When organizations first acquire a data scientist or an analytics team, they may be unsure how to properly maximize their new-found talent. Analytics teams cannot create business value in isolation. This requires engagement from stakeholders across verticals and pay-grades. Below are the “prototypes” of stakeholders needed to make the most of a company’s analytics team.
A business leader who champions analyses from the data team and shields them from inconsequential work. Ideally, this is someone in a strategic position that is well-regarded within the organization. Migrating from a managerial-driven to a data-driven strategy can be difficult in companies with traditional…
Data science roles in tech businesses fall into three categories:
These approaches can be very different in practice, requiring different tools, expertise, and operational processes. Nonetheless, businesses are only starting to differentiate these forms of data science. By properly differentiating these roles, businesses can more effectively hire, cultivate, and retain data science talent.
In this article, we will…
Interviewing for analysts and data scientists can be challenging when you do not have a technical background; however, there are simple questions (to ask, not necessarily answer) that will vet a potential hire’s analytical acumen. These questions are designed to gauge how analysts conceptualize data and analytics, rather than test technical skills.
Note: some questions below have been tailored for a data analyst and data scientist role. The specifics of these questions can differ — what is most important is that the question captures what we want to assess.
Data scientists and analysts love to discuss technical details of their work. Unfortunately, those details may be lost by the stakeholders who ultimately make business decisions; or, analysts may miss the “big picture” of a project because they focus excessively on the minutia.
By taking a “street smart” approach to analytics, rather than a strictly academic perspective, data scientists can maximize the business impact of their work.
In a way, “street smart analytics” is basically applying the Zen of Python principles to how we communicate insights. In fact, the quotes below are sourced from these principles. …
The depth and variety of skills that fit under the analytics umbrella are extensive. Different roles — such as strategic analysts, digital analysts, data scientists, data engineers — require distinct skillsets and varying levels of technical expertise. However, a handful of statistical processes are so common that every analyst should be acquainted with them. Further, it’s beneficial to know how to code these in at least one programming language (or if not, in Excel).
Below, are 4 of the most common and versatile statistical methods used in business, along with examples and educational sources.
Many graduates have curiosity in data science. However, aside from tech companies, they may be uncertain where such opportunities lie. Fortunately, the number and variety of analytical roles are vast and ever-expanding. Below is an overview of companies and industries that have roles in data science.
Corporations. These are household name companies — AT&T, Capital One, P&G, Nike. These companies generally have analytics teams within the marketing, business strategy, operations, and finance business units. Data and analytical processes vary depending on the industry. …
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.
After a certain point, startups must build a Business Intelligence (BI) infrastructure. However, without expertise in BI, it’s difficult to know when or to what extent an organization should invest in analytics. Drafting a structured roadmap during this “analytics onboarding” process will allow business to scale their BI or Analytics team in a positive ROI manner. Below is a roadmap for bringing analytics into startups or growing SMBs.
Data is, of course, the key ingredient in analytics. However, structuring data is the most difficult and time-consuming part of any analytsis. It’s even more difficult at the enterprise level — we…
Marketers increasingly want to provide personalized messaging to customers. However, personalizing CRM at scale can be challenging. A key ingredient to successful personalization is to ensure the strategic goal of the campaign aligns with the targeting method used.
By layering the following forms of targeting, marketers can deliver relevant and tailored content at scale. Below are 3 forms of targeting that comprise a robust “targeting framework” (note that this framework was created for email marketing, but can be leveraged across any form of Below the Line or direct marketing).
This targeting leverages traditional CRM…
Making data matter.