Data Leader Kerstin Frailey Emphasizes the Need for Quality Data

Kerstin Frailey, who leads data science at market research company Numerator, recently participated in data analytics school Promotable’s webinar series. While presenting concepts such as Machine Learning and the overly propagandized “Big Data,” there was this sign-of-the-times statement from her:

“It’s hard not to care about data quality when you see what happens with data. Because data underlies every algorithm that is automatically approving or denying you a mortgage, that is automatically dismissing or accepting your application to go on to a recruiter to see. It underlies all of the automated admissions that next or current generations are having … That is all built on data. As soon as that data starts to get a little sticky, oh, the world we create in there.”

Data and the modern era do stir wonder. One constant is that data keeps accruing—becoming its own multiverse where the possibilities of use are grand and endless. In the startup ecosystem, “data-driven” is a popular prefix to distinctly qualify a product or service. When elegantly executed, it demonstrates how business, design and technology can be systematized. The emphasis by Kerstin on data’s “underlying” nature feeds into visualizing data as a shifting, sprawling tectonic layer (which, no doubt, it is) influencing everyone and everything. In its composition and expanse, data (for all its content, support and magical potential) is infrastructure.

The last line of Kerstin’s proclamation includes this poetic phrase: “the world we create.” In context, it sparkles with analytics aspiration, coupled with prospective capabilities—for the better. The wellspring here is data—running through several, practical, important applications she noted: mortgages, hiring submissions, school admissions, among a great many processes. The data-propelled world, shaped humanely, co-exists with a world energized by data that’s steered toward inflicting alternative effects—when viewed through a literary lens, they can be characterized precisely as Kafkaesque, even Orwellian.

Though not surprising, it is refreshing to hear Kerstin speak about the importance of critical thinking. Working with data makes it a must-do (as opposed to a no-brainer) for Data Quality to undergo rigor in how it’s managed. From Kerstin, this body of scientific disciplines consists of these principles:

1. Accuracy
2. Timeliness
3. Validity
4. Consistency
5. Completeness

If quality of data suggests the quality of decision-making, then critical thinking is essential. More so, when data faces duality, exacerbated by cross-generational disparity uncovered by these pandemic times, which exposed data-driven systems not behaving as data-driven solutions. From breakages in delivering public education, to filing unemployment claims, to receiving healthcare, to booking a vaccination appointment, and so on.

Kristen's focus on Data Quality hones in on making reality an honest one—these days, a collective movement reinforced. With the beauty of objectivity in mind, here’s to the people having at it to create a world—where data helps bring out the best in everyone.

Thanks again to Promotable who pair their virtual workshops with talks organized regularly online! Explore their channel on YouTube.

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LinkedIn’s Insights Manager Hallie Moldawer Reinforces Enablement When it Pertains to Launching Products and Services

In her recent talk with data-skills school Promotable, Hallie Moldawer, Senior Insights Manager at LinkedIn, stated: “A big piece of it is enablement.” She referred to the practical reception of data-driven products and services. The awareness, use and ultimate success of these creations relies on enablement.

“Enable” is one of those go-to business proclamations. However regularly it comes up in project stakeholder gatherings and the media, “enable” is essentially–in the product-and-service-building sense—about helping people accomplish a task, a goal, an effort. Nudging them steadily toward gaining moments of productivity. 

For example, the software industry is ripe with enablers, from tool tips to notifications to voice-commands to touchless transactions and more. All of which are debatable in their benefits and side effects (such as “notification addiction”). Yet, the drive “to enable” the user stays in line with the aggressive and aspirational plans of business, design and tech.

Paired with her acknowledging the benefits of enablement, Hallie grounded this persistent talking (and implementing) point among managers, strategists, designers, engineers, et al., with a plainspoken technique—bluntly put as: “So what?” Early on in her career as a data analyst when she was putting together and presenting data-driven models, her audience, particularly director-level types, reacted with responses such as: “So how is this relevant?” or “Why does this matter?” In essence: “So what?” A direct question, straightforward and succinct—so Hemingway. 

At the same time, prudent to have a so-what attitude where “so” is the critical-thinking qualifier: so how does this analysis inform a business decision; so how does this initiative enhance company culture; so how does this research improve a product’s usability and adoption … So what? It’s a tried-and-true prompt to take thoughts to the next level.

When it comes to problem-solving, when data is involved, when expectations matter in making business, when design and tech are steered continually to work together favorably, Hallie's push of rigorous enablement is not subject to complacency—so not the time!

Thanks again to Promotable who amplify their virtual workshops with talks organized regularly online! Explore their channel on YouTube.

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Marketing Expert Anita Alkhimovich Goes with The Flow of Data

For a recent talk hosted by workforce accelerator Promotable, Anita Alkhimovich, a Senior Marketing Manager at software company LeaseAccelerator, unpacked the basics of data-driven marketing. From her point of view, broadly attracting customers is mere marketing. But attracting the most appropriate customers is done by minding data specifically related to consumers and customers, in this case, how they spend their attention to marketing messages among other pertinent areas. One of the straightforward assertions which Anita expressed was this insight:

“Nobody has time to read a lot of watery stuff about your product … nobody likes a sales, cheesy approach … everybody likes when it hits straight to the goal … you give water to a thirsty person.”

I appreciated the analogy between marketing and water. Both are fluid mediums: the former in its discipline, the latter in its nature. Data shares the quality of fluidity. Water is one of the best comparisons to the concept and reality of data. But Anita’s distinction, though applied to marketing, can be applied readily to the universe of data—and it makes a difference. “Watery” equates to shallow decision-making, where work lacks, even dismisses, the value of data. “Water” equates to the opposite process which is informed as much as possible—with data as a major input among a cast of other supplements.

