DMP, Clean Rooms, CDP: Yada yada yada.

Recently, I have been getting many questions or inquiries on migrating from DMP, CDP, and Clean Rooms, how they are related, and where they each sit in today's data management mess.   These questions I received prompted the notion of cookieless profiles, Identity Use Case scenarios, and the migration from DMP to CDP. These are endless topics written about endlessly by every vendor on the planet.

Let me take a shot at answering some of these questions – I am most likely not going to be 100% accurate or frame every answer to everyone's liking on every facet – but generically, the article might help uncover some definitions and give you some lists and tables that provide additional value – which I love, to help you in your work with these tools in our crazy MarTech mess?

So, let's start with cookieless - since it drives the premise of these platforms. Due to privacy concerns, cookieless marketing is shifting away from relying on third-party cookies for ad targeting. Instead, it emphasizes using other methods like contextual advertising, first-party data, and alternative technologies for audience targeting. Unfortunately, anonymous broad-based acquisition marketing doesn't go away. It can't. Businesses must grow; they can't stay static or maintain the status quo. Without growth, they will eventually decline and cease to exist.

The CDP has been the primary driver of the contextual/authenticated marketing campaign methods. However, the methods are not new marketing methods or tactics – the cookieless trend has prompted the evolution from the DMP to the CDP.  As I have said before, the CDP is not new – it's just an evolution of what is now an older approach – the DMP – not to be confused with the Modern Data Stack, MDM (Master data management), or data management platforms like Databricks or Snowflake. Which are core infrastructures that are abstracted by the DMP and the CDP UI/UX, a.k .a. CX. While a CDP could conceptually incorporate all these other platforms – its primary capability was transforming the DMP approach, which was primarily non-PII and anonymous to PII and known profile marketing and targeting.

Here is a super old under-the-cover look at Adobe's Old FORMAT of Data Collection (TMS)+DMP+Web Analytics – all integrated, synchronizing, and calibrating the profile from Anonymous to Known. Note how deep the infrastructure is in this graphic. It's insane – the CDP is not any different. It has layers and layers of tech to do nearly the identical profile curation and syndication happening years ago - just in a different UI. Each composable component - OH – Composable isn't new!, has its own independent infrastructure that gets abstracted into a common UI. What is new is the infrastructure is converging into a more common layer that fits together more easily and is more modern to handle scale and volume.

With the evolution into the CDP ecosystem landscape, the cookieless shift has been propelled by changes in regulations and browser policies favoring user privacy. Google Chrome has delayed cookie deprecation several times.  Google recently said they will start the process in Q1 – 2024 for 1% of their Chrome users, increasing this over time. [searchenginejournal]. They keep pushing the date out – I will believe it when I see it.  The revenue loss is huge if they can't propose a good migration to the known user or the advertising and content/data supply approaches mentioned in the above paragraphs.

Many ideas about cookieless, DMPs, CDPs, and Google's deprecation can be validated by articles written by Google, Adobe, and Salesforce – the predominant revenue generators in the Martech landscape. But many of the smaller CDP players and even the insignificant players are writing incessantly about this topic – They should all be worried. The revenue loss for all of them is immense – they are all trying to convince the Fortune 5000 to buy more software to replace the old software MarTech stacks, notably the DMP or even possibly the older web analytics stacks and Tag management solutions(the latter should have been ripped out years ago).

The reality is the F5000 does need something new that consolidates Data collection, Audience management, and Analytics into a more streamlined workflow.  Some vendors will be better than others.  I have an idea to list my recommendations on the best vendors for particular purposes and scenarios. Still, I have so many ideas for articles that I have difficulty deciding what to write about. Please send me suggestions. 

I believe two approaches exist for the new MarTech data supply-chain re-tooling for the F5000: Composable components or a single platform.  The scenario each client organization chooses will be unique to them.

As mentioned above, the real villain is revenue loss. The agencies are worried, and so are the data brokers who sold all the enrichment and device ID data to target these anonymous profiles for the older DMP method – billions in revenue could be lost if newer, more automated content and data supply chains are not modernized and realized to replace the older methods. The CDP will not 100% fix the new Acquisition issues related to anonymous customers – how do you target someone who has never visited you or doesn't even know your brand yet? Therefore, anonymous brand and product marketing are still viable for specific brand organizations.

