пятница, 6 октября 2017 г.

The Flare and Focus of Successful Futurists


The ability to plausibly forecast the future requires alternating between broad and narrow ways of thinking.



Futurists are skilled at listening to and interpreting signals, which are harbingers of what’s to come. They look for early patterns — pretrends, if you will — as the scattered points on the fringe converge and begin moving toward the mainstream. The fringe is that place where hackers are experimenting, academics are testing their ideas, technologists are building new prototypes, and so on. Futurists know most patterns will come to nothing, so they watch and wait and test the patterns to find those few that will evolve into genuine trends. Each trend is a looking glass into the future, a way to see over time’s horizon. This is the art of forecasting the future: simultaneously recognizing patterns in the present and thinking about how those changes will impact the future so that you can be actively engaged in building what happens next — or at least be less surprised by what others develop. Futures forecasting is a learnable skill, and a process any organization can master.
Joseph Voros, a theoretical physicist and senior lecturer in strategic foresight at Swinburne University of Technology in Melbourne, Australia, offers my favorite explanation of futures forecasting, saying it informs strategy making by enhancing the “context within which strategy is developed, planned, and executed.”1 The advantage of forecasting the future in this way is obvious: Organizations that can see trends early can better prepare to take advantage of them. They can also help shape the broader context, with an understanding of how developments in seemingly unconnected industries will affect them. Most organizations that track emerging trends are adept at conversing and collaborating with those in other fields to plan ahead.
Although futures studies is an established academic discipline, few companies employ futurists. That’s starting to change as more leaders become familiar with the work futurists do. Accenture, Ford, Google, IBM, Intel, Samsung, and UNESCO all have had futurists on staff, and their work is quite different from what happens within the traditional research and development (R&D) function.
The futurists at these organizations know that their tools are best used within a group — and that the group’s composition matters tremendously to the outcomes they produce. Here’s why. Within every organization are people whose dominant characteristic is either creativity or logic. If you’ve been on a team that included both groups and didn’t have a great facilitator during your meetings, your team probably clashed. If it was an important project and there were strong personalities representing each side, the creative people felt as though their contributions were being discounted, while the logical thinkers — whose natural talents lie in managing processes, projecting budgets, or mitigating risk — felt undervalued because they weren’t coming up with bold new ideas. Your team undoubtedly had a difficult time staying on track, or worse, you might have spent hours meeting about how to have your next meeting. I call this the “duality dilemma.”
The duality dilemma is responsible for a lack of forward thinking at many organizations. It contributed to the decline of BlackBerry Ltd.’s smartphone business; the company (formerly known as Research in Motion Ltd.) never had an executable plan to remake the phone’s form factor and operating system in the age of the iPhone. Right-brained creatives wanted to make serious changes to the phone, while left-brained process thinkers were fixated on risk and maintaining BlackBerry’s customer base.2 The future of the business hinged on the company’s ability to bring both forces together to forecast trends and plan for the future.
BlackBerry’s experience suggests that forecasting the future of a product, company, or industry should neither be relegated to inventive visionaries nor mapped entirely by left-brain thinkers. Futures forecasting is meant to unite opposing forces, harnessing both wild imagination and pragmatism.

Turning a Dilemma Into a Dynamic

Overcoming the duality dilemma — and getting full use of both your creative- and logic-oriented team members — in order to track emerging trends and forecast the future is possible. But counterintuitively, it’s a matter of highlighting — rather than discouraging or downplaying — the strengths of each side. Stanford University’s Hasso Plattner Institute of Design (also known as the d.school) teaches a brainstorming technique that addresses the duality dilemma and illuminates how an organization can harness both strengths in equal measure by alternately broadening (“flaring”) and narrowing (“focusing”) its thinking.3
When a team is flaring, it is finding inspiration, making lists of ideas, mapping out new possibilities, getting feedback, and thinking big. When it is focusing, those ideas must be investigated, vetted, and decided upon. Flaring asks questions such as: What if? Who could it be? Why might this matter? What might be the implications of our actions? Focusing asks: Which option is best? What is our next action? How do we move forward?
The forecasting method I have developed — one, of course, influenced by other futurists but different in analysis and scope — is a six-step process that I have refined during the past decade as part of my work at the Future Today Institute. The first four steps involve finding a trend, while the last two steps inform what action you should then take. (See “A Six-Step Forecasting Methodology.”)

