Sunday, October 11, 2020

Twilio Buys CDP Segment for $3.2 Billion

Friday afternoon brought an unconfirmed Forbes report that communications platform Twilio is buying CDP Segment for $3.2 billion. (The all-stock deal was officially announced on Monday.)  It's Twilio’s third acquisition this year, following much smaller deals in January for telephony platform Teravoz and in July for IoT connector Electric Imp.  It comes two years after Twilio’s $3 billion purchase of email platform SendGrid.

The deal is intriguing from at least three perspectives:

Valuation: the $3.2 billion price is impressive by any standard. Segment’s current revenue isn’t known, although one published estimate put it at $180 million for 2019.  That sounds a bit high for a company with 450 employees at the time, but let's go with it and assume $200 million for 2020 revenue.  This has Twilio is paying 16x revenue, which is less than the 20x that Salesforce paid for Mulesoft ($6.5 billion on roughly $300 million) but in line with the 15x that Adobe paid for Marketo ($4.7 billion on $320 million)  or 14x that Twilio itself paid for SendGrid ($2 billion on $140 million when the deal was announced; the $3 billion price reflects the subsequent rise in Twilio’s stock). Note that these prices are well above the run-of-the-mill SaaS valuations, which are below 10x revenue.

Twilio: the SendGrid acquisition marked a major movement of Twilio beyond its base in telephone messaging to support a broader range of channels. If they’re to avoid the fragmentation that has plagued the larger marketing clouds, which also grew by acquisition, they need a CDP to unify their customer data. The big clouds (Oracle, Adobe, Salesforce, Microsoft, SAP) all chose to build their CDPs internally, but Twilio is much smaller and lacks the resources to do the same in a timely fashion. (Even the big clouds struggled, of course). On the other hand, Twilio’s surging stock price makes acquisition much easier. So buying a CDP they can deploy immediately gains them time and a mature product. It also offers entry to 20,000 accounts that might buy other Twilio products, especially given Segment’s position at the heart of their customer data infrastructure.

Of course, if Twilio really wants to compete with the marketing clouds, it will need to support other channels, most notably Web site management and ecommerce. Note that vendors beyond the clouds are pursuing the same strategy, including Acquia (which bought CDP AgilOne), IBM-spinoff Acoustic, MailChimp, and HubSpot. So the strategy isn’t unique, but it may be the only way for companies like Twilio to avoid being marginalized as apps that depend on major platforms controlled by other vendors. By definition, apps are easily replaced and are therefore easily commoditized. That’s a position to escape if you have the resources to expand beyond it.

CDP Industry: Segment is/was the largest independent CDP vendor, although Tealium and Treasure Data are close. Other recent CDP acquisitions were mostly mid-tier vendors (AgilOne, Evergage, QuickPivot, Lattice Engines, SessionM). Of these deals, only AgilOne seemed central to the product strategy of the buyers. Segment’s decision to sell rather than try to grow on its own may signal a recognition that it will be increasingly difficult to survive as a general-purpose independent CDP. We’ve already seen much of the industry shift to more defensible niches, including integrated marketing applications and vertical industry specialization. There’s certainly still a case to be made for an independent CDP as a way to avoid lock-in by broad marketing clouds. But there’s no doubt that the marketing cloud vendors’ own CDPs will grab some chunk of the market, and more will be lost to CDPs embedded in other systems (email, ecommerce, reservations, etc.), offered by service vendors (Mastercard, Vericast, TransUnion, etc.) and home-built on cloud platforms like Amazon Web Services and Google Cloud.

Given these pressures, we’re likely to see additional purchases of CDPs by companies who are trying to build their own complete marketing platforms, including Shopify, MailChimp, HubSpot, and a number of private-equity backed roll-ups. Faced with a daunting competitive situation, many CDP vendors will be interested in selling, even at prices that might not be as high as they once hoped.

Ironically, none of this bodes ill for the fundamental concept of the CDP itself. Companies will still need a central system to assemble and share unified customer profiles. It is indeed the platform on which the other platforms are built. Whether their CDP is stand-alone software or part of a larger solution doesn’t really matter from the user’s perspective: what matters is that clean, consistent, complete customer data is easily available to any system that needs it. Similarly, companies will still need the skills to build and manage CDPs.  Marketing, data, and IT departments will wrestle with customer data long into the future, and the winners will be best positioned to achieve business success. 


