Follow Datanami:
July 22, 2019

Why Investing in Your Team’s Data Culture Could Be the Most Important Money You Spend

Satyen Sangani

(pgraphis/Shutterstock)

Most executives today recognize the need to become data-driven and view their “digital transformation” initiatives as the means to do so. More technology means more data, right? Often, these efforts stumble and fail because being data-driven is not just about having more data. According to a recent survey from New Vantage Partners, 69% of executives report they have not created a data-driven organization — and the percentage of companies identifying themselves as data-driven has fallen each of the past three years.

Despite the $1.25 trillion in worldwide digital transformation spending expected in 2019, according to IDC, the allure of digital transformation has left too many enterprises focused on the shiniest, newest technology instead of what actually moves business forward: culture and people. Without a culture focused on enabling and empowering people to make better decisions, technology investment goes to waste. According to the same study by New Vantage Partners, nearly three-fourths (72%) of executives say they have yet to forge a data culture.

Adopting data and analytics tools is one step in moving toward data-driven decision-making, but enterprises must combine these efforts with cultural change. Changing culture is difficult, not least because it’s hard to define the terms, much less the goal line. That does not mean that we should not try, particularly because innovative companies that are committed to creating a data culture have been validated with success.

The Innovators Have Already Reinvented Themselves

Digital transformation is often a part of reinvention, but the many successful transformations put as much, or more, emphasis on transforming the way people work and think about data. These companies actively nurture a data culture along with a scientific mindset — ideas move from observation to hypothesis to knowledge, once the hypothesis is proven or rendered false. A number of innovative companies have already made this leap with impressive and exciting results:

GoDaddy: From Developer-led to Customer-first

Domain registry giant GoDaddy has committed to using data to become a customer-first organization, rather than a self-described “developer-led community.”

Making data accessible to more people so they can better understand the customer was an important part of this strategy. But with more than 2,000 consumers of data across dozens of projects — many of whom with limited knowledge of SQL or how to join or otherwise manipulate data sources, simply providing access to data wasn’t enough.

To put the right data into the hands of those who needed it, GoDaddy created a cross-department team devoted to curating data packages called Unified Data Sets. UDSs are used to package data by domain, such as orders, web traffic and marketing details to simplify the ability to access and answer 80% of their questions, freeing users to allocate more time to understanding and helping the customer and less time digging into data. By building a culture of shared ownership, GoDaddy is able to better understand the customer through data — a transformation that has led to unparalleled revenue results over the last 12 months, as well as a stock price increase of more than 36%.

Munich Re: Launching a New Business in Green Energy

Meanwhile, Munich Re, the world’s largest re-insurance company, has been able to generate a breakthrough data product as part of its efforts to lead the green revolution — becoming the first insurer to re-insure wind turbine projects end-to-end.

In parallel with the introduction of a new data lake, Munich Re also created a new way for actuaries and business experts to explore new product concepts and test new markets, like introducing automated processes to reduce the manual nature of damage assessment and implementing a data catalog to enable people with different areas of expertise, working in different geographies to have access to the same data. With one place to communicate on data, share and further the work of others, Munich Re is able to breakdown organizational silos and allow collaboration to happen organically among experts who are all trying to mitigate the risk of high-impact crises.

Pfizer: Discovering New, Innovative Drug Applications

And finally Pfizer, the pharmaceutical powerhouse, has created one platform that could support searching, analyzing and visualizing different types of structured and unstructured data, including physician notes, lab reports, demographics and comorbidities. This Virtual Analytics Workbench is helping employees from different divisions collaborate to identify patients with rare diseases that might previously have gone undiagnosed. By implementing a Virtual Analytics Workbench, even non-data scientists have all the data and tools they need to create new drug applications — and in one case, they were even able to spot a rare form of heart failure.

Organizations are adopting data and analytics technologies to move toward data-driven decision making, but technology alone won’t provide a competitive edge. Today’s innovative organizations are pursuing digital transformation with an eye on the people who make their businesses tick.

About the author: Satyen Sangani is the CEO of Alation and a co-founder. In founding Alation, he aspired to help people dispassionately observe the world around them, empowering them to passionately work to improve it. Before Alation, Satyen spent nearly a decade at Oracle, where he ran the Financial Services Warehousing and Performance Management business. Prior to Oracle, Satyen was an associate with the private investment firm, Texas Pacific Group and an analyst with Morgan Stanley & Co. He holds a Master’s in economics from the University of Oxford and a Bachelor’s from Columbia University.

Related Items:

Five Tips for Winning at Data Governance

Five Reasons Synthetic Data Is the Electrolyte to Speed Up Your AI Initiatives

Big Data Meltdown: How Unclean, Unlabeled, and Poorly Managed Data Dooms AI

 

 

Datanami