The $69 billion buyout of the health insurance company Aetna brought again big data on the agenda. Indeed, these massive storehouses of information have always been there. But now they are being analyzed and interpreted and it’s the most radical change happening in health care. This radical change in healthcare is interpreted as the new ‘gold rush’. Throw in the stuff from medical claims, clinical trials, prescriptions, academic research and more and the yield is something on the order of 750 quadrillion bytes every day; or some 30 per cent of the world’s data production.

IT BEGAN IN DECEMBER

with CV’s proposed $ 69 billion buyout of insurer Aetna. In January, three more corporate behemoths-Amazon JP Morgan Chase and Berkshire Hathaway-said they were forming a joint venture aimed at reducing health care costs and improving outcomes for their combined 1 million or so employees. Then in March, Cigna said it would buy pharmacy benefits manager Express Scripts for more than $50 billion. Just about everyone agrees that America’s healthcare system is broken. Is better data-and the ability to harness it-the medicine we’ve been looking for? Fortune goes deep into tech’s next big wave.

What’s driving this frenzy of healthcare related deal making? On first glance, you might think it’s merely the pursuit of mass itself. But in truth, there’s a more powerful catalyst and that’s data.

More specifically, it’s you data: your individual biology, your health history and ever-fluctuating state of well-being, where you go, what you spend, how you sleep, what you put in your body and what comes out… The amount of data you slough of everyday-in lab tests, medical images, genetic profiles, liquid biopsies, electrocardiograms, to name just a few- end the yield is something on the order of 750 quadrillion bytes every day or some 30 per cent of the world’s data production. These massive storehouses of informations have always been there. But now, thanks to a slew of novel technologies, sophisticated measuring devices, ubiquitous connectivity and the cloud and yes, artificial intelligence, companies can harness and make sense of this data as never before. “It’s not the data, “says Eric Topol, director of the Scripps Translational Science Institute. “It’s the analytics. Up until three-to-five years ago, all that data was just sitting there. Now, it’s being analyzed and interpreted. It’s the most radical change happening in health care.”

The quest to retrieve, analyze and leverage that data has become the new gold rush. And a vanguard of tech titans-not to mention a bevy of hot startups-are on the hunt for it.

Alphabet life sciences arm Verily is aiming to create a “baseline” of human health by tracking all kinds of biometric information from 10,000 volunteers. Apple just released an iPhone feature offering users in several big health systems instant access to their own medical record; an effort that joins its ongoing heart study with Stanford, testing if wearables can detect serious cardiac conditions.

PROVIDING HEALTHCARE TO PATIENTS, HARNESSING THE MEDICAL EXPENSES

According to the Centers for Medicare and Medicaid Services, tapping this reservoir, say many, will ultimately improve patient health and decrease medical costs, which are projected to rise 5,3 per cent in 2018 alone. That’s a noble aspiration, certainly. But not lost on anyone is that it’s sure to make for a potentially blockbuster business too. David Friend, managing director at BDO, points out that data-rich Facebook and Google make their money on advertising- a business worth $200 billion, he estimates. “Health care is 15 times bigger than that” he says.” We spend $3 trillion. In theory, if this is done right, you’ll have 15 Facebooks and 15 Googles. That’s what’s up for grabs.”

Which is why so many old-guard health care companies, from hospitals and insurers to benefits managers and drug and device makers are hastily recombining and reinventing themselves. The realignment promises not only to drastically reshape the health care landscape for companies overall, but for you as well.

Optimists like Accenture’s chief technology and innovation officer Paul Daugherty, predict that the “information asymmetry” will soon favor patients whose ownership of their own biological data will give them new power.

To see what the new balance of power will look like in the coming years-and what it looks like right now-Fortune interviewed more than three dozen executives at companies across the health care continuum, along with entrepreneurs, doctors, patients and other experts. Here’s how the big-data revolution is-and isn’t transforming medicine.

To hear the folks at Amgen tell it, big data has upended the California biotech’s drug development process and significantly reshaped its pipeline. That story begins in 2011 when R&D chief Sean Harper started making trips to Iceland. He was trying to solve his company’s-which is also the industry’s- ‘failure problem’, which is summed up by the fact that 90 per cent of drug candidates fail to make it to market. For Harper, Iceland seemed to offer an unparalleled pool of health-related data. The collection of that data-the genetic sequences of 160,000 citizens, along with their medical and genealogical records-was made possible by the Icelandic government and the storage and analysis of that data was overseen by deCode, a Reykjavik-based human genetics outfit that, since its founding in 1996, had struggled to stay afloat financially.

Despite its solvency issues, deCode had become a prolific publisher of genetic discovery. Its trove of data allowed the company to mine the population for genetic variants and connect those variants to clinical outcomes in diseases ranging from cancer to schizophrenia. As the cost of sequencing plummeted in sync with the rise of computer processing power, Harper saw an undervalued asset for drug discovery: Amgen bought the company in 2012 for $415 million.

