President Donald Trump has vowed to bring manufacturing jobs back to the U.S. through new policies and regulatory reform. But this effort faces a strong headwind: In all walks of life, human employment is being challenged.
Many manufacturing jobs have been replaced by robots. Meanwhile, drivers are on their way to being displaced by driverless cars, tax professionals by software, and much more.
Recently Trump turned his attention to the financial services industry, signing two directives aimed at repealing portions
But regulatory change isn’t likely to repel the march of the robots that is transforming the financial services business. FinTech — the finance industry equivalent of robots in manufacturing — is too far along for that. If future investors and consumers of financial services begin to trust FinTech platforms as they have done in retail and travel, then fewer humans will be working in finance.
While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talents are structured and managed.
How can organizations realize the promise of the evolving disciplines that we broadly call analytics?
Although financial firms were among the first to recruit “quants” to use sophisticated mathematical models and high-powered computing hardware, analytics groups have now taken hold in areas ranging from health care to political campaigns to retailing to sports. Organizations like these can benefit from the insights gained by financial service firms on how best to manage teams doing advanced analytics. It requires skills and philosophies that are different from those that arise in managing other groups of smart professionals.
Rather than just involving oversight and planning, managing a data science research effort tends to be a dynamic and self-correcting process; it is difficult to plan precisely either a project’s timing or final outcomes. For those unused to this type of work, this process can seem quite messy — an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.
Believe it or not, about 20 years’ worth of potentially life-saving drugs are sitting in labs right now, untested. Why? Because they can’t get the funding to go to trials; the financial risk is too high. Roger Stein is a finance guy, and he thinks deeply about mitigating risk. He and some colleagues at MIT came up with a promising new financial model that could move hundreds of drugs into the testing pipeline. (Filmed at TED@StateStreet.)
Roger Stein wants to bring financial engineering to the world of drug funding.
Before companies can profit from big data, they often must deal with bad data. There may indeed be gold in the mountains of information that firms collect today, but there also are stores of contaminated or “noisy” data. In large organizations, especially financial institutions, data often suffer from mislabeling, omissions, and other inaccuracies. In firms that have undergone mergers or acquisitions, the problem is usually worse.
Contaminated data is a fact of life in statistics and econometrics. It is tempting to ignore or throw out bad data, or to assume that it can be “fixed” (or even identified) somehow. In general, this is not the case.