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Podcast: Capitalizing on the Data Imperative

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This roundtable debate centers around the data imperative: examining how data can be used ethically and the ways teams can become more digitally literate.

The data imperative model centers the methods of processing and controlling data flows and data quality in order to produce the desired outcomes for businesses. 

With Miya Knights moderating, the speakers of this roundtable include:

  • Dr. Joe Perez, Senior Systems Analyst at North Carolina Department of Health and Human Services and CTO at Solontek Corporation
  • Meeraiah Guddensetty, VP of Digital Technology Products at Assurant
  • Lauren Walker, Managing Director at Accenture Interactive
  • Diana Ascher, Director, Information Studies Lab UCLA and Head of Research at EDMC
  • Douglas Laney, Innovation Fellow, Data & Analytics Strategy at West Monroe and Professor of Infonomics at the University of Illinois
  • Russ Lewis, ‘The Agilizer’ at Storm Consulting

The significance of data quality

“We are inviting leaders to look more at measures rather than spoken words”, says Russ Lewis. The Agilizer at Storm Consulting, Russ believes the way in which we introduce data to clients has become more empirical and measured. On the other hand, he argued that problems arise from a lack of specific data. “People don’t necessarily know what to look for or measure”, he said. “They don’t know what to do with the data that is presented to them.”

Innovations Fellow and Professor of Infonomics at the University of Illinois, Douglas Laney, believes that many organizations today are doing a good job at collecting and generating data. But, he adds that they “don’t really manage [data] like an asset” despite the fact that other organizations are. Doug states that another challenge is that the term ‘data quality’ is used too generally. Organizations aren’t aware that they can measure a dozen different components of data including accuracy, completeness, timelines, integrity, subjectivity and granularity, he added.

“Organizations with a bottom up and top down perspective are ensuring that measures are in line with their values”, said Diana Ascher, Director of the Information Studies Lab at UCLA and Head of Research at EDMC. Currently working on a project looking at return on investment in data, she deduced that one of the biggest challenges is figuring out which measurements are the ones that matter.

When thinking about data quality and data imperative, what comes to mind for Accenture Interactive’s Managing Director, Lauren Walker, is consumer data. Coming from a data-aggregated perspective, she offers cookies as an example of low quality data. “Marketeers want to measure the effectiveness of their spend in reaching actual people and not bots”, she said. According to Lauren, data quality in the marketing world is about how close you can get to an individual on the other side of that digital divide. 

Data quality and how organizations drive insights from the data that is generated is a priority for Assurant’s VP of Digital Technology Products, Meeraiah Guddamsetty. 

Dr. Perez feels that: “one of the biggest hallmarks of [data] quality in any organization is a willingness to question the status quo”. According to Dr. Perez, they need to be constantly on the lookout for better ways of doing things. Another challenge he points out is the silo mentality organizations have adopted: “There isn’t anything wrong with silos; they don’t want to get past their natural bias”. He wants people within these organizations to overcome their long-time reluctance to change the way they do things.

Successful and ethical uses of data

Miya Knights prompted the panelists to think about the successful and ethical uses of data within their respective organizations. What does ‘good’ look like from a data quality standpoint?

Russ believes that ‘good’ is the small and ordinary change being made behind the scenes, the things no one hears about. “It’s not revolutionary change, it’s not big stuff”, he stated, emphasizing that bringing down ordinary tasks and turning that into something achievable is what people should focus on doing.

Furthering Russ’ point, Doug added that they don’t have to create big data enterprise solutions to be considered inspirational. To expand on his point, he drew from his own experience with clients. “I started compiling news cases on how organizations are using big data and analytics in innovative ways”, he said. Doug described these stories as inspirational. Despite being considered ‘pedestrian’ they were enough to shame business and IT executives into doing more with their data. 

“We have a responsibility, for those of us who understand data, to make sure our communities understand how our data is being used by a lot of the brands and companies we work with”, Lauren stated. She added that certain company models are built on us, the consumers, giving up information about ourselves. From her data aggregated perspective, she boils it down to one question: “Are consumers okay with that value exchange?”

The code of ethics factors into a lot of the panelists’s answers. Diana stressed that organizations need to make sure they are using people’s data in ways they would have anticipated. It’s about making sure that the outcomes are aligned with who you are as a company. Diana wants companies to: “make sure (their) promises are upheld”. 

“It’s all about mission, vision and goals. How are you portraying your organization? What is it that you are gathering?”said Dr. Perez. Using the example of oil and gas supply, he described how there were significant issues with failure in equipment because oil wells are inefficient or performing at an unacceptable level. “When these industries adopt a successful strategy, that advocates for preventative maintenance. They go a step ahead and use data to achieve predictive maintenance”, he stated. He explains that this type of case study is both successful and ethical.

The type of data collected, according to Meeraiah, is important when it comes to determining what ‘good’ looks like. When it comes down to it, he looks at the situation from three different perspectives. The first is the organizational perspective, where you determine your value system. The second focuses on process and considering what tools you have at hand. What ethical practices are being used? Are there data review boards in place? The third is the people aspect, where he considers whether or not the organization is following their own code of ethics that aligns with the company’s values. 

The lack of data literacy

Dr. Perez thinks that data literacy is something that needs to be addressed. He recalls a recent Forbes study where 83 percent of leaders said that they want their organizations to be data-led, while only 33 percent of employees were comfortable working with data. This, in his opinion, presented a huge disconnect. He points out that this “data literacy is only for data scientists” attitude is the kind of mentality that leads to illiteracy or it proