Why Salesforce Data Cloud’s Unknown Cost Shouldn’t Scare You (Or Your CFO)
How Data Cloud’s Credit Model Increases Cost Transparency and Will Likely Save You Money
Next week I’m heading to Salesforce’sTrailblazerDX event where Data Cloud features prominently on the agenda. By now, most organizations understand the value of having a complete, consistent, current, contextual, and compliant 360 understanding of their Customer relationships.
For companies considering implementing Customer 360 for first time, the unknown costs in Data Cloud’s consumption-based pricing model may be scary. Other organizations may be skeptical because past investments into these initiatives have not provided clear returns.
As an old hand at this, having implemented operational data stores and data warehouses in the 1990s, customer master data management starting in 2006, I’ll explore why historical approaches to solving Customer 360 have been so expensive and slow, where hidden costs ballon total cost of ownership, and why I believe Data Cloud is a more cost-effective option.
Data Cloud Consolidates Must-Have Customer 360 Capabilities to Help Organizations Do More with Less, Faster
As I’ve stated before, when discussing Salesforce’s Customer 360 journey, I consider Salesforce Data Cloud to be the best technology choice for organizations, regardless of size. I also consider it a highly cost-effective solution compared to alternatives. Why is this? To understand, let’s look at what a Customer 360 solution must accomplish.
Any successful Customer 360 initiative requires you to:
- Bring data together from disparate sources, regardless of data schemas or data content.
- Understand the data content to determine what data is valuable and actionable for different business purposes.
- Decide on how best to resolve data content differences to power enterprise AI, automation, activation, and analytics needs.
Traditionally, companies solved this with a complex technology stack, with different vendor solutions solving distinct capability requirements:
On top of these requirements, you still need data security, job scheduling, and exception management controls in each technology, plus scheduling across all flows. The resulting stack looks something like the following diagram.
For many organizations, this continues to be their current enterprise data solutions strategy, even today. Marketers have their own CDPs, Data Scientists their data lakes, Sales leaders and CFOs their data warehouses or other analytics repositories, and IT is seeking to meet the needs of all of these different stakeholders. This is extremely inefficient in terms of cost, never mind time to value or data inconsistency concerns. So many single sources of truth…
When you look at the big picture, it is easy to see how the cost of selecting, licensing, learning, implementing, integrating, and maintaining each and every one of these technologies adds up quickly.
Maintaining Separate Customer 360 Stacks for Data Security Is Expensive
In the past, segregating databases was seen as essential to maintain simple data security management and comply with contextual or regulatory data delivery requirements. As a result, companies often opted for different technology providers and even developed separate stacks for Marketing (CDP) and non-Marketing (Data Lakes) use cases, increasing costs even more.
While maintaining parallel database infrastructure can meet data security and compliance objectives, it significantly increases financial and operational burdens for companies. In addition to redundant expenses for compute power, licenses, and human resources, this approach also leads to duplicative maintenance and support needs.
Salesforce Data Cloud has an add-on feature called “Data Spaces”, which essentially allows you to control what subset of data should be exposed to different stakeholders how, with fine grain controls.
While the work of defining data access policies, i.e. who is allowed to see what for what business purpose, remains the same, the ability to specify controls for different stakeholders under a common umbrella can significantly reduce your costs, monitoring complexity — especially when change happens, and thus risk, often associated with monetary implications.
Data Cloud Increases Cost Transparency
Cost transparency absolutely matters. It’s important to note that consumption-based pricing is the industry default. Developing solutions on Snowflake with profiles created by an MDM technology running on AWS, GCP, Azure, etc., integrated via Kafka or other integration technologies, will also incur credits.
These credits, of course, add up. However, when vendor contracts are managed by different budget owners — or business functions purchase entirely separate stacks — the total credit cost can be difficult to determine. Not to mention the overhead costs of integration, maintenance, and maintaining data security controls across multiple components all but ensures the total cost of ownership of legacy stacks will be higher that Data Cloud in the long run.
The complexity of previous architectures makes understanding the total cost of ownership elusive. You may not know exactly how much your Customer 360 initiative costs without significant efforts involving many different teams.
Expanding Total Cost of Ownership Advantages with the AppExchange
Salesforce Data Cloud stands out because it’s not just a solution that consolidates capabilities. It’s a platform that can be quickly extended to accelerate Customer 360 initiatives. Time to value and Trust matter. The faster we can deliver business value in a secure and predictable way, the better we can fulfill our objectives as delivery professionals.
There is a growing collection of Data Cloud solutions on AppExchange, which integrate seamlessly into the Salesforce data architecture (and often the Salesforce UI) and meet security standards set by Salesforce.
- Data Enrichment offerings simplify and improve identity resolution, data quality assessment, segmentation, and action decisions. Since these are pre-integrated with Data Cloud, instead of worrying about how many different integrations you need to put in place with your data enrichment providers, you can control them centrally.
- Data Cloud is already differentiated from traditional, marketing-focused CDPs, by supporting all business functions such as Customer Service and Sales. Data Activation solutions allow you to accelerate the value you can get from the data you unified in additional endpoints beyond Salesforce applications.
- Data Management solutions, the latest category for Data Cloud applications, are enabling users to assess and improve data natively, without adding integration or security complexities.
Each of these areas simplifies implementation and value realization while reducing overall complexity.
Predicting the Data Cloud Consumption Needs
It’s natural, especially for Finance stakeholders or those responsible for budget requests, to want clarity on the number of credits required. As a former customer and product manager tasked with predicting and understanding the operational costs of a multi-tenant solution, I fully grasp the challenge and complexity involved.
Having cost clarity helps, but understanding costs is not the same as understanding future demand. You can spend a lot of time creating models based on hypotheticals. My best advice is to use actual consumption to predict future costs.
Start with Data Cloud free credits to implement your solution and measure the incremental credit costs between the first two batches. As you get ready to go live, monitor incremental credit consumption weekly, then monthly, to identify the pattern.
In probably 6–12 months, as you go from your first phase go live to second and third phase deployments, you will have a model that can track what your initial cost (and business benefits) are and what the incremental cost impact is. Take the long view. Such technology investments should be looked at with a 3–5 year+ horizon. And find solace that tracking and modeling costs with a single technology platform is much much easier than how 3–7+ technology platforms will work together.
Skeptical? Love it? Have more questions? Post a comment or send a message, and I’ll do my best to respond.
