3 levels of analytical knowledge, and why they’re critical in every organization

When dealing with technical subjects such as analytics, data science or programming, individuals have varying depths of technical expertise. Employees with these different levels of technical knowledge play valuable roles within the “talent fabric” of an organization. However, it is important that all parties are aware of their level of expertise and know where they fit within the business’ collective knowledge base.

We have grouped employees based on technical expertise — Communicators, Replicators and Builders. For each of these groups, we also provide the following context.

• Who they are. Describes the employees with the given expertise level
• Why They’re Critical. All levels of technical expertise provide value differently
• Where to Master these Skills. Resources in case you would like to reach the given level of mastery!

Level 1: Communicators

Who They Are. As the name suggests, these individuals conceptually understand certain analytical processes or software. Within consulting, these are often client-facing individuals who can articulate technical analysis at a high level, but also have specialized knowledge of their client’s business or industry. This is why analytical consulting teams often deliver presentations with a ‘Level 1’ Consultant (who is more involved in client engagement) and a ‘Level 2 or 3’ Analyst (who can field any deep technical questions).

Within corporations, ‘Level 1’ knowledge is extremely valuable as well. Someone who can extract insights from a model or data set is as valuable as the person who actually executes the analysis. ‘Level 1’ business stakeholders are often savvy in Excel and/or SQL, but can also extract and articulate salient business insights. In sum, ‘Level 1’ stakeholders are most valuable when they combine a base analytical understanding with a deep understanding of the business’ operations or strategy.

Why They’re Critical. Within consulting, having a baseline analytical acumen is imperative. Whenever a consultant is presenting in a room with a technical stakeholder, having a basic understanding of the underlying data and calculations gives instant credibility.

Within corporations, these individuals bridge the gap between Analytics and Operations or Strategy. They are critical to bolstering a data-driven culture within an organization.

Where to Master these Skills

• Coursera offers Business Analytics Specialization. Offered by Wharton, this specialization provides instruction across verticals within Business Analytics — Customer, Operations, People (HR) and Accounting Analytics.
• Coursera offers Excel to MySQL: Analytic Techniques for Business Specialization. Duke has partnered with AirBnb to show users advanced Excel techniques, Tableau dashboard creation and analytical approaches to strategic business problems.
• Note: the “Excel to MySQL” Specialization focuses more on the tools used in ‘Level 1’ analytics, whereas the “Business Analytics” Specialization focuses on conceptual elements of ‘Level 1’ analytics.
• Through edX, Berkeley offers a Marketing Analytics Series, including Competitive Analysis & Segmentation, Price & Promotion and Marketing Measurement.
• Through edX, Microsoft offers courses on leveraging Excel for data analysis and extracting data insights.

Level 2: Replicators

Who They Are. These are analysts who conceptually understand given analyses, as well as the analyses’ underlying code. This skill set allows analysts to take pre-existing code and apply it to novel projects. This group generally comprises more junior analysts. Taking this step in an analyst’s career is valuable because they learn best practices by viewing more senior analysts’ code. However, when working with a ‘Level 2’ analyst, it is important to give them support through coaching and Quality Assurance (QA). As Analysts progress from ‘Level 2’ to ‘Level 3’, they are bound to make mistakes (as a wise man once said, “only those who dare to fail greatly can ever achieve greatly”).

Why They’re Critical. ‘Level 2’ Analysts have a “multiplicative impact” — once a set of code is built, these analysts can implement the same code on other projects. This makes the path to executing an end-to-end analysis much quicker. And when junior analysts can generate 80% of the outputs for an analysis, this allows senior analysts to do custom, technical and QA work across multiple projects.

Where to Master these Skills

• Through Coursera, John Hopkins offers a Data Science Specialization that provides a thorough introduction to the skills needed to be a Data Scientist
• edX and Microsoft also offer Data Science courses for Python and R, allowing analysts to better wield their weapon of choice

These courses provide robust instruction on Data Science principles without diving too deeply into technically advanced subjects. These are ideal courses for someone transitioning into Data Science.

Level 3: Builders

Who They Are. These are your technical gurus. They can start with a blank screen, and build you an end-to-end analysis or analytical product. This level of expertise can take years to achieve; therefore, these analysts command relatively higher salaries. Note that all ‘Level 3’ talent isn’t created equally. For instance, two key differentiators among ‘Level 3’ Analysts are conceptual competency and technical specialty.

Conceptual Competency is a requirement for any great analyst. It is essentially a high-level understanding of analytical tasks at hand. This skill manifests in a variety of ways:

• Understanding of the business’ current state and implications of any strategic/operational change
• Considering multiple approaches to an analysis, and pursuing the most simple (but robust) solution
• Writing dynamic and flexible code that doesn’t require future manual adjustments
• Building simple but scalable code

Technical Specialty is the toolkit that analysts possess. It is generally better to hire someone who has some previous business experience with a particular skill set, rather than waiting for them to build that skill set on the job. These technical specialties include:

• Data Extraction & Processing: Python, Rugby, Cloud IAAS, API Development
• Data Manipulation: SQL, Hadoop, Python
• Data Analysis: SAS, R, Python
• Data Visualization: MS Excel, BI tools (e.g. Tableau, Chartio), R shiny, D3.js

Note that these “specialty areas” range in complexity and ‘Level 3’ analysts may fall anywhere within each range. Also, note that data extraction isn’t necessarily a skill set that analysts will have (this is often managed by a data developer or BI engineer).

Why They’re Critical. These are your ‘analytical innovators’. They have the ability to add novel features to a product or solution. Or, even better, they can build new solution! These analysts also tend to be keepers of companies’ best practices for analytics. That is, they establish the parameters and statistical processes to be used across all analytical teams. Because of this level of responsibility, these analysts should have at least a Masters in a quantitative field (however most consultancies also have PhDs on their analytical teams).

Where to Master these Skills

• Coursera has recently teamed with the University of Illinois at Urbana-Champaign to offer a Masters in Data Science, which can be completed entirely online at a fraction of the cost of other Masters programs (<$20K for the program)!
• Berkeley offers a comparable Master of Information and Data Science, which is available online but comes at a premium ($60K for the program)
• For more engineering-focused Data Scientists not pursuing a Masters, edX and Berkeley provide a Series on using Spark for Data Science, which contains 5 classes on different aspects of Big Data analytics

Note: while I’ve only included online courses, there are many traditional Masters programs and independent educational companies that provide instruction on more advanced Data Science topics.

For more information on analytical skills and when to learn them, see my post 4 Statistical Processes that Every Analyst Should Know.