Part 2 of a two-part series. Read Part 1
In the first blog in this series, we explored how organizations can create dynamic business strategies rather than get stuck in cement. Here, we’ll look at four key concepts for supporting that vision.
Key #1: A common vision
It all starts with a common vision – a shared mission – so that everyone on the team and across the ecosystem understands the organization’s “True North.”
For our data science community, that common vision or “True North” is around helping our customers (both internal and external) become more effective at leveraging data and analytics to power (optimize) their business models and uncover new sources of customer, product, and operational value (Figure 1).
Figure 1. Big Data Business Model Maturity Index
The Big Data Business Model Maturity Index (BDBMI) helps organizations:
- Map or benchmark where they sit today vis-à-vis data and analytics best practices (to understand what “good” looks like)
- Provide a roadmap to navigate the BDBMI to become more effective at leveraging data and analytics to power their business models
Key #2: A common language
It is important that organizations have common language and terminology, so there is no confusion about what is being said, and a standard engagement “framework” that guides their value identification, capture, and operationalization process. For our data science team, that is the Data Science Value Engineering Framework.
A flexible, malleable framework is more important than a rigid methodology – think guardrails, not railroad tracks – in allowing the team the necessary flexibility to bend the process in response to customer and market conditions. But there’s still a common framework so that everyone understands the organization’s goals and the KPIs that define measures of progress and success (Figure 2).
Figure 2. Data Science Value Engineering Framework
The Data Science Value Engineering Framework is very simple. (If it’s complicated, then it’s too hard to swap team members in and out of the process while retaining structural integrity.)
- Start with a targeted business initiative: a business or operational goal that the organization is trying to achieve or accomplish over the next 12 to 18 months.
- Identify and engage the key business stakeholders (subject matter experts) who either impact or are impacted by the business initiative.
- Interview and conduct brainstorming (facilitated workshops) to identify, validate, value, and prioritize the decisions the key stakeholders need to make in support of the targeted business initiative.
- Have key stakeholders collaborate with the data science team to identify the predictions that will need to be created to support the top-priority decisions. See the “Thinking like a Data Scientist Series” for more details.
- The data science team will then determine (explore/discover) the data sources necessary to drive the predictions that optimize the top-priority decisions. In an IoT engagement, this may also germinate into a sensor instrumentation strategy.
- Finally, use the analytics, data, and instrumentation strategy to drive technology and architecture decisions. See “Disposable Technology: A Concept Whose Time Has Come” for more insights into creating an agile technology infrastructure.
Key #3: Organizational improvisation
Like a great basketball or soccer team (channeling the Women’s World Cup), successful organizations exhibit organizational “improvisation” in their ability to move team members in and out of teams while maintaining operational integrity. We have this organizational improv challenge in every one of our data scientist engagements. You can’t just add another data scientist to a high-functioning data science team and expect it to keep operating at its optimal efficacy.
To address that challenge, we create data science “pods” with clearly defined roles, responsibilities, and expectations (not to mention a common vision and common languages, as we discussed earlier). But we also understand that the head of the pod is free to morph the team’s structure and reassign roles, responsibilities, and expectations during the heat of the battle (see Figure 3).
Figure 3: Data Science Pods
By the way, the pods must have a senior person who can make the hard tie-breaker decisions. Otherwise, analysis paralysis can set in (especially with a bunch of data science nerds).
Our pods also contain a design thinker who can not only help drive innovative thinking on that team with respect to the problem that the data science team is trying to address but is also responsible for the cultural fitness of the team. As I wrote in “Is it a Floor Wax or a Dessert Topping?”:
“Design thinking accelerates two important outcomes: 1) fuels innovative thinking around identifying, validating, testing, refining, and valuing ideas, and 2) drives organizational alignment around those ideas.”
Key #4: A culture built on sharing, openness, and trust
Marc Andreessen once claimed that software is eating the world. But my personal experience says culture sits on top of the food chain. Culture is hard for me to define; you know a good culture when you’re in one, but it’s hard to explain how to create a culture that supports scaling innovation.
So instead of a statement or directive on how to create a culture for scaling innovation, I have found these guidelines useful:
- Be humble but not diminutive.
- Be confident but not arrogant.
- Be inclusive while still remaining decisive.
- Be open but results-oriented.
- Have fun but still get stuff done.
- Empower individualism but don’t disrupt the team.
- All ideas are worthy of consideration (which does not mean that all ideas are good).
- Be respectful and cordial…always!
- Be open to sharing and to learning something new and unlearning something old.
Maybe the most important guideline is trust. But you cannot just dictate trust; trust has to be earned.
Trust accumulates with every transaction and engagement. Trust builds slowly. But if a team can establish an atmosphere of trust, then nothing is impossible.
Scaling innovation summary
I recently read “What it really takes to scale artificial intelligence” from McKinsey that discussed why organizations are failing in the adoption of artificial intelligence (AI). This paragraph summarized the challenge nicely:
“Many organizations aren’t spending the necessary (and significant) time and resources on the cultural and organizational changes required to bring AI to a level of scale capable of delivering meaningful value – where every pilot enjoys widespread end-user adoption and pilots across the organization are produced in a consistent, fast, and repeatable manner. Without addressing these changes up front, efforts to scale AI can quickly derail.”
Scaling innovation may be the biggest challenge for organizations in the 21st century, given the onslaught of new digital technologies, data sources, analytic capabilities, and communications channels. It will require more whiteboards than maps to flourish with all of these changing dynamics.
As General McChrystal says in Team of Teams, the modern enemy operates in smaller, highly cohesive teams, exhibits extreme flexibility in the execution of its objectives, learns/unlearns/relearns rapidly, and shares a common vision and language.
The future of competition will look very different in the 21st century. And as the Golden State Warriors and the 2019 World Cup champion U.S. Women’s National Team have taught us, while strong individuals will always play a role in creating new sources of innovations, it takes a team to scale innovation.
Want to find out how to build, integrate, deploy, and operate an intelligent application with SAP Data Intelligence? Join us on September 17 and get to know the capabilities of SAP Data Intelligence.
This article originally appeared on LinkedIn and is republished by permission. Hitachi Vantara is an SAP global technology partner.