Why you need a digital data architecture to build a sustainable digital
business
Companies
that succeed at meeting their analytics objectives let business goals drive the
technology. Here’s how they structure a data architecture that works.
Data architecture has been consistently
identified by CXOs as a top challenge to preparing for digitizing business.
Leveraging our experience across industries, we have consistently found that
the difference between companies that use data effectively and those that do
not—that is, between leaders and laggards—translates to a 1 percent margin
improvement for leaders. In the apparel sector, for instance, data-driven companies have
doubled their EBIT margin as compared to their more traditional peers.
Using data effectively
requires the right data architecture, built on a foundation of business
requirements. However, most companies take a technology-first approach,
building major platforms while focusing too little on killer use cases. Many
businesses, seeing digital opportunities (and digital competition) in their
sectors, rush to invest without a considered, holistic data strategy. They
either focus on the technologies alone or address immediate, distinct use cases
without considering the mid- to long-term creation of sustainable capabilities.
This goes some way toward explaining why a 2017 McKinsey Global Survey found
that only half of responding executives report even moderate effectiveness at
meeting their analytics objectives. The survey found the second-largest
challenge companies face (after constructing a strategy to pursue data and
analytics) is designing data architecture and technology infrastructure that
effectively support data-and-analytics activities at scale. We found that eight out of ten
companies embark on digital data enablement by making their IT departments
responsible for the data transformation—with very grand implementation
programs—and a small set of business use cases.
This strategy is quite
different from that employed by next-generation digital leaders, who typically
embark on transformation from a business perspective and implement supporting
technologies as needed. Doing the technology first produces more problems than
successes, including:
·
Redundant
and inconsistent data storage. Only two in ten banks we’ve worked with have
established a common enterprise data warehouse, which is essential for creating
a single source of truth for financial and customer data.
·
Overlapping
functionality. Every bank we’ve
worked with has at least one business function supported by three different
technological systems.
·
A
lack of sustainability. The
solutions at which financial institutions typically arrive are often quick
fixes that ignore the enterprises’ larger aspirations for datafication. For
example, one insurance company extracted and replicated data from its warehouse
each time it was needed rather than building data architecture that would allow
it to store each customer element only once, thereby reducing costs and
eliminating inefficiencies.
These problems have
real business consequences. Meeting leading-edge business requirements, such as
real-time customer and decision support, and large-scale analytics requires the
integration of traditional data warehousing with new technologies.
The two-speed data-architecture imperative
Today, enterprises must
cope with increasingly large and complex data volumes (worldwide, data storage
doubles every two years) coming from diverse sources in a wide variety of
formats that traditional data infrastructures struggle, and most often fail, to
operationalize. Developing new business capabilities—such as individual pricing
for customers based on real-time profitability, as some insurance companies
have done, automating credit decisions that lead to improved outcomes for banks
and greater customer satisfaction, or running automated, more cost-effective strategic
marketing campaigns as we’ve seen in the chemicals sector—demands new ways of
managing data.
This does not mean,
however, that legacy data and IT infrastructures must be trashed, or that new
capabilities need to be bolted on. It does mean that the traditional data
warehouse, through which the organization gains stability and financial
transparency, must be scaled down and integrated with the high-speed
transactional architecture that gives the organization the capability to
support new products and services (as well as real-time reporting). This is
the two-speed principle.
This new, complex
technical environment requires companies to closely examine business use cases
before making costly technology decisions, such as needlessly ripping out and
replacing legacy architectures. Instead, it is preferable to use a
capability-oriented reconceptualization of data management as an enabler of digital
applications and processes.
To implement an
end-to-end digital data architecture, an enterprise needs first to develop a
point of view on its current and, if possible, future business requirements,
sketch its desired, flexible data-management architecture, and create a roadmap
for implementation. To begin, one must identify the key business use cases.
To do this, we
recommend a thorough review of best-practice use cases across industries that
address common value drivers (financial transparency, customer satisfaction,
rapid product development, real-time operational reporting, and so on). Then,
the company should compare those use cases with its market position and
strategic direction, prioritizing those that best reflect the company’s situation
and aspirations. Once those reference use cases are identified, the company can
begin to define target data-architecture capabilities. In this process, the
business leads and technology follows.
