Negotiations to acquire data management software company Informatica fell through after Salesforce, a business software giant, and Informatica couldn’t reach an agreement on terms. Discussions between the two companies were reportedly well underway in April, and a successful deal would have been one of Salesforce’s largest acquisitions.

A Missed Opportunity or a Blessing?

Was this a missed opportunity, or could it be a blessing in disguise for both companies and their customers? Let’s explore some potential reasons why the failed acquisition might not be all bad:


One concern with large acquisitions is vendor lock-in. If Salesforce had acquired Informatica, some Informatica customers might have felt pressured to adopt Salesforce’s entire suite of products, even if they weren’t the best fit for their data governance, data quality, and data catalog needs. Informatica, remaining independent, can continue to focus on providing data management solutions that can integrate with various platforms, giving customers more flexibility. However, it’s important to note that Salesforce customers would likely also face pressure to adopt the Informatica platform if the acquisition had gone through, potentially limiting their choice among the strong alternatives in the data management market. See the latest Forrester ‘The Seven Providers That Matter Most And How They Stack Up‘ report. 

Focus & Innovation

Large acquisitions can sometimes lead to a loss of focus for both M&A parties. With the Informatica deal off the table, both Salesforce and Informatica can concentrate their resources on core business software development and continue to innovate in their own respective spaces.

Conflicting Product Portfolio – Informatica vs Mulesoft

Salesforce already owns Mulesoft, another integration platform. There might have been overlap in functionalities between Informatica and Mulesoft, leading to product rationalization and confusion regarding future product roadmaps for both platforms. Confusion around future product roadmaps would create uncertainty for customers. They might not know which platform to invest in or how long their current platform (Informatica or Mulesoft) would be supported. This uncertainty could lead to a higher risk of rework or reinvestment as customers adapt to changes or migrate to a different platform.

Market Preference – Best-of-Breed vs All-in-One-Platform

Nowadays the majority of businesses prefer a “best-of-breed” approach, using the best tools from different vendors for specific tasks. An Informatica acquisition could have pushed Salesforce more towards an “all-in-one” platform strategy, which might not resonate with all customers who favor a more flexible approach. The simplicity of an all-in-one-platform or best-of-suite solution is appealing – fewer tools to manage and potentially lower costs with a single vendor. But real-world experience often reveals hidden drawbacks.


Overall, the failed Salesforce-Informatica deal allows both companies to remain their focus and better cater to their customer preferences in a competitive market that offers a variety of data management solutions. 



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In the ever-evolving landscape of data-driven decision-making, organizations are increasingly recognizing the critical interplay between Master Data Management (MDM) and Data Governance. These two pillars, seemingly distinct in their functions, converge to form a symbiotic relationship that is instrumental in driving efficiency, ensuring compliance, and fostering a data-driven culture within enterprises.

MDM – Orchestrating Data Symphony

At its core, Master Data Management is the discipline of managing an organization’s critical data, including master and reference data, to ensure uniformity, accuracy, and consistency across the entire organization. This involves establishing and maintaining a single, authoritative version of the truth for core data entities such as customers, products, assets, and employees. MDM acts as the custodian of data integrity, fostering reliability in decision-making processes and supporting various business functions.

Data Governance – Setting The Stage For Data Harmony

On the other hand, Data Governance is the framework that defines how organizations manage and control their data assets. It involves establishing policies, procedures, and standards to ensure data quality, security, and compliance with regulatory requirements. Data Governance provides the necessary oversight and control mechanisms to safeguard data assets and align them with organizational objectives.

In Unison

The link between MDM and Governance lies in their shared objective of ensuring data accuracy, consistency, and reliability. MDM provides the tools and processes to create a single, trusted source of master data, while Governance defines the rules and policies that guide the creation, usage, and maintenance of this data. Together, they form a formidable alliance that addresses the challenges of data silos, inconsistency, and lack of accountability.

Effective MDM and Governance synergies offer a range of benefits to organizations. Firstly, they enhance data quality by establishing standardized processes for data creation, validation, and maintenance. This, in turn, leads to improved decision-making, as stakeholders can confidently rely on accurate and consistent data. Moreover, compliance with regulatory requirements becomes more manageable, as Governance ensures that data practices align with legal and industry standards. Next to Data Quality, Data Governance typically also embodies a number of other data management capabilities, such as a Business Glossary to properly manage shared business terms and definitions and the associated data governance workflows and processes. Having this properly in place is also fundamental for MDM.

Furthermore, the collaborative efforts of MDM and Governance foster a data-driven culture within organizations. Employees are empowered with reliable data, breaking down silos and encouraging cross-functional collaboration. This not only enhances operational efficiency but also facilitates innovation and agility in responding to market dynamics.



In conclusion, the link between Master Data Management and Governance is not just a technical integration but a strategic alliance that underpins the success of modern enterprises. By aligning MDM and Governance initiatives, organizations can unlock the full potential of their data assets, driving informed decision-making, ensuring compliance, and fostering a culture that values and leverages the power of data. The journey towards data excellence begins with recognizing and nurturing this symbiotic relationship.


