Enough Marketing Technology, not Sufficient Integration
Data Integration and Quality Made The Bottom Of This Big Data List
One thing that I've learned across several big data and data science initiatives is that the more data discovery and analytics capabilities you enable, the more data you expose to a larger number of people (including customers), the more likely you'll expose data quality issues and the need to automate data integration. I would suggest CDO, CIO and other digital leaders balance their investments and include more of these technologies and services in big data programs.
Citizen Data Scientists to Enable the Enterprise
Lastly, Myles Suer discusses the concept of citizen data scientists as way to fill the talent gap many organizations have in analyzing and processing their data. Myles describes the personas of data scientists as Explorers and Miners that are poorly enabled by data warehouses and BI technologies of the last decade because of the structures they impose.
With miners and explorers, we can enable safe collaboration at the front end of the business intelligence process, and use their collective efforts to lower costs while increasing the business relevance of anything they measure. This is a big opportunity
My experience is that more CIO and organizations are open to self service practices, but some of the tool providers have fallen behind enabling these scientists and developers.Are these platforms easy to learn? Do they enable version control, code reuse, and deployment practices?
Digital Transformation and Data Management
CMOs getting enabled, new data analytics capabilities, and new talent - but the underlying data management practices around integration, automation, quality, virtualization, preparation fall behind? These practices need attention to fully enable digital transformation.