Credit: Data Science Central
Did you ever have a concept that you knew was right, but just couldn’t find the right words to articulate that concept? Okay, well welcome to my nightmare. I know that Data Science and Design Thinking share many common characteristics including the power of “might” (i.e., that “might” be a better predictor of performance), “learning through failing” (which is the only way to determine where the edges of the solution really reside), and the innovation liberation associated with “diverge to converge” thinking (where all ideas are worthy of consideration).
A recent McKinsey article titled “Fusing data and design to supercharge innovation” confirmed the integrated potential of Data Science and Design Thinking:
“While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits.”
The combination of Design Thinking and Data Science is a powerful combination, but they must be fused on deriving and driving new sources of value and actionable outcomes. To make Data Science and Design Thinking more actionable, we must begin with an end in mind. And to make the conversation more fun, I have created the 7 “Economics of Data” playing cards because I don’t want you gambling on the future of your organization!
The Big Data Business Model Maturity Index (BDBMI) provides that desired end; a frame around which we can drive the organization’s data monetization efforts (see Figure 1).
Playing Card 1: Big Data Business Model Maturity Index
The BDBMI helps organizations leverage data and analytics to:
- Uncover customer, product and operational insights buried in the data
- Leverage these customer, product and operational insights to optimize key business processes
- Build analytic profiles and digital twins that create new monetization opportunities (new products, new packaging, new services, new consumption models, new channels, new audiences, new markets).
- Ultimately lead to the Digital Transformation of the organization
Figure 2 details the stages of the BDBMI.
Playing Card 2: Big Data Business Model Maturity Index Phases
BDBMI is ultimately an economic model. And we need to interweave Data Science and design thinking with BDBMI to help organizations exploit the economic value of data and analytics; to help them become more effective at leveraging data and analytics to power their business and operational models.
Data Science is a method of data analysis that automates analytical model building. Data Science uses combinations of different analytic techniques (data mining, predictive analytics, machine learning, deep learning, artificial intelligence)to find hidden insights buried in the data – patterns, trends, associations and relationships – without being explicitly programmed where to look. Data Science is a business discipline (branch of knowledge) that:
- Identifies those variables and metrics that might be better predictors of performance
- Identifies, codifies and determines the strength of the customer, product and operational patterns, trends, associations and relationships buried in the data
- Quantifies “cause-and-effect” and “goodness of fit” in order to know when a predictive model is “good enough” given the costs of False Positives and False Negatives
- Acts, automates and continuously learns from every customer and device transaction and interaction
To support our Data Science development efforts at Hitachi Vantara, we have developed a Data Science methodology called DEPPA: Descriptive, Explorative, Predictive, Prescriptive, Automation (see Figure 3).
Playing Card 3: DEPPA Data Science Development Methodology
DEPPA is an agile, iterative Data Science maturation process that enhances an organization’s analytics capabilities as they advance along Big Data Business Model Maturity Index. DEPPA is comprised of the 5 stages:
- Descriptive: identify, alert and report on business and operational results; Confirm analytics scope via business-prioritized use cases that captures use cases business value and potential implementation challenges and risks
- Exploration: establish hypotheses, assess variable behaviors and relationships between variables; Establish use case hypotheses and conduct data explorations to assess variable behaviors and relationships between variables
- Predictive: predict outcomes, consequences, costs, or effects; Exploit wide-ranging, detailed data sets to train models that predict outcomes, consequences, costs, and effects, and quantifies the drivers (causes) of those predictions developed through machine learning algorithms
- Prescriptive: quantifies successful outcomes that drive recommended actions; Optimize use case with prescriptive and preventative outcomes driven through recommended actions developed through deep learning algorithms
- Automation: automates steps to prevent problems, exploit business opportunities and create intelligent apps and smart spaces; Automate implementation of prescriptive analytics in intelligent assets to address / prevent problems, exploit next best business opportunities, business decisions and intelligent asset creation
Figure 4 provides more details on the specific stages of DEPPA.
Playing Card 4: DEPPA Stages
The DEPPA Data Science development methodology supports many of the challenges associated with non-linear thinking that I covered in my blog “Why Data Science is like Playing Game Boy® Final Fantasy” (except with different menacing monsters and bosses).
The DEPPA Data Science methodology lives to support the Big Data Business Model Maturity Index which is designed to help organizations become more effective at leveraging data and analytics to power their business models.
Design Thinking leverages human engagement, collaboration and envisioning techniques to discover unmet needs within the context and constraints of a particular situation. Design Thinking frames the opportunity and scope of innovation, generating creative ideas, testing and refining solutions. It creates a repeatable and scalable process for innovation. Design Thinkingis a human-first development and engagement approach that is all about:
- Building a deep empathy with the people for whom you are designing
- Generating lots of ideas from a variety of different, even conflicting, perspectives
- Rapidly roughing out prototypes and storyboards
- Sharing those prototypes and storyboards with the people for whom you are designing
- Trying, failing and learning from each failure
- Operationalizing your innovative new solution with the acknowledgement the no solution is perfect
Design Thinking is a customer-obsessed process to uncover and validate human (SME) requirements and insights as organizations advance along Big Data Business Model Maturity Index (see Figure 5).
Playing Card 5: Design Thinking Stages
Design Thinking complements Data Science by uncovering and codifying the deep knowledge and insights of the organization’s subject matter experts. Check out the blog “Using Design to Drive Business Outcomes, or Uncovering What You Don…” for more details on our thinking about the application of Design Thinking. See Figure 6 for more details on the stages of the Design Thinking Maturation Model.
Playing Card 6: Design Thinking Maturation Model
The Big Data Business Model Maturity Index helps organizations leverage their data (Big Data, IoT) with advanced analytics (machine learning, deep learning, AI) to:
- optimize key operational and business processes,
- mitigate security and compliance risks,
- uncover new sources of customer, product, operational and market revenue, and
- create a differentiated, compelling customer experience.
Data Science and Design Thinking support the BDBMI in helping to identify and codify the customer, product and operational insights – patterns, trends, associations and relationships – buried in the data or in the heads of the Subject Matter Experts (see Figure 7).
Playing Card 7: Data and Analytics Monetization Framework
Enjoy playing with the cards, and hopefully they’ll someday be as valuable as a Nolan Ryan rookie card (and yes, I have one!).
- Design Thinking and Data Science empower the Big Data Business Model Maturity Model (BDBMI).
- The 7 “Economics of Data” playing cards provide a roadmap for helping organizations to become more effective at leveraging data and analytics to power their business models.
- Data Science uses combinations of different analytic techniquesto find hidden insights buried in the data – patterns, trends, associations and relationships – without being explicitly programmed where to look.
- Design Thinking leverages human engagement and envisioning techniques to discover unmet needs within the context and constraints of a particular situation.
- Monetization is about… well, making more money…