четверг, 5 октября 2017 г.

10 top skills of the best Innovation Managers


By definition, an innovation role covers many different areas of expertise. We’ve listed the most prominent skills you might be looking for on the resume of job candidates. A T-shaped profile will probably be the one you’re looking for. You need an emphatic generalist that can quickly jump from one domain to the other.

Example interview questions
For each, we’ve added a couple of example questions. Use this list to write your own interview script. 

1. Business Case oriented
Working with limited info should not be a problem. An innovation manager should be able to work with a brief list of assumptions to build the basic of an innovative business case.
  • What could be the selling price of Virtual Reality Goggles for surgeons?
  • What would need the biggest investment: Building & maintaining a support chatbot or hire an extra support colleague?

2. Project oriented
Many innovation managers have to follow up on dozens of innovation projects. Make sure to look for people with a decent level of project mgmt experience. This person will have to manage your innovation process. You don’t want a creative dreamer at the wheel, right?
  • Which project management tools or services have you used before. Why (or why not) were those tools the best solution for the task at hand?
  • What is the most difficult part of managing a new business or innovation project? 

3. Manage failure and exceptions
The more disruptive the project, the bigger the chance failure can occur. You’re walking in new territory, so an innovation manager will need to prepare others around her that future is always very uncertain.
  • What is the biggest threat to an innovation project? How to manage this risk?
  • How many client/users would you talk to before going public with a new innovation?

4. Willing to change
Talking about change is not enough. Look for candidates that are willing to take the first steps themselves. By doing so, they can inspire others to follow.
  • When and how did you initiate a new idea that was out of the comfort zone for most people?
  • Name a situation where you managed to change how your organization worked? 

5. Curious
Look for people that are eager to find new information. People that want to deep dive into new domains.
  • Tell me something most people don’t know about? 
  • Do you follow niche websites, people or sources about a very specific topic?
6. Optimistic
Innovation leads will need to see opportunities everywhere. Obstacles are just a trigger for change.
  • Name a failed innovation but that you could relaunch in the market? Explain how.
  • When would you stop an innovation program or project?

7. Fast and Furious
Thinkers that take 2 years develop an idea are probably not the leaders to steer an innovation team. You need people that will look for shortcuts. People that understand that done is better than perfect.
  • Have you ever launched a project that you’re ashamed of today? Why?
  • What was the fastest cycle you ever experience to go from the first idea to public launch? What was your role in this initiative?

8. Cross-industry experience
Ideally, an innovation manager has a mixed background with practical experience in multiple industries. The best ideas emerge when different views collide.
  • When did you solve a problem in your organization, based on info you learned elsewhere? 
  • How do you keep yourself up to date, what sources do you follow? (generalist will mention a mix of domains)

9. Able to manage internal politics / buy-in 
A corporate is packed with internal politics. Innovation projects are the perfect fuel to spark internal fires. An innovation manager will need to spend a lot of time on internal communication.
  • Explain a situation where an important project got blocked in the ‘red tape’ of your organization. What did you do to move this project forward?
  • When working on a new innovation project, who needs to stay in the loop? (other roles/departments/…) Who needs to stay out?

10. Strategic Sighted
Innovation project fall by definition out of your companies comfort zone. Can this person inspire others with a clear vision where else the company needs to look?
  • Can you give an example of a promising technology that most people see as the next big thing, but that you don’t agree with?
  • Which small or medium-sized company has a strong innovation strategy & why? (avoid that people just talk about Apple, Facebook, Google or Amazon)

https://goo.gl/ZXswpL

вторник, 26 сентября 2017 г.

