Predictions 2015: Data management will get more sophisticated yet easier to use.
By Olly Downs December 3, 2014
Prediction 2015: This scientist believes the role of data in business strategy and tactics will continue to grow in 2015. Do you have a prediction to share? Send it to: [email protected] We’ll post the ones we think readers will find interesting.
The intersection of data management with low-friction, high sophistication analytics will enable data science at scale.
Todays big data analytics solutions are kludges, where data is forced into the proprietary data model of the analytics solution, analyzed, then pushed back into the underlying big data repository. In 2015, we will see the data scientist focus on how to place commoditized, straightforward analytics and insights in the hands of less-analytical functions across the business. What will be key is guiding the procurement of technologies and the curation of data management platforms that allow simple access and accurate pivoting of data making the most repetitive and straightforward elements of analytical work as repeatable and scalable as possible. This will enable data scientists to use analytics more creatively and apply their understanding of sophisticated techniques in ways that have maximum business impact.
Seattles top analytical talent will raise the bar for technology environments and organizational skillsets.
These quantitative doers will be looking for businesses who are savvy of and oriented to using analytics to impact their bottom line. Businesses must be willing and able to respond in an agile manner to analytics output so analytical talent can determine whether opportunities exist to impact the business. This will require technology environments where data is accessible and tools like Python and R can be leveraged against a repository of well-curated data. Raw data preservation will be key, eliminating as much friction of the analytics process as possible. We will also see potential hires evaluating the skills and understanding of machine learning in the software development organization to ensure solutions can be optimized for analytics. An understanding of distributed computing in the organization will be considered a key indicator that analytics output can easily be operationalized in the business.