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how Important data is for Companies

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In July 2018, Facebook’s market price suffered the most important one-day fall in US stock exchange history—$120 billion. After alleged election tampering by the Russians and therefore the Cambridge Analytica scandal, Facebook’s costs to manage data security and privacy have skyrocketed, negatively impacting earnings.

To put the loss in perspective, that’s quite the market cap for GE, UPS, or American Express.

Many companies interpreted the news as a cautionary tale. But that’s a misread of the larger story, which has played out over the last decade and can likely predict the increase and fall of industry leaders over subsequent several years:

Today, every company may be a data company.

Whether they realize it or not.

A decade ago, Internet companies began to satisfy the promise of the 90s dot-com boom. Companies like Apple, Amazon, Alphabet (Google), and Facebook began an unchecked ascent, taking full advantage of the Internet’s frictionless reach to rewrite the principles of nearly every major industry.

They also crowned themselves the foremost valuable companies within the world.

In 2011, Marc Andreessen wrote that each company must be a software company to survive. Many companies spent billions on software, a binary race. Yet the info paints a bleak picture of the results. Over the last decade, nearly three-quarters of Fortune 1,000 companies are replaced—despite aggressive investments in software.

The real story of the Facebook decline is how the social network amassed value so quickly it could lose $120 billion and still be one among the ten most precious companies within the world. It’s a story of the facility of knowledge.

Facebook may be a data company.

NewVantage Partners recently released the results of its annual Big Data Executive Survey. The survey featured responses from C-level executives representing the most important bellwether blue-chip firms—American Express, Bank of America, Bloomberg, Capital One, Charles Schwab, Farmers Insurance, Fidelity Investments, Ford Motors, Goldman Sachs, MetLife, and Verizon, among others.

The findings underscore the increasing urgency around data. 79.4% of executives report fear of disruption by data-driven competitors.

Over the last 20 years, we’ve both participated in the fast-evolving world of knowledge management. At NewVantage Partners, Randy Bean has advised and helped companies to leverage data as an asset and to become data-driven. Jedidiah Yueh has invented data products that have generated over $4 billion in revenue as founding CEO of Avamar (acquired by EMC) and therefore the founding CEO of Delphix. At Delphix, he has been privileged to figure on innovative programs for several of the foremost advanced companies in Silicon Valley and around the world—a chance to peek under both tents.

And the differences between big tech companies and mainstream legacy companies are proving to be pivotal for the worldwide economy.

We’ve both learned an excellent deal from our customers. The very first customer to ever put Delphix into production was the previous CIO of Facebook, Tim Campos. He showed how it had been possible to extend project velocity by accelerating data flow between systems. He also showed the way to live by the Facebook ethos emblazoned on the company’s walls: “Move Fast and Break Things.”

And, wow, did they ever move fast and break things.

Under a darkening regulatory cloud, Facebook has begun to mature, but only after their data practices catapulted them to quite half a trillion in market cap.

Even in IT, they use data to innovate, creating Facebook CRM (customer relationship management) and Audience Insights—products that helped them scale from a billion to over $40 billion in revenues.

Legacy companies are burdened by regulatory constraints and decades-old systems. They constantly struggle with what products to create and if they need the proper digital transformation strategy. They scrutinize programs and budgets with an account

entrant’s attention to details and risk.

But at Facebook, Campos quickly learned that “data wins arguments.” They built their products first then proved the worth with results. they need to adopt the fail fast, learn faster mantra.

Herein lies a difference between data companies and lots of traditional companies.

Data companies aggressively leverage data as core assets. They drive continuous returns by purposely instrumenting their companies to gather data then experiment to develop value.

Legacy enterprises, on the opposite hand, often treat their data as liabilities. They obsess over risk, costs, and management—especially those in heavily regulated industries. Too often, they need to understand the results a priori—before they invest.

Take financial services firms, for instance. Too many collect data as a byproduct of their transactions—not the core product that creates up their business and differential value within the world. They answer new regulations and news of security breaches by adding ever more belts and suspenders (that still fail)—aggressively conservative.

We could also be standing at the brink of Ray Kurzweil’s singularity, a flash when artificial superintelligence changes the trajectory of human history forever. As we accelerate into that future, tech companies in Silicon Valley have embraced a knowledge gold rush.

Yet too many legacy companies are still optimizing for pennies when facing a potentially existential crisis.

There’s an AI maturity model at adding the planet today.

At level one, companies run AI programs that drive operational efficiency. These are the “dabblers,” companies that drive tens of billions in revenues a year and save a few million using AI to automate tasks previously done by employees.

At level two, companies run AI programs to drive significant earnings or revenue impact. These are the “practitioners,” like Farmers Insurance and PayPal. They layer machine learning through their businesses and use it to rework user experience and customer value.

At level three, companies run AI programs that drive industry change and transformation. this is often the domain of massive tech—the “experts.” Facebook determines what we see in our feeds with AI. Apple uses AI and AI chips to power marquee iPhone features like Face ID and Siri. Google, Amazon, and Microsoft use a variety of AI services like machine learning, deep learning, and image recognition to run their businesses and also sell them as products in their clouds to arm the remainder of the planet.

In a world where every company may be a data company, the fast eat the slow. Level three AI companies, like Amazon, are modern predators, consuming market prices by feeding on legacy prey.
Change in market price.

Change in market price. Jedidiah Yueh

The timid too often obsess over data liabilities and risk bankrupting their companies, while the bold—who pan for gold and strike it data-rich—work aggressively to inherit the industries of the longer term.

You can’t specialize in defense and expect to win the longer term. Leveraging data—in an ethical manner—has to be at the guts of your company and merchandise strategy. Collect, sift, then monetize.

Because within the end, data companies will reinvent the planet.

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