Friday, September 25, 2020

Software Review: Skypoint Cloud Combines CDP and Privacy Management

There are obvious similarities between Customer Data Platforms and privacy systems: both find customer data in all company systems; both assemble that data into unified profiles; and both govern access to those profiles. Indeed, some CDP vendors have expanded into privacy management by building consent modules to their systems or by integrating third-party consent managers.

Still, the line between CDP and privacy managers is usually clear: CDPs store customer data imported from other systems while privacy managers read the data in place. There might be a small gray area where the privacy system imports a little information to do identity matching or to build a map of what each source system contains. But it’s pretty easy to distinguish systems that build huge, detailed customer data sets from those that don’t. 

There’s an exception for every rule. Skypoint Cloud is a CDP that positions itself as a privacy system, including data mapping, consent management, and DSR (Data Subject Request) fulfillment. What makes it a CDP is that Skypoint ingests all customer data and builds its own profiles. Storing the data within the system actually makes fulfilling the privacy requirements easier, since Skypoint can provide customers with copies of their data by reading its own files and can ensure that data extracts contain only permitted information. Combining CDP and privacy in a single system also saves the duplicate effort of having two systems each map and read customer data in source systems.

The conceptual advantages of having one system for both CDP and privacy are obvious. But whether you’d want to use a combined system depends on how good it is at the functions themselves. This is really just an example of the general “suite vs best-of-breed” debate that applies across all systems types. 

You won’t be surprised that a young, small vendor like Skypoint lacks many refinements of more mature CDP systems. Most obviously, its scope is limited to ingesting data and assembling customer profiles, with just basic segmentation capabilities and no advanced analytics or personalization.  That’s only a problem if you want your CDP to include those features; many companies would rather use other tools for them anyway. There’s that “suite vs best-of-breed” choice again.

When it comes to assembling the unified database, Skypoint has a bit of a secret weapon: it relies heavily on Microsoft Azure Data Lake and Microsoft’s Common Data Model. Azure lets it scale effortlessly, avoiding one set of problems that often limit new products. Common Data Model lets Skypoint tap into an existing ecosystem of data connectors and applications, again saving Skypoint from developing those from scratch. Skypoint says they’re the only CDP vendor other than Microsoft itself to use the Common Data Model: so far as I know, that’s correct. (Microsoft, Adobe, SAP, and others are working on the Open Data Initiative that will map to the Common Data Model but we haven’t heard much about that recently.) 

How it works is this: Skypoint can pull in any raw data, using its own Web tag or other sources, and store it in the data lake. Users set up a data flow to ingest each source, using either the existing or custom-built connectors. The 200+ existing connectors cover most of the usual suspects, include Web analytics, ecommerce, CRM, marketing automation, personalization, chat, Data Management Platforms, email, mobile apps, data stores, and the big cloud platforms.

Each data flow maps the source data into data entities and relations, as defined in the Common Data Model or adjusted by the user. This is usually done before the data is loaded into the data lake but can also be done later to extract additional information from the raw input.  Skypoint applies machine learning to identify likely PII within source data and lets users then flag PII entities in the data map.  Users can also define SQL queries to create calculated values. 

Each flow has a privacy tab that lets the user specify which entities are returned by Data Subject Requests, whether data subjects can order the data erased, and which data processes use each entity. The data processes, which are defined separately, can include multiple entities with details about which entities are included and what consents are required. Users can set up different data processes for customers who are subject to different privacy regulations due to location or other reasons.

Once the data is available to the system, Skypoint can link records related to the same person using either rule-based (deterministic) matches or machine learning. It’s up to the client define her own matching rules. The system maintains its own persistent ID for each individual. Matches can be either incremental – only matching new inputs to existing IDs – or can rebuild the entire matching universe from scratch. Skypoint also supports real-time identity resolution through API calls from a Web tag.

After the matching is complete, the system merges its data into unified customer profiles. Skypoint provides a basic audience builder that lets users define selection conditions. This also leverages Skypoint's privacy features by first having users define the purpose of the audience and then making available only data entities that are permitted for that purpose. Users can also apply consent flags as variables within selection rules. Audiences can be connected with actions, which export data to other systems manually or through connectors.

Users can supplement the audience builder by creating their own apps with Microsoft Azure tools or let external systems access the data directly by connecting through the Common Data Model.