However, many articles mention moving towards the "known user profile" and abandoning broad-based anonymous targeting. That myopic approach is not viable. Removing Anonymous targeting 100% has substantial revenue implications across the Acquisition MarTech landscape – more prominent players, smaller software vendors, ISV hangers-on, and the ad agencies that support the content and data supply chain and the quarterly pitches that fund the industry will all be impacted. It is a massive piece of the $1 trillion annual global marketing advertising spend.

Conversely, we are seeing massive layoffs across all software right now. Still, more notably in Martech – it's just too saturated with vendors with terrible software that doesn't work, are too hard to use, or are approaching the problem domains in a contrarian obtuse way to show some differentiation. Most are failing miserably and not admitting their failure. The customer is suffering because of this lack of truth.

Cookieless Challenges

What are some challenges with the 3^rd party Ad cookie going away? Marketers face several challenges in a cookieless environment. One major issue is the loss of data granularity, as third-party cookies have been instrumental in providing detailed user insights for targeted advertising. Without them, there's a reliance on first-party data, which may not offer the same level of insight.

Apple, for example, has implemented all sorts of new consumer-based features to stop the cookie and implemented technical restrictions they call Link Tracking Protection(LTP) that strips the URL parameters for Ads. There are 100's of articles already written on Apple's new restrictions – I, for one, am pleased about it. These parameters and managing them have been the BANE of the web analytics industry and agencies' managing campaigns for their clients in EXCEL for 20 years. 

When I worked at Omniture from early 2004 to late 2008 - Google and DoubleClick were the guilty parties pushing for all these parameters attached to the ClickURL – It was so painful having to explain the differences between Omniture's approach vs. Google's. Their product managers were lazy in how they technically approached the problem - since they had the scale - they also had the bigger voice.  They piggybacked on the cookie, and the clickURL URI stem vs. not using a single ID like Omniture promoted using a single variable in the JS called s.campaign – you could grab the lone Ad-ID and then update the campaign ID with all the metadata behind the scenes. Since we were not big enough, our voice was too small to promote the single campaign ID approach.

Google and DoubleClick could have leveraged this approach with all their backend metadata structures and programmatically simplified this process, but they never took the initiative. So now, we are where we are and dealing with this huge mess.

Today, a great company that has managed to automate all the campaign metadata is Claravine. Their solution cures the campaign metadata and other data standards problems for agencies and their F5000 client organizations without relying on the UTMs or parameters that cause this entire cookie and clickURL debacle. If I were a CDP vendor, I would buy Claravine and add their metadata enrichment and data standards process for campaigns.  It is just a brilliant tool. If you would like an introduction to them, please let me know.

Here is a table created by Litmus.com of many parameters being stripped by Apple with LTP. Many more articles analyze this issue and the parameters that will impact your tools. Just search LTP or Apple Link Tracking Protection. This table is an example.

Additionally, the phasing out of third-party cookies affects marketers' ability to engage with customers effectively, find quality prospects, and measure the effectiveness of digital ad campaigns. The cookieless shift could lead to marketing signal loss, and as I said above, impact revenue and require marketers to use new tools, like buying data science tools, to gain customer insights.  There are a slew of articles on these topics, too.

On the upside, which I advocate, moving away from third-party cookies will mitigate ad fraud, enhance brand safety, and save costs by avoiding investments in ineffective or fraudulent channels. It also presents an opportunity to undo bad digital marketing habits formed over the years of relying on third-party cookies; using UTMs and campaign parameters fastened to the clickURL was a complete and utter disaster. As I said above, it was the lazy approach by some product managers over 20 years ago. It caused everyone to jump on this crazy train. The industry probably deserves the pain this issue is causing.

Cookieless Strategies

Various solutions and strategies have been proposed to navigate the cookieless terrain.  I have listed a few below:

Collecting First-Party Data: Encourage customers to share their data by offering value in return, establishing a "value exchange." It boils down to getting the user to register and authenticate as a known user. However, I want to see the user control this process vs. the corporation. I briefly discussed this in one of my Identity articles about the DID and Sovereign IDs – consumers could have multiple IDs, like how they have multiple email addresses. One CDP moving a bit in this direction is https://fuzecdp.com. These user-controlled IDs are then attached with varying degrees of personal information the marketer uses to target them.  In some circles, many would call this ZERO-PARTY. The consumer fully controls it – but even this concept has been manipulated and bastardized by the powers that be in the industry to drive their agenda further because revenue is shrinking. The idea with user-controlled IDs is that the consumer can limit what they want to share for the brands they admire or restrict for the brands they don't like. The ID is not stored by the CDP or the marketing organization but accessed locally from the user's account when granted permission and could be on a timer when saved to the CDP for marketing. The CDP would use a querying mechanism to use the profile only when needed and syndicate it to the edge on a 1:1 basis. Future case - this is too slow today, and no deep infrastructure exists to deliver the marketing. So we are back to registration and authentication.

