KACHARAGADLA

Thirty-five years after Robert Waterman’s observation in In Search of Excellence that companies were “data rich and information poor,” little has changed. For sure companies are “data richer,” having exponentially more data at their disposal. But they are still information poor, even as leaders have implemented a wide array of programs aimed at exploiting data. Most still struggle to build data into their business strategies and, conversely, to align their data efforts to the needs of the business. There are a host of reasons, from lack of talent to unreasonable expectations to culture. Solving these problems is essential for those that wish to unleash the power of data across their organizations. It should come as no surprise that data is not yet strategic for many organizations. Business is already complex enough: When setting a company strategy, there are customers to satisfy, competitors to fend off, uncertain regulatory environments to accommodate, and skills gaps that must

“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”  This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled differences between data analytics and data science.  Data Analytics vs. Data Science  While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Culture Amp was founded in 2011 (Photo courtesy of Culture Amp/www.cultureamp.com) Startups talk about culture all the time. Building it, championing it, spreading it. Its importance is recognized by the broader ecosystem because it represents a key foundation to growing a company. Businesses are encouraged to create a workplace that facilitates both employee empowerment and prolific performance. But the challenge of building such a culture is rooted in a lack of trackable data. If culture isn't measured, how can it be deliberately improved? An Australian startup called  Culture Amp  is addressing this problem by giving companies analytics and data on their culture. Culture Amp is an “employee feedback and analytics platform” founded by Didier Elzinga, Jon Williams, Douglas English and Rod Hamilton. By using “research-backed surveys,” Culture Amp collects data on teams by asking for honest and relevant feedback. The responses given by employees, which boast an “80%

Data has been called  the new oil   and for good reason. While as recently as a decade ago our ability to use data effectively was limited by technology, today our access is nearly unlimited. New platforms like   Hadoop   and   Spark  allow us to analyze hundreds —or even thousands — of databases at once.   Open data initiatives   have expanded our reach even further. Still it’s becoming clear that there is a  data divide  opening up.  James Manyika , a director at the  McKinsey Global Institute , told me that he sees vast differences in effectiveness even among companies in the same industry, with similar IT budgets, competing for the same consumer. Gautam Tambay , CEO of  Springboard , founded his company to help close the data divide. His company offers online education in data science that pairs executives with personal mentors in order to help them navigate an increasingly data intensive world. So to gain some insight I talked to him about what managers today need to know