Back to privacy. Skypoint creates an online Privacy Center that lets customers consent to different uses of their data, make data access requests, and review company policy statements. It creates an internal queue of access requests and tracks their progress towards fulfillment. Users can specify information to be used in the privacy center, such as the privacy contact email and URLs of the policy statements. They can also create personalized email templates for privacy-related messages such as responses to access requests or requests to verify a requestor’s email address.

This is a nicely organized set of features that includes what most companies will need to meet privacy regulations. But the real value here is the integration with data management: gathering data for subject access requests is largely automated when data is mapped into the system through the data flows, a major improvement over the manual data assembly required by most privacy solutions. Similarly, the connection between data flows, audiences, and data processing definitions makes it easier to ensure the company uses only properly consented information. There are certainly gaps – in particular, data processes must be manually defined by users, so an undocumented process would be missed by the system. But that’s a fairly common approach among privacy products.

Pricing for Skypoint starts with a free version limited mostly to the privacy center, consent manager, and data access requests. Published pricing ranges past $2,000 per month for more than ten data integrations. The company was founded in 2019 and is just selling to its first clients.

Sunday, September 13, 2020

Software Review: Osano Manages Cookie Consent and Access Requests

The next stop on our privacy software tour is Osano, which bills itself as “the only privacy platform you’ll ever need”.  That's a bit of an overstatement: Osano is largely limited to data subject interactions, which is only one of the four primary privacy system functions I defined in my first post on this topic. . (The other three are: discovering personal data in company systems, defining policies for data use, and enforcing those policies.) But Osano handles the interactions quite well and adds several other functions that are unique. So it’s certainly worth knowing.

The two main types of data subject interactions are consent management and data subject access requests (DSARs). Osano offers structured, forms-based solutions to both of these, available in a Software-as-a-Service (Saas) model that lets users deploy them on Web sites with a single line of javascript or on Android and iOS mobile apps with an SDK.

The consent management solution provides a prebuilt interface that automatically adapts its dialog to local laws, using the geolocation to determine the site visitor's location.  There are versions for 40+ countries and 30+ languages, which Osano updates as local laws change. Because it is delivered as a SaaS platform, the changes made by Osano are automatically applied to its clients. This is a major time-saver for organizations that would otherwise need their own resources to monitor local laws and update their system to conform to changes.

Details will vary, but Osano generally lets Web visitors consent to or reject different cookie uses including essential, analytics, marketing, and personalization. Where required by laws like the California Consumer Protection Act (CCPA), it will also collect permission for data sharing. Osano stores these consents in a blockchain, which prevents anyone from tampering with them and provides legally-acceptable proof that consent was obtained. Osano retains only a hashed version of the visitor’s personal identifiers, thus avoiding the risk of a PII leak while still enabling users to search for consent on a known individual.

Osano’s use of blockchain to store consent records is unusual. Also unusual: Osano will search its client’s Website to check for first- and third-party cookies and scripts. The system will tentatively categorize these, let users confirm or change the classifications, and then let site visitors decide which cookies and scripts to allow or block. There’s an option to show visitors details about each cookie or script.

Osano also provides customer-facing forms to accept Data Subject Access Requests. The system backs these with an inventory of customer data, built by users who manually define systems, data elements, and system owners. Put another way: there’s no automated data discovery. The DSAR form collects the user’s information and then sends an authentication email to confirm they are who they claim.  Once the request is accepted, Osano sends notices to the owners of the related systems, specifying the data elements included and the action requested (review, change, delete, redact), and tracks the owners’ reports on completion of the required action. Osano doesn’t collect the data itself or make any changes in the source systems.

The one place where Osano does connect directly with source systems is through an API that tracks sharing of personal data with outside entities. This requires system users to embed an API call within each application or workflow that shares such data: again, there’s no automated discovery of such flows. Osano receives notification of data sharing as its happens, encrypts the personal identifiers, and stores it in a blockchain alone with event details. Users can search the blockchain for the encrypted identifiers to build a history of when each customer’s data was shared.

Perhaps the most unusual feature of Osano is the company’s database of privacy policies and related information for more than 11,000 companies. Osano gathers this data from public Web sites and has privacy attorneys review the contents and score each company on 163 data points.  This lets Osano rate firms based on the quality of their privacy processes. It runs Web spiders continuously check for changes and will adjust privacy ratings when appropriate. Osano also keeps watch on other information, such as data breach reports and lawsuits, which might also affect ratings. This lets Osano alert its clients if they are sharing data with a risky partner.