Investing in Cookieless Identity Solutions: Utilize technologies like Unified ID 2.0 and Liveramp's IdentityLink to target high-value segments without relying on cookies. A lot is written about these two, and the Unified ID 2.0 is one I like.  Time will tell how much traction either will get on the Anonymous acquisition campaign landscape or if they will get superseded by the DID or Zero-party approaches.

Using De-aggregation Solutions: These solutions can connect aggregate user profile data to user-level insights for more granular targeting but make it anonymous and non-PII. Chiefmartech talks about the entire infrastructure within the platform as aggregated, and Kilowatt.com discusses it from the perspective of analytics and insights. This looks like a newer name for the DMP, and it reflects what the DMP did for the anonymous audience and targeted ad segments. It could be construed that this is a Clean Room. It's not new; it's a new name, a wrapper, and potentially more modern features and capabilities in the UI. The ideas around this concept are interesting as they blend the Clean Room and CDP, converging and extending the capabilities to displace the DMP entirely without explicitly stating this is the case.

Rethinking Ad Measurement Practices: Invest in market research and reset measurement baselines to adapt to new digital ad measurement challenges. Agencies will have to do this because their revenue is in jeopardy. The web analytics tools are already in migration – look at Adobe Journey Analytics, Adobe Product Analytics, and the newer Market Mix Modeler solution. mParticle has added and ramped up its analytics with Indicative.  Other CDPs have lots of analytics but are scattered, bolted-on, or half-baked. Or they don't have a CDP and only focus on Analytics, creating a fragmented workflow. You need comprehensive, broad-based data alongside the granularity of campaign and profile data to generate rich insights for decision-making and campaign creation.

Focusing on Frequency Capping and Authentic Identity: Emphasize managing ad exposure and ensuring accurate user identification without relying on cookies. This goes without saying, but many Agencies and Vendor CDP product teams forget about frequency capping. I still see it in RFPs and am surprised it is not a higher priority. When I worked for Adobe, it was one of the first features asked about in DMP evaluations. However, the marketing organizations within companies don't forget about it because they have a limited budget; they have to watch their ad budgets like a hawk. Frequency capping was/is a crucial feature in the DMP/DSP ecosystem, and you do not even see a notion of it in CDPs, mainly because the CDP vendors have no deep domain knowledge of the marketing and campaign data supply chain of the past. It's like it never existed. Authentic identity is a targetable and addressable profile for DMPs; this means a device ID. In a CDP, it is a known user. For broad-based anonymous Ad campaigns with large acquisition budgets where the user is still unknown, organizations need to do these frequency cap campaigns – it might be based on their product lifecycle or to grow their customer base. This capability is heavily required and could be programmatically created via machine learning or AI. However, unfortunately, the CDPs still do not do Acquisition marketing very well compared to the DMP/DSP combo. Until CDP/Clean Room integration grows deeper into the Ad network landscape, the DMP/DSP may still need to be used by certain brands.

Exploring New Methods of Measurement: Adapt to new ways of measuring advertising impact as traditional methods like multi-touch attribution may no longer be viable. This is a new name for Attribution Analysis and marketing – we see new capabilities in the Journey analytics tools.  Adobe has many tools along this notion, but many smaller vendors focus on Data-driven attribution to help make audience decisions. Some I mentioned above in the Ad Measurenment bullet. The better approach is to build AI or ML into the audience to curtail or even automate adjustments and audience curation with the syndication pipelines to the destinations. In a previous article, I talked about a Marketing AI control plane.  The idea would be to curate and analyze profiles at a 1:1 attribution, group them using LLM technology, and then send them up to a Reinforcement engine to target them based on previous history and existing present event actions. AKA Personalization engine in an omnipresent/channel approach.  The Reinforcement engine would hold all the criteria for use cases, strategies, and business rules. If you take Louis Kirsch's depiction of reinforcement and overlay marketing inputs from the CDP and LLM foundation and then overlay marketing personalization outputs in each fishbone in the diagram, the aspect of the diagram becomes an AI marketing control plane for the CDP/Clean Room combo across all channels. The idea is that this can be a new layer of capability for all sorts of marketing control and delivery to the destinations - a type of Command Center. It could converge all the strategies in this list into one Marketer CX that coordinates the profile for all the channels across delivery systems.