Living through a pandemic escalates the necessity of data to better energize, organize and mobilize—as opposed to politicize—systems to help people. Flowing throughout these efforts is the evidential power of data collection, analysis and science. When it comes to the continual thirst for improved outcomes in business and across society, data is water—because it holds up.

Thanks again to Promotable who amplify their virtual workshops with talks organized regularly 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!

Kenny Ly of Accenture Mines Business Processes to Extract Valuable Information Regarding Performance and Speed

It has become a pattern to attach the word “mining” to business-driven nouns to amplify their meaning, such as data mining and intention mining. At a recent webinar hosted by workforce accelerator Promotable, Kenny Ly, a Senior Manager of Data Analytics at consulting firm Accenture, offered such a linguistic combo: process mining. This means analyzing the data resulting from a sequence of activities executed internally (stakeholder) and externally (consumer, customer) to accomplish a goal. Following are a couple of areas Kenny addressed which got my attention:

Qualitative Data → Kenny anchored the qualitative (or “anecdotal” as he pegged it) with the quantitative. This enables corroboration between two types of data amassed in carrying out a process. The quantitative complements the qualitative. But they feed each other. Besides being one of the world’s best, natural resources, words constitute basic data. They’re valuable—demonstrated constantly by self-expression, characterized by diction, sentiment, tone and viewpoint. These dimensions apply to sentiment analysis—otherwise called opinion mining.

Shortcuts → When Kenny stated, “Not all conformance is bad,” I smiled (cautiously). A process is elastic. It can be improved to gain efficiency. Or a well-intentioned improvement can unintentionally, as the high-fantasy writer J. R. R. Tolkien put it, “make long delays.” The fidelity of calibrating complexity is a tricky exercise—nonetheless, worthwhile, concerning quality and ultimately: safety. Jason Fried, who co-founded web-based project management software Basecamp, gave this mindful directive that connects with the time and energy swallowed by processes: “Beware [of] many shortcuts in a row.”

From deconstructing the qualitative to devising shortcuts as they relate to data analytics and science, mine fully—decide wisely.

Thanks again to Promotable who fuse their virtual workshops with talks organized regularly 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!

Coping with COVID–19: Econometrician Jerrod Begora Channels the Timely Forces of Communication and Creativity

Jerrod Begora, Director of Analytics at marketing solutions provider Quad, recently spoke about the impact of COVID–19 on economic activities at a virtual talk hosted by workforce accelerator Promotable. He gave a lean tour of sheer disturbance (and disruption) across varied industries, from food processing to retail to hospitality, affected bottom-up, top-down by the pandemic. From his narration of consumer behavior and its potential staging of opportunities for data analytics/science in these unsettling times, I kept channeling (gerund surely intended) a major source of ingenuity. Rather than referring to the world of econometrics which Jerrod geeks on, I contemplated on the world of dance—specifically the pioneering artist Martha Graham (1894–1991), whose prime directive was:

“Keep the channel open.”

Underscore “channel.” In relation to Jerrod’s presentation, the meaning of this word and concept is twofold:

Channel—as in communication channel. Jerrod framed communication within marketing. Content strategy is also a part of this in addition to user experience (UX). To marketers, Jerrod strongly encouraged an omnichannel-data outlook regarding which communication channels are capitalized on by people sheltering in place, along with learning and working remotely. Beyond marketing, communication is a staple of business infrastructure. Pre-pandemic, it may have been regarded as a no-brainer. Post-pandemic, communication is an indispensable capability. To communicate via a variety of methods (omnichannel) will never be observed as a passive priority. One example: communication is at the core of Microsoft’s Workplace Analytics experiment of studying their teams’ adaptation to producing and collaborating from a distance. From its summary: “Human connection matters a lot, and people find a way to get it.” Communication can obviously be channeled in a variety of technological ways—to not be taken for granted.

Channel—as in creative channel. When asked what he would revisit and adjust early on in his career as a Data Analytics Analyst, Jerrod revealed that he “pulled back too much” in sharing ideas. In the search for ideas, Jerrod essentially nudged people to express, not repress. Ideation is a continuum energized by creativity—to be kept channeled.

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

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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!

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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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!

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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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!

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.

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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!

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.

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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!

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.

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* 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!

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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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!

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.

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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!

Business Intelligence Manager Sam Koperski of Kin Insurance on How to Build and Democratize a Data Org

Kin Insurance is a Chicago-based startup that matches customers in disaster-prone regions with optimal home insurance policies. The staple ingredient for this mission is data—massive amounts of it. Their Senior Manager of Business Intelligence, Sam Koperski, was tasked with building a “data org.” He shared steps to make this happen, including:

Communicate objectives to leadership about the data-org effort from the get-go. If an executive member sparked the initiative of building a data org, this doesn’t necessarily mean that all members of leadership are aware of it (nor aligned).

Canvas the current data infrastructure. Scout for what data continues to be collected and its sourcing within the business.

Chip away at the inherited punch list of data-related issues. Extinguishing existing data-fires can also become quick wins to further validate the necessity of building a data org.

Treat the process of building a data org as an ensemble cast. No data silos allowed!

The part that intrigued me most was Kin Insurance’s deeper goal of making a data-driven work culture. From Carl Anderson, Director of Science at Warby Parker, in his book “Creating a Data-Driven Organization”:

“Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.”

Exciting to hear Sam’s step-by-step account of co-leading the charge to elevate Kin Insurance from a data-driven business to one that behaves in lots of data-driven ways—from the inside out.

Thanks again to Promotable who regularly tie in their web-based workshops with informative perspectives through their organizing of regular talks online! Explore their channel on YouTube.

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Support Design Feast on Patreon!
Your readership 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 with a recurring monthly donation—starting at $1. 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! And keep healthy!