Disruptive Analytics

This is an introduction to my book, Disruptive Analytics, available now from Amazon and  Apress.

DA Cover

Disruption: in business, a radical change in an industry or business strategy, especially involving the introduction of a new product or service that creates a new market.

From its birth in 1979, Teradata led the field in data warehousing. The company built a reputation for technical acumen, serving customers like Wal-Mart and Citibank; analysts and implementers alike rated the company’s massively parallel databases “best in class.”  After a 2007 spinoff from NCR, the company grew by double digits.

On August 6, 2012, Teradata released its earnings report for the second quarter.  Results excelled; revenue was up 18% and earnings per share (EPS) up 28%.  Teradata stock traded at $80, five times its value four years earlier.

“We are increasing our guidance for constant currency revenue growth and EPS for 2012,” wrote CEO Mike Koehler.

In retrospect, that moment was Teradata’s peak. Over the next three and a half years, the company lost 75% of its market value, as it repeatedly missed revenue and earnings targets. In 2015, Koehler announced a restructuring and sale of assets; several top executives departed. Finally, after a brutal first quarter earnings report, Koehler himself stepped down in May 2016.

Management blamed many factors for the sluggish sales: long sales cycles, a sluggish economy, and unfavorable currency movement.  But worldwide spending on business analytics increased during this period, and some vendors reported double-digit revenue growth.

One can blame Teradata’s struggles on poor leadership, but the truth isn’t that simple. The company’s growth problems in the last few years are not unique: in the same period, Oracle and IBM suffered declining revenue; Microsoft and SAP failed to grow consistently, disappointing investors; and SAS had to walk back embarrassing projections of double-digit growth, recording low single-digit gains.

In short, while businesses continue to invest in analytics, they aren’t buying what the industry leaders are selling.

Meanwhile, a steady stream of innovation creates new value networks in the business analytics marketplace:

Open Source Analytics. With substantial gains in the last several years, open source software makes deep inroads in the analytics community. Surveys show that working data scientists prefer to use open source R and Python more than any brand of commercial software. Technology leaders like Oracle, IBM, and Microsoft rush to get on the bandwagon.

Hadoop and its Ecosystem. As Hadoop matures, it competes successfully with data warehouse appliances, even displacing them. Technology consultant Gartner estimates that 42% of all enterprises now use Hadoop. A few years ago, data warehousing vendors laughed at Hadoop; they aren’t laughing today.

In-Memory Analytics. As the cost of memory declines, fast and scalable high-performance analytics are within reach for any organization. Adoption of open source Apache Spark increases exponentially. With more than a thousand contributors, Spark is the most active open source project in Big Data.

Streaming Analytics. Organizations face a growing volume of data in motion, driven in part by the Internet of Things (IoT). Today, there are no less than six open source projects for streaming analytics in the Apache ecosystem. In-memory databases position themselves as streaming engines for hybrid transactional/analytical processing (HTAP).

Analytics in the Cloud. When Amazon Web Services introduced its Redshift columnar database in 2012, it lacked many of the features available in competing data warehouses. For many businesses, however, Amazon offered a compelling value proposition: “good enough” functionality, at a fraction of the cost of a Teradata warehouse. The leading cloud services all report double-digit revenue growth; Gartner estimates that 44% of all businesses use the cloud.

Deep Learning. Cheap high-performance computing power makes deep learning practical. Nvidia releases its DGX-1 chip for deep learning, with the power of 250 servers; Cray announces its Urika-GX appliance with up to 1,728 cores and 35 terabytes of solid-state memory. Meanwhile, Google releases its TensorFlow framework to open source and declares that it uses deep learning in “hundreds” of applications.

Self-Service Analytics. With an easy-to-learn user interface and robust connectors to data sources, Tableau disrupts the business intelligence software industry and grows its revenues tenfold.

We do not hype Big Data in this book; petabytes of data are worthless unless they answer a business question. However, the tsunami of data produced by the digital economy is a fact of life that managers and analysts must address. Whether you manage a multinational or drive a truck, your business generates more data than ever; you will either use it or discard it, but one way or the other, you must decide what to do with it.

In a disrupted business analytics market, managers must focus ruthlessly on needs for insight, then build systems and processes that satisfy those needs. Understanding the innovations described in these chapters is a step towards that end, but the focus must remain on the demand for insight and the value chain that delivers it.

Innovations do not spring fully formed from the mind of an inventor; they are the result of a long process of tinkering. Many of the most significant innovations we describe in this book are more than fifty years old; they emerge today for various reasons, such as the long-run decline of computing costs. We present a historical perspective at several points in this book so the reader can distinguish between that which is new and that which is merely repackaged and rebranded.

In the middle chapters of this book, we present a survey of key innovations in business analytics. These chapters include detailed information about available software products and open source projects. In general, we do not cover offerings from industry leaders, under the premise that these companies have ample marketing budgets to build awareness of their products.

We close the book with a handbook for managers: specific strategies to profit from disruptive innovation. Some of these strategies may seem radical; if this disturbs you, put this book down – it’s not for you. But if you are ready to embrace disruptive innovation, and profit by it, read on.

5 thoughts on “Disruptive Analytics”

  1. Looking forward to the book. The ecosystem around analytics (whether open source or closed) has grown so much that it’s hard to get ones arms around the subject matter in a coherent way.

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