Also want to take your data agenda to the next level and address MDM & Data Governance? Would you like to find out how Datalumen can help?


Customer & household profiling, personalization, journey analysis, segmentation, funnel analytics, acquisition & conversion metrics, predictive analytics & forecasting, …  The marketing goal to deliver a trustworthy and complete insight in the customer across different channels can be quiet difficult to accomplish.

A substantial amount of marketing departments have chosen to rely on a mix of platforms going from CEM/CXM, CDP, CRM, eCommerce, Customer Service, Contact Center, Marketing Automation to Marketing Analytics. A lot of these platforms are best of breed and come from a diverse number of vendors who are leader in their specific market segment. Internal custom build solutions (Microsoft Excel, homebrew data environments, …) always complete this type of setup.

78% According to a Forrester study, although 78% of marketers claim that a data-driven marketing strategy is crucial, as many as 70% of them admit they have poor quality and inconsistent data.

The challenges

Creating a 360° customer view across this diverse landscape is not a walk in the park. All of these marketing platforms do provide added value but are basically separate silos. All of these environments use different data and the data that they have in common, is typically used in a different way. If you need to join all these pieces together, you need some magical super glue.  Reality is that none of the marketing platform vendors actually have this in house.

Another point of attention is your data scope. We don’t need to explain you that customer experience is the hot thing in marketing nowadays. However marketeers need to do much more than just analyze customer experience data in order to create real customer insight.

Creating insight also requires that the data that you analyze goes beyond the traditional customer data domain. Combining customer data with i.e. the proper product/service, supplier, financial, … data is rather fundamental for this type of exercises. This type of extended data domains is usually lacking or the required detail level is not present in one particular platform.

38% Recent research from  KPMG and Forrester Consulting shows that 38% of marketers claimed they have a high level of confidence in their data and analytics that drives their customer insights. That’s said, only a third of them seem to trust the analytics they generate from their business operations.

The foundations

Regardless of the mix of marketing platforms, many marketing leaders don’t succeed in taking full advantage of all their data. As a logical result they also fail to make a real impact with their data driven marketing initiatives. The underlying reason for this issue is that many marketing organizations lack a number of crucial data management building blocks that allow them to break out of these typical martech silos. The most important data capabilities that you should take into account are:




Master Data Management (aka MDM)

Creating a single view or so called golden record is the essence of Master Data Management. This allows you to make sure that a customer, product, etc is consistent across different applications.


Business Glossary

Having the correct terms & definitions might seem trivial but reality is that in the majority of the organizations noise on the line is reality. However having crystal clear terms and definitions is a basic requirement to have all stakeholders manage the data in the same way and prevent conflicts and waste down the data supply chain.


Data Catalog

Imagine Google-like functionality to search through your data assets. Find out what data you have, what’s the origin, how and where it is being used.


Data Quality

The why of proper data quality is obvious for any data consuming organization. If you have disconnected data landscape, data quality is even more important because it also facilitates the automatic match & merge glue exercise that you put in place to come to a common view on your data assets.


Data Virtualization

Getting real-time access to your data in an ad hoc and dynamic way is one of the missing pieces to get to your 360° view in time and budget. Forgot about traditional consumer headaches such as long waiting times, misunderstood requests, lack of agility, etc.



We intentionally use the term capability because this isn’t a IT story. All of these capabilities have a people, process and technology aspect and all of them should be driven by the business stakeholders. IT and technology is facilitating.

The results

If you manage to put in place the described data management capabilities you basically get in control. Your organization can find, understand and make data useful. You improve the efficiency of your people and processes, and reduce your data compliance risks. The benefits in a nutshell:

  1. Get full visibility of your data landscape by making data available and easily accessible across your organization. Deliver trusted data with documented definitions and certified data assets, so users feel confident using the data. Take back control using an approach that delivers everything you need to ensure data is accurate, consistent, complete and discoverable.
  2. Increase efficiency of your people and processes. Improve data transparency by establishing one enterprise-wide repository of assets, so every user can easily understand and discover data relevant to them. Increase efficiency using workflows to automate processes, helping improve collaboration and speed of task completion. Quickly understand your data’s history with automated business and technical lineage that help you clearly see how data transforms and flows from system to system and source to report.
  3. Reduce data and compliance risks. Mitigate compliance risk setting up data policies to control data retention and usage that can be applied across the organization, helping you meet your data compliance requirements. Reduce data risk by building and maintaining a business glossary of approved terms and definitions, helping ensure clarity and consistency of data assets for all users.

42% of data-driven marketers say their current technology in place is out of date and insufficient to help them do their jobs. Walker Sands Communications State of Marketing Technology report.


The data you need to be successful with your marketing efforts is there. You just have to transform it into useable data so that you can get accurate insights to make better decisions. The key in all of this is getting rid of your marketing platform silos by making sure that you have the proper data foundations in place. The data foundations to speed up and extend the capabilities of your datadriven marketing initiatives.

Need help unlocking your marketing data?