Creating Contextual Targeting Campaigns: Utilize first-party data to learn about contextual signals for personalization and engaging target audiences. This type of targeting is where the CDP comes in and targets the known user. The issue arises if the PII and known user identifiers must be stripped out for acquisition marketing to target unknown users who have never visited or know about the brand.  This type of broad-based marketing can happen if the CDP has imported purchased profiles with device IDs or other IDs that can be matched up to the DSP or Ad networks like Google or Meta/Facebook. What most CDPs integrate are the C-APIs – customer APIs or conversion APIs.  These are becoming more prevalent, and I discuss how the integration generically works in the details below. Some vendors are better than others, but the goal is a directly targetable user match – this improves ROAS (Return on Ad Spend) and CAC(Customer Acquisition Cost). A CDP/CR would improve this approach with added features. 

Setting Up Cookieless Tracking Strategies: Develop new marketing strategies that respect user privacy and deliver valuable insights. Mike Gullaksen and his Team at NP Digital deliver posts by their founder, Neil Patel. He talks about setting up tracking strategies. Neil suggests many of the areas I listed here, but I would go a step further in my suggestion on Data Collection. The first step is to rid yourself of all Tag Management vendors and any vendor that charges you to deploy tag containers.  I would start to migrate away from tags to SDKs and APIs native to your domain – eliminating the TMS JS and their cookies from all your properties - this is a huge added expense you do not need today - it's legacy and not the most modern approach.

• MOVE to an in-house single API/SDK or Scripting agent approach with your own cookie to manage the Customer IDs. 
• PASS your data to your WEB SERVER and use a data collection SDK like mParticles SDKs or Adobe Edge Server API SDKs, or take even more control into your hands with MetaRouter or Jitsu – put your entire tracking strategy Server-side – you control your cookies and your customer IDs and pass them via Server-side API's and SDK's. Removing Tag Managers should be the go-forward strategy.
• You DO NOT NEED TO BUY A 3rd-Party Tag Management tool – Save money, modernize to the server-side approach, and control your data, cookies, and IDs.

Profile IDs and Ad IDs from activation to engagement

The journey of Profile IDs and Ad IDs from activation to engagement with an ad campaign and the loopback for analysis is a cycle that involves multiple steps.

This cycle is iterative and designed to continuously improve campaign performance, audience targeting, and data accuracy. Each step in the journey of Profile IDs and Ad IDs(offsite and onsite) is crucial for comprehensively understanding user behaviors and preferences, which informs better campaign strategies and higher engagement rates over time.

The process of associating a Profile ID with a click event, especially in the absence of cookies, is very complex. Here's how many vendors approach destination management for Acquisition marketing with the Google and Facebook C-APIs.  Note that this is a generic mapping and data flow and not specific to any vendor – it's only an illustration to help you understand the flow and steps. Here is the generic approach:

