Want To Lead a Data Driven Organization? Build a Mathematical Mindset First

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While organization is nothing but an amalgamation of ideologies / methodologies of people working in it. Leaders play a vital role. Leaders must cast aside their old ideas and apprehensions about machines and intelligence, and proactively use the power of machine of their advantage. If leaders want to be the forerunners in this new machine-human work culture they must start educating themselves, and create a thriving dataphilic culture before they begin an organizational transformation.

Josh Sullivan and Angela Zutavern, author of The Mathematical Corporation have a lot to say about this topic through their book. Let’s dive in.

What new leadership mindset will be needed to lead the mathematical corporation?

Josh:

Leaders will need to move away from managing what they know. How they think about the future is constrained by what they’re already working with. Instead they should ask, “What are the big unknown factors about the future that I want to answer?” and then use their intelligent machines to go figure that out.

Most leaders today are driving strategy, managing a financial envelope, or pushing their product line. They’re not trying to imagine where they’ll take their business next, and challenging machines to use an ocean of data and the ability to learn to help answer the question. They’re not even trying to imagine the question to ask yet.

Angela:

People look at the past. It’s so engrained in people leading organizations, in society, even in our education system. You get a report card based on what you did. The big shift is moving from the past to looking at the future. That requires people to hit the “I believe” button and shatter their constraints about what they think is even possible to know.

In what ways will organizational culture and structure need to shift in order to glean the benefits of the mathematical corporation?

Angela:

There are two pieces needed to become a mathematical corporation: learning how to think differently, and learning how to act differently.

Learning how to think differently is about shattering our own long-held constraints about what we believe is or isn’t possible. To do this, you need to assemble an asymmetrical team—people with different backgrounds, skills, and perspectives than you might otherwise work with. Experimentation is critical, and the diversity of the people you bring to the table has a big impact on how successful you are in creating breakthroughs.

Learning how to act differently involves spreading the techniques, thinking, and technologies of the mathematical corporation to the rest of the organization.

Josh:

An important factor in learning to act differently is the redistribution of time. Most leaders spend half of it on administrative tasks. They need to reexamine how they spend time, and think of AIs as “workers” instead of decentralized, separate functions. This shift will change the very fabric of an organization.

What are the most critical intelligence technologies that leaders need to know?

Angela:

There are five:

  1. Data collection, storage and preparation;
  2. Algorithms applied to data to learn new information;
  3. Algorithms applied to the information learned;
  4. Applications to visualize, interpret and act; and
  5. Infrastructure technologies to make everything possible.

Leaders need to look into how they are going to collect, integrate, and make data available. Then, they need to determine what they’ll do with the data—the algorithms and the analyses. Then there’s the storytelling piece of how they’re going to communicate the data with visualizations and get buy-in and change. And then there’s an action piece: what are you going to do differently? You need to look into technologies for all of those. Not all leaders are going to be deep experts in these technologies, but there are some they should understand and be conversant with if they want to lead a mathematical corporation.

Josh:

In general, there are two primary ways humans learn, and five main ways machines learn. The technology of how that happens will continue to evolve with the rapid progress in AI, but the broad categories are important to know for leaders. IT architecture changes quickly, but the categorizations of how people and machines learn hasn’t changed in the last 50 years.

What’s the most important first step leaders should take right now to start transforming their organizations into mathematical corporations that use data to alter their future?

Angela:

Start by shattering your own constraints. It’s harder than it sounds, but it starts with educating yourself on what’s possible to begin to open your mind to new possibilities, and then actively working on exercises that change the way you ask questions. You can do that through innovation workshops, leadership retreats, etc.

People can also work through these four main questions:

  1. What is the ecosystem in which your mathematical corporation will operate?
  2. What impossible problems, if you solved them, would change everything?
  3. What impossible solutions can you come up with?
  4. How can you best experiment with solutions and learn from your mistakes?

Josh:

Recognizing that you have constraints in your thinking is the key to breaking them. Constant failure is a learning approach to leadership. Most leaders wouldn’t want to say they failed, but to get on this journey, you have to try to new approaches and algorithms. Successive failure leads to learning, even in itself. You also need to establish a strategic imperative at the leadership or board level—it’s not just a six-month program. It needs to be a leadership transformation that drives an organizational transformation.

The future of work means differently to each one of us: some see it as more technology and less human, some expect a more humanized space and some others imagine it to be a no-workplace world. In our journey to unwrap FutureofWork, Work2.org invites leaders from various industries to help our global community to understand what the posterity holds for workers, leaders and organizations. While our team is busy at bringing this fresh ideas directly to you, we would appreciate our community help in making it possible. If you like what you’ve read, we would appreciate if you could spread the word within your circles and let us know if anything you want us to bring into this #FutureOfWork conversation.

Originally posted at AnalyticsWeek