Osano is offered in a variety of configurations, ranging from free (cookie blocking only) to $199/month (cookie blocking and consent management for up to 50,000 monthly unique Web site visitors) to enterprise (all features, negotiated prices). The company was started in 2018 and says its free version is installed on more than 750,000 Web sites.

Sunday, September 06, 2020

When CDPs Fail: Insights from the CDP Institute Survey

We released a new member survey last week at the CDP Institute. You can (and should) download the full report, so I won’t go through all the details. You can also view a discussion of this on Scott Brinker's Chief Martech Show.  But here are three major findings. 

Martech Best Practices Matter 

We identified the top 20% of respondents as leaders, based on outcomes including over-all martech satisfaction, customer data unification, advanced privacy practices, and CDP deployment. We then compared martech practices of leaders vs. others. This is a slightly different approach from our previous surveys but the result was the same: the most successful companies deploy structured management methods, put a dedicated team within marketing inside of martech, and select their systems based on features and integration, not cost or familiarity. No surprise but still good to reaffirm. 




Martech Architectures are More Unified 

For years, our own and other surveys showed a frustratingly static 15%-20% of companies reporting access to unified customer data. This report finally showed a substantial increase, to 26% or 52% depending on whether you think feeding data into a marketing automation or CRM system qualifies as true unification. (Lots of data in the survey suggests not, incidentally.)


 

CDPs Are Making Good Progress 

The survey showed a sharp growth in CDP deployment, up from 19% in 2017 to 29% in 2020. Bear in mind that we’re surveying members of the CDP Institute, so this is not a representative industry sample. But it’s progress nevertheless. 


Where things got really interesting was a closer look at the relationship of customer data architectures to CDP status. You might think that pretty much everyone with a deployed CDP would have a unified customer database – after all, that’s the basic definition of a CDP and the numbers from the two questions are very close. But it turns out that just 43% of the respondents who said they had a deployed CDP also said they had a unified database (15% with the database alone and 28% with a database and shared orchestration engine). What’s going on here? 


 

The obvious answer is that people don’t understand what a CDP really is. Certainly we’ve heard that complaint many times. But these are CDP Institute members – a group that we know are generally smarter and better looking and, more to the point, should understand CDP accurately even if no one else does. Sure enough, when we look at the capabilities that people with a deployed CDP say they expect from a CDP, the rankings are virtually identical whether or not they report they have a unified database. 

(Do you like this chart format? It’s designed to highlight the differences in answers between the two groups while still showing the relative popularity of each item. It took many hours to get it to this stage. To clarify, the first number on each bar shows the percentage for the group that selected the answer less often and the second number shows the group that selected it more often. So, on the first bar above, 73% of people with a unified customer database said they felt a CDP should collect data from all sources and 76% of those without a unified database said the same. The color of the values and at the tip of the bar shows which group chose the item more often: green means it was more common among people with a unified database and red means it was more common among people without a unified database. Apologies if you’re colorblind.) 

Answers regarding CDP benefits were also pretty similar, although there begins to be an interesting divergence: respondents without a unified database were more likely to cite advanced applications including orchestration, message selection, and predictive models. Some CDPs offer those and some don’t, and it’s fair to think that people who prioritized them might consider themselves having a proper CDP deployment even if they haven’t unified all their data. 


But the differences in the benefits are still pretty minor. Where things really get interesting is when we look at obstacles to customer data use (not to CDP in particular). Here, there’s a huge divergence: people without a unified database were almost twice as likely to cite challenges assembling unified data and using that data. 


Combining this with previous answers, I read the results this way: people who say they have a deployed CDP but not a unified database know quite well that a CDP is supposed to create a unified database. They just haven’t been able to make that happen. 

This of course raises the question of Why? We see from the obstacle chart that the people without unified data are substantially more likely to cite IT resources as an issue, with smaller differences in senior management support and data extraction. It’s intriguing that they are actually less likely to cite organizational issues, marketing staff time, or budget. 

Going back to our martech practices, we also see that those without a unified database are more likely to employ “worst practices” of using outside consultants to compensate for internal weaknesses and letting each group within marketing select its own technology. They’re less likely to have a Center of Excellence, use agile techniques, or follow a long-term martech selection plan. (If the sequencing of this chart looks a bit odd, it's because they're arranged in order of total frequency, including respondents without a deployed CDP.  That items at the bottom of the chart have relatively high values shows that deployed CDP owners selected those items substantially more often than people without a CDP.)

