Data Driven Innovation: A Primer

Data Driven Innovation A Primer
Data Driven Innovation A Primer

We are hearing all the hoopla about Big-data. How it is radically changing the way we look at company data and provide data driven reasoning for better and less risky decision making. Innovation is one such area. Big-data could provide a real lift to DDI. Having a data driven approach will help in better, targeted and relevant innovations that are craved by the clients/ customers. This bottom-up approach at its most effective form could be easily conceived by a good data driven innovation strategy.

Now let’s get into the primer on DDI- What is entails and how could one leverage that. Here is the Who, What, Where, Why and When of Data Driven innovation.

Why use DDI?
Let us address why do we actually need to use DDI- what will it buy us. Consider a situation where you have to come up with your next best product/feature/innovation. Where would that come from? From your gut based on some hunch or from hardcore actual data from right sources. Hunch based discoveries are great but their failure rate is higher. Also, they are difficult to validate as the implementer has to do various focus groups which in themselves are flawed to certain extent. Now consider a case where your product-customer interactions, operations fill you up on what is relevant and you can use data to understand its impact on the organizations. This helps you identify what matters most and helps you choose the idea with substantial data to back up the theory. So, there is no need for spending money on focus groups, but there is a need to leverage real interactions with the real customers/people leading to real results. This ultimately means lesser chance of failure and cost effective way to find the next big thing that is most craved by your customer or organization. This reduces the risk of failure substantially and puts you at ease. So, DDI is important and it could provide a sustainable and continuous way to innovate, iterate and improve.

What is DDI?
Data driven innovation, as name suggests is the way through which data is used for learning about new features, modifications, product ideas that is most cherished by your current customers, market landscape. However, its usage and manifestation in an organization could be different based on its structure, maturity, usage and implementation. Its definition could very well incorporate the application and purpose it is set to achieve. For some organization DDI is a way for finding process improvements, for others it is way to learn from customers and how they use products to learn about next features and/or products, for some it is a sustained source of learning about people, process and technology. But, I would put it in generic and call it “A method to innovate/iterate/improve using sustainable & continuous ways using data based decision process, where data is sourced to help learn about people, process and technology critical to your organization”.

Who could use DDI?
Data driven innovation is not everyone’s play. Not that it is too difficult to implement or it requires too much investment, but it requires certain maturity in your data handling capability before getting started. If you are diligent about using data to learn about your processes and its effectiveness, it will be easier. If you are not yet focused towards using data around your product and processes, you still might have some distance to travel before you delve into data driven methodologies. It is never late to start planning and executing strategies to introduce and leverage data points that go beyond your traditional direct customer & product data. So, in short, DDI could be used by any organization that is serious about learning from data. In Fact, smaller the firm, the better it could be implemented and lesser it would cost. The more silos, more complicated product/process structure, the more it is going to cost, to execute. In short, you could safely tag your DDI initiative on your management, the more selling your management requires for a data driven project, the farther you are from pursuing a full scale DDI. So, first get the leadership buyin on its value and then start shaping your organization to implement DDI.

Where will DDI take place?
Yes, you could figure out that DDI is a system that runs on data driven insights and data is everywhere in an organization so, it could show up anywhere. But, it is a bit trickier than that. The toughest part is not when data driven decision making is running in an organization’s DNA but the time when organizations decides to get started. DDI requires some careful understanding of how data works and how it could be used to get insights. Therefore, place where it should start is important. The best starting place for DDI could be around project managers, or if your organization is big enough to accommodate project management office, for agile companies, it should be around group leads. In short, DDI should start from a place that is not a stranger to data and understands how to handle it. So, in short, it could exist everywhere but it should start from a place that provides the most amiable surroundings required by a data driven project. Project managers, supervisors, PMOs are meant to keep a tap on the progress, so they possess some basic skills to function as data driven professionals and therefore, could help the best in understanding and executing a good DDI strategy.

When is the time to delve into DDI?
In short, the sooner the better. DDI requires substantial amount of preplanning and dedication. The sooner organizations delve into data driven innovation, the better will be its execution and value to the organization. A good data driven innovation implementation requires some practice and iterations on data models, validations, analysis and reporting. So, a successful implementation will rarely emerge from first implementation and would require some iteration. Also, the sooner the organization will start in direction of implementing DDI, the better it is because organizations will start acting in ways to facilitate smart data handling, which will have its own benefits. But, one caveat is that organization should have data to play with. Doing DDI sooner when data handling capabilities are not established could confuse the processes and steer the implementation in wrong directions. So, we could reword our sooner as “the sooner the organizations have started embracing data based decision making process, the better”.

To summarize, DDI is important and beneficial to any organization. It has the tendency to make any organization grow sustainably without having to invest too much into research and development. It support continuous improvements and that too without investing too much money and it could re-utilize the same infrastructure for sustainable leanings.

As a treat, here is a video on Big-Data and Innovation:

Originally Posted at: Data Driven Innovation: A Primer by v1shal

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