Database Price Quotes

When copies are free, you need to sell things that cannot be copied. Well, what can’t be copied? Trust, for instance. Trust cannot be reproduced in bulk. You can’t purchase trust wholesale. You can’t download trust and store it in a database or warehouse it. You can’t simply duplicate someone’s else’s trust. Trust must be earned, over time. It cannot be faked. Or counterfeited (at least for long). Since we prefer to deal with someone we can trust, we will often pay a premium for that privilege. We call that branding. Brand companies can command higher prices for similar products and services from companies without brands because they are trusted for what they promise. So trust is an intangible that has increasing value in a copy-saturated world.

Dimensional models implemented in relational database management systems are referred to as star schemas because of their resemblance to a star-like structure. Dimensional models implemented in multidimensional database environments are referred to as online analytical processing (OLAP) cubes, as illustrated in Figure 1.1. Figure 1.1 Star schema versus OLAP cube. If your DW/BI environment includes either star schemas or OLAP cubes, it leverages dimensional concepts. Both stars and cubes have a common logical design with recognizable dimensions; however, the physical implementation differs. When data is loaded into an OLAP cube, it is stored and indexed using formats and techniques that are designed for dimensional data. Performance aggregations or precalculated summary tables are often created and managed by the OLAP cube engine. Consequently, cubes deliver superior query performance because of the precalculations, indexing strategies, and other optimizations. Business users can drill down or up by adding or removing attributes from their analyses with excellent performance without issuing new queries. OLAP cubes also provide more analytically robust functions that exceed those available with SQL. The downside is that you pay a load performance price for these capabilities, especially with large data sets.

Organizations seeking to commercialize open source software realized this, of course, and deliberately incorporated it as part of their market approach. In a 2013 piece on Pando Daily, venture capitalist Danny Rimer quotes then-MySQL CEO Mårten Mickos as saying, “The relational database market is a $9 billion a year market. I want to shrink it to $3 billion and take a third of the market.” While MySQL may not have succeeded in shrinking the market to three billion, it is interesting to note that growing usage of MySQL was concurrent with a declining ability of Oracle to sell new licenses. Which may explain both why Sun valued MySQL at one third of a $3 billion dollar market and why Oracle later acquired Sun and MySQL. The downward price pressure imposed by open source alternatives have become sufficiently visible, in fact, as to begin raising alarm bells among financial analysts. The legacy providers of data management systems have all fallen on hard times over the last year or two, and while many are quick to dismiss legacy vendor revenue shortfalls to macroeconomic issues, we argue that these macroeconomic issues are actually accelerating a technology transition from legacy products to alternative data management systems like Hadoop and NoSQL that typically sell for dimes on the dollar. We believe these macro issues are real, and rather than just causing delays in big deals for the legacy vendors, enterprises are struggling to control costs and are increasingly looking at lower cost solutions as alternatives to traditional products. — Peter Goldmacher Cowen and Company

Roomba, made headlines when the company’s CEO, Colin Angle, told Reuters about its data-based business strategy for the smart home, starting with a new revenue stream derived from selling floor plans of customers’ homes scraped from the machine’s new mapping capabilities. Angle indicated that iRobot could reach a deal to sell its maps to Google, Amazon, or Apple within the next two years. In preparation for this entry into surveillance competition, a camera, new sensors, and software had already been added to Roomba’s premier line, enabling new functions, including the ability to build a map while tracking its own location. The market had rewarded iRobot’s growth vision, sending the company’s stock price to $102 in June 2017 from just $35 a year earlier, translating into a market capitalization of $2.5 billion on revenues of $660 million.1 Privacy

But this wasn’t the case when running a quantum computer. These computers could run Shor’s algorithm and come to the answer almost immediately. Which basically saved an infinity of time. And who could put a price on that? The government had begun using symmetric cryptographic algorithms, such as AES, which stood for Advanced Encryption Standard, for enciphering major databases and protecting classified data. But a quantum computer could easily break these also, this time using Grover’s algorithm.

Progressive tackled risk assessment in a different way, building a massive database with more granular indicators that better predicted the probability of accidents. It used this data to spot the good risks in pools that looked like bad drivers to other insurers. For example, among drivers cited for drinking, those with children were least likely to reoffend; among motorcyclists, Harley owners aged forty-plus were likely to ride their bikes less often. Progressive used information like this to set prices so that even the worst customers could be profitable.

To do index arbitrage, PNP developed techniques in the mid-1980s for finding baskets of stocks that did a particularly good job of tracking an index. We used this very profitably the day after “Black Monday,” October 19, 1987, to capture a spread of over 10 percent between the S&P 500 Index and the futures contracts on it. Quants have honed this to a fine art and, through their trading, generally keep the price discrepancy very small. To cut taxes, start with a tracking basket and, each time a stock drops, say, 10 percent, sell the loser and reinvest the proceeds in another stock or stocks chosen so the new basket continues to track well. If you want only short-term losses, which is usually best, sell within a year of purchase. I advise anyone considering doing this in a serious way to study it first with simulations using historical databases.