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Given the circumstances we all need to face nowadays, CDO Exchange 2020 was organized last week in an adjusted online way. Regardless of the non-traditional approach, this still turned out to be an interesting forum for leaders active in the data domain (CDO – Chief Data Officers and others). The event was chaired by Bloor Research Brian Jones.

The event opened with a strong focus on data program fundamentals like stakeholder management and business case creation.  The second part was all about AI and data driven value creation including the necessary attention on the important data ethics topic.

Key Takeaways

  • No data program or initiative without a purpose. Sounds like basic but so true. It’s not the first time that that a data initiative is kicked off because it’s innovative but at the end doesn’t address any real business need.
  • In order to be successful you need to inform business leaders at all levels. Not just C-level but all leadership in your business stakeholder community. Of course, these business leaders also need to be open to listen. Reading tip: Also have see our post about MDM business case building.
  • Correctly translating business needs is important but also understanding the right priorities is key. What are my real burning data platforms?
  • In the AI, ML, …. Basically the data science context, we are happy to see that a substantial number of business leaders managed to gain understanding of the most important principles of data science. Pay the necessary attention to this in your organization and make sure that you also remove the data  & data science ‘language barrier’.
  • Don’t forget the company politics. In some organizations you will need to cope with individuals attempting to sabotage constructive change if it was ‘not invented here’ or ‘owned by us’. This point of attention was valid before, but unfortunately is still there and definitely present in the context of data programs.
  • Ethics is not about a choice, it’s an obligation. Data can be powerful and can potentially deliver huge value. Besides this potential, it also comes with a duty of care. In an era where customer centricity is vital, you need to make sure that your data management is objective, trustworthy and transparent to your customers and other stakeholders.
  • Ethics carry a cost. However the cost of not doing things right is much higher. Think about the overall and longer term reputationally and commercially cost.

Do you have a data management question or require some level of support with a data initiative in your organization? Feel to reach out to us and schedule a free sync session.


The holiday season is the most important sales moment of the year. Nevertheless, a Zetes study reveals that retailers miss about 35 percent sales due to products not being immediately available or the lack of product information.

A quarter of consumers leave a store without actually buying anything if they do not immediately see the product that they are looking for or if that is not immediately available. It is one of the conclusions of a market research conducted by supply chain expert Zetes. The study specifically focused on buyer behavior during the annual peak period between November and January and analyzed both physical and online retail. 120 European retailers and over 2000 consumers were interviewed for this study in the January 2018 timeframe.

Time is money

The study states that stores miss 35 percent of sales due to the unavailability of products. The main reason for this is the expectation of the customer: more than in the past, customers will simply leave a store if they do not immediately see a product and they do not bother to talk to a shop assistant. 

If customers do however approach a shop assistant, they expect to receive more information about the product availability within two minutes. A rather limited window of opportunity especially if you know that the study also calculated that 51 percent of shop employees need to go to a cash desk to obtain the necessary information, and that 47 percent also needs to check the warehouse to verify the availability of a product. Both actions cost time and time is very expensive during the peak period. The study also reveals that 62 percent of retailers do not have access to real-time product data.  

Return Management

Also deliveries and returns typically cause extra problems during these busy months. It is common knowledge that people tend to buy quicker when they are sure that they can possibly return a product. The processing of returned parcels also causes problems. 26 percent of retailers indicate that they are having problems during the peak, so that only 39 percent of the returned goods are available for sale within 48 hours.  


“A lack of visibility of data is the core of these sales problems during the holidays,” the report states. “Consumers want choices, and they want to be informed. Instead of a general “not available” message, a retailer has a much greater chance of securing sales by telling the customer that a product will soon be back in stock and delivered within three days or will be available for click & collect.” There is still a significant room for information management improvement with direct sales optimization as a result. 

More information

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Gartner recently released an updated version of the Magic Quadrant for Master Data Management Solutions report and positioned Informatica & Orchestra Networks as leaders in this MDM market segment. 



Our key takeaways:

  • Specialized MDM vendors still lead the market and offer biggest added value. Vendors, delivering niche data solutions directly linked to i.e. specific business applications, seem to lack the focus and as result don’t meet the function & features of the leaders.
  • The multi-domain data trend continues to happen. This is the first edition of the Gartner Magic Quadrant on Master Data Management where Gartner consolidates the different data domains. This translates also a trend that we see it in the market where customers need an MDM solution that covers all their domains and not only customers, products, assets, etc. Reality is that customers need an MDM solution that they can use to address their complete data agenda.
  • Informatica still makes a difference compared to other vendors due to its end-to-end data capabilities which are also key for a successful MDM implementation. Data Quality, Data Governance and Data Integration are key components of any MDM platform and are available as integrated functionality in the Informatica MDM solution.


Learn more. Download the full Gartner report here

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document.

Gartner does not endorse any vendor, product, or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner, Magic Quadrant for Master Data Management Solutions, Bill O’Kane, Terilyn Palanca, Michael Patrick Moran January 2017.

If you are preparing or in the process of implementing an MDM solution and you are interested in getting additional advice, get in touch with us and we are happy to help you out.