1. Profile ID Activation: When the Profile ID is uploaded to an advertising platform, it is often associated with a particular campaign or Ad ID. This association can be maintained in a server-to-server environment where identifiers can be passed securely between systems. This premise was originally deployed in the Martech DMP environments like Adobe AudienceManager, Salesforce Krux, and Oracle BlueKai and eventually migrated to the CDP integrations. 
2. Deployment of ClickURL: The ClickURL deployed in the ad can contain parameters that help identify the campaign, Ad ID, or even the Profile ID if privacy regulations permit. This approach is getting more difficult with Apple's restrictions on UTMs and other parameters embedded in the ClickURLs. Therefore, authentication practices after click-through are a more advisable approach. You then can associate other IDs like the AD-ID, and custom-IDs tied to the profile's authentication. It will give you higher resolution and less signal loss.
3. Click Event Tracking: Upon a click, the parameters within the ClickURL can be captured and sent back to the advertising platform and/or the CDP. As in the above bullet, you will need to monitor this signal loss closely to see how these parameters are affected due to the type of responses you get from specific sources. Apple is currently the biggest signal loss culprit due to clickURL parameter restrictions. If this occurs, you must create a non-parameter scheme in your clickURLs and parse the URL via code on the landing page or only use designated parameters Apple does not restrict. Lastly, another approach is to use the Full-URL path with a UID that can be mapped back to an array of metadata - you could use a tool like Claravine to help with multi-ad network metadata for all your campaigns. These would solve the signal loss on clickthrough. Note Google and Facebook are the most restricted by Apple. Adobe(Omniture) Analytics never used Google's approach to UTMs, so their approach, along with Claravine, is a much more elegant and automated way to track click-URLS. You can leverage this approach without using Adobe's solutions.
4. User Authentication: If the user is logged in, the Profile ID can be easily associated with the click event. The advertising platform or the website can send this information back to the CDP, associating the click event with the Profile ID. Note that you want a CDP that stores historical data.  
5. Profile Re-resolution: In the absence of a login, re-resolution may be required. This would involve capturing data from the click event and using it to resolve the Profile ID within the CDP. This process can be complex and may require matching on various data points to resolve the Profile ID accurately. Not many vendors do ID Resolution very well. Amperity, Zingg.ai, and a few others are leaders with more defined solutions. Adobe, Salesforce, and other older legacy CDPs are not great ID Resolution solutions. While they have ID Res., they are barebones at best unless the user authenticates consistently with the same ID. Incorporating a 3rd party like Zingg.ai or even something custom with AWS if you use Adobe AEP with their I/O platform to perform custom ID RES. Typically you would need to augment most CDPs with more robust ID RES solutions if you have complex use cases and many data sources outside of the digital sources.
6. Data Enrichment: The click event data can enrich the CDP profile, adding to the understanding of user interactions and preferences. Enrichment is another term that is often misdefined. It can mean filling gaps in data within a record, or it may involve deriving data like a lead score or supplementing the address fields with updated information like the zip code extension for your mailers. Enrichment can happen at many stages. CDP vendors have decided to muddle the conversation of what they can do vs the reality, so be very careful about these capabilities and what you need.
7. Privacy Compliance: Ensuring compliance with privacy regulations is crucial throughout this process. For instance, including Profile IDs in URLs could be subject to privacy regulations, so alternative association or anonymization techniques may be necessary.
8. Cross-Device Identity Resolution: Cross-device identity resolution techniques may associate the click event with the correct Profile ID if the user engages with the campaign across different devices.
9. Server-Side Tracking: Server-side tracking can capture click events and associate them with Profile IDs in a more secure and privacy-compliant manner than client-side tracking. I advocate moving to 100% ownership of your data collection and all client-side tracking to Server-side tracking under your control and on your servers. I mentioned some vendors above and suggested removing your Tag managers. Google and Adobe are increasingly moving towards 100% Server-side collection via SDKs and APIs. It is only a matter of time until Tag Management goes away completely.
10. Data Synchronization: Regular synchronization between the advertising platform and the CDP ensures that the Profile IDs and click events are accurately associated and updated in near real-time.

DMP, CDP, and Clean Room Platform Comparisons

Now that we have looked at some of the definitions, processes, challenges, and steps for a cookieless and types of acquisition marketing in this new landscape. Let's look at the 3 platforms that play a part or can play a part in the stack based on your individual scenario, what you have licensed, how much longer those licenses have left on them, where the platform type fits, and how it can be used for your situation. Data Management Platforms (DMPs), Customer Data Platforms (CDPs), and Clean Rooms can all have distinct roles in this new landscape of cookieless profiles and tracking. Each plays a critical part in an omnichannel marketing strategy. As mentioned, CDP and Clean Rooms have a natural synergy to integrate or even evolve into a new platform. I suggest a Marketing Control plane or Command center with a reinforcement engine inside. It would also be the next step, in my opinion. I suspect we will all have to wait for that solution or feature set in the CDP. It may never come.

