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A look inside Coca-Cola’s digital transformation, led by digital skills training

In its first year, the company’s digital academy trained more than 500 people in digital skills.

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Digital skills and talent are in high demand these days — bolstered by McKinsey research that found one in every 16 workers will need to transition to new roles by 2030.

In July 2021, Coca-Cola set up a digital academy to upskill managers and frontline team leaders across operations. Using go-and-see visits, boot camps, and e-learning modules, more than 500 people were given digital skills training in the first year. Now, training is being rolled out to about 4,000 employees company-wide.

In the latest episode of the McKinsey Talks Operations podcast, Coca-Cola’s Iain McLaughlin (VP of commercial product supply) and Gigy Philip (transformation director) joined Daphne Luchtenberg, director of reach and engagement in McKinsey’s London office, and Roberto Migliorini, a partner also based in McKinsey’s London office, to discuss the company’s upskilling process as part of their digital transformation strategy.

Here are three highlights from the episode.

On addressing supply chain challenges through digital adoption

“One of the things that has been most helpful to us is that early in 2021, we built a digital twin of our manufacturing network to support business continuity planning, as well as network optimization. Previously, we had relied on experience, we had relied on know-how within the organization in order to do business continuity planning. But by digitizing it, we actually brought all of the data together into a single model, which has helped us enormously in reacting to situations during the pandemic.” – Iain McLaughlin

On avoiding ‘pilot purgatory’

“We did a lot of research and prework to understand the potential impact of Industry 4.0, and we did that as part of our overall planning for the transformation. So we were aware of the pitfalls of pilot purgatory. But pilots are still important, right? You have to understand if there’s value, where the value is, and how you can capture that value. Pilots have an important role to play, and we did one. There was a significant pilot at our largest facility in Ireland. But having done that, and having seen the fact that there was tremendous value to be captured, what we then did was take time out to plan the strategy. So we always started from a viewpoint that we were taking this across the network. Therefore, by the time we started executing the strategy, we already knew we were going to multiple sites. And that has really helped us to move at speed and scale. So, pilots are important, I’m not dismissing them. But pilots for the sake of pilots are not going to get you the transformation that you might be looking for.” – Iain McLaughlin

On their digital academy

Our digital academy is designed really to build foundational knowledge and skills on core digital, analytics, and agile topics so that our associates can be successful operating in a digital world. All of our 3,000-odd employees across CPS [commercial product supply] will be participating in the academy. In terms of the design mechanics, we grouped all the roles within the organization into six cohorts based on learning needs and used a very structured approach to develop customized learning journeys for each of the cohorts. Across the six learning journeys, we developed 25 unique modules, covering three major areas. The first area is around creating awareness and excitement. The second is around transformation skills, and the third is around digital and analytics skills. – Daphne Luchtenberg

To read an edited version of the conversation, or to listen to the full podcast episode, head to McKinsey’s website.

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The county receiving the most Small Business Administration loans in each state

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Flippa identified the county in each state where applicants were approved for the most Small Business Administration loan funds per capita in 2023.
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The Small Business Administration backed loans worth $27.5 billion through its primary lending program in 2023—rising well above pre-COVID-19 pandemic levels as government officials aim to stabilize the economy.

Many small businesses get their start and scale up with SBA loans, which increased lending to Black, Latino, and women entrepreneurs in the past few years in step with efforts to become more equitable.

Flippa found the county within each state where applicants were approved for the most SBA loan funds per capita in fiscal year 2023, which ended in September. The analysis was based on the SBA’s most common loan program, known as 7(a) loans. States are listed in alphabetical order.

SBA’s 7(a) program provides extra security to lenders when they loan money to small businesses that might otherwise be considered too risky to grant. Loans can be for up to $5 million, but in 2023, nearly 7 in 10 loans were for amounts of $350,000 or less. Small businesses can use these funds for real estate acquisitions or improvements, working capital, supplies and equipment, and for other business startup or acquisition purposes.

Barriers do still exist for eligibility, including income, credit history, and location, but SBA loans can be fruitful for founders who don’t qualify for conventional business financing. They can also provide protection against high and volatile interest rates, as SBA-backed loans have maximum interest rates that are predictable and often lower than other loans.

