John D. Rockefeller and Data

John D. Rockefeller built a monopoly on oil starting in the late 19th century.

His Standard Oil Company become so powerful and far-reaching that the US Government forced him to dissolve it into 34 separate companies in 1911. 

Never one to accept a setback, Rockefeller went about running his oil empire as usual, and these separate entities did even better.

Check out the list of companies that were created AFTER the government broke up his business:

1. Standard Oil of New Jersey – became Exxon
2. Standard Oil of New York – became Mobil (eventually merged with Exxon)
3. Standard Oil of California – became Chevron
4. Standard Oil of Ohio – became Sohio (later acquired by BP)
5. Continental Oil Company – became Conoco (later merged with Phillips Petroleum to become ConocoPhillips)
6. Standard Oil of Indiana – became Amoco (eventually merged with BP)
7. Atlantic Refining Company – became ARCO (eventually merged with BP)
8. Chesebrough Manufacturing – became part of Unilever (produced Vaseline)
9. The Ohio Oil Company – became Marathon Oil
10. The Prairie Oil & Gas Company – was absorbed into other companies

Imagine a business so big that it became the foundation for 10 of the largest oil producers in the world, still operating today over 100 years later... 

And contrary to what the government expected, the breakup actually proved to be extremely profitable for shareholders.

Here's why:

• The breakup caused the shares of each of the smaller companies to increase in value, as they were able to operate more independently and take advantage of regional markets.
• The sum of the parts exceeded the whole: Each of the newly created companies grew rapidly in a burgeoning oil market, benefiting from increased competition and focus.
• John D. Rockefeller, the largest shareholder, saw his wealth increase. By 1913, just two years after the dissolution, his net worth had grown to nearly $900 million, making him the world's first billionaire. This was largely due to the rising stock prices of the spun-off companies.
• When the trust was dissolved, shareholders of Standard Oil were granted proportional shares in each of the 34 companies. This means Rockefeller retained significant ownership in all the successors of Standard Oil, and as the companies grew and prospered, so did his wealth.

The original billionaire.

Cool story, but you might be thinking how on earth is this related to Salesforce?

This is how: Data is the modern oil.

Amazon, Netflix, Facebook, Google, Tesla - all the big tech companies - hone their algorithms on oceans of data. 

Data is valuable.

And while we're not likely building advanced driving or social media algorithms, the companies we work for still benefit from good clean data.

So, how clean is your data?

If you're anything like me, the environments you work in always have room to improve their data quality.

And one way to do just that is with a simple formula field. 

This formula field checks four separate fields (FirstName, Email, MobilePhone, and BirthDate) on the contact object and assigns a score based on how many are filled in.

Only one data point filled in gets a score of 25%.

All four gets a 100%

We could then run a report on all contacts to check out our data quality score across our entire Salesforce database.

With this simple field we can use reports to get an average score, a score by contact owner, a score by year, etc etc.

It's cool!

What gets measured gets improved, and this helps you measure something important.

Other methods to improving data quality include:

• Leveraging duplicate rules
• Marking more field as required
• Using conditional field requirements (if an Opportunity is Closed Lost, fill out a reason why)
• Installing AppExchange products like CloudDingo to auto-merge dupes

We don't need to drop everything and focus on getting all records to 100% data quality.

But small changes like this, over time, will add up to big improvements.

And while your company may not be the next Standard Oil, it will benefit from better, cleaner data.

So the key takeaways from today are:

1. Data is valuable but messy (like oil)
2. Simple formula fields, adjusted for your environment, can help you measure data quality
3. There are other tools you can leverage too (Dupe rules, AppExchange)
4. Your company will love you for improving their data quality

And so, in the words of Forrest Gump, that's all I have to say about that.

Hope you have a great weekend.

Best,

Nick