An agile culture looks at capabilities differently, as we noted in our recent post about how businesses can achieve enterprise agility.

While the core function of capabilities remains the same in the traditional sense – being able to create significant value for its customers — those capabilities must now function more quickly in an agile organization. To do move with speed, — to support an agile business — they need to be fueled by data and automation. Let’s take a closer look at data-centricity.

It’s common to hear that a big-data mindset is key to unlock these capabilities; it allows organizations to predict what’s next in the form of disruption and customer need and find ways to be more efficient. Data paired with the right continuous integration/continuous delivery pipeline is one potent combination. However, that big data competency takes time to stand up and to having a mature system of insight requires considerable investment.

Why Small Data Matters

But what about small data? Small data is data that is gathered by primary research in less-than-automated ways.  Small data is gathered by observation to uncover insights; when it is purposefully gathered, it is more than a suitable catalyst to agility. Small data pairs perfectly with incremental updates to existing products or creating entirely new minimum lovable products (MLPs), and the design sprints that power them. Small data in this application allows organizations to be data-centric, because they are actively listening to customers. Small data gets translated into immediate business intelligence, allowing organizations to adapt to their changing needs, with velocity. The best part about small data is that it requires little investment and is usually right in front of them.

Small Data and Minimum Lovable Products

For MLPs, data gathering should focus on validating leap-of-faith assumptions or conversely, disproving said assumptions. The type of data to gather should facilitate fail fasts and form new questions to allow a quick pivot or to go all in. When hard volumes of transactions and referral rates aren’t within reach, consider user-centered metrics that allow measurement of the user experience on a large scale. These metrics can then be used for decision making in the product development process (read: Google’s HEART framework).

In Practice

At Moonshot, we recently collaborated with a client to explore, define, and build a two-sided marketplace in the industrial materials space. With small data, we tracked task success, engagement, and overall happiness via a prototype, yielding key learnings that determined the primary search use cases and overall on-boarding experience for its first MLP.

Recently, a nationally recognized fast casual brand is using small data sets as an early indicator for potential additions to their menus. Said additions are tested at just a few stores before undergoing tests at scale (80-100 stores), and only then would they be considered for release on a national scale. Leveraging small data enables the brand to smartly navigate changing customer preferences, which has contributed to their stock price  setting record highs and a forecast of 140 new stores by the end of 2019.

Recap

The benefits of data-centricity don’t have to be depend solely on big data. Certainly, big data is a part of the grander equation which won’t happen overnight. In the meantime, consider small data. Small data allows organizations to continuously deliver or create net new value with minimal investment. The consistency of quick wins allows organizations to in parallel make sufficient investments in their infrastructure to make that big data play as their product scales and matures. At Moonshot, we’re here to help update your capabilities to keep up with your customer’s rapidly evolving needs. Reach out anytime.

Chris Mui

Chris Mui

Product Manager

Bitnami