Thinking Outside The Box

In digital, it’s all about personalization. Isn’t it time pharma caught up with other industries?



By Adam Chapman


Soon, the pharma industry will face a stern ultimatum – diversify or die. The internet – that majestic marketplace of ideas – has been busy empowering consumers through unfettered access to information and services. Now that customers know what they want (and when they want it), holding their hold their attention means tracking and reacting to the minutiae of their online movements. 

This sophisticated idea is at the center of Volkswagen’s marketing model, says Adel Baraja, Sales and Marketing Consultant at Volkswagen, who believes a high level of personalization will provide exponential growth in the pharma industry.

“Marketing channels like outdoors, out-of-home, pay-view or TV deliver one message that aims to suit everybody,” he says. “In digital, we are trying to give our customers or prospects a totally different message.”

It’s mass media but it’s also custom, he says. “We highlight people's interests, from either their browsing behavior or online habits, and we identify the people in the marketplace who want to buy a car. In a way, it’s not invasive at all; Netflix or Amazon make suggestions based on books read or programs watched in a similar way.” 

In tracking a customer’s online activity, connectedness is the starting point, says Baraja. “We are looking for the connected customer, people that use their phone or multiple devices to get online. Those are the ones that are always searching, always looking up items – recipes, advice, do-it-yourself guides.”

Where does Volkswagen mine this data? “We work with multiple agencies and service providers, who gather and analyze all this big data. We do not have to do it manually, we can’t even see them – the data or the people – as it's anonymous. We just know that they have set the criteria that we are looking for,” he says.

Pooling data from third-parties can provide a general overview of behavioral patterns, then Volkswagen uses sophisticated metrics to tailor its message to each individual visitor on its website. “We know how these customer are coming in, for example, if they clicked on a banner, but we also know now – through advanced algorithms – that somebody browsing a website has an attention span of eight seconds – that’s less than a goldfish. They get distracted by another website or by a TV set and they leave your website,” says Baraja.

Many marketers would give up at this point – after all, they’ve served the ad, had a click and a visit, and formed a bond. “We keep connecting to that same client; we might be able to serve him a different ad. If he has seen the exterior of the car, we serve him another ad inviting him to see the interior,” he says, adding that if a customer leaves the website midway through customizing a car, a third ad can then be deployed on a third-party website asking them to come back and complete the customization. “The whole idea is to re-target the same customer again and again because they are the highest potential, rather than focusing on a new one with the least potential.” 

Baraja rejects the idea that this approach would not work in pharma. “A lot of people say: If people are healthier, pharmaceutical companies are going to lose money. But look at cooking shows; chefs show you their recipe and they show you how to make the food, but you will still go to the restaurant to eat it. It doesn't mean that if you are being helpful, showing them how to be healthy, people will stop coming back to you. They know you, you will become the one they take advice from, they will listen to you. If you throw anything at them they will buy it because they trust you.”

The right attitude and internal awareness are essential to successfully implement a personalized digital strategy. The increasing ubiquity of the digital world must be mirrored by the marketing strategy, he says. “Digital is no longer a separate strategy to the sales strategy, conventional marketing strategy or branding strategy. It is one strategy that takes a different direction, developing and evolving.”

The impact across the organization requires a top to bottom understanding, he says. “Marketing has become involved in IT – there is a lot of software and processes involved and it will continue to become more sophisticated. We will always have a CEO that understands where both digital and marketing is going. A CEO that also supports the vision and understands this is the requirement,” says Baraja.

вторник, 12 сентября 2017 г.