 

So, whatever the problems with their IT staff, it seems at least some of their problems reflect martech management weaknesses as well. 

But There's More...

The survey report includes two other analyses that touch on this same theme of management maturity as a driver of success. The first focuses on cross-channel orchestration as a marker of CDP understanding.  It turns out that the closer people get to actually deploying a CDP, the less they see orchestration as a benefit. My interpretation is that orchestration is an appealing goal but, as people learn more about CDP, they realize a CDP alone can't deliver it.  They then give higher priority to less demanding benefits.   (To be clear: some CDPs do orchestration but there are other technical and organizational issues that must also be resolved.)  


We see a similar evolution in understanding of obstacles to customer data use. These also change across the CDP journey: organizational issues including management support, budget, and cooperation are most prominent at the start of the process. Once companies start deployment, technical challenges rise to the top.  Finally, after the CDP is deployed, the biggest problem is lack of marketing staff resources to take advantage of it. You may not be able to avoid this pattern, but it’s good to know what to expect. 


The other analysis looks at CDP results. In the current survey, 83% of respondents with a deployed CDP said it was delivering significant value while 17% said it was not. This figure has been stable: it was 16% in our 2017 survey and 18% in 2019. 

I compared the satisfied vs dissatisfied CDP owners and found they generally agreed on capabilities and benefits, with orchestration again popping out as an exception: 65% of dissatisfied CDP owners cited it as a CDP benefit compared with just 45% of the satisfied owners. By contrast, satisfied owners were more likely to cite the less demanding goals of improved segmentation, predictive modeling, and data management efficiency. Similarly, the satisfied CDP users were less likely to cite coordinated customer treatments as a CDP capability and more likely to cite data collection. (Data collection still topped the list for both groups, at 77% for the satisfied owners and 65% for the others.) 

When it came to obstacles, the dissatisfied owners were much more likely to cite IT and marketing staff limits and organizational cooperation. The divergence was even greater on measures of martech management, including selection, responsibility, and techniques. 


In short, the dissatisfied CDP owners were much less mature martech managers than their satisfied counterparts. As CDP adoption moves into the mainstream, it becomes even more important for managers to recognize that their success depends on more than the CDP technology itself. 

There’s more in the report, including information on privacy compliance, and breakouts by region, company size, and company type. Again, you can download it here for free.

Thursday, August 27, 2020

Software Review: BigID for Privacy Data Discovery

Until recently, most marketers were content to leave privacy compliance in the hands of data and legal teams. But laws like GDPR and CCPA now require increasingly prominent consent notifications and impose increasingly stringent limits on data use. This means marketers must become increasingly involved with the privacy systems to ensure a positive customer experience, gain access to the data they need, and ensure they use the data appropriately. 

I feel your pain: it’s another chore for your already-full agenda.  But no one else can represent marketers’ perspectives as companies decide how to implement expanded privacy programs.  If you want to see what happens when marketers are not involved, just check out the customer-hostile consent notices and privacy policies on most Web sites.

To ease the burden a bit, I’m going to start reviewing privacy systems in this blog. The first step is to define a framework of the functions required for a privacy solution.   This gives a checklist of components so you know when you have a complete set. Of course, you’ll also need a more detailed checklist for each component so you can judge whether a particular system is adequate for the task. But let’s not get ahead of ourselves. 

At the highest level, the components of a privacy solution are:

  • Data discovery.  This is searching company systems to build a catalog of sensitive data, including the type and location of each item. Discovery borders on data governance, quality, and identity resolution, although these are generally outside the scope of a privacy system. Identity resolution is on the border because responding to data subject requests (see next section) requires assembling all data belonging to the same person. Some privacy systems include identity resolution to make this possible, but others rely on external systems to provide a personal ID to use as a link.

  • Data subject interactions.  These are interactions between the system and the people whose data it holds (“data subjects”).  The main interactions are to gather consent when the data is collected and to respond to subsequent “data subject access requests” (DSARs) to view, update, export, or delete their data. Consent collection and request processing are distinct processes.  But they are certainly related and both require customer interactions.  So it makes sense to consider them together. They are also where marketers are most likely to be directly involved in privacy programs.

  • Policy definition.  This specifies how each data type can be used.  There are often different rules based on location (usually where the data subject resides or is a citizen, but sometimes where the data is captured, where it’s stored, etc.), consent status, purpose, person or organization using the data, and other variables. Since regulations and company policies change frequently, this component includes processes to identify changes and either automatically adjust rules to reflect them or alert managers that adjustments may be needed.

