July 14, 2020

To Embark on a Career in Data Analytics and Data Science, Data Analyst Sean Sullivan Points to Self-Kindness as Key in Building an Accomplished Portfolio of Work


How to build a data analytics/science portfolio that increases your hiring chances was the topic of a recent talk hosted by data-upskill school Promotable. Sean Sullivan, a data analyst at media agency Spark Foundry, offered solid steps, from finding a data set that genuinely interests you to minding and communicating your process throughout the data-portfolio-creation effort. The high-order bit I found the most important from Sean’s presentation was this grounded prompt:
“Be kind to yourself.”
Sounds like Sean was channeling Brené Brown and Arianna Huffington, the champions of well-being. With an economy constricted and a job market deflated, Sean’s self-care directive was meaningfully apropos in these tensely dramatic times. Making a portfolio of work, focused on data analytics/science in this case, is an accrued testimony of lessons and accomplishments. Done to continuously gain knowledge and launch a professional path—requiring precious variables: time, energy, speed and money. Progress is an all-consuming goal—worthwhile for having the career most desired. Professional portfolio-building, like any thirsty pursuit, is inherent with challenges, disappointments and positively formative moments. As Sean concisely prescribed, best to be kind to the determined protagonist in your ambitious story: you.

Thanks again to Promotable who further nurture their virtual workshops with expert perspectives through their generation of regular events online! Explore their YouTube channel and Events at LinkedIn.


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June 30, 2020

Sean MacCarthy of Mega-Retailer Claire’s Upholds Curiosity as an Admirable Variable when Working with Data


Fashion is taste-making. Its industry depends on identifying and seizing the pulse of personal, aesthetic expression. Data is at the core of this cultural enterprise. The unbridled coverage and tracking of what styles escalate to peak interest (and purchase) relies on data analytics/science. Enthusiasm for this kind of data-driven work was palpable throughout the talk given by Sean MacCarthy, a strategy and insights executive at retailer Claire’s, in a lecture hosted by data-skills school Promotable. His nerdy proficiency of data amplified in the fashion industry—particularly channeled and harnessed by AI (artificial intelligence) and ML (machine learning)—was apparent.

Claire’s business model is a contemporary template for every company taking advantage of the operational benefits afforded in data—collecting, analyzing and managing it. Data remains the super staple food for a brand to excel.

When asked about how he hires for his data-inquisitive team, Sean scouts for these characteristics:
“Really curious. Really hungry. And self-starting attitude.”
It’s no surprise that the first emphasis was on curiosity, because it’s not only one of the most PR’d qualifications, it’s also perishable. The next work-trait of “hungry” turns curiosity into diligence for seeing ideation and problem-solving through. Then “self-starting” is the built-in drive to put things into motion and achieve productivity (another championed job requirement).

Such pristine attributes rank high in Sean’s professional criteria, a greatly essential list, in attracting the best minds over matter—the digital chemical of data in this case. The beauty of such a hiring menu is that it’s not only beholden to job-screening data analytics analysts and data scientists. Curiosity. Drive. Motivation. These are durable indicators in seeking ideal members to join a work culture—of the positively geeky persuasion.

Thanks again to Promotable who expand on their virtual workshops with expert perspectives through their planning of regular events online! Explore their channel on YouTube.


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Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!

June 23, 2020

Minding and Mining the Truth: Strategic Analytics Analyst Kate Lee Distinguishes between Data Analytics and Data Science


Kate Lee is a Strategic Analytics Analyst at big-data company IRI which specializes in CPG (consumer packaged goods). At a recent event hosted by data-upskill school, Promotable, she discussed the differences between the roles of data analytics analyst and data scientist. From her perspective, the former is essentially focused on reporting and insights-generation compared to the latter whose foremost concentration is parsing causal relationships to inform predictability. The scale and scope of the data sets collected and examined also varies between the two disciplines. Kate’s elaboration of the distinct differences between these two fields provides a great primer for anyone, data-literate or not, who is curious about them as potential career paths.

