If your organization is new to data science, then your job as the Chief Data Officer is going to be dominated by the activities of a librarian putting a library back together after a hurricane. Yeah, you've got the books, but nobody will be able to find anything they want until you've got them put back in order.
If your organization's data holdings are extensive, then you as the CDO need to find a small variety of data engineers. They will form a team to apply data structuring concepts suited to your diverse data sets. Good data engineering will not only facilitate faster search processing, but allow your end users to ask fuzzier questions and still get good, usable answers.
Cleaing up bad data is ugly work...and some people absolutely love to do it. More than that, the results these people can produce add to the value of the conclusions that any analytical process may produce while using them. If you know your data archives have things to teach you, then cleaning up errors is a vital step to learn these things.
Digital Clones can supply pre-paid, retained services for major project design and launches. Retainers based on a 160 hour (four week) engagement.
Book a Launch Engagement
We would also be happy to supply speakers to your organization to present on the principles of LO+FTTM and how they work in research and development teams.
Book a Speaking Engagement
Optimizing Luck is our primary case study on leadership in high-stakes, high-tech businesses. Get your copy while they're still available.
Buy Optimizing Luck
If your data are scattered all over the place, and everything is full of errors, you aren't ready to do data science at the large enterprise level. And if your data sets are numerous and extremely diverse in nature, a handful of APIs ain't gonna get the data collection job done the way you really want it done.
Good data engineering fixes this in a number of ways. Minimally, it gives you a flexible and increasingly error free archival structure. Additional results include improved data quality with data quality assessments. Your re-engineered data holdings should also exhibit various forms of data standardization and include meta-data features to enable wide interoperability.
On the real-time side, your IT infrastructure should include mechanisms for dynamic data acquisition and data product deployment. While your historical records are important, the future is being formed from the present. Real-time awareness, built on well-managed data flows, can generate greater lead time to engage the future.
Even in pure research contexts
it's all about problem solving.
Problem solving always begins with
careful problem characterization.
Innovation is the art of turning
a great solution into a great application.