Creating Digital Flow for Agile Analytics

Steve BurgUncategorized0 Comments


In order for agile analytics to work properly, the correct digital flow must exist.


Executives put large sums of money into marketing their technology. In fact, companies spend an average of $7.4 million on data-related initiatives in 2015. However, most of this investment created more digital data silos. We have Google Analytics for web traffic, Omniture for segmentation, Salesforce as a CRM, and countless other programs — all producing data. The issue is that none of them communicate with each other.


Establishing a digital flow that will ensure optimized performance requires a different digital architecture for each organization. It must be dynamic, able to shift as the business does without interruption, as well as provide opportunities for innovation as the data dictates.


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While building this philosophy of agile analytics, we came across an amazing ebook from Forbes that discusses this exact issue. They break it down into three essential building blocks: design thinking, platform thinking, and open thinking.


For this post, we will concentrate on design thinking and follow up on the others in separate posts.


Design thinking


Design thinking, as described by leading proponent Tim Brown, president and CEO of IDEO, is “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”


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In other words, placing design at the beginning rather than the end of the process. The next step is, how do we apply this to the digital flow necessary for agile analytics?


Design thinking has three major stages, each can be applied to the agile analytics digital flow:


  1. Invent a Future: Set goals about what you want to get out of your company’s data that you are not currently getting. Rather than poll employee for this information, simply observe how the data is being used and look for other opportunities for application.
  2. Test Your Ideas: Start slowly bringing new data to different teams. Watch how they respond to the new data sets. Adjust the data, data sources, and other variables accordingly until it becomes useful.
  3. Bring it to Life: When you’ve got a winner, identify the activities, capabilities, and resources your company will need to produce and distribute the new digital flow to the rest of your company.


How does this work in real life?


A great real life example of design thinking using a data focused process comes from P&G’s Oil of Olay. When executives were looking to rebuild the brand they took a look at the two primary locations that were selling the brand: mass retail channels and high-end department stores. They observed their customers in real life and came to a startling realization.


The industry was targeting women over 50 who were primarily worried about wrinkles, while ignoring the audience of 30-50 year olds who had other skincare concerns. The product and the market were not aligned.


As a result of this discovery, they used data to test other skin products that had multiple uses and protection capabilities.


That’s how it was done years ago.


The P&G process meets agile analytics


Using the agile analytics model, we can update this process and rework it completely to make this decision take hours instead of weeks. First, we would pull our 1st party credit card data to see who was purchasing skin care products and where. Second, we would overlay 3rd party data from any number of companies to learn what was important to those making purchases.


We can then test the new products in key markets, taking into account outside parameters (age, income, packaging, store placement, etc.), after which we would roll out the product nationwide.


The whole process is significantly shortened. We don’t have to spend weeks observing people in stores. We have the capability to see exactly who our customers are and decide if they are the customers we want. We know who is going into department stores, who is purchasing online, and we can overlay 3rd party data to figure out the why.


The above is just another example of the efficiency of agile analytics. Design thinking just got a substantial upgrade.


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