, Why Legacy Companies Struggle With Data Cultures — And What Leaders Can Do, The Nzuchi News Forbes

Why Legacy Companies Struggle With Data Cultures — And What Leaders Can Do

While data and analytics are crucial today for informing decision-making and optimizing business performance, putting them to use effectively is often easier said than done for legacy companies. The problem lies in the established nature of these enterprises.

Mature companies often are decades or even a century old and have many thousands of employees in multiple business units and divisions globally.  Their information systems have been built over many years, on multiple platforms for multiple purposes. Their business processes and operations are complex and, most fundamentally, they have entrenched cultures and structures for decision authority and how decisions are made. 

And yet, leaders and managers in legacy enterprises often hear that they should approach their business as if it were Amazon, Netflix or Spotify. These great companies have built enormously successful and admirable businesses; however, their models and solutions characterize twenty-year-old e-commerce companies that were designed to be data-driven before they even launched. That model often doesn’t generalize to a hundred-year-old global manufacturing company. 

I’ve observed the reactions of executives to being told their legacy enterprises should be modeled on high-profile tech startups whose models, cultures, structures, and systems bear little resemblance to their companies and experiences. Often, these admonitions come across as simplistic and, in many cases, irrelevant. The use of data and analytics is often a case in point; now the same applies to the adoption of artificial intelligence (AI) and machine learning.

The Data Culture Clash

The starting point for leaders in mature companies is to understand what a data culture really is: one that expects, enables, and encourages people to use data to make decisions and optimize the business. If people make a recommendation, they should expect to be asked, “Do you have data and analytics to support that?” People also must be enabled by having access to the data they need — and encouraged to present their analyses, even if that means sharing unwelcome insights. 

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By design, Silicon Valley startups were founded on this premise.  Legacy companies, however, have organizational structures and systems that predate the use of data analytics and, now, AI for business optimization based on predictive analytics. An executive I know was recruited away from a top data-driven startup to lead the development of data analytics at a large consumer packaged goods (CPG) company. “Total culture shock,” he told me after discovering that the business practices, decision processes, and systems he had taken for granted at the startup were missing at the CPG company and, in some cases, actively resisted.

, Why Legacy Companies Struggle With Data Cultures — And What Leaders Can Do, The Nzuchi News Forbes

Established organizations are too often fragmented, siloed, and parochial in their data use, with entrenched impediments to information sharing. There’s a human element as well. Data accessibility and analytics inevitably create transparency, question the conventional wisdom,  and produce what may be unwelcome insights. This can be interpreted as questioning the established authority structure (e.g., business unit heads). And it can be crushed by executives who don’t like the answers generated. 

For example, a colleague of mine tells the story of doing an analysis for a financial services company to identify which retail branches should be closed. When the recommendation was presented, the head of retail operations said, “I’m in charge of retail branches, I don’t agree with your analysis, and we’re not closing any.”

At another firm, the head of analytics demonstrated how advertising media buying could be optimized for greater effectiveness and proposed that his department take over media-buying decisions to do so. The head of advertising reacted very negatively to the suggestion, and an organizational rift ensued.

Improving Performance

Despite these challenges, there is a large and growing number of legacy companies that successfully embrace data and data analytics. 

Rather than trying to replicate a Silicon Valley approach, legacy companies should integrate the use of data and analytics into their businesses. A few suggestions:

  • Get comfortable with transparency. Data that used to reside only within one department must be analyzed and the outcomes shared more broadly across the leadership team. But a carte blanche isn’t necessary. Transparency can relate largely to business performance data, whereas personnel decisions, for example, can stay confidential. 
  • Heighten accountability. With increased transparency comes greater accountability. It’s not enough for a department or division to say a particular strategy or product launch is effective; methodologically-sound data and analysis are required to support the results.
  • Embrace unwelcome answers. Data analyses often challenge conventional assumptions and reveal what leaders may wish they did not learn — such as poorer-than-thought performance in actuality or conventional wisdom that is contradicted by data.

Creating a data culture is an imperative for continuously advancing business performance and adopting AI and machine learning. Rather than being told to act like tech startups, legacy companies need to concentrate on expecting, enabling and encouraging the use of data and analytics in their culture, decision-making and organization.

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