Enough Marketing Technology, not Sufficient Integration

Chiefmartec produced his annual state of marketing technologies showing an explosive growth in the number of vendors. I blogged about this last year to illustrate that the CMO and CIO should partner to help select the optimal technologies, but when I look at this year’s landscape and almost another 2x of technologies that are eligible go be on the CMO’s shopping list, it’s the integration landscape that scares me. Trying to implements several of these solutions can be the Achilles heel of having an end to end platform that enables transformation.

Any volunteers to map out and rate the integration matrix? Implement it poorly and you’ll have a 3874 x 3874 grid that will make a very ugly visual.

Data Integration and Quality Made The Bottom Of This Big Data List

Review the top Big Data Technologies from Gil Press and you’ll notice that the sexier big data, nosql, search, analytics, data visualization, and other analysis or delivery oriented technologies occupy the top positions while integration, data preparation and data quality fill in the bottom ones. I suppose there is some success that these less exciting data platforms made the list at all, but it got me thinking, “How many organizations have implemented all the analytics and data visualization without considering data quality or insuring robust automation in data integration?” Another question, “How many IT departments have provided robust self-service BI and data visualization capabilities with limited services around data integration and preparation?”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

Success comes by enabling these data scientists and emerging ones to be successful while balancing their data access based on security, privacy, regulatory, and other governance.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.