- Create a product in isolation and push it out through advertising
- Customer focus groups
- Lean / Design Thinking / Customer Discovery
- Data Science
- Now we need to integrate 3 & 4
I had a follow up to the Cluster Analysis kick off hosted at the UMN last September 2014. Joining me on the call was the MN Director of innovation and we where exploring partnership opportunities around the collection of economic data in both the public data and innovation space. My particular interest was in capturing addition data around innovation centers the full lifecycle of start-up maturation on a regional level. Though partnerships with centers and through integration with public data sources we could get a level deeper in the economic activity happening in the corporate and entrepreneurial areas. By building on the standards already set forth in the cluster analysis we hope to define the next level of data in this area so that all regions could capture data consistently for analysis. We are looking to develop analytics for the health and activity within clusters around the time, cost, and progress made by start-ups and corporate innovation initiatives. The innovation centers provide a great base where much of this activity is happening and can be one of key sources of data for the overall model. I’ve been working with economist and other data analytics specialists to develop economic data models to this end and identifying the public, private, and NGO data partnerships that could provide valuable in a integrated data capture strategy.
In future blogs I will dive more deeply into each of these considerations to explore how different organizations approached each area and what outcomes came for their efforts.
See more images of the event and speaker agenda in the gallery
I attended an executive salon event hosted by one of the global marketing agencies in Minneapolis. The theme of the presentations focused on the roles of aggregating social data for big data analysis. Two concepts jumps out during the conversation that drove most of the post presentation discussions.
The first was the topic of cross linking social accounts with corporate CRM systems. This was starting to expand a big data picture of the customer. While this is common practice today, they extended the concept to include cross liking with other identity sources such as LinkedIn or Public Tax Records. This started to create a vastly larger profile set of individuals outside of your customer base. These larger sets of profiles could be used to identify trends and patterns that could be leveraged for approach and enticing new customers to your brand or new offerings.
The second topic built on the first but was much more elaborate. They had some guest speakers from new ISVs that where building tools for markets to access a massive big data pool that had been assembling. Several years prior they had launched a backend platform that was constantly listening and recording many social media channels. The platform would be analyzing the content and generating additional meta data and tagging of content to aid in ongoing analysis. An elaborate architecture of meta tag hierarchies where defined to provide categorization of subject matters. Even more impressive was the ontologies that where defined between the hierarchies to cross relate topics. The end result in the analysis seemed to be an enormous multiplier in the ability to cross-relate cross channel data and inter-relate thematic trends and insights. Since seeing this demo I’ve noticed several new companies building out these types of solutions. While the science side of the platforms to do this is fairly straight forward it is the Art of creating the inter-relationships of the ontologies across the hierarchies that will define the state-of-the-art of competitive analysis.