Balancing the ‘Scale Paradox’

By John Lucker

Historically, large businesses have behaved and functioned differently than small and midsize organizations, even within the same industry. Smaller firms often had advantages over larger competitors in many areas, including innovation and the time needed to achieve value from insights. Larger companies, in turn, tended to have capabilities and assets that brought them returns that were not always available to smaller firms. 

But evolving disruptive technologies, combined with advanced analytics, are starting to challenge these historical yin-yangs. That is the “Scale Paradox.”

Bombarded with new technologies and software tools, both large and small businesses should continuously re-examine and, when indicated, alter how they innovate and compete. Leveraging advanced information management and advanced analytic methods—combined with the wide availability of external data products and assets—small companies can now achieve insights that were once exclusive to large enterprises.

Conversely, large enterprises are using these disruptive forces to shorten product development cycles and expedite decision making in ways that help obliterate limitations that were once a function of corporate silos, bureaucracy and management hierarchy. The Scale Paradox can reset competitive forces and induce a flip-flop in how large and small businesses operate—and where they may gain a competitive edge.

Less than a decade ago, the enterprise advantage was one of might: Large companies had more money, more people, more resources, more intellectual property and more deployable capital, which gave them an analytic head start.

Today, free or very low cost open-source platforms and high-end computational tools, cloud computing, social media and analytics have drastically altered—or flattened—the large-enterprise scale advantage. Analytic power and might are more available to companies of all sizes.

Small and midsize businesses are using analytics to better understand and know their customers, thereby achieving a sense of intimacy that’s often felt lacking from larger organizations. Advanced analytics and more intuitive technologies paint a clearer picture of customer wants and needs.

This allows small businesses to build loyalty, often displacing larger rivals. The customer or end user may never know–or need to know—that the company they are dealing with is dwarfed by a large traditional service or product provider.

While the Scale Paradox offers small companies the opportunity to function beyond their size and scale, it also allows large organizations to become more nimble. Analytics offers new levels of flexibility and agility that were once the domain of small businesses and startups. Large organizations that take advantage of the Scale Paradox learn to experiment with methods that make them more strategically fleet-footed and open to change.

The Scale Paradox is also redefining how small and large businesses attract, nurture and retain top talent. While analytics software abounds—even in free, open markets—experienced data analysts and the evolving professional persona of the data scientist are in increased demand and diminished supply.

While such analytic professionals may prefer a more hands-on small business environment, large enterprises have traditionally cast a much broader recruiting net. But the talent landscape is evolving for every size organization with the emergence of crowdsourcing, competitive predictive modeling, and other disruptive technologies and analytics.

Regardless of size, the Scale Paradox is pushing today’s businesses outside the boundaries of their traditional modus operandi. It’s no longer enough just to think outside the box. Competitive enterprises—large, midsize and small—need to perform outside the lines as well.

Disruptive forces are eroding the historical scale advantage once held by large enterprises, and smaller firms may no longer have an agility advantage. Applying analytics allows enterprises of various scales to more effectively address this disruption.

John Lucker is Deloitte’s Global Advanced Analytics & Modeling Market Leader and a leader for Deloitte Analytics. He is also a leader of Deloitte’s Advanced Analytics & Modeling practice. John provides clients with strategy, business, operational and technical consulting services in the areas of advanced business analytics, predictive modeling, data mining, scoring and rules engines, and other analytic business solution approaches.  

This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication.

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