  • Policy application.  This monitors how data is actually used to ensure it complies with policies, send alerts if something is not compliant, and keep records of what’s done. Marketers may be heavily involved here but more as system users than system managers. Policy application is often limited to assessing data requests that are executed in other systems but it sometimes includes actions such as generating lists for marketing campaigns. It also includes security functions related specifically to data privacy, such as rules for masking of sensitive data or practices to prevent and react to data breaches. Again, security features may be limited to checking that rules are followed or include running the processes themselves. Security features in the privacy system are likely to work with corporate security systems in at least some areas, such as user access management. If general security systems are adequate, there may be no need for separate privacy security features. 

Bear in mind that one system need not provide all these functions.  Companies may prefer to stitch together several “best of breed” components or to find a privacy solution within a larger system. They might even use different privacy components from several larger systems, for example using a consent manager built into a Customer Data Platform and a data access manager built into a database’s core security functions. 

Whew.

Now that we have a framework, let's apply it to a specific product.  We'll start with BigID.

Data Discovery

BigID is a specialist in data discovery. The system applies a particularly robust set of automated tools to examine and classify all types of data – structured, semi-structured, and unstructured; cloud and on-premise; in any language. For identified items, it builds a list showing the application, object name, data type, server, geographic location, and other details. 

Of course, an item list is table stakes for data discovery.  BigID goes beyond this to organize the items into clusters related to particular purposes, such as medical claims, invoices, and employee information. It also draws maps of relations across data sources, such as how the transaction ID in one table connects to the transaction ID in another table (even if the field names are not the same). Other features highlight data sources holding sensitive information, alert users if these are not properly secured from unauthorized access, and calculate privacy risk scores. 

The relationship maps provide a foundation for identity resolution, since BigID can compare values across systems to find matches and use the results to stitch together related records. The system supports fuzzy as well as exact matches and can compare combinations of items (such as street, city, and zip) in one rule.  But the matching is done by reading data from source systems for one person at a time, usually in response to an access request. This means that BigID could assemble a profile of an individual customer but won’t create the persistent profiles you’d see in a Customer Data Platform or other type of customer database. It also can’t pull the data together quickly enough to support real-time Web site personalization, although it might be fast enough for a call center. 

In fact, BigID doesn’t store any data outside of the source systems except for metadata.  So there's no reason to confuse it with a data lake, data warehouse, CRM, or CDP.

Data Subject Interactions

BigID doesn’t offer interfaces to capture consent but does provide applications that let data subjects view, edit, and delete their data and update preferences. When a data access request is submitted, the system creates a case that is sent to other systems or people to execute. BigID provides a workflow to track the status of these cases but won’t directly change data in source systems. 

Policy Definition 

BigID doesn’t have an integrated policy management system that lets users define and enforce data privacy rules. But it does have several components to support the process:

  • "Agreements" let users document the consent terms and conditions associated with specific items. This does not extend to checking the status of consent for a particular individual but does create a way to check whether a consent-gathering option is available for an item.

  • “Business flows” map the movement of data through business processes such as reviewing a resume or onboarding a new customer. Users can document flows manually or let the system discover them in the data it collects during its scan of company systems. Users specify which items are used within a flow and the legal justification for using sensitive items. The system will compare this with the list of consent agreements and alert users if an item is not properly authorized. BigID will also alert process owners if a scan uncovers a sensitive new data item in a source system.  The owner can then indicate whether the business flow uses the new item and attach a justification. BigID also uses the business flows to create reports, required by some regulations, on how personal data is used and with whom it is shared. 

  • “Policies” let users define queries to find data in specified situations, such as EU citizen data stored outside the EU. The system runs these automatically each time it scans the company systems. Query results can create an alert or task for someone to investigate. Policies are not connected to agreements or business flows, although this may change in the future. 

Policy Enforcement

BigID doesn’t directly control any data processing, so it can’t enforce privacy rules. But the alerts issued by the policy, agreement, and business flow components do help users to identify violations. Alerts can create tasks in workflow systems to ensure they are examined and resolved. The system also lets users define workflows to assess and manage a data breach should one occur. 

