Building a market data business is not rocket science.

Intriguingly Dr John Kubiatowicz of the University of California, Berkeley calculated that filling a 4GB Kindle to its storage limit would increase its weight by a billionth of a billionth of a gram. Russell Seitz went further estimating the internet’s entire weight to be approximately 50 grams, or the same as a large strawberry. Though since the year he worked that out the internet could well be a small apple by now.   

The point? The 8 Rules for Building a Data Business are designed to be lightweight guiding principles to keeping things nice, simple, clean, and straightforward.

8 Rules provides a guide to the more salient points to be considered when delivering information services to the global financial community. Success requires research, investigation, and attention to detail, while trying to keep business as simple as possible. Very important is maintaining the highest standards of:

• Effective application of Intellectual Property Rights

• Continuous data integrity

• Efficient operational management

• Complete document and contract management

Any form of degradation in service levels must be exposed, evaluated, and then expunged. Even a short period of under-performance will have long term repercussions and consequences for your data business.

To recap the 8 Rules and summarise:




Who has built successful data businesses?

The market is littered with data businesses that either do not live up to their potential, or are relative failures compared to their peers. Stagnation, acceptance of under-performance, simple lack of basic resourcing to take advantage of opportunities, an avoidance or unwilling to adapt, are sadly qualities many managements seem almost perversely delighted to revel in.

On the positive side of the ledger such ‘qualities’ allows good data businesses to flourish. So who stands out as data role models?

Activ Financial. Better known for datafeeds, Activ has arguably the best cloud native data publication and distribution platform currently available. Data accessibility is a vital component of 2020s data workflows

Australian Securities Exchange. Developing forward looking business models combining the application of IPRs, knowledge of dataflows with an understanding of how clients use their data

Bloomberg. Dominant in the terminal space. Its relatively simple to understand business model focused on the ‘all-in-one’ Bloomberg Professional terminal has proven to be the benchmark for market data vendors

Intercontinental Exchange. ICE data combines core evaluated pricing services with an increasing range of complementary analytics services built upon data from its constituent exchanges. In 2020 this strategy was reinforced with ICE Data splitting into 1. ICE Fixed Income Data & 2. Exchange data & connectivity

London Stock Exchange. Again founded upon its principal exchange data, this could go either way, FTSE Russell is a first class franchise marrying benchmarks with analytics, but without corporate surgery, the Refinitiv merger leaves the group unbalanced

MSCI. Global index business with value added analytics is delivering a high margin, ‘sticky’ client business backed by ruthlessly enforced IPRs

S&P Global/IHS Markit. Credit Ratings, Indices, Benchmarks, combining a vast catalogue of in demand proprietary data globally with IP and value added services. This IP driven data business will become the number one financial information vendor in 2021

TP ICAP. Inter-dealer broker which built its core data business around partnerships with Bloomberg and Refinitiv, and now deciding its strategic choices for the next phase of its business development

There are common denominators in these data businesses, they are either IP owners and/or have great business models

None of these data businesses are perfect and each faces unique challenges, but importantly they have key advantages to either build future success or continue earning the right to sit at data business’ top table.

Keiren Harris 31/03/2021

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