The cornerstones of large-scale technology transformation PART II
Building in-house capabilities
An essential component
of achieving scale in a technology-enabled transformation is having sufficient
in-house technology expertise and talent. One proven model for building a
technology bench is the “technology factory.” Such a factory is wholly at the
service of the business and governed by the business. It provides the sort of
work setting that is necessary to attract technology talent and achieve high-velocity development.
Scotiabank, a large
international bank, set up such a factory in 2015. Headed by the bank’s chief
digital officer, the factory employs 700 technologists and functional
specialists, who are grouped into small agile teams that share expertise,
development tools and methods, and proprietary software and analytics.
Scotiabank structured its factory as a network of five hubs, with one
co-located in each of its five core geographic business units to promote close
collaboration. Scotiabank’s factory ordinarily develops 20 to 25 solutions at a
time. Over the past two years, factory-built solutions have helped the bank to
nearly double the share of sales made through online channels from 11 percent
of revenues to 20 percent, on the way to a medium-term goal of 50 percent.
Scotiabank’s factory,
like other successful ones we’ve seen, exhibits several distinguishing
features. Depending on the size of the company, a technology factory typically
employs between 50 and 1,000 technology specialists: designers, software
developers, data scientists, data engineers, platform architects, AI experts,
automation engineers, analytics translators, product owners, and digital
marketers, among others. The composition, scale, and skill set of the factory’s
workforce reflect the portfolio of solutions and the development pace specified
in business units’ technology road maps. With road maps focused on optimizing
customer journeys, Scotiabank initially skewed its technology bench toward
designers and software developers.
To fill out a factory’s
roster, companies usually have to search far and wide. In our experience, it’s
not unusual for half of a factory’s staff, particularly in technical domains,
to be recruited externally, which is partly why it can take 12 to 18 months to
set up a well-functioning factory. A staffing campaign of this scale will
falter if it is not directed by a leader with a proven ability to recruit and
retain digital talent. At Scotiabank’s factory, external hires make up about 60
percent of the workforce, and the remainder hail from the bank’s IT department
and other business units. Scotiabank also provided training to help the
factory’s workforce establish a common working style and set of methods.
Internally hired business and technology experts, for example, received
coaching in agile development if they weren’t already familiar with it.
Arguably, it’s even
more important to spread knowledge of advanced technologies and their uses
throughout the business. Interventions to effect cultural change and skill
building can take any number of forms. At DBS Bank, CEO Piyush Gupta has noted, “One
of the big things we focused on was how to get the company technology
literate.” After it learned that “classroom sessions didn’t work,” DBS staged a
series of 72-hour hackathons in which its employees teamed up with people from
tech start-ups to build apps. Coming out of the hackathons, Gupta said, “The
renewed confidence and self-belief among employees was astounding.”
By contrast, one of the
world’s leading steel plants, the Tata Steel IJmuiden plant, in the
Netherlands, offered on-the-job technology training with a “field and forum”
approach. The company provided some 200 operations managers and engineers with
enough training in advanced analytics that they could serve as analytics “translators,” capable of spotting potential
new opportunities to use sophisticated techniques and then deploying them or
acting as business champions. Tata achieved this by cycling cohorts of managers
through classroom training forums while having them perform hands-on projects
in the field. The training curriculum left managers with a shared vocabulary
and understanding of concepts such as agile, technology stacks, data
governance, and data management. This common understanding of technology
enables senior executives and managers to quickly align in the pursuit of new
opportunities and to “pull” for the services and support of technology
specialists (versus having IT “push” solutions).
Modernizing the technology environment
Two features define the
core of a modern technology environment: a data platform and a development
environment for producing software and analytics code. Without these, a
company’s tech-enabled transformation quickly stalls and becomes mired in
complexity. The good news is that technological tools have evolved rapidly over
the past two to three years, and it is now possible to deploy these cloud-based
solutions quickly and at relatively low cost.
