impact of Covid-19 in a Post-Pandemic World

5/18/20

5 Steps to Improve Your Data Syndication

5/7/20

Marketing Data Quality: Why Is It Important and How to Get Started

3/26/20

Panic Buying: Navigating Long-term Implications and Uncertainty

3/24/20

Women in Master Data: Ditte Brix, IMPACT

2/20/20

Get More Value From Your CRM With Customer Master Data Management

2/17/20

Women in Master Data: Nagashree Devadas, Stibo Systems

2/4/20

How to Create Direct-to-Consumer (D2C) Success for CPG Brands

1/3/20

Women in Master Data: Anna Schéle, Ahlsell

10/25/19

Women in Master Data: Morgan Lawrence, Infoverity

9/26/19

Women in Master Data: Sara Friberg, Acando (Part of CGI)

9/13/19

Improving Product Setup Processes Enhances Superior Experiences

8/21/19

How to Improve Your Product's Time to Market With PDX Syndication

7/18/19

8 Tips For Pricing Automation In The Aftermarket

6/1/19

How to Drive Innovation With Master Data Management

3/15/19

Discover PDX Syndication to Launch New Products with Speed

2/27/19

How to Benefit from Product Data Management

2/20/19

What is a Product Backlog and How to Avoid It

2/13/19

How to Get Rid of Customer Duplicates

2/7/19

4 Types of IT Systems That Should Be Sunsetted

1/3/19

How to Use Customer Data Modeling

11/15/18

How to Reduce Time-to-Market with Master Data Management

10/28/18

How to Start Taking Advantage of Your Data

9/12/18

6 Signs You Have a Potential GDPR Problem

8/16/18

GDPR: The DOs and DON’Ts of Personal Data

6/13/18

How Master Data Management Supports Data Security

6/7/18

Frequently Asked Questions (FAQ) About the GDPR

5/30/18

Understanding the Role of a Chief Data Officer

4/26/18

3 Steps: How to Plan, Execute and Evaluate Any IoT Initiative

2/20/18

How to Benefit From Customer-Centric Data Management

9/7/17

3 Ways to Faster Innovation with Multidomain Master Data Management

6/7/17

Product Information Management Trends to Consider

5/25/17

4 Major GDPR Challenges and How to Solve Them

5/12/17

How to Prepare for GDPR in Five Steps

2/21/17

How Data Can Help Fight Counterfeit Pharmaceuticals

1/24/17

Create the Best Customer Experience with a Customer Data Platform

1/11/17
Did you like this blog post?

Sign up to get the latest blog content in your inbox.