Until Kate’s talk, I perceived data analytics and data science as synonymous. Not only was this assumption of mine corrected, she clarified their respective purposes which are jointly vital in helping people and organizations navigate this era of the brutally “new normal.” Though the objectives and focal points are different between data analytics and data science, this statement from Kate’s opener rung true:
“We tell the truth.”
This is a claim, a call-to-action and an oath wrapped up in a bite-size proclamation. Whatever is revealed by data is also supported by it—whether the revelation is fancied or not. Especially now, truth and outcomes matter a lot. More than ever, how data is utilized helps make the decision-making process much less shallow.

Although practitioners of data analytics and data science may differ in their roles, they share the same mission: to inform choices.

Thanks again to Promotable who further contextualize their virtual workshops with relevant perspectives through their planning of regular talks online! Explore their channel on YouTube.


Support Design Feast on Patreon!
Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!

June 4, 2020

Trust and Triangulation: Abbott’s Jayant Rajpurohit on Data Analytics during the Coronavirus Pandemic


In these times inflicted by COVID–19, data is a necessity. How people and resources are organized and mobilized depend on it. In a recent webinar organized by data-skills school Promotable, the role of data analytics in this unfamiliar climate was addressed by Jayant Rajpurohit, a Global Lead for Market Research and Strategic Analytics in the Transfusion Medicine division at healthcare and medical devices company Abbott. Two data-centric factors that resonated the most with me from his presentation were:

Data Trust → Rigorous governance of data cultivates trust. As Jayant posed, “Can these data metrics be trusted into the future?” Only trustworthy sources lend themselves to be trusted—over time.

Data Triangulation → Instead of just, as Jayant put it, “spitting out data,” make sure it’s reliable—not rote. Continually cross-validate the data to ensure it’s correct and achieves consistency.

Data analytics stirs discussion and vice versa. The productivity of data-driven interactions counts on trust and triangulation. They provide quality data to inform quality decision-making. Helps to foster certainty when uncertainty spreads.

Thanks again to Promotable who augment their virtual workshops with relevant perspectives through their planning of regular talks online! Explore their channel on YouTube.


Support Design Feast on Patreon!
Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!

May 25, 2020

How Data Scientist Tomeka Hill-Thomas Achieved Integration at Ernst & Young


One of the business goals I’ve heard on repeat is “integration”—its repetition in the corporate and consulting worlds reaches the magnitude of myth. This is why it was refreshing to learn about an actual case of successful integration, as it pertains to data, shared by Tomeka Hill-Thomas, a People Analytics Expert and Senior Data Scientist at management firm Ernst & Young, in the latest webinar hosted by data-skills school Promotable. Tomeka initiated the huge task of building a desperately needed employee database—modernizing it and, most of all, integrating it with more relevant types of employee-related content. This bringing-it-together effort encompassed these dynamics:

Inheritance to Improvement
The starting employee data set was your basic garden-variety, consisting of standard facts: birthday, gender, cultural heritage and so on. Fundamental but lacked density. It was expanded into a more muscular body of data in sync with the employee’s business domain, job performance and more.

Separate to Singular
The data inherited was fragmented—documented in mixed ways and housed across different sources. It was centralized for common findability and access.

Minor to Major
The initial employee data set was underwhelming—adequate for satisfying rote initiatives, for example, noting work anniversaries. It was advanced to enable better applications, far more strategic ones—like employee retention.

Kudos to Tomeka for sparking and leading the charge of making a big project happen—one that benefits in dividends. The integrated database established by her and her team* began as a short-term boost but ultimately plays the long game. Proverbial advantages have been realized and are advancing—such as time savings and efficiency gains, along with data accuracy, on-demand reporting, in-depth analytics and more. All of these benefit Ernst & Young’s workforce. They also qualify a business template of optimizing other, if not all, areas of the organization, company-wide.

Superficial as it sounds, this long-standing wish intensifies as a modern directive: integrate or… evaporate.

Thanks again to Promotable who align their virtual workshops with relevant perspectives through their organizing of regular talks online! Explore their channel on YouTube.


* During the Q&A session after her presentation, considering the growing quantity and quality of data collected and visualized, I asked Tomika if UI/UX designers were on her team. She confirmed their involvement. Great to know that they are integral to the project’s marathon-success. 👍


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Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!

May 18, 2020

Strategist Jefferson McMillan-Wilhoit Seeks to Craft Amazing Stories Driven and Backed by Data


“Pietà” (1498–1499) by Michelangelo (1475–1564). Photo by Art Gallery ErgsArt.