Nutrien, a global
supplier of agricultural inputs, built a data platform—a cloud-based middle
layer—that centralizes information from 13 different in-house systems, as well
as from external data sources. The platform makes the data readily available to
a range of newly built employee- and customer-facing applications, such as
online commercial transactions and agronomic services for farmers. Technology
architects linked both legacy systems and new digital applications to the data
platform through application programming interfaces (APIs). Whenever a core
system or a digital application is upgraded or added, architects unhitch the
old program from the data platform and hook up the new one—with minimal
disruption. Introducing a data platform made Nutrien’s enterprise architecture
modular and flexible, creating a so-called two-speed architecture that easily integrates
fast-evolving customer- and user-facing solutions with slow-evolving legacy
systems.
In addition, Nutrien
set up a modern software-development environment. The environment enables
multiple developers to work on the same application in parallel and automates
software testing and in-production release of new applications, reducing cycle
times from months to hours. This new way of working is key to developing and
improving software at a swift pace, especially once a company moves beyond the
pilot phase of its transformation.
Data platforms and
code-development environments should be among the first investments that
companies make to facilitate the expansion of their technology programs.
Although the cost and complexity of such efforts increase with the number of
legacy systems and external data sources, as well as with the volume,
sensitivity, and real-time nature of data in play, these additions are now
easier to make with modern, cloud-based tools. The Nutrien business unit
described above went from concept to live operations in less than six months,
using off-the-shelf tools, and spent less than $10 million.
Overhauling data strategy and governance
Every executive
understands that data are a source of competitive
advantage, but surprisingly few
have put in place the business practices to capitalize on the value of data. As
companies move beyond piloting solutions, they find that their data are messy,
hard to access, and undifferentiated from their competitors’. Scaling beyond a
few solutions becomes complex and slow for them, and often yields unimpressive
results because the underlying data are poor.
It doesn’t have to be
this way. The value of data is directly related to the
technology solutions that the data enable. Data strategies therefore should
start with the technology road map described earlier and, for each tech
solution, articulate the data needed. If you want to automate insurance
underwriting by relying solely on the customer’s name (rather than using
medical tests and customer form filling), you need a vast array of external
data—and a permissive regulator. If you seek instead to automate claims
management, your data requirements may be very different. Prioritizing the data
domains that support the initial set of solutions on the technology road map is
a critical first step.
Next, the prioritized
data domains should guide the data ingestion efforts, be it from legacy systems
or external data sources. Value in data is often unlocked by linking data from
very diverse sources. For example, Aston Martin substantially reduced the
development time of new luxury cars by linking data from around 30 different
sources, ranging from team composition and product drawings to parts features.
In parallel, the chief data officer should be developing the appropriate
data-management processes, such as establishing conventions (a “master data
model”) for defining data down to the syntax of customer names and assigning,
to explicit data owners, responsibility for maintaining high-quality data. Data
management has become an essential capability to any successful technology
transformation.
Many leading players
regard their data strategies and models as a long-term, multiperiod chess game.
Ping An, a leading Chinese financial institution, started with data in banking
and insurance and over time developed a customer-data ecosystem across nine
industries ranging from automotive to healthcare. (For more see, “Building a
tech-enabled ecosystem: An interview with Ping An’s Jessica Tan,” forthcoming
on McKinsey.com.) Some companies obtain data assets through M&A. IBM’s
acquisitions of Explorys and Phytel, for example, were healthcare-data plays.
Many innovative companies, not satisfied with the data “exhaust” they can collect
or buy, strive to create new data that are directly relevant to their
anticipated use cases. An energy-trading business, for instance, is deploying
webcams next to power-generation sites to understand the volume and mix of
fossil fuels being burned and to better predict future regional demand.
Examples such as these speak to the iterative nature of data-strategy efforts
and to the importance of continually enriching your data assets. We strive for
the same at McKinsey, by conducting an annual strategic process to consider
which data sources and partnerships, among nearly 200 functional and industry
data domains, should be expanded in the following year.