All but two of the #1 ranked counties had populations of less than 500,000—most smaller than 100,000. That’s not surprising, as the Census Bureau classifies about 99% of U.S. counties as small. Still, it signifies that these smaller communities are building successful entrepreneurial environments. In most cases, their small businesses are able to succeed beyond those within the major U.S. population centers—at least in terms of success in gaining SBA funding.

Read on to see whether your county was among those receiving the most SBA loans.


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Alabama: Cleburne County

– SBA loan funds approved: $5.6 million (About $375 per resident)
– Number of loans: 5

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Alaska: Sitka Borough

– SBA loan funds approved: $6.1 million (About $716 per resident)
– Number of loans: 4

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Arizona: La Paz County

– SBA loan funds approved: $3.1 million (About $185 per resident)
– Number of loans: 1

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Arkansas: Lawrence County

– SBA loan funds approved: $8.5 million (About $524 per resident)
– Number of loans: 3

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California: Madera County

– SBA loan funds approved: $29.0 million (About $186 per resident)
– Number of loans: 16

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Colorado: Summit County

– SBA loan funds approved: $20.6 million (About $662 per resident)
– Number of loans: 23

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Connecticut: Hartford County

– SBA loan funds approved: $95.6 million (About $106 per resident)
– Number of loans: 212

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Delaware: New Castle County

– SBA loan funds approved: $49.8 million (About $88 per resident)
– Number of loans: 121

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Florida: Gilchrist County

– SBA loan funds approved: $5.6 million (About $317 per resident)
– Number of loans: 2

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Georgia: McIntosh County

– SBA loan funds approved: $10.0 million (About $888 per resident)
– Number of loans: 3

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Hawaii: Kauai County

– SBA loan funds approved: $4.1 million (About $56 per resident)
– Number of loans: 8

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Idaho: Shoshone County

– SBA loan funds approved: $4.8 million (About $365 per resident)
– Number of loans: 4

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Illinois: Logan County

– SBA loan funds approved: $8.2 million (About $291 per resident)
– Number of loans: 2

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Indiana: Bartholomew County

– SBA loan funds approved: $16.4 million (About $201 per resident)
– Number of loans: 10

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Iowa: Chickasaw County

– SBA loan funds approved: $2.5 million (About $207 per resident)
– Number of loans: 6

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Kansas: Gove County

– SBA loan funds approved: $2.0 million (About $721 per resident)
– Number of loans: 1

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Kentucky: Owen County

– SBA loan funds approved: $5.1 million (About $456 per resident)
– Number of loans: 2

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Louisiana: Claiborne Parish

– SBA loan funds approved: $6.0 million (About $412 per resident)
– Number of loans: 5

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Maine: Knox County

– SBA loan funds approved: $5.3 million (About $132 per resident)
– Number of loans: 19

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Maryland: Allegany County

– SBA loan funds approved: $6.5 million (About $95 per resident)
– Number of loans: 9

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Massachusetts: Nantucket County

– SBA loan funds approved: $3.3 million (About $240 per resident)
– Number of loans: 8

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Michigan: Keweenaw County

– SBA loan funds approved: $4.3 million (About $2,101 per resident)
– Number of loans: 5

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Minnesota: Marshall County

– SBA loan funds approved: $5.1 million (About $559 per resident)
– Number of loans: 4

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Mississippi: Smith County

– SBA loan funds approved: $7.3 million (About $506 per resident)
– Number of loans: 14

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Missouri: Pettis County

– SBA loan funds approved: $17.4 million (About $406 per resident)
– Number of loans: 9

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Montana: Sweet Grass County

– SBA loan funds approved: $4.8 million (About $1,312 per resident)
– Number of loans: 1

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Nebraska: Nuckolls County

– SBA loan funds approved: $2.2 million (About $521 per resident)
– Number of loans: 1

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Nevada: Carson City

– SBA loan funds approved: $13.3 million (About $229 per resident)
– Number of loans: 15

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New Hampshire: Rockingham County

– SBA loan funds approved: $35.3 million (About $113 per resident)
– Number of loans: 117

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New Jersey: Cape May County

– SBA loan funds approved: $26.7 million (About $280 per resident)
– Number of loans: 27

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New Mexico: Torrance County

– SBA loan funds approved: $4.2 million (About $280 per resident)
– Number of loans: 1

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New York: Essex County

– SBA loan funds approved: $11.5 million (About $306 per resident)
– Number of loans: 8