Data-Driven Transformation: Accelerate at Scale Now


Data-driven transformation is becoming a question of life or death in most industries. But initiatives to embed data in operations throughout a company often fail. This is because companies start by trying to reinvent their core IT systems—a multiyear effort that can run to hundreds of millions of dollars. Sadly, most of this money is wasted, because these massive centralized efforts take far too long. When the rules of business are being rewritten on a quarterly basis, companies need an approach to transformation that is agile, focused on results, and manageable.
Most CEOs recognize the power of data-driven transformation. They certainly would like the 20% to 30% EBITDA gains that their peers are racking up by using fresh, granular data in sales, marketing, supply chain, manufacturing, and R&D. And they may even dream of joining the ranks of data-driven companies that have shoved aside traditional players among the world’s most valuable companies. (See Exhibit 1.)

Yet CEOs are right to wonder how their organizations—where managers and executives already complain about a lack of data skills and where overburdened IT systems seem unlikely to be able to handle a tenfold increase in company data—can pull off such a transformation. These CEOs want to find a reliable way to move their companies into the data-driven future so that they can set up their companies to survive—and not put them in danger in the process.
There is a better way to approach data transformation. In our experience, these initiatives can succeed only if they are cost effective, incremental, and sustainable. Transformations should start with pilots that pay off in weeks or months, followed by a plan for tackling high-priority use cases, and finishing with a program for building long-term capabilities. Working with clients across industries, we have developed a three-phase approach to data-driven transformation. It starts with small-scale, rapid digitization efforts that lay the foundation for the broader transformation and generate returns to help fund later phases of the effort. (See Exhibit 2.) In the second and third phases, companies draw on knowledge from their early wins to create a roadmap for companywide transformation, “industrialize” data and analytics, and build systems and capabilities to execute new data-driven strategies and processes.

This three-step approach is faster, less costly, and more likely to succeed than a system-wide overhaul. Using existing data systematically and combining it with external data (from social networks, for example) for marketing or customer issue resolution can deliver fast results. We have seen companies achieve 15% to 20% of the potential of a full data-driven transformation in six to nine months.
Use quick wins to learn and fund the digital journey. In this first phase, companies identify the low-hanging fruit—discrete, rapid digitization efforts that can deliver quick wins. These projects immediately move the needle on performance in a key area—sales support or supply chain, for example. And rather than taking years, implementation occurs in months and starts paying back almost immediately. The pilot projects show that the company can benefit from digitization, and they provide important lessons in how to roll out digital transformation across the company. Crucially, the extra value that the quick wins create can help pay for longer-term efforts, potentially making the transformation self-funding.
Design the companywide transformation. In the second phase, which can begin while the first initiatives are still underway, the company draws a roadmap for company-wide transformation. This involves building a portfolio of opportunities—identifying and prioritizing functions or units that can benefit most from transformation. It also involves locating and starting to address roadblocks to transformation. During the design phase, companies also invest in framing and communicating the vision for the transformation to build support for needed changes, and they invest in systems to industrialize data analytics—making analytics a resource for every operation.
Organize for sustained performance. With a detailed roadmap in place and with the experience and funding available from the early projects, the company is ready to undertake a full-fledged digital transformation. In this phase, digital and data-driven processes and work methods spread to every corner of the company. Employees learn to work across silos to enable data-driven processes, and leaders make the organizational changes necessary to sustain the new approaches. The company creates a data-driven culture by investing in capabilities to use analytical insights and by launching a change management program to embed new mindsets, behaviors, and ways of working.

