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Four Rules for Data Success

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Database technology is a fast-moving field filled with innovations. Michael Manoochehri describes the current state of the field and discusses the four rules for data success.
  • The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.
  • —Bill Gates

The software that you use creates and processes data, and this data can provide value in a variety of ways. Insights gleaned from this data can be used to streamline decision making. Statistical analysis may help to drive research or inform policy. Real-time analysis can be used to identify inefficiencies in product development. In some cases, analytics created from the data, or even the data itself, can be offered as a product.

Studies have shown that organizations that use rigorous data analysis (when they do so effectively) to drive decision making can be more productive than those that do not.1 What separates the successful organizations from the ones that don’t have a data-driven plan?

Database technology is a fast-moving field filled with innovations. This chapter will describe the current state of the field, and provide the basic guidelines that inform the use cases featured throughout the rest of this book.

When Data Became a BIG Deal

Computers fundamentally provide the ability to define logical operations that act upon stored data, and digital data management has always been a cornerstone of digital computing. However, the volume of digital data available has never been greater than at the very moment you finish this sentence. And in the time it takes you to read this sentence, terabytes of data (and possibly quite a lot more) have just been generated by computer systems around the world. If data has always been a central part of computing, what makes Big Data such a big deal now? The answer: accessibility.

The story of data accessibility could start with the IT version of the Cambrian explosion: in other words, the incredible rise of the personal computer. With the launch of products like the Apple II and, later, the Windows platform, millions of users gained the ability to process and analyze data (not a lot of data, by today’s standards) quickly and affordably. In the world of business, spreadsheet tools such as VisiCalc for the Apple II and Lotus 1-2-3 for Windows PCs were the so-called killer apps that helped drive sales of personal computers as tools to address business and research data needs. Hard drive costs dropped, processor speeds increased, and there was no end to the amount of applications available for data processing, including software such as Mathematica, SPSS, Microsoft Access and Excel, and thousands more.

However, there’s an inherent limitation to the amount of data that can be processed using a personal computer; these systems are limited by their amount of storage and memory and by the ability of their processors to process the data. Nevertheless, the personal computer made it possible to collect, analyze, and process as much data as could fit in whatever storage the humble hardware could support. Large data systems, such as those used in airline reservation systems or those used to process government census data, were left to the worlds of the mainframe and the supercomputer.

Enterprise vendors who dealt with enormous amounts of data developed relational database management systems (RDBMSs), such as those provided by Microsoft SQL Server or Oracle. With the rise of the Internet came a need for affordable and accessible database backends for Web applications. This need resulted in another wave of data accessibility and the popularity of powerful open-source relational databases, such as PostgreSQL and MySQL. WordPress, the most popular software for Web site content management, is written in PHP and uses a MySQL database by default. In 2011, WordPress claimed that 22% of all new Web sites are built using WordPress.2

RDBMSs are based on a tried-and-true design in which each record of data is ideally stored only once in a single place. This system works amazingly well as long as data always looks the same and stays within a dictated size limit.

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