Viewing entries tagged
morningstar

Get it Right ... Or Else!

Why is data getting so much attention these days? Why is it such a good business? Why is it so profitable? Well, there are numerous reasons, but the one I’d like to highlight today is that increasingly, data matters.

What do I mean by that? Simply that data, to a degree you don’t see with other forms of content, gets relied on to make serious decisions, some of which have significant, business, economic and personal impact. Some people (many of them rich data publishers) have understood this for a long time. For others, this insight is a new one. And one consequence of data’s growing importance is that it is increasingly the focus of lawmakers. Consider just a few examples:

In a true "only in Hollywood" moment, the state of California now has a law that says data providers cannot publish the ages of people in the entertainment industry. Yes, actors have long been skittish about putting their ages out there, but in the old days, they simply lied about their ages. Now, they have the force of law behind them. The ostensible purpose of this law is to help prevent age discrimination, however, the law also specifically includes everyone in the videogame industry as well, so go figure.

Across the pond, UK financial regulators have taken Morningstar, the mutual funds data company to court. Its offense? A number of the funds to which it gave high ratings ended up under-performing relative to their benchmarks. Apparently your predictions are now required to always be accurate. Of course, if Morningstar could identify top-performing funds with 100% accuracy, my strong recommendation to Morningstar would be to get out of the data business and into the investing business, pronto.

We also have the example of health insurance company physician directories. Every health plan publishes a directory of participating physicians, and in many cases, these directories are woefully inaccurate. Examples abound of plan directories with physicians who have left the plan, moved offices (sometimes hundreds of miles away), retired and even died. This would be just another everyday annoyance except for the fact that many people select their health plans, and spend thousands of dollars, based on the network of physicians a health plan claims to offer. The federal government has stepped in on this one, and not to be outdone, California (surprise!) has its own legislation covering physician directories.

These examples are just the tip of the iceberg. Consider all the various laws around credit data, for example.

Back to my original point, all these rules and laws simply illustrate that data at its core is all about helping people to identify, select, assess and decide. And as databases proliferate, so does their influence and impact. There is power in data, which is why, increasingly, data producers are being held to higher standards of quality and accuracy. While painful for some, in the aggregate, good data is good for all of us.

How Do You Rate?

Morningstar, the financial information giant, today announced that it will be licensing a ratings system from Sustainalytics, a Dutch company that assesses and rates public companies along three dimensions: environmental and social responsibility and governance. Morningstar will adapt this methodology and apply it to mutual funds.

Why the rush by Morningstar to add still more ratings to its data platform? And why license a ratings system when Morningstar already has demonstrated expertise in this area? Indeed, Morningstar has been rating mutual funds on their stewardship (akin to governance) for a number of years now.

The answer, in a word, is that ratings systems are hot. While they don’t look like much on the surface, they offer to users what they most want today: fast answers. You could even go so far as to say that the other reason ratings system are so popular is that they do the research – if not the thinking – for you.

Most importantly of all from a data perspective, a ratings system provides a consistent, normalized and sortable data point. This is especially valuable in the investment world, which is in the business of finding needles in haystacks. Ratings systems and other filters significantly streamline this process.

Imagine if someone asked to you identify the ten best restaurants in Dallas. Without Yelp and Zagat and the other existing restaurant rating services, this would be a nearly impossible task, particularly if you were looking for a comprehensive and objective answer. But these services in effect conduct mass-scale surveys, asking people to condense their opinions of restaurants into a predefined ratings scale. This user-generated approach to ratings has all sorts of imperfections, but most people believe that with enough people participating, the truth will present itself.

A step up from these open surveys are the professionally administered ratings systems. These distinguish themselves by identifying and rating companies against a fixed set of criteria. The goal of the exercise is to be objective as possible. That’s why data are used in place of opinions whenever possible. The more rigorous the system, the more valuable it tends to be. That’s because in addition to being normalized and consistent, these ratings systems allow you to make dependable comparisons. Companies rated “A,” for example, are all rated that way because they met a certain specified set of criteria. That means you can place more trust in the ratings system.

Interestingly, most ratings systems happily publish their underlying criteria and ratings methodologies. While this might seem to be their highly proprietary “secret sauce,” the reality is that nobody wants to undertake the same laborious ratings work if somebody else has done it, and publicizing the underlying methodology builds credibility and trust. In fact, the underlying methodology of most professional rating systems is central to their marketing efforts.

Rating systems reflect the fundamental shift we are seeing from data publishers selling vast piles of raw data to high value, more analytical datasets. The next opportunity is to actually do the analysis for them.

You can learn more about how publishers are using their data to produce a wide range of high value products at this year's Business Information and Media Summit. Hope to see you there!