Why Salesforce Certification Really Matters

When I joined Salesforce in 2013, I did not know I would be required to become Salesforce Certified. I had designed, deployed and used a variety of Salesforce applications dating back to 2003, but I had not taken an exam of any kind since graduating from university. Preparing for, and ultimately taking, exams was not on my list of things I ever wanted to do again. I was excited to begin my new career, but in truth I was not excited to be tested. Not only did I learn that I needed to get certified, but also I learned I needed to complete five certifications.

What happened next truly surprised me. It turns out that a well designed certification process, like the one developed by Salesforce University, is about training people not to miss the proverbial forest because of the proverbial trees. Understanding the holistic value of the platform is much more important than being able to stand-up and administer Salesforce CRM, to deploy Service Cloud for a B2C call center or empower 1-to-1 Customer Journeys through the Marketing Cloud. Certification is about learning to identify the connections between the different products for the purpose of deriving exponentially greater value from the platform as a whole.

The boilerplate reasons for any technical certification, whether Salesforce or some other enterprise platform, are typically the same: Companies are looking for proven professionals; and companies who use certified cloud specialists see smoother deployments and better use of Salesforce. Getting certified boosts your career and enables you to contribute even more to your organization’s success. All of these are true. But what I didn’t realize was how certification was more about career development than I originally thought.

Across the pantheon of the Salesforce Universe, certifications are loosely divided into four categories: 1) Technical / Developer; 2) Administrative 3) Marketer and 4) Consultant. As a customer-facing employee, I was required to pass two administrative exams, one developer exam and three consulting exams. Salesforce’s products are industrial strength and enterprise-class. From the perspectives of functionality, features and administration, this means the products are both wide and deep, which makes for challenging examinations. After completing my second administrative exam, I realized unexpectedly that I was really taking one certification in six parts. This realization helped me, for the first time, become less narrow-minded. The certification process was not simply a hoop to jump through; rather, it was giving me a wide-angle lens through which I could see the entire Salesforce product universe.

Since completing my initial round of five required certifications, I have gone on to complete an additional four. What I know now to be absolutely true (at least when it comes to the Salesforce platform)…

It is easy to miss the Salesforce forest because of the product trees. And the whole Salesforce universe is much greater than the sum of its individual parts.

Our job is not just to help Salesforce customers find solutions to the challenges they have today, but also to help develop innovative solutions to the challenges our customers do not even know they have. From this vantage point, as a Salesforce Certified professional, I am better prepared to help our customers exceed their own expectations.

About the author:

Tal Golan (@TalGolan) is the Chief Strategy Officer at VERB.

Big Data is a Verb (Not a Noun)

Big Data (BD) is not a “thing” (noun). Big Data is a set of actions (verb), orchestrated by people, using specialized tools (software, hardware, algorithms) to organize data streams for the purpose of gaining additional insight. The quantity of data, in unto itself, does not equate to Big Data. The processes used to correlate and draw inferences from (potentially) disparate data streams, ultimately leading to insight, is the soil from which BD thrives.

Nestlé Waters North America, as documented by Marketing Land, demonstrates the power of a creative Big Data strategy.

Antonio Sciuto (CMO @ Nestlé Waters North America): “We listen [to] our consumers on social platforms to understand: conversation topics, share of conversation by social platform, tone, consumer sentiment, roles and consumer engagement rules by media touch-point.

This understanding is enabling us to define the right opportunities to engage consumers with content and the right calls to action by online and offline touch-points. Our whole consumer journey is managed by leveraging… marketing cloud solutions, powered by Salesforce, that allow us to listen, analyze, and engage consumers by automating consumer interactions.

Our mission is to build communities around our brands and content based on the real needs of our audience, offering them a truly personalized omni-channel experience to deepen their engagement with our brands. Success to be measured in market share and loyalty to our brands.”

In 1998 a company was launched with the following mission:

“To organize the world’s information and make it universally accessible and useful.”

With the clarity provided by 17+ years of hindsight, Google’s sublime mission statement not only signals the modern era of Big Data, it provides a succinct, human-readable, and aspirational “true north” when setting expectations for all Data Science, including BD, outcomes.

The following, based on Google’s mission statement, attempts to illustrate a theory of Big Data relativity:

    B = DQE

    B = Big Data Cost (a.k.a. cost to achieve a “useful” and / or “insightful” result)

    D = Number of discrete data streams (a.k.a. “information”)

    Q = Average quantity of information per data stream (a.k.a. “information”)

    E = Effort required to process

Working with the assumption that a “useful” and / or “insightful” result is the objective of any BD exercise, we see that although the quantity of inputs (data streams) impacts the cost, the more significant driver is the level of effort required to achieve a result. From a practical business perspective, the key to implementing a cost effective Big Data strategy is not the quantity of data or the number of streams, but the human and machine effort required to achieve a “useful” or “insightful” result.

If we accept the premise that “Big Data” is a verb, any organization looking to achieve cost effective results from the analysis of their data must consider the following:

  1. Do you have the expertise (a.k.a data scientists), from the beginning, to build an effective strategy?
  2. Do you have the software and hardware tools to accomplish the required level of data ingestion and analysis?
  3. Are your resources (human, software, hardware) capital expenses, or are they elastic (operational expenses)?

Understanding (and appreciating) the linguistic differences between Big Data (verb) and data (noun) will allow individuals and enterprises to more effectively collaborate on strategies which achieve outcomes of insight and value.

About the author:

Tal Golan (@TalGolan) is the Chief Strategy Officer at VERB.