Back in 2012, the Harvard Business Review declared the Data Scientist the “sexiest job of the century,” and since then, marketers have been challenged to assemble a mix of analysts, statisticians, developers, mathematicians, data scientists and more, to fulfill on the promise of a data-driven business and to stay ahead of the competition in an increasingly digital world.
One of the main struggles for organizations is in finding the resources with the right combination of skill sets to support an analytics program. Recruiters are renowned for sending unsolicited “perfect job” descriptions comprising bulleted items like these:
- 10+ years of analytics experience
- A/B and Multivariate Testing
- Advanced degree
- Define KPI’s
- Manage multiple client engagements
- 5+ years working with Ensighten
- Expertise in SAS, SPSS, R, Python
- Develop strategic vision
- Competitive salary $65-80k
Anyone see the red flags? This company is apparently seeking a single resource to perform tasks across the entire analytics program; from defining the strategy and KPIs, to coding, testing, deployment, analysis, client management, and more! The requirements for an advanced degree, the five+ years of Ensighten experience, and this salary range are red flags for me as well.. An advanced degree is not needed and the salary is under market for the skill set. These two items will be explored further in a subsequent post.
However, asking for a skill set that exceeds the number of years a tool or business entity has been available (Ensighten), is a clear indication the hiring company is going to need more mentoring than a single resource can deliver.
Organizations trying to support an enterprise level analytics program with a single resource will waste time, money, and credibility. Don’t be fooled! There is no single, self-branded analytics “guru” who can successfully execute the full lifecycle of skills required to launch an enterprise level program by themselves. Even seasoned analytics veterans, who may have performed every one of the skills required from concept to reality at some point in their career, may need a refresher when it comes to coding, accessing APIs, or tweaking a web server. To be successful, organizations must have multiple resources in place to implement and support an enterprise class analytics program.
In a very basic support model, there are three distinct functional roles to address: Development, Analysis, and Strategic. The diagram below describes the three roles. There’s potential for overlap with the skills required, however, notice the span from highly technical and across to more strategic and business skills.
- Database Administration
- Key Performance Indicators (KPIs)
- Testing: A/B, Multivariate
- Data Governance/Integrity
- Reporting Tool Administration
- Tag Management
- Marketing Channel Optimization
- R, Python, SAS, Tableau
- Google Analytics, Unica, Adobe
- Dashboard Creation/Distribution
- Business Requirements
- Process Improvement
- Strategy Definition
- Project Management
- Team Building
Still thinking that a single resource can get you to analytics nirvana? Consider a common career progression (diagram below) where the dotted line depicts a developer or analyst through their career path. In this case, they initially stressed technical expertise, but then migrated towards the strategic functions. Notice the labels in the boxes, from “High Tech/Low Strat” in the upper left to “Low Tech/High Strat” in the lower right. If we reverse the x and y axis and box labels, it would display an initially business focused person moving into the technical field.
There are always exceptions to the rule, the people who dance between the technical and strategic throughout their careers, and the true thought leaders of our industry, but it doesn’t mean they will be in the top right corner as a master of both strategy and technical disciplines. Moreover, our well-respected industry leaders are not likely on the market as a full-time staffer.
Job Title Evolution
In just the past five years, the list of job titles has grown exponentially and with Marketing spending a lot of the technology budget these days, many of these new titles are warranted. It used to be fairly easy to determine what type of task or function one would perform by the advertised job title. There still are positions for Project Managers, Data Analysts, and Database Administrators, but it seems creativity has taken root with the explosion of hybrid offerings like; Technical Business Analyst, Data Systems Designer, Marketing Modeler, Solution Design Integrator. Companies are blurring the lines between these three functions which can set up everyone involved – and the analytics program – for failure.
