Organizations committed to improving their data-driven decision-making processes are increasingly formulating an enterprise analytics strategy to guide their efforts in finding new patterns and relationships in data, understanding why certain results occurred, and forecasting future results. Using business intelligence (BI) tools and strategies, organizations can tackle enterprise analytics in an IT setting, mining data and applying insights to inform better business decisions.
In this CIO guide to enterprise analytics strategy, learn how organizations can use analysis methodologies from various industries, BI tools and the availability of big data to its utmost potential.
This enterprise analytics strategy guide is part of SearchCIO.com's CIO Briefings series, which is designed to give IT leaders strategic management and decision-making advice on timely topics.
Table of contents:
Sports analytics and the CIO: Five ways IT can win at enterprise analytics strategy
A lot of people -- CIOs included, we imagine -- turn to sports as a form of escape from everyday life, including their job. But not so fast: The growing sports data craze means that analytics and number crunching are coming to a team near you.
That's right: Just as big data is informing new forms of business intelligence, sports teams and leagues are pouncing on the proliferation of newly captured data that can help them win games and present a better fan and viewer experience. The sports data movement has spawned the MIT Sloan Sports Analytics Conference, held each year in Boston. Since its inception in 2007, the conference's attendance has bloomed from 200 to 2,700 in 2013, including some of the biggest names in sports analytics and management.
"Big data coming to the sports scene was a huge development this year," said Daryl Morey, one of the conference's co-founders, and number-crunching general manager of the National Basketball Association's Houston Rockets. Sports analytics, he said, "has become mainstream because it works."
Visit this slideshow to find out what CIOs can learn from the sports analytics movement.
A video primer to guide your enterprise analytics strategy
More of a visual learner? Check out our latest video interviews on enterprise big data analytics and strategy.
Where business intelligence has been and where it's going next
"Business intelligence" (BI) and "analytics" are terms as common in our world as peanut butter and jelly. In deference to our global readership, to avoid any possible local or regional bias, we should perhaps additionally consider fish and chips, borscht and potatoes, hummus and falafel, and likely many others, but I don't want to belabor the point.
As best I could determine in my look back at the history of BI, the term was first used by H. P. Luhn in an article titled A Business Intelligence System, published in an IBM research journal in 1958. Luhn defined BI as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal." Sounds straightforward.
During the next 30 years, the original concept evolved through various stages of maturity: decision support systems, or DSS, and executive information systems, or EIS, were quite in vogue during the 1970s and 1980s. In 1989, a major milestone was achieved when Gartner Inc. analyst Howard Dresner described BI as "concepts and methods to improve business decision making by using fact-based support systems."
What's coming next for enterprise analytics strategy? Find out in Harvey Koeppel's column.
Terminology to drive better enterprise analytics
Before you deep-dive into your data, makes sure you know what you're looking for. Here are some terms to know.
Metadata practices make a name for themselves
After he was labeled a person of interest in a murder investigation, antivirus software pioneer John McAfee fled his home in Belize, but he didn't disappear. For the next month, as he hid from police, the founder of McAfee Inc. remained visible to the public through blog posts, tweets and media reports.
The Silicon Valley legend might have continued to elude police while maintaining a virtual presence if it weren't for a single, sizable electronic bread crumb: a photograph of McAfee and a tag-along journalist posted to the website of Vice, a New York magazine on arts and culture. The image revealed little, but the same couldn't be said for the information embedded within the image. There, for law enforcement officials or any reasonably tech-savvy person to see, were the coordinates documenting precisely where the photo was taken.
The information not only led to McAfee's capture and arrest, it also pushed metadata -- geocode, in this case -- into the limelight. Metadata, commonly referred to as "data about data," is rapidly making a name for itself -- and not just in relation to tracking suspected criminals. Companies investing in big data tools will need to think beyond storing and analyzing large data sets to consider the tags or labels that give the data context over time, according to experts. Without metadata, companies will forfeit some of the deep insights big data can yield, including the identification of important business trends by analyzing detailed data over time.
Read more about big data analytics and metadata.
Data collection and analysis methods: A strategy quiz for CIOs
Do you know the best data collection and analysis methods for your IT organization? Take our quiz on business intelligence and analytics to find out.