Technology 

 As previously mentioned, BigID reads data from source systems without making its own copies or changes any data in those systems. Clients can run it in the cloud or on-premises. System functions are exposed via APIs which let the company, clients, or third parties build apps on top of the core product. In fact, the data subject access request and preference portal functions are among the applications that BigID created for itself. It recently launched an app marketplace to make its own and third party apps more easily available to its clients. 

Business 

BigID has raised $146 million in venture funding and reports nearly 200 employees. Pricing is based on the number of data sources: the company doesn’t release details but it’s not cheap. It also doesn’t release the number of clients but says the count is “substantial” and that most are large enterprises.

Tuesday, August 18, 2020

Data Security is a Problem Marketers Must Help Fix


Everything you need to know about 2020 is covered by the fact that “apocalypse bingo” is already an over-used cliché. So I doubt many marketers have found spare time to worry about data security – which most would consider someone else’s problem. But bear in mind that 92% of consumers say they would avoid a company after a data breach. So, like it or not, security is a marketer’s problem too. 

Unfortunately, the problem is a big one. I recently took a quick scan of research on the issue, prompted in particular by a headline that nearly half of companies release software they know contains security flaws.  Sounds irresponsible, don't you think?  The main culprit in that case is pressure to meet deadlines, compounded by poor training in security procedures. If there’s any good news, it’s that the most-used applications have fewer unresolved security flaws than average, suggesting that developers pay more attention when they know it’s most important. 

The research is not reassuring. It may be a self-fulfilling prophecy, but most security professionals see data breaches as inevitable. Indeed, many think a breach is good for their career, presumably because the experience makes them better at handling the next one. Let’s just be grateful they're not airline pilots. 

Still, the professionals have a point. Nearly every company reports a business-impacting cyberattack in the past twelve months. Even before COVID-19, fewer than half of IT experts were confident their organizations can stop data breaches with current resources.

The problems are legion. In addition to deadline pressures and poor training, researchers cite poorly vetted third-party code libraries, charmingly described as “shadow code”; compromised employee accounts, insecure cloud configurations, and attacks on Internet of Things devices.

Insecure work-from-home practices during the pandemic only add new risk. One bit of good news is that CIOs are spending more on security,  prioritizing access management and remote enablement. 

What’s a marketer to do?  One choice is to just shift your attention to something less stressful, like fire tornados and murder hornets. It’s been a tough year: I won’t judge. 

But you can also address the problem. System security in general is managed outside of most marketing departments. But marketers can still ensure their own teams are careful when handling customer data (see this handy list of tips from the CDP Institute). 

Marketers can also take a closer look at privacy compliance projects, which often require tighter controls on access to customer data. Here’s an overview of what that stack looks like.  CDP Institute also has a growing library of papers on the the topic.

Vendors like TrustArc, BigID, OneTrust, Privitar, and many others, offer packaged solutions to address these issues. So do many CDP vendors. Those solutions involve customer interactions, such as consent gathering and response to Data Subject Access Requests.  Marketers should help design those interactions, which are critical in convincing consumers to share personal data that marketers need for success. The policies and processes underlying those interfaces are even more important for delivering on the promises the interfaces make. 

In short, while privacy and security are not the same thing, any privacy solution includes a major security component. Marketers can play a major role in ensuring their company builds solid solutions for both. 

Or you can worry about locusts

 

Saturday, July 25, 2020

Don't Misuse Proof of Concept in System Selection

Call me a cock-eyed optimist, but marketers may actually be getting better at buying software. Our research has long shown that the most satisfied buyers base their selection on features, not cost or ease of use. But feature lists alone are never enough: even if buyers had the knowledge and patience to precisely define their actual requirements, no set of checkboxes could capture the nuance of what it’s actually like to use a piece of software for a specific task. This is why experts like Tony Byrne at Real Story Group argue instead for defining key use cases (a.k.a. user stories) and having vendors demonstrate those. (If you really want to be trendy, you can call this a Clayton Christensen-style “job to be done”.)

In fact, use cases have become something of an obsession in their own right. This is partly because they are a way of getting concrete answers about the value of a system: when someone asks, “What’s the use case for system X”, they’re really asking, “How will I benefit from buying it?” That’s quite different from the classic definition of a use case as a series of steps to achieve a task. It’s this traditional definition that matters when you apply use cases to system selection, since you want the use case to specify the features to be demonstrated. You can download the CDP Institute’s use case template here.