Jefferson McMillan-Wilhoit is the Director of Health Informatics and Technology at the Lake County Health Department and Community Health Center. He recently spoke of storytelling’s critical role in data analytics/science as part of modern data-skills school Promotable’s series of events. He stated—and restated—the importance of this step:
“Take the data and have it tell its story.”
A prime directive. No magic formula. Obstacles are always in play against good data storytelling. Jefferson urged minding the ingrained bias and the quality of the data itself. The former is significant—if unchecked, data analytics gets skewed toward cognitive predisposition, notably confirmation bias (among a great many others). The latter reinforces what previous Promotable presenters have also stated—that the quality of the data is in direct correlation to the quality of its analysis.

A point by Jefferson that stood out most to me was how much he enjoys, as he put it, “Amazing Stories.” He shared his fandom for good storytelling in literature and movies. As it applies to data analytics/science, Jefferson referenced the primary building blocks possessed by a good story: the opening scene, episodes of crises and the convergence toward denouement, all happening along a timeline. Intellectual nourishment is found in stories. Jefferson encouraged making the thorough and transparent effort in achieving this outcome as it applies to the utilization of data. In essence, storytelling of data to promote data-driven understanding to then contribute to evidential decision-making.

Storytelling also brings a sense of wonder, even awe. Jefferson’s repeated ask of “Is this telling a good data story?” recalls one amazing account of creativity—a true story. Michelangelo di Lodovico Buonarroti Simoni (1475–1564) created masterpieces of art. From amorphous stone, he shaped compelling sculpture. His motto: “Beauty is the purgation of superfluities.” Through the lens of data analytics/science, “superfluities” could refer to analytical bias, dirty data or other nonessentials. Like a data analyst/scientist telling the story of a specific set of data, Michelangelo was telling the story of another kind of raw material: stone.

Great data. Great analysis. No superfluities. In key ways, Jefferson, a classically trained data analyst, is channeling the clarity also sought by Michelangelo. Whereas the Renaissance artist used marble, Jefferson and his team use data—using it because it makes the best job of the truth. Amazing.

Thanks again to Promotable who connect their virtual workshops to relevant perspectives through their organizing of regular talks online! Explore their channel on YouTube.


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Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!

April 23, 2020

Slalom Consulting’s Erinn Mitchell on Having Data Not Getting Lost in Translation


In the Harvard Business Review article “You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role,” the authors highlighted the emerging discipline of “Analytics Tanslator” as crucial in the increasingly converging worlds of data and business:
“At the outset of an analytics initiative, translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. These may be opportunities within a single line of business (e.g., improving product quality in manufacturing) or cross-organizational initiatives (e.g., reducing product delivery time).”
This is a precursor-job description for the current responsibility of “Data Translator” advocated by Erinn Mitchell, a Data & Analytics Consultant at professional services firm Slalom. This role calls for a specialist who is strategically (and happily) nestled between the business side—regarding goals and strategy, and the data side—regarding information that is collected and measured. The Data Translator’s instincts and skills are focused on turning complex, large data sets into actionable steps.

Adjacent to the rising importance of industry expertise, data visualization, storytelling, et al., the factor that intrigued me the most from Erinn’s presentation was putting a spotlight on a sought-after virtue: trust. Translating data into useful (potentially insightful) information for a business-schooled-and-minded audience is ultimately a workflow of trust.

Considering the absolute integrity and security of data in this systems-intense era (when reliability is both lossy and fragile), trust is the absolute requirement. The description given by Erinn for the Data Translator, whose purpose is vigilantly working across the areas of analytics and business, was apt: a relationship. To make it work, trust must be the basis (absolutely).

Thanks again to Promotable who supplement their virtual workshops with relevant perspectives through their organizing of regular talks online! Explore their channel on YouTube.


Support Design Feast on Patreon!
Your visiting means a lot. Lots of hours are put into making Design Feast—because it’s a labor of love to provide creative culture to everyone. If you are able to contribute, please consider becoming a Patron to support this long-term passion project of mine with a recurring monthly donation—every bit of support makes a difference in allowing me to generate all of this content on a regular basis. Thank you for your consideration!