USE QUICK WINS TO LEARN AND FUND THE DIGITAL JOURNEY

Moving a big company in a new direction is a huge challenge for management. The best-conceived and most urgent transformation programs—digital or otherwise—are sometimes no match for organizational inertia. This may explain why 70% of publicly announced transformation programs fail to meet the company’s ambition, its timeline for the transformation, or both.
But large organizations can overcome resistance and build the enthusiasm needed for change to succeed if they approach transformation in the right way. By starting a transformation journey with a small number of quick initiatives that demonstrate what can be achieved by using new approaches, companies greatly increase their chances of eventual success.
Leaders should choose quick-win initiatives carefully, on the basis of several critical criteria: they must have a high chance of success, a significant and rapid payback, and visibility across the company. A major industrial company, for example, started by digitizing high-profile processes, including inventory management. (See “Building Momentum Through Pilots.”)
A large industrial company contemplated a massive digital transformation to increase its efficiency and to compete more effectively in markets where producers have limited pricing power. The company did not want to tie up capital in a massive change program and wait years for a payback. To avoid that outcome, the company first identified a few quick-win initiatives that could pay off within a month or a quarter.
The first initiatives it selected were in inventory management and capacity optimization—analyzing output and shifting production to sites that made the most profitable products. For these quick wins, the company used static data and created one-off solutions. But the projects led to significant savings and more sales of high-profit items, which generated immediate value. In nine months, quick wins generated $20 million in value. Once the projects based on static data were up and running, the company went back and built the systems it needed to manage these processes and functions continuously, using real-time data flows.
Applying the lessons from its early wins, the company has created a roadmap for ten major data transformation initiatives in areas as varied as demand forecasting and managing the outbound sales force. The company has also made plans for new companywide resources to support data-driven approaches and make them sustainable, including building a data lake. And it has begun identifying new data-driven business models. The overall goal for the transformation is to unleash $200 million in value over three to five years and to help the company raise its EBITDA margin by 2% to 4%.
Initial projects may be limited in scope, but it is essential that they succeed and serve as a convincing advertisement for the benefits of digital transformation. For this reason, companies should not only choose projects carefully but also be pragmatic about execution. It is best to avoid projects that would require fundamental changes in data handling—projects that would entail building a new data repository, for instance. Companies should use agile methodologies to build any new analytics models, with short sprints and tight timelines for developing a minimum viable product that can be tested and used to define additional requirements and refinements.
Quick-win projects should require no more than four to six months to complete, and their value should be demonstrable within weeks. During the quick-win phase, companies can build their ability to focus and execute swiftly and to work across silos—critical capabilities for pursuing large-scale transformation efforts. Quick wins can also energize and inspire managers and employees who have seen change initiatives bog down in the past.

DESIGN THE COMPANYWIDE TRANSFORMATION

As soon as it is clear that the early digital transformation projects are off to a solid start, the company can start preparing the roadmap for extending digital transformation across the enterprise. This starts with a high-level vision, which company leaders translate into a portfolio of initiatives (or use cases) to be rolled out in a logical order, on the basis of factors such as size of impact and competitive needs or opportunities. Then the company must agree upon some underpinnings of digital operations—analytics, data governance, and data infrastructure. (See Exhibit 3.) Creating a roadmap for use cases and projects to build data infrastructure and other resources needed for data-driven operations can not only make the transformation run more smoothly but also ensure that these investments pay. (See “Driving Fast Value from Data Transformation in Logistics.”)

For more than 30 years, a major global logistics company led its industry in the use of information technology, and its leaders believed that data was its competitive differentiator. The company took the long-term view and had spent more than five years implementing a new ERP system that cost hundreds of millions of dollars. But after massive investments of time and money, the company could not demonstrate that it had gained any competitive advantage in cost or revenue.
Then the company took a new, agile tack. This time it created a detailed roadmap for transformation based on two primary considerations: an examination of the data needed monthly, weekly, and in real time to optimize functions or operations and to generate the most impact for the company; and an assessment of the systems and data already available to fill the newly identified business needs. On the basis of this roadmap, the company began a series of pilot projects, using benchmarking data to optimize important cost drivers such as fuel consumption, maintenance, and labor. Another project aimed to improve pricing performance by accessing data such as customer P&L through new analytics.
Over the course of three years, the company systematically completed the list of projects for every major value driver in the business. After dozens of projects in areas such as pricing, fuel consumption, and network, the company went from sitting in the middle of the pack in operating performance to becoming the industry leader on EBIT performance.
Before attempting to define its vision, a company needs to have a thorough understanding of where it stands in terms of data, digitization, and current capabilities. As a preliminary step, then, the company should quickly and objectively assess its situation and gauge how its capabilities stack up against best practices in its industry. One option in this area is a diagnostic developed by BCG that weighs 21 factors in assessing a company’s starting point in data and analytics capabilities and assets, backing up the assessment with extensive, continually updated benchmarks.
Five Critical Steps for a Successful Data Transformation
The assessment of data capabilities gives the company the information it needs to carry out five critical steps.
Build a vision. When planning a data-driven transformation, a company must set the appropriate vision for its business. For some companies, the transformation will mostly be about using data to improve operations and to compete more effectively. For others, it might involve building new business models. The visioning exercise should include identifying the macro use cases—the most important projects that the company wants to undertake.
Select the portfolio of initiatives. Using its vision and its list of macro projects for reference, companies can create a full list of transformational initiatives. The company should use a structured ideation process to compile the list, and it should use a rigorous prioritization methodology to set the schedule. Factors such as data availability, regulatory compliance, and technical or modeling difficulty, as well as dollar value, customer benefits, and strategic importance must also be weighed.
Devise an analytics operating model. Before investing in new data analytics capabilities, a company should specify how it wants the data analytics function to work. After analyzing its internal capabilities, it can decide which components of the analytics function to seek in-house and which to outsource.
Establish data governance. To ensure the quality and integrity of the data it will use for business decisions—with and without human intervention—a company must have strict governance rules and a data governance structure. It must also define data quality and establish ways to continually improve it.
Define data infrastructure. A company moving toward data transformation should address the following questions: Can our current infrastructure support our future data value map? Should we make or buy? Should we go to the cloud? Do we need a data lake? What role should our legacy IT systems play in our data transformation? The company should design a data platform (or data lake) that can accommodate its product map and should use that platform to progressively transform its legacy systems.
Industrialize Data Early to Ensure Full Transformation and Long-Lasting Impact
While the company continues to sketch the transformation roadmap—if not sooner—it needs to begin industrializing its data and analytics. This means setting up a way to standardize the creation and management of data-based systems and processes so that the output is replicable, efficient, and reliable. Digital systems are the new means of production, and they need to have all the attributes of industrial machinery, including reliability and consistency. Above all, the company needs to have a way to guarantee that it generates and harnesses high-quality data and has an efficient data environment.
A centralized or hub-and-spoke operating model can ensure clear, consistent strategy and execution; rationalize investments; and ensure economies of scale. Business units and functions that will rely on new data-driven systems and processes should have input into system design and data quality assurance, but these groups must rely on the core data management organization for data governance.
The second element in industrializing data consists of determining the appropriate architecture to support data analytics across the organization. A flexible open architecture that can be updated continuously and enhanced with emerging technologies is generally the best option. Rather than embracing an end-to-end data architecture, companies should adopt a use-case-driven approach, in which the architecture evolves to meet the requirements of each new initiative. The data governance and analytics functions should collaborate to create a simplified data environment; this will involve defining authorized sources of data and aggressively rationalizing redundant repositories and data flows.

ORGANIZE FOR SUSTAINED PERFORMANCE

As is the case with any change program, the success of a data transformation is measured by sustained results—and those will not materialize without making the company and its culture data centric. To prepare its organization for a digitized future, the company needs to move on four fronts: creating new roles and governance processes, instilling a data-centric culture, adopting new ways of working, and cultivating the necessary talent and skills.
Many companies may be capable of managing this change on their own; but if a company faces competitive challenges that require a rapid transition, or if it is far behind in digitization or lacks the resources and capabilities to manage the transformation, it may benefit from adopting a build-operate-transfer model (which we discuss below). This involves creating a dedicated organization—usually run with the guidance of an outside expert partner—that takes over the organizational change effort.
Define new roles and governance rules. To ensure the sustainability of the benefits it obtains through the adoption of new digital processes, a company needs to make clear who has responsibility for building and running new systems and maintaining specific types of data—and how to manage those people. The changes begin at the top: senior leaders should adopt data-driven objectives and cascade those goals throughout the organization. Top management may want to set up data councils to extend the work to all sectors of the organization and to carry it out more effectively. The company should promote data awareness by using data champions to disseminate data-driven practices. The company can set up a change management function under the chief data officer, too. The data awareness effort should extend to all work, including tasks that the digital transformation does not directly affect. For example, the company might create data-based metrics for functions such as HR, perhaps measuring the number of applications processed per job filled.
Build a data-first culture. Not everyone needs to become steeped in data analytics or learn to code in order for digital transformation to work. However, everyone does need to adopt a less risk-averse attitude. To move quickly and to continually find new ways to apply data, companies should behave a bit like software development operations, embracing a test-and-learn culture that encourages experimentation, accepts—even celebrates—failure, and is always learning. Companies can also encourage the desired cultural change through organizational moves, such as creating internal startup units where employees can focus on experimentation or co-locating data labs within operating units. The company can also promote the new culture by using cross-functional teams that share data across silos, thereby encouraging openness and collaboration throughout the organization.
Adopt agile ways of working. The entire organization does not have to become expert in agile, but the company can adopt many of the tactics of the agile method and use them in everyday operations to increase the organization’s responsiveness and adaptability. It can establish scrum teams with squads and tribes to tackle specific problems—and accelerate the pace with weekly sprints, rather than months-long efforts. Teams and groups can implement morning standups and weekly demos (reviews) as part of governance. Overall, the new ways of working should emphasize autonomy and reduce hierarchy.
Cultivate the necessary talent and skills. For data-based transformation to work, the company must have talent with the right skills to execute data-driven strategies and manage data-based operations. This presents a workforce planning challenge, starting with assessing current employees and defining future needs. The company should create an inventory of the talents and skills that its employees will need, and it should identify where the gaps are in the current workforce. Companies will need to retrain current employees, hire new talent, or use a partnership to get the right capabilities. To recruit people with digital skills, the company may need to rethink the value proposition it offers—work, opportunity, rewards, career path, and so on—in relation to what tech companies offer.
Consider the build-operate-transfer model. In some instances, a company may need to adopt bolder steps to accelerate its data-driven transformation. This may be because it is starting from far behind its competitors or because it lacks the capabilities and resources needed to drive the transformation internally. In such situations, using the build-operate-transfer model makes sense. Adapted from the construction industry, this model involves creating a stand-alone organization in partnership with an outside vendor that has the expertise to run transformation initiatives. The organization focuses on managing transformation efforts and is staffed by employees from both the firm and the outside partner. It takes responsibility for setting up and running the use case projects and other elements of the transformation. Over time, as projects are completed, the partner withdraws its employees. Eventually it transfers all work and resources back to the company, and the stand-alone organization dissolves.



The promise of data-driven transformation has captured the imagination of leaders throughout the business world and is driving change in the public and social sectors, too. Executives are inspired by the idea of using data to make better decisions and digitizing all sorts of processes to improve performance. They are also motivated by fear that they won’t be able to keep up with competitors who are ahead of them in data-driven digital transformation. These forces can encourage companies to try to achieve sweeping, companywide change to go digital—which can lead to counterproductive overreaching. This contest will not be won by making huge bets. The winners will be agile, pragmatic, and disciplined. They will move fast and capture quick wins, but they will also carefully plan a transformation roadmap to optimize performance in the functions and operations that create the most value, while building the technical capabilities and resources to sustain the transformation.
Antoine Gourévitch , Lars Fæste , Elias Baltassis , and Julien Marx