But I suspect the real reason use cases have become so popular is that they offer a shortcut past the swamp of defining comprehensive system requirements. Buyers in general, and marketers in particular, lack the time and resources to create complete requirements lists based on their actual needs (although they're perfectly capable of copying huge, generic lists that apply to no one).  Many buyers are convinced it’s not necessary and perhaps not even possible to build meaningful requirements lists: they point to the old-school “waterfall” approach used in systems design, which routinely takes too long and produces unsatisfactory results. Instead, buyers correctly see use cases as part of an agile methodology that evolves a solution by solving a sequence of concrete, near-term objectives.

Of course, any agile expert will freely admit that chasing random enhancements is not enough.  There also needs to be an underlying framework to ensure the product can mature without extensive rework. The same applies to software selection: a collection of use cases will not necessarily test all the features you’ll ultimately need. There’s an unstated but, I think, implicit assumption that use cases are a type of sampling technique: that is, a system that meets the requirements of the selected use cases will also meet other, untested requirements.   It’s a dangerous assumption. (To be clear: a system that can’t support the selected use cases is proven inadequate. So sample use cases do provide a valuable screening function.)

Consciously or subconsciously, smart buyers know that sample use cases are not enough. This may be why I’ve recently noticed a sharp rise in the use of proof of concept (POC) tests. Those go beyond watching a demonstration of selected use cases to actually instal a trial version of a system and seeihow it runs. This is more work than use case demonstrations but gives much more complete information.

Proof of concept engagements used to be fairly rare. Only big companies could afford to run them because they cost quite a bit in both cash (most vendors required some payment) and staff time (to set up and evaluate the results). Even big companies would deploy POCs only to resolve specific uncertainties that couldn’t be settled without a live deployment.

The barriers to POCs have fallen dramatically with cloud systems and Software-as-a-Service. Today, buyers can often set up a test system with a just a few mouse clicks (although it may take several days of preparation before those clicks will work). As a result, POCs are now so common that they can almost be considered a standard part of the buying process.

Like the broader application of use cases, having more POCs is generally a good thing. But, also like use cases, POCs can be applied incorrectly.

In particular, I’ve recently seen several situations where POCs were used as an alternative to basic information gathering. The most frightening was a company that told me they had selected half a dozen wildly different systems and were going to do a POC with each of them to figure out what kind of system they really needed.

The grimace they didn’t see when I heard this is why I keep my camera off during Zoom meetings. Even if the vendors do the POCs for free, this is still a major commitment of staff time that won’t actually answer the question. At best, they’ll learn about the scope of the different products. But that won’t tell them what scope is right for them.

Anther company told me they ran five different POCs, taking more than six months to complete the process, only to later discover that they couldn’t load the data sources they expected (but hadn’t included in their POCs). Yet another company let their technical staff manage a POC and declare it successful, only later to learn the system had been configured in a way that didn’t meet actual user needs.

You’re probably noticing a dreary theme here: there’s no shortcut for defining your requirements. You’re right about that, and you’re also right that I’m not much fun at parties. As to POCs, they do have an important role but it’s the same one they played when they were harder to do: they resolve uncertainties that can’t be resolved any other way.

For Customer Data Platforms, the most common uncertainty is probably the ability to integrate different data sources.  Technical nuances and data quality are almost impossible to assess without actually trying to load each system.  Since these issues have more to do with the data source than the CDP, this type of POC is more about CDP feasibility in general than CDP system selection. That means you can probably defer your POC until you’ve narrowed your selection to one or two options – something that will reduce the total effort, encourage the vendor to learn more about your situation, and help you to learn about the system you’re most likely to use.

The situation may be different with other types of software. For example, you might to test q wide variety of predictive modeling systems if the key uncertainty is how well their models will perform. That’s closer to the classic multi-vendor “bake-off”.  But beware of such situations: the more products you test, the less likely your staff is to learn each product well.

With a predictive modeling tool, it’s obvious that user skill can have a major impact on results. With other tools, the impact of user training on outcomes may not be obvious. But users who are assessing system power or usability may still misjudge a product if they haven’t invested enough time in learning it.  Training wheels are good for beginners but get in the way of an expert. Remember that your users will soon be experts, so don’t judge a system by the quality of its training wheels.

This brings us back to my original claim.  Are marketers really getting better at buying software?  I’ll stand by that and point to broader use of tools like use cases and proof of concepts as evidence. But I’ll repeat my caution that use cases and POCs must be used to develop and supplement requirements, not to replace them. Otherwise they become an alternate route to poor decisions rather than
guideposts on the road to success.