Sometimes science can be personal. When my father, who was living in Greece at the time, was diagnosed with stage IV gastric cancer in 2007, I set out to find the best possible care for him. As is the case with many patients with advanced disease, drug therapy was his best course. So, after unsuccessful surgery in Greece, he came to the US for treatment.
I contacted the most prestigious cancer hospitals in the country and found that they all used different drugs in different treatment regimens to treat advanced gastric cancer. As both a son and a scientist, I was surprised to discover that there was no standard treatment – something I would later realise was true of many different kinds of late-stage cancers.
Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar
Professor of Information Technology, Director, The MIT Initiative on the Digital Economy
From Harvard Business Review
Growing opportunities to collect and leverage digital information have led many managers to change how they make decisions – relying less on intuition and more on data. As Jim Barksdale, the former CEO of Netscape quipped, “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Following pathbreakers such as Caesar’s CEO Gary Loveman – who attributes his firm’s success to the use of databases and cutting-edge analytical tools – managers at many levels are now consuming data and analytical output in unprecedented ways.
This should come as no surprise. At their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery.
At the same time, large corporations are not always nimble creatures. How quickly are managers actually making the investments and process changes required to embrace decision-making practices rooted in objective data? And should all firms jump on this latest managerial bandwagon?
The data breach at the law firm of Mossack Fonseca in Panama sent shock waves around the world recently with the prime minister of Iceland stepping aside, Swiss authorities raiding the headquarters of the Union of European Football Associations, and relatives of the president of China linked to offshore companies. The size of the breach was also shocking with 2.6 terabytes of data leaked. That’s 30 times bigger than the WikiLeaks release or the Edward Snowden materials. However, the most shocking part of the “Panama Papers” story is that the breach and exploit of the popular open source project Drupal was totally preventable.
Everyone knows that law firms manage large amounts of highly sensitive information. Whether the data involves an individual’s estate plan, a startup’s patent application, or a high-profile merger and acquisition, clients expect their information to be secure. Indeed, lawyers are required to keep this information both confidential and secure. Yet, despite the very high level of security owed this information, many firms lack an IT staff and outsource the creation and maintenance of their data management and security services. Once outsourced, there is an assumption that someone else will effectively manage the data and ensure its security.
This is many firms’ first mistake. Even if they aren’t managing their own IT, law firms still have an obligation to make sure that data is properly secured. This means asking frequent questions about security and ensuring that the vendor is implementing reasonable security measures.
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.
The West African Ebola outbreak first hit Sierra Leone in May 2014, followed by an explosion of cases in the capital Freetown in the autumn. The epidemic now counts more than 10,500 cases across Sierra Leone, with signs that the spread is slowing.
The early days of the crisis were characterized by a sense of immense fear, anxiety and alarm, regionally and globally. In Sierra Leone, a three-day, countrywide, military-led lockdown in September fed the fear in West Africa and beyond. Many flights originating in unaffected African countries were restricted. African students were prevented from attending some American schools, and there were countless reports of discrimination against Africans across the globe. Pictures of health workers in full protective suits became a ubiquitous symbol of the panic.
Misleading reports, speculation and poor projections from international agencies, government ministries and the media about the Ebola outbreak exacerbated the problem. The fear that was spread by the dramatic reports that accentuated the negative, undermined confidence, made it harder to encourage people to seek care, and misdirected attention away from Sierra Leone’s urban areas, where data suggest the economic effects of Ebola have been concentrated.
Valid, credible and timely data is essential during a global crisis. Without reliable data, efforts to assist affected people and to rebuild damaged communities can be misdirected and inefficient.