DMP, CDP, Clean Room Platform Comparison

Feature Comparison Table

 Use Case Comparison

Note: The utility of these platforms may vary based on the specific technologies and strategies an organization employs and the evolving nature of privacy regulations and technology. The move towards a cookieless future has particularly impacted the relevance of DMPs. CDPs and Clean Rooms are gaining prominence due to their focus on first-party data and secure data handling. Clean rooms and CDPs should converge and displace the DMP 100% - it seems only natural for this to occur, as mentioned above. CDP vendors should look at this capability and either include it or deeply integrate and create a capability within or for Snowflake, Databricks, AWS, Azure, or GCP. While ensuring the best features of the DMP for acquisition marketing, like Frequency capping and anonymous targetable assurance for the addressable market.

Below is a table on how the Profile ID might be used during specific customer journey scenarios for each platform. Note that this is not every scenario and would most likely be used to slowly migrate off your DMP environment to only having an integrated Clean Room/CDP environment. The continued use of the DMP would only make sense for those industries that are highly new acquisition-focused with low authenticated profiles. You will want to strategize, however, on how to get these profiles to a known and authenticated state to grow your business more predictively, but in the interim, using the DMP is not a bad option in the short term until the CDP vendors start to deeply integrate into the Clean Room metaphor and those CR's can integrate deeply to the DSP and ad networks.

Identity Use Case Table:

Migration Steps

Migrating from a DMP (Data Management Platform) to a CDP (Customer Data Platform) and Clean Room approach involves a strategic shift in how customer data is collected, managed, and utilized. However, if broad-based and anonymous acquisition campaigns are still a key strategy for customer growth, the DMP can still play a significant role.

Here's a strategy to integrate these systems effectively:

1. Assess Current Data Capabilities and Needs:

• Evaluate the current capabilities of your DMP and identify what first-party data is being collected.
• Understand the gaps in your current data strategy, particularly regarding first-party data and privacy compliance. 

2. Integrate DMP with CDP:

• Implement a CDP to start aggregating and managing first-party data from various sources like CRM systems, websites, apps, etc.
• Use the DMP to enhance the CDP's data with third-party data where appropriate, considering privacy regulations.

3. Data Cleaning and Transformation:

• Ensure the data migrated from the DMP to the CDP is clean, consistent, and usable.
• Transform any usable third-party data into actionable insights within the CDP.

4. Privacy and Compliance Alignment:

• Ensure the data practices align with current privacy regulations (GDPR, CCPA, etc.).
• Incorporate privacy-by-design principles in both CDP and DMP operations.

5. Leveraging Clean Rooms:

• Use data clean rooms to securely analyze and share data with partners or third parties without compromising customer privacy.
• Start to integrate Clean Room insights into the DMP and CDP for enhanced targeting and personalization.

6. Developing an Omnichannel Strategy:

• Utilize the CDP for a unified view of the customer, enabling personalized omnichannel marketing strategies.
• Continue to use the DMP for broader, anonymous audience targeting and acquisition campaigns.

7. Testing and Iteration:

• Test the effectiveness of integrated data strategies in small-scale campaigns.
• Iterate based on performance data and feedback.

8. Employee Training and Change Management:

• Train your marketing team on the functionalities and best practices of using a CDP and data clean rooms.
• Manage the change in processes and workflows within your marketing team.

9. Continuous Monitoring and Optimization:

• Regularly monitor the performance of both platforms.
• Optimize data collection, analysis, and campaign strategies based on performance data and evolving market trends.

10. Future-proofing and Scalability:

• Ensure that your CDP and DMP strategy is scalable and adaptable to future technological advancements and changes in privacy laws.
• Regularly update your data strategy to remain compliant and effective.

A DMP can still be vital in an enterprise's marketing strategy, especially for broad-based, anonymous campaigns. Integrating it with a CDP and utilizing data clean rooms can offer a comprehensive approach that balances the need for broad reach with personalized, privacy-compliant marketing. Eventually, CDP/CR combos will be the better approach - so watch the Martech landscape for the vendors putting this on their roadmap. Additionally, remove TMS vendors and migrate to server-side data collection. Look for future AI/ML product roadmaps that create a master control plane for marketers leveraging LLM and Reinforcement learning engines that sit on top of the CDP/CR combo. Lastly, ID RES. most CDPs are terrible at this capability. Be aware and look for vendors with ID Res. solutions that let you customize it in a composable framework depending on your data complexity.

I hope this article was valuable and gave insight into the 3 platforms being marketed to the Fortune 5000. Do not get confused - the vendors want you confused, so you keep buying bad software. Please, as always, email me your ideas for new articles and your thoughts on this one or others.

Happy holidays wherever you might be this month.