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North Carolina: Dare County

– SBA loan funds approved: $13.3 million (About $362 per resident)
– Number of loans: 8

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North Dakota: Oliver County

– SBA loan funds approved: $384,000 (About $208 per resident)
– Number of loans: 1

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Ohio: Putnam County

– SBA loan funds approved: $7.4 million (About $214 per resident)
– Number of loans: 10

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Oklahoma: Craig County

– SBA loan funds approved: $4.4 million (About $311 per resident)
– Number of loans: 2

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Oregon: Wasco County

– SBA loan funds approved: $6.1 million (About $229 per resident)
– Number of loans: 7

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Pennsylvania: Jefferson County

– SBA loan funds approved: $6.8 million (About $153 per resident)
– Number of loans: 8

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Rhode Island: Kent County

– SBA loan funds approved: $14.9 million (About $88 per resident)
– Number of loans: 39

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South Carolina: Jasper County

– SBA loan funds approved: $5.5 million (About $192 per resident)
– Number of loans: 5

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South Dakota: Deuel County

– SBA loan funds approved: $1.5 million (About $341 per resident)
– Number of loans: 1

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Tennessee: Decatur County

– SBA loan funds approved: $3.0 million (About $262 per resident)
– Number of loans: 2

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Texas: Menard County

– SBA loan funds approved: $1.5 million (About $745 per resident)
– Number of loans: 1

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Utah: Piute County

– SBA loan funds approved: $1.4 million (About $746 per resident)
– Number of loans: 1

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Vermont: Windham County

– SBA loan funds approved: $9.2 million (About $201 per resident)
– Number of loans: 15

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Virginia: Richmond County

– SBA loan funds approved: $6.9 million (About $777 per resident)
– Number of loans: 22

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Washington: Columbia County

– SBA loan funds approved: $1.3 million (About $331 per resident)
– Number of loans: 3

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West Virginia: Marshall County

– SBA loan funds approved: $5.3 million (About $172 per resident)
– Number of loans: 3

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Wisconsin: Vilas County

– SBA loan funds approved: $13.6 million (About $597 per resident)
– Number of loans: 8

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Wyoming: Sheridan County

– SBA loan funds approved: $13.9 million (About $451 per resident)
– Number of loans: 7

Story editing by Ashleigh Graf. Copy editing by Paris Close. Photo selection by Michael Flocker.

This story originally appeared on Flippa and was produced and
distributed in partnership with Stacker Studio.

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How has US wealth evolved since the 1980s?

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How do people allocate their wealth? The Wealth Enhancement Group analyzed data published by the Federal Reserve to answer this question.
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America’s economy has exploded since 1989.

Gross domestic product, which measures all of the goods and services produced in a year, grew from $9.9 trillion to $22.5 trillion from 1989 to 2023 (after accounting for inflation), according to the Bureau of Economic Analysis. This figure represents a massive increase in economic output.

This increased productivity has fed into a similarly significant increase in wealth. The Wealth Enhancement Group used data from the Federal Reserve to look at how the assets held by U.S. households has evolved over time.

Data shows that American households owned a combined $161 trillion in assets in the third quarter of 2023, up from $24 trillion in 1989. That makes for a roughly 570% increase, or 170% after adjusting for inflation.

After accounting for debt, such as mortgages, America’s total household net worth grew to $142 trillion, up from $20 trillion. Although the number is down by about 1% from its peak in the second quarter of 2022, it still reflects a dramatic increase over time.

The most valuable asset class the typical American family holds is real estate. Besides a significant drop during the 2000s subprime mortgage crisis and a brief dip following interest rate hikes in 2022, housing has been a reliable generator of wealth for the middle class.


Line chart showing the rise of household assets in the US between 1989 and 2023, which rose from $24 trillion to $161 trillion.

Wealth Enhancement Group

Household assets have skyrocketed since 1989

For Americans in the bottom half of the wealth distribution, housing made up 51% of their assets. Wealthier households, in contrast, tend to have higher shares of their savings in equities.

Households in the top 0.1% held 60% of their assets in shares of public and private companies in 2023. Meanwhile, households in the bottom half of wealth in the United States held only around 6% of assets in equities.

Yet, despite how much housing has grown in value, its ascent pales compared to the fastest-growing asset class: public equities.