Of course with technological advancement, there will always be a need for new skills. Subject Matter Experts (SME’s) with experience in Tableau, R, HTML-5, Ensighten, Mobile App Development, Search Engine Optimization, Display Marketing, and Data Visualization are in demand. But you can’t just add a line item to a job description without understanding what function that specialization supports. You wouldn’t list Mobile App Development for a Business Analyst or a Project Manager. Iit would go under a title that supports the Development function.
The message here is to keep titles simple, focused, and separate the functions, especially when you are kicking off your analytics program.
The Skills Matrix
How do you choose the right set of skills or even know how many people to hire for a successful analytics initiative? Well, we have established that we need to support the three functions: development, analysis, and strategy/business. And we know we will need multiple resources, but which ones will achieve the optimal outcome?
The link below contains a high-level matrix that differentiates the core skills and groups them across the three functions. The top row contains five sample Titles/Column Headings. The column with examples simply shows a brief list of potential skills, tools, and/or software vendors.
Using your job description defined by your business:
- Highlight the skills/rows that are a close match, use the example column as needed
- Look for the “High” ratings and cross reference those with the Title column
In the optimal scenario, all of the “High” ratings would be under a single title. However, more than likely “High” ratings will be under multiple titles. For instance, you may find that there are four “High” ratings under Developer, five under Data Analyst, and three under Business Analyst. In this case, you should consider hiring three resources. Of course, if the three under Business Analyst are rated as “High” across Data Analyst as well, then maybe only two resources will work.
Feel free to adapt this matrix to suit your unique organization. This is intended to be used as a guide, a basic tool to identify the appropriate skill sets across functional areas, and define which resources you need as well as the expectations for those resources.
The Job Description
Build the job description from the skills matrix. The “High” ratings under that title are your core, must-have talents. The “Med” can be listed as “preferred” or “optional” or “nice to have.” Keep in mind, with each bulleted skill, you are reducing the hiring pool. There are some quality candidates that may not be proficient in a specific skill and may not respond to the advertisement because they don’t fully meet all of the job requirements. These are also people who could easily be brought up to speed with some training. In this case, you might want to drop the “Low” rated skills from the job description and hire another resource where those skills have “High” ratings under the appropriate title. Expecting too much from a single resource not only limits your potential pool of qualified candidates, but also demonstrates a lack of knowledge around how to build out an enterprise-level analytics program.
Lastly, anyone can learn a tool if they have experience with a competing tool. For example, if you have experience with Python, you can quickly get up to speed in R. In the job description, instead of listing a particular required expertise, you may want to phrase it like: “Experience navigating statistical applications like Matlab, SAS, SPSS, Python, R, etc…” Using this approach, you are casting a broader net as well as displaying your level of understanding of the rapid changes in technology and supporting an analytics program.
An Analytics Foundation to Build On
Understanding what resources to hire and the corresponding skill sets is critical in building out a successful analytics program. Don’t be fooled and don’t fool yourself: there are no shortcuts when it comes to establishing a solid foundation for your analytics. Hopefully this helps you both build and grow your team to support your business goals.
Get Professional Help
Few organizations are doing this alone. The rapid developments in this industry makes it really tough to stay on top of advancements in marketing technology and digital marketing techniques, while enabling data-driven insights to your stakeholders across the enterprise.
We can even help you to further define the kinds of skills you need to bring in-house, and what to outsource, for best results.
At Cardinal Path, we pride ourselves on a simple vision of sharing our knowledge and being our clients’ competitive advantage. Our teams help organizations to develop the analytics strategy, identify KPIs, deploy the tools, integrate the data, optimize for search engines, conduct multivariate tests, perform data science, train your teams, and provide vendor-agnostic guidance for technology selection, supporting a full spectrum of vendors like the Adobe Marketing Cloud, Google Analytics, Tealium, Ensighten, Optimizely, Tableau and Klipfolio to name a few.
Looking to get your analytics program on-track? Bring in Cardinal Path’s senior-most advisors for an onsite strategy session to assess your situation across needs and stakeholders and design a custom plan so you can start delivering on the promise of digital data analytics.