Customer Data Culture: The Innovators Have Already Reinvented Themselves

Satyen Sangani
Satyen Sangani
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Alation, automated intelligence, data catalog, data culture, decision science, einstein analytics, forrester wave, Gartner, GoDaddy, Machine Learning Data Catalog, munichre, pfizer, self-service analytics,

After 116 years in business, legendary guitar maker Gibson filed for bankruptcy in 2018. Prior to the filing, Gibson’s CEO Henry Juszkiewicz was one of the most staunch advocates for “innovation” that you could find. During his tenure as CEO, he sought to transform Gibson from a guitar manufacturer into a “music lifestyle brand.” While he successfully built a culture of innovation, he missed a key ingredient, customer demand. Gibson’s push to innovate for innovation’s sake left behind the company’s most loyal customers and ultimately failed.

It concerns me that a similar approach has taken over the IT world with the recent avalanche of “digital transformation” initiatives being kicked-off in major enterprises. “Digital transformation” is often described as technology-led innovation, but innovation for innovation’s sake can run you into the ground, a la Gibson. Without focus on the needs and pain points of the customer, digital transformation initiatives rarely lead to a positive return on investment.

The allure of the digital transformation can become all-consuming, enticing executives to pay too much attention to new technology and not enough attention to driving long-term sustainable growth. Sustainable innovation is often customer-centric if not customer-led. It requires organizations to adopt a scientific mindset of testing and iteration to find the truth, rather than using data to support a predetermined path.

These organizations have a culture that is willing to listen to the needs of the customer, and often embrace a single source of reference to guide that understanding consistently throughout the organization. These are organizations that embrace a change in culture while those that stay stagnant are bound to fail.

According to a new survey from NewVantage Partners, 72% of executives say they have yet to forge a data culture, let alone a customer data culture. In fact, according to the survey, the percentage of firms identifying themselves as data-driven has declined each year for the past three years. The reason? Too much emphasis on technology and too little emphasis on creating a data culture.

“We hear little about initiatives devoted to changing human attitudes and behaviors around data. Unless the focus shifts to these types of activities, we are likely to see the same problem areas in the future that we’ve observed year after year in this survey.” — Big Data and AI Executive Survey 2019

The Innovators Have Already Reinvented Themselves

The companies that succeed in the age of digital transformation are reinventing themselves and embracing a customer data culture. They leverage data to more accurately understand their customers and to gain a competitive advantage.

GoDaddy: From Developer-led to Customer First
GoDaddy is one of the rare companies that has successfully built a customer-first culture. GoDaddy’s transformation has driven unparalleled revenue results for the domain registry giant over the last 12 months and led to a stock price increase of more than 36%. What’s driving that change? A commitment to using data to become a customer-first organization rather than a self-described “developer-led community.”

Learn how GoDaddy uses Alation and Tableau to bring trust to data »

At the beginning of last year, GoDaddy’s CEO Scott Wagner said in their earning’s call, “2018 is off to an exceptionally strong start, with first quarter revenue up 29%. We feel confident about our ability to both acquire new customers in markets around the world, and deepen relationships with existing customers, which we believe can fuel solid growth for GoDaddy well into the future.”

Part of GoDaddy’s transformation was to get the right customer data consolidated in one place and make it accessible to every employee for data-driven decision making. This meant a large Hadoop deployment, self-service analytics tools available to every employee with Tableau, and a data catalog from Alation. This technology lowered the barrier to gaining a comprehensive view of the customer, but it was a shift towards a customer data culture that made all the difference.

Today a cross functional, cross organizational team meets weekly to define and describe a business glossary that makes customer data more easy to interpret by any employee. A team of data stewards certify reports and dashboards for accuracy and publish Unified Data Sets to all employees for use in tools like Tableau. They’ve been so successful at creating a data culture in the organization that 80% of end user data needs are now readily available in the Alation Data Catalog and re-using existing data assets has eclipsed the need for ad hoc development, allowing users to focus on deeper analysis. By making customer data accessible, GoDaddy has created a culture of shared ownership that has empowered to better understand the customer with data.