Between 1989 and 2023, the value of public stocks held by American households grew by nearly 1,700%, rising from $2 trillion in value to $37 trillion. This trend, coupled with the fact that shares in companies are held disproportionately by the rich, has caused the share of American household assets held by the top 0.1% to increase from 8% to 12%.

A stacked bar chart showing the top 0.1% have most of their wealth in equities where housing makes up for 51% of the assets of people in the bottom half of wealth in the United States.

Wealth Enhancement Group

The wealthy tend to own shares in companies

Some economists argue that, in theory, the ratio of a country’s wealth to its economy, as measured by GDP, should be constant over time.

Yet, data from the Bureau of Economic Analysis and the Federal Reserve data shows that the ratio of the net worth of American households and nonprofit organizations to GDP rose from around 3.6 in the 1980s to 5.5 in the third quarter of 2023.

In 2022, YiLi Chien and Ashley Stewart, two researchers at the St. Louis Federal Reserve, offered a few theories to explain how this ratio has increased over time. They suggest that American companies might now have greater market power, allowing them to charge more. The authors also note that since the internet era, many of America’s biggest companies, such as Meta and Google, offer their services to consumers for free—while investors may value their economic contributions, they do not count for much in the GDP numbers.

However, assets are not net worth. The rich are more likely to own their homes outright. In the third quarter of 2023, households from the top 0.1% owned $1.83 trillion worth of real estate while owing just $70 billion in mortgages. In contrast, households in the bottom 50% of wealth owned $4.87 billion of real estate against $3 billion of housing debt.

Story editing by Ashleigh Graf. Copy editing by Kristen Wegrzyn.

This story originally appeared on Wealth Enhancement Group and was produced and
distributed in partnership with Stacker Studio.

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Deepfakes cause 30% of organizations to doubt biometrics, Gartner finds

A look at AI deepfakes, it’s impact on security, and ways to mitigate the risks

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A fake moustache and trenchcoat isn’t a convincing disguise, right? But a digitally altered video that makes your face identical to someone else’s? 

That’s a different story. 

Deepfakes are artificial images or videos that imitate a person’s likeness so convincingly that it can be nearly impossible to recognize they’re fake. Hackers use them to impersonate people’s faces and voices. This can have monumental impacts — even $25 million worth, which is what one undisclosed company lost in a deepfake scam. 

Even with all the money a company spends on voice authentication and facial biometrics, it can all be in vain if a deepfake hacker manages to fool them. 

Gartner explores the impact of deepfakes on organizational policy, and we’ll share some risk management considerations to address the trend. 

30% of organizations can’t rely on facial recognition software and biometrics

Biometrics rely on presentation attack detection (PAD) to assess a person’s identity and liveness. The problem now is that today’s PAD standards don’t protect against injection attacks from AI deepfakes. Once a bulletproof security strategy, biometrics are now inefficient for 30% of companies surveyed by Gartner. 

“These artificially generated images of real people’s faces, known as deepfakes, can be used by malicious actors to undermine biometric authentication or render it inefficient,” 

— Akif Khan, VP Analyst at Gartner 

The solution is a demand for more innovative cybersecurity tech. Gartner advises organizations to update their minimum requirements from cybersecurity members to include all of the following 

  • PAD
  • Injected attacks detection (IAD)
  • Image inspection

On top of that, you can beef up security with: 

  • Device identification: Numerical values or codes to identify a user’s device
  • Behavioural analytics: Machine learning algorithms to detect any shifts in day-to-day online behaviour

So, how can you account for deepfakes risks and mitigation in practice? Here are a few more tips to consider: 

  • Educate employees: Hold monthly or quarterly meetings with experts in the field to help your employee identify common signs of deepfakes, including blurred or pixelated images in a person’s video, or distorted audio. Greater awareness of what to look out for can allow employees to flag suspicions. 
  • Don’t rely on one authentication process: Multi-factor authentication demands 2+ pieces of evidence to verify a user before admitting them into a network. Include email, phone, or voice verification in addition to biometrics. 
  • Invest in deepfake detection software: Consider a subscription Sensity AI, Deepware Scan, Truepic, or Microsoft Video Authenticator. 

Gartner plans to share more findings and research on deepfakes at their security and risk management summits taking place in various countries around the world. 

Read more about those summits and see the news release here.

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