That purchase has utterly transformed Amgen’s R&D process. Prior to deCode acquisition, only 15 per cent of Amgen candidate molecules had been validated against specific genetic targets. After the purchase, Amgen began evaluating all of its drug candidates against deCode’s database. The review exposed some clear losers; in the case of 5 per cent of its candidate molecules, there was the evidence the agent wouldn’t work. Managers killed those programs (including one highly anticipated drug aimed at coronary disorders that was about to head into human trials) and prioritized others where there was a clear genetic target for the drug. Amgen also greenlighted more than a dozen drugs for which it found confirmation in deCode’s genetic data. Today, three-quarters of Amgen’s pipeline is based on genetic insights largely gleaned from the database, says Harper, and the company has more than earned its investment back.

HIDDEN FIGURES: THE UNTAPPED VALUE OF MEDICAL RECORDS

A random assortment of health information doesn’t mean much if it doesn’t meet at least two critical criteria, says oncologist and former Duke professor Amy Abernethy: quality and context. “Anyone who doesn’t understand the core aspects of practicing medicine can’t understand how messy it is” says Abernethy, who four years ago became the chief medical officer at Flatiron Health, a startup backed by Google Ventures (GV).

Take cancer records-Flatiron’s specialty-as an example. Many of the most useful nuggets in an oncology EHR may reside in doctors’ notes that aren’t structured into specific data fields. These are the sorts of observations that can’t be neatly packaged into categories on a form. “Historically, these electronic records are billing and collection tools, documentation we have to comply with to get paid” explains Jeffrey Patton, a physician and CEO of Tennesse Oncology, a community-based health system that treats the largest number of cancer patients in the state and is one of the hundreds of community cancer centers that now uses Flatiron’s system. Flatiron’s selling point, ironically, is humans. When it comes to this type of data, it seems, people can figure out things a purely computer-driven system might miss. The real challenge isn’t to gather the data but to “clean it up” says Abernethy. “And that’s really hard without an understanding of context.”

Flatiron now has data from 20 per cent of active cancer patients in the US “and it’s extremely well structured” says Daniel O’Day, CEO of Roche Pharmaceuticals, a unit of Roche Holding AG, which snapped up Flatiron in a $ 1,9 billion deal announced in February. “What set Flatiron apart was that it was able to create regulatory grade, real-world data” O’Day tells. Data that O’Day claims is so well curated that it “could have theoretically replaced the ‘control arm’ of one of Roche’s own clinical trials for the cancer immunotherapy drug Tecentriq.

In theory, Flatiron’s system could have a broader impact on clinical trials accrualfor generations, one of the most stubborn challenges in cancer drug research-making it easier, for example, to match up specific patients with appropriate drug studies.

A couple of years ago, executives at Geisinger Health discovered that, late at night, a number of the Pennsylvania health system’s doctors were still logged into the system’s electronic health records. The reason? Physicians were staying up doing all the administrative work they couldn’t get done during the day. Geisinger CEO’su David Feinberg knew that burnout had reached epidemic levels within America’s medical community, largely for this reason-and so, after reviewing the data, he made a bold decision: He doubled the length of appointment time given to patients 65 and older.

Geisinger physicians now spend 40 minutes with those patients and as a result, see many fewer per day- an expensive decision for the system, it would seem. But in time, the data proved it was financially sound. Here’s why: Older patients, having undergone more thorough exam, were far less likely to show up at the ER or require hospitalization for an issue that didn’t get discussed in the visit.

HEALTH CARE’S SAUDI ARABIA

If data is the oil of the modern day, Geisingerwhich serves 3 million patients in rural Pennsylvania- may just be health care’s Saudi Arabia. Founded in 1915 as an integrated system-it both cares for patients and insures them (and, more important, gets data from both roles)-Geisinger became, in 1996, one of the first US healthcare systems to adopt a full-featured EHR. And they’ve invested in it ever since. Patients can even carry their records on an iPhone.

With such clinical and claims data merged together in a searchable form, Geisinger physicians can query data- even in unstructured forms like doctor’s notes and medical images-in real time and as easily as a Google search.

In the past few years, its newfound capabilities have helped it rationalize the use of ORs (previously, every operation was slotted for 45 minutes, even if surgeons were known to run over), pinpoint costly variability in the system and prioritize the review of medical images. An AI trained computer, for instance, identifies those of potential stroke victims.

Creating innovative, lifesaving medicines, say pharmaceutical company bosses, requires a sufficient return on investment. But lately, that ROI stinks. In 2017, according to Deloitte, the 12 largest biopharma companies got a mere 3,2 per cent return out of their drug research arms. In 2010, that number was 10,1 per cent.

How can pharma break out of this rut?

One avenue might be the use of artificial intelligence to improve drug discovery at the earliest stages. “A.I. can help analyze large data sets from sources such as clinical trials, health records, genetic profiles, and preclinical studies; within this data, it can recognize patterns and trends and develop hypotheses at a much faster rate than researchers alone” says Deloitte.

And Big Pharma names like Merck, Sanofi and AstraZeneca are already taking it to the lab. In 2017, Astra Zeneca struck a partnership with startup BERG, to use the latter’s A.I. platform to home in on promising biological targets and possible agents against neurological diseases such as Parkinson’s.

So how does it work? Says BERG CEO Niven R.Narain, by going “back to biology”. Tissue samples are taken from both healthy and sick patients, analyzed on multiple molecular levels, combined with clinical data and then fed through BERG’s A.I.platform.