MunichRe: Launching a New Business in Green Energy
MunichRe, the world’s largest re-insurance company, is helping to lead the green revolution as the first insurer to re-insure wind turbine projects end-to-end. The re-insurance product that they introduced was inspired by collaboration between geographically dispersed teams coming together through the Alation Data Catalog. With the introduction of a new data lake, MunichRe created a new way for actuaries and business experts to explore new product concepts and test new markets. With the data catalog, energy industry experts in Houston, experts of risk data science in New Jersey, and business experts in Munich all have access to the same data. Collaborating together, they were able to align interests and algorithms to generate a breakthrough data product. This has led the CDO of MunichRe to say that, “Alation helps us to break down silos. We are able to think differently.”

Learn how MunichRe used Alation’s data catalog for immediate productivity gains »

Pfizer: Discovering New, Innovative Drug Applications
Pfizer is using a Data Catalog to lead a transformation in healthcare. The pharmaceutical leader is identifying patients with rare diseases that might have previously gone undiagnosed. The use of a Virtual Analytics Workbench enables even non-data scientists to leverage all the data and tools they need to create new drug applications and in one case, they were able to spot a rare form of heart failure – transthyretin cardiomyopathy. The symptoms for that rare disease are very similar to other forms of heart failure and less than 1% of the heart failure population is affected by this disease, making it extremely difficult to detect.

“While data science is a critical skill set in our company, you don’t need a mathematician or data science expert to use it,” said Jeff Keisling, CIO at Pfizer.

Learn how Pfizer uses a data catalog to identify patients with rare diseases that might previously have gone undiagnosed »

How are These Companies Doing it?

Technology is important but a customer data culture is driven by people who are using data to better understand customers. That’s no simple task. The most successful companies are following some or all of the following principles:

Scientific Mindset: At the recent Gartner Data and Analytics Summit in London, Alys Woodward, senior director analyst, laid out one of the biggest failings in how we leverage data, stating, “Most individuals only use data as a Machiavellian tool to validate the path we already chose.”

To really leverage data, we need to think with a scientific mindset where ideas move from observations to hypothesis to theories to laws of business. Organizations should be leveraging data to uncover insights and must be open to what the data reveals, even if that means that the original hypothesis was wrong and they have to go back to the drawing board.

Automated Intelligence: Humans are quickly overwhelmed by the sheer volume of data and the complexity of systems. And often, your experts, the ones who understand the customer best, don’t have the technical skills needed to work with data. Machine learning can take out the manual work and provide recommendations that make data accessible, allowing humans to think creatively and find deeper insights.

Data Context: Despite big investments in data and analytics (projected to be some $370 billion by 2022, according to Gartner), organizations aren’t connecting analytics to decision making. A big part of that is the inability to trust the data. Only by understanding where the data came from, who created it, why it was created, and how it is being used can you build the foundation for trusted decision-making.

Access to Data: To get data into the hands of data curious employees, organizations need to provide self-service access to raw data. While BI tools and cloud infrastructure can help, stewardship and curation are critical to ensuring that the right data gets into the hands of the right people.

Decision Science: Even when armed with the best data and powerful machine learning algorithms, humans can unknowingly introduce bias, make misjudgments, and introduce all kinds of nuances into the decision-making process. Decision science is a new area that companies are exploring to help humans make smarter decisions.

Farsighted executives understand that their organizations need to be data-driven and doggedly pursue digital transformation. They acquire all of the latest solutions to bring data to the forefront, but unless they combine that with a broad cultural shift and a deep understanding of how to use that data inside business processes, they will continue to struggle.

Organizations are adopting data and analytics technologies to move toward data-driven decision making, but technology alone won’t provide a competitive edge. Without a data culture focused on leveraging data to better understand customers, all of that technology investment goes to waste. Today’s innovative organizations are pursuing digital transformation with the customer as their North Star and implementing the underlying principles that support a customer data culture.

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