The high-level
structure in the exhibit above represents a layered data architecture that has
been applied successfully by many organizations, across many industries,
especially in finance. It extends to accommodate new digital capabilities such
as collecting and analyzing unstructured data, enabling real-time data
processing, and streaming analytics.
The exhibit shows a
reference architecture that combines both the traditional requirements of
financial transparency via a data warehouse and the capability to support
advanced analytics and big data. In a phrase, it’s a two-speed approach.
The two-speed architecture adheres to three core
principles:
1.
A limited number of
components with a clear demarcation of capabilities to manage complexity while
providing the required functionalities, such as advanced analytics and
operational reporting
2.
Layers that enable the
transparent management of data flows and provide a single source of truth to
protect against silos and data inconsistencies (through the data warehouse,
which models, integrates, and consolidates data from various sources)
3.
Integration of
state-of-the-art solutions with traditional components, such as the data
warehouse, to satisfy such new requirements as real-time processing, and an
operational data store (ODS) based on new database technologies
We have used this model
to:
·
Help clients think
through and evaluate their options on an architectural level before discussing
concrete technical solutions.
·
Map technology
components against capabilities to manage and avoid redundancies while
identifying gaps.
·
Create plans for
stepwise transformations driven by business value while limiting business
disruption.
Getting physical with digital
For example, one of the
largest banks in Scandinavia, understanding the business potential of advanced
analytics, big data, and better data management to improve fraud detection and
prevention, ATM location, and other initiatives, was eager to begin its digital
data journey. It was facing intense competition, and was considering making a
massive, multimillion-dollar investment in its IT and data architecture.
A lot was riding on
what the bank decided to invest in, where it decided to invest it, and how.
It began by identifying
key use cases that reflected the organization’s most compelling strategic
requirements: improved fraud detection, optimized location and allocation of
branches, and more granular customer segmentation.
Based on this
determination, we helped the bank outline a target architecture, founded on the
best-practice reference model, that would enable the capabilities the bank
desired and assess available solutions. Instead of ripping out its entire IT
infrastructure, the bank decided to add a single Hadoop solution that allowed
for storage and distributed processing of the bank’s extremely large and
frequently unstructured data sets across thousands of individual machines. This
was especially useful in scaling the bank’s high-frequency requirements for its
online fraud-detection processes.
For branch location,
allocation, and optimization, a Hadoop data lake (a management platform that
processes flat, nonrelational data) used the bank’s geospatial and
population-growth data to determine where best to locate new branches and ATM
machines. To improve its customer segmentation, the bank tested a new customer
algorithm on the Hadoop database before rolling it out on its legacy data
warehouse. This eliminated the typically costly and time-consuming
back-and-forth process of develop, pilot, assess, validate, tweak, and pilot
again that characterizes traditional data developments.
In this way, the bank
achieved its primary business goals. It added new, differentiating
capabilities, such as real-time analytics, and created real enterprise value
with a relatively small technology investment, not the massive one originally
contemplated. This was achieved by deciding what to invest in, where to invest
it, and how—before buying systems and software that might not have
served it nearly as well. Crucially, instead of first buying the technology,
the bank built an in-house analytics team, skimming off the cream of the local talent in the process.
Today, the bank is
considered the leader in financial analytics in its market and sells analytics
services to other financial institutions.
The bank knew that the
time was ripe to get serious about digital transformation, made it a priority,
and in doing so achieved what may well be an enduring competitive advantage,
all without disrupting its business with a big-bang technological
transformation. It started with a clear view of its business goals, kept them
front and center, and created a two-speed data architecture that worked.
The lesson here is that
for many companies, it is both doable and cost-effective to add analytics
capabilities to an existing IT environment. But that requires a sound data
architecture, and a well-grounded approach to data management.
By Sven Blumberg, Oliver Bossert, Hagen
Grabenhorst, and Henning Soller. November 2017
https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/why-you-need-a-digital-data-architecture?cid=other-eml-alt-mip-mck-oth-1711
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