To date, businesses have primarily seen open data as an asset that could create value for consumers and themselves. Our understanding of the weather (The Weather Company), neighborhoods and real estate (Zillow/Trulia), and family genealogy ( are largely rooted in open data. More recently, companies have started embracing and contributing to the open data community in ways that echo their participation in the open source software community. They have begun producing and contributing open data to create value and pursue corporate strategy.

Bloomberg LP is one of these pioneering companies. It is known as an information services powerhouse that investors and financial markets professionals deeply rely upon to do their jobs as well as a global media juggernaut operating a wire service, a television network, a radio station, and multiple magazines. Bloomberg has embraced open data, recognizing that breaking down siloes with open data and frameworks induces innovation.

I interviewed Richard Robinson, Bloomberg’s Open Symbology Strategy Executive, about his company’s open data activities and about corporate attitudes towards open data. I hope you enjoy reading Richard’s answers as much as I have enjoyed hearing his perspective.

Can you share a bit about Bloomberg’s current open data activities and your goals for these efforts?

Bloomberg has supported open data efforts for many years, which is evidenced in both the Financial Instrument Global Identifier (FIGI) as well as our recent accreditation as a Local Operating Unit for issuance of Legal Entity Identifiers (LEI’s).

This is in recognition that the best way to gain better data quality and transparency in the financial markets is by establishing an environment of shared open data for key reference information used throughout the industry. There is no value-add in closed identification systems, or systems that establish barriers to access or sharing.

With common, shared, and open understanding of a baseline of a reference data, efforts are better focused on generating value, through generating high quality information about those shared, open concepts.

Bloomberg is active in the adoption of open, standardized identifiers — LEI and OpenFIGI being notable examples. Why do you think these are important, and what do you think is going to power their adoption?

It’s about the potential for future growth and change.

Our industry is based on trading something with someone. For too long, we have not had a single open framework for describing either of these concepts. Traditional approaches look to identify a single ‘thing’, without contextual or relationship perspective, and create lock-in to services or particular functions.

Traditional standards focus on identifying a thing for a purpose. New standards, like FIGI, are focused on the methodology for identifying a thing. It’s about the framework, not a single specific purpose.

In the industry, we always talk about silos, and the need to break down silos. But we continue to use the same approaches to data — and that simply re-enforces those silos.

Implementing a framework standard like FIGI creates a foundation for evolving past silos and truly being able to bring new functionality and value. The vision of LEI, as it moves to ‘Level 2’ data, is much like FIGI in that respect — moving past simple one-for-one identification and into more of a framework.

Finally, open data allows firms and providers to focus on creating and competing on value-add activities. If something is not open — even if it is a ‘standard’ — and it is mandated, it creates lock in, which stifles innovation. With an open data foundation, competition is fostered by forcing firms to compete on quality and breadth of services — not footprint.

Bloomberg has been a leader in corporate contributions to the open data community. Can you talk about how the idea for Bloomberg’s open data efforts came to be and the journey to get these efforts off the ground?

I try to avoid pointing back to the 2008 market turmoil as a reason for many things. But in data, it shone a light on the issues around transparency. In reality, there wasn’t really some plot to hide everything. It’s that the methodology for representing the data just didn’t exist. Bloomberg realized that internal efforts at creating a baseline framework for identifying financial instruments could be a powerful tool for the industry as a whole to help address at least a bit part of that issue.

Internally, there wasn’t much effort. Senior management immediately understood that this would be good for the industry. Bloomberg doesn’t have a stake in locking customers in through proprietary identifiers. Bloomberg’s focus is on delivering the highest quality data and information as possible, therefore making it better and more valuable than other providers. As I mentioned before, competition breeds value and creates better quality data for the end consumer.

But for a very long time, because the way identifiers were traditionally created — either through standards organizations or proprietary — firms relied on their monopoly position and ownership of licensing of a standard to maintain their position as a data provider and drive revenue, as opposed to the value-add activities they could provide.

That is why we approached the Object Management Group to standardize what would eventually become the FIGI, and dedicated FIGI and the related open metadata to the open data cause.

Today, around the world, over 130 vendors and other providers — unrelated to Bloomberg — consume FIGI for free, and can provide FIGI to their customers at no cost to them or their customers. This is in addition to all the data consumers and financial firms across the global that use FIGI and associated data in their daily operations.

Bloomberg is a very active contributor to the open source software community. Do you plan to participate at a similar level in the open data community?

We already are fairly active in the open data community through various working groups and industry groups. We continue to look for opportunities to contribute expertise to ongoing efforts.

Have you seen company attitudes to contributing to the open data community change over the course of your work in the space?

There are four different paths I have seen different firms take. On one hand, you have companies that see open data as a threat, much like many firms saw a free and open Internet as a threat. These firm retrench, and actively fight against open data efforts. They see open data like FIGI as competition to standards they own the licensing to, as opposed to the framework for evolving products and services. Barnes&Noble refused to accept the realities of the Amazon business model, and instead vilified Amazon.

Many firms wait and see, or stick a toe in the water. They include FIGI in their database, or maybe a data feed. They’re still working through where the transition point is within their firm between open data and value-add data and services. Which is an important line to clearly define. That determines your future business model. Too little, and the open data doesn’t really have any purpose and is unusable. Too much and you may sacrifice the unique value your firm does bring to the table.

Third, there are firms that jump in and embrace open data, and find exactly where they can add value. Firms like EDI, who have fully embraced FIGI — without sacrificing their own value — have actually increased the services and value they can provide to clients. FinTech firms like Intrinio may not have been able to even build their business and evolve their services as quickly as they have without utilizing FIGI as a basis.

Finally, you have the fourth type of organization, which presents a more nuanced challenge. This type of firm is a challenge to continued growth and acceptance of open data, and is characterized by providers that use the open data or ‘standards’ label, while actually not conforming to the true spirit of open data. They look to co-opt the ‘open data’ label. They are much like the first type of firm that retrenches, and they really are fighting against the open data movement. They hide behind ‘cost recovery’ or policies restricting equal access in the name of ‘fairness’. These policies are many times meant to protect a monopoly or some commercial interest based on licensing of exactly that data. This confuses the open data message, and makes decision makers question or not immediately trust true open data offerings. There is always a good rationalization on why you should pay for it. But it isn’t open data.

You can see the distinct difference between firms in the third group and these last types of firms that co-opt the open data label. In the former, they are creating added value while the core remains open, unrestricted, free and still has value of its own. In the latter, there is always some condition of use on the core data — whether it is as innocuous as tracking who is using it, to restrictions on use depending on some type of payment.

Lastly, and hopefully a fun question… What’s your favorite open data set? Why?

Oh, there are so many choices. Energy is a great open data set — the progress of renewable energy sources, consumption patterns over time and seasons. Education and science and research also have fantastic applications. But NOAA data, climate and related oceanographic data is likely my favorite. As a surfer, I use it constantly to self-predict when we might have a window of decent swell here on the East Coast. We’re not as blessed with consistently predictable waves coming in — so using that data to help forecast based on wind, pressure systems, ocean buoys and so on likely tops my personal list. isn’t just for open data. If you need to securely manage distributed access across many partners, read how you can use for data distribution here.