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How to Use Data Science The Ten Types Of Innovation

Data is now the key to successful innovation

20Nov

The first decade of this century was all about business intelligence as a competitive advantage. Companies created and pursued metrics, gathered information and refined it into smart charts and used it as a base to develop new products, reach new markets or come up with new business models. The second decade of this century is taking this trend to the next level. The age of Big Data, of massive unstructured information, requires different tools to harness the knowledge trapped in exabytes of logs and recordings. This is the time when data science consulting services can help companies define their next generation innovation tools.

Enterprise-level challenges

Designing any type of IT system at an enterprise level comes with specific problems related to security, stability, scalability, and adaptability. When using Big Data, additional obstacles should be considered, imposed by the 3 Vs. (volume, velocity, and variety).

Furthermore, data science is a new discipline, and the qualified workforce is still scarce and spread out. Finding the right people, motivating them and creating cross-functional teams is always tricky since most experts come from other domains like statistics, engineering, and even business analysis and need to find common ground.

Another barrier is the transferable knowledge problem that hinders innovation, as most specialists are not used to seeing the applications of their analysis or the synergic connections that could lead to breakthroughs.

Innovation with data science

Although the problems above should be acknowledged appropriately, these are not real barriers to using data science for innovation, but things to consider for a successful implementation. There are plenty of ways to incorporate the information provided by this discipline in innovation attempts.

Success stories

This annual ranking from Fast Company acknowledges the world’s most innovative companies each year. By looking at the awards given in 2017, all the top honors were received for using data science. The reasons for the distinctions were improving speed and accuracy of service (Amazon), developing photographic memory (Google), accelerating autonomous driving (Uber) and even enticing artists with data (Spotify). These are just a few examples of how manipulating information the right way can create new revenue streams, optimize delivery or enhance a current offering.

Ten types of innovation

Larry Keeley's book regarding the types of innovation still stands as cornerstone content in the world of business model definition. We aim to give a few examples how this framework can be adapted to the era of data science by focusing on each type of innovation at a time.

  • Profit model - Crunching numbers can identify untapped potential hidden in the profit margins or pin-point insufficiently used revenue streams. Simulations can also show if specific markets are ready. Data can help you apply the 80/20 principle and focus on your top clients.
  • Network - Data recorded and analyzed by one company can benefit others in numerous ways, especially if the two entities are in complementary businesses. Just imagine how a hotel could boost their bookings by using the weather and delayed flights information collected by a nearby airport during their regular operations.
  • Structure - Algorithms to ingest organizational charts with augmented information from thousands of companies and produce models of the best performing. It could offer recipes for the gender and educational composition of a Board to maximize talent. This could end artificial efforts of having more women on the board and produce even recommendations of possible candidates by scanning professional profiles.
  • Process - Data science consulting company InData Labs states that using analytics in the company’s operations is the best way to handle uncertainty by teaching staff to guide their decisions on results and numbers instead of gut feeling and customs.
  • Product performance - One company which already does this through their newsfeed automation is Facebook. They have innovated the way it looks for each individual user to boost their revenue from PPC ads. By employing data science in every aspect of user experience, you can create better products and cut development costs by abandoning bad ideas early on.
  • Product system - The ability of Big Data to serve as a base for recommendation engines makes the creation of product systems not only efficient but dynamic and perfectly adapted to the user’s preferences. Target’s achievement in creating a product system recommendation for pregnant women is a relevant example and a warning sign at the same time.
  • Service - One of the primary goals of using data science in an organization is to create better services and to impress customers. By using all the information provided by website logs, in-person visits to retail stores, redeeming loyalty points and other actions, the company can create innovative ways to impress the customer by targeting their specific pain points. For example, innovating the check-out process for returning customers or offering free shipping for items placed in the basket and not bought yet or creating an intelligent chatbot to help them with trivial questions are all results of data science.
  • Channel - Implementing data science to listen to social media word of mouth and using Big Data to learn more about the traffic on each channel can help a company innovate the ways they are reaching clients and grabbing their attention for long enough to make a difference.
  • Brand - By sentiment analysis and natural language processing the enterprise can learn the satisfaction degree of the clients and if they tent to associate their trademark with certain feelings, groups or communities. Such insight can have a direct impact on marketing campaigns, brand voice and exterior signs like the packaging. These innovations, done right, should increase the market share and ROI.
  • Customer engagement - Before Big Data, measuring customer engagement was done indirectly, by counting redeemed coupons or looking at sales. Now every mention of the company or visit of the website can be recorded and analyzed to determine how where is the visitor in the sales funnel. Innovations in the way an enterprise allows their followers to interact with them build awareness, positive word of mouth and create communities. Just look at what gaming companies are doing for inspiration.

These are just a few examples of how data science can help demystify the “unknown unknowns.”

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