As the inventor and co-founder of Startups@NSN, I was one of the drivers behind a successful incubation program within a large (70K) and complex multinational. We coached global employees into generating hundreds of ideas and converted them into 6 prototypes in 2 months. After customer feedback, 4 commercial products were launched in months of which one won a prestigious international innovation award.
Having gone through the whole process, if given the chance to do it again, I would make some substantial changes.
We overestimated a couple of aspects.
1) Employees have very innovative ideas
2) Employees understand customer’s problems
3) Employees can let go of unproductive products
Several employee ideas were very innovative but the majority were just small changes to existing products. Most corporate employees are good at incremental innovations but have a hard time imagining innovative products on top of unknown technology innovations like Cloud Computing (jan 2010) or M2M (2011).
Also using employees as a substitute for understanding customer needs is not a great idea. Nothing beats real customer contact.
Finally people fall in love with their prototypes too easily. They are blinded and can not understand that their brainchild is an ugly duck instead of a beautiful swan.
So how can you do it better?
My first suggestion would be NOT to start with a technology but to start with the customer. Identifying some real important customer problems before identifying solutions is key.
Secondly, using employee but also external ideas (e.g. Via a competition) to generate minimum viable product requirements on paper should be done before building any prototype. The solution definitions should be reviewed by customers to get early feedback. In addition to solutions also other elements should be evaluated, e.g. Price, customer channel, unique value proposition, customer acquisition costs, etc. A good framework to use is the Lean Canvas.
Only after the customers have validate your lean canvas and minimum valuable product design should you go and build a prototype or even better the minimum valuable product. Launching the product in months and only adding features after the initial product has been successful should lower your initial costs and risk of failure.
If you are looking for ways to launch new innovate products quickly, why don’t we talk (Maarten at telruptive dot com)…
With Big Data in the news all day, you would think that having a lot of high quality data is a guarantee for new revenues. However asking yourself how to generate new revenues from existing data is the wrong question. It is a sub-optimal question because it is like having a hammer and assuming everything else is a nail.
A better question to ask is:”What data insight problems potential customers have that I could solve?” Read more…
Finally a solution for real-time distributed machine learning: Jubatus. Jubatus differs from Mahout and other distributed machine learning solutions that its focus is real-time instead of batch. Algorithms are for online classification, regression, recommendation, graph operation (queries, centrality, shortest path), etc. Zookeeper is used to keep the distributed Jubaclassifiers synchronized. Multiple clients connect to the Juakeeper (based on Zookeeper). Jubatus has a plugin framework to convert unstructured data on the fly into feature vectors. Performance seems to be linear for 16 nodes. Jubatus is another solution that Big Data Architects should evaluate…
Impala is the open source version of Dremel, Google’s proprietary big data query solution. A first beta is available and the production version is foreseen for Q1 2013.
However the real revolution will only get better when Doug Cutting [the creator of Lucene, Hadoop, etc.]’s Trevni is integrated into Impala. Trevni is a new columnar data storage format that promises superior performance for reading large columnar stored data sets.
Impala+Trevni is promising real-time big data queries with multiple joins that are on par in performance but have more functionality than Google’s Dremel…