Changing the operating model to capture
technology’s value
Scotiabank’s road map
for enhancing its online credit-card application highlighted an array of
technology solutions: digital marketing tools to find, target, and attract
customers on the web; a streamlined application process that would cut the rate
at which customers abandoned partly completed applications; and advanced
analytics to improve pre-approvals, for example. While these solutions stood to
improve customer satisfaction and reduce unit costs, Scotiabank could only
capture the benefits by making corresponding operating-model changes across many
different areas such as rebalancing online and offline marketing investments,
and reducing staffing levels in the back- and mid-offices. These changes, some
of which are still underway, are helping the bank to increase online card sales
substantially while cutting acquisition costs compared with in-branch
applications.
Time and again, though,
we have seen companies succumb to the last-mile challenge, deploying new
technologies in one area of the business but failing to make value-creating
adjustments to its operating model in other areas. Last-mile value capture must
begin with understanding how technology will change the business model and its
underlying economics. By tracking the expected impact of technology
systematically across many organizational units, companies can learn to work
across silos and capture the full benefit (see sidebar, “The big roadblock to
digital implementation”). Reconciling competing incentives across
organizational units is a classic example of this. A plan to sell more credit
cards online, for instance, might go over badly with the head of the
retail-branch network who is rewarded for in-branch revenue increases.
We’ve seen several CEOs
accelerate their companies’ technology transformations by appointing a senior
executive to a multifaceted leadership role that includes driving cross-unit
collaboration, mapping where technology benefits are expected, holding leaders
accountable for capturing those benefits, resolving conflicting incentives, and
removing impediments to value-capture efforts. Once issues such as these are
clarified for business-unit and functional leaders, it’s easier to lock in
their value-capture commitments, to link that value to real-world performance
improvements, and to help them recognize the necessary, supporting changes to
their operating model. For example, asking a bank’s head of back-office
operations to reduce her staff by one full-time equivalent for every 1,000
credit cards sold online (rather than through the branch network) helps the
bank progressively capture the benefits of its online credit-card application.
Ultimately, a
technology-enabled transformation calls for a continuous, enterprise-wide effort to improve the operating
model. It is no longer a one-time, big-bang, IT system deployment. As customers
and internal users adopt technology solutions, every business area that is
affected adjusts its processes accordingly. That can happen rapidly when the
technology is disruptive or a new digital business is being created, but more
often, the change unfolds progressively.
At many large
traditional companies, a moment of reckoning has arrived. Not only is it
difficult to scale up technology transformations beyond a handful of pilot
projects, but broad-based efforts to apply and integrate advanced technologies
are placing new demands on senior leaders. They must define technology road
maps to drive strategic use of resources, invest in technology-development
capabilities while training their managers, build a modern technology
environment that can support multiple, fast-evolving solutions, ensure a
strategic evolution of data assets across the enterprise, and reinforce a
commitment to operating-model changes that will capture the business value of
new technology solutions.
These enterprise-wide
changes are critical to seizing today’s technology opportunities, and
tomorrow’s. After all, the real competitive edge comes from repeatedly being
first to market with innovative technological solutions and integrating them
deeply into the operating model of the enterprise. This is a final lesson from
the lean-management revolution. Lean methods were widely known, yet Toyota and
other companies still developed competitive advantages by using lean to orient
their organizations comprehensively—from the CEO to the shop floor—toward the
achievement of world-class performance. The information-technology revolution
is playing out in a similar way. The companies that derive a true competitive
advantage from technology will be those that make tech-enabled transformation a
permanent business discipline.
By Michael Bender, Nicolaus Henke, and Eric
Lamarre
https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-cornerstones-of-large-scale-technology-transformation?cid=other-eml-alt-mkq-mck-oth-1810&hlkid=a614684228fd463b9d87c98041678121&hctky=1627601&hdpid=e1670b14-4631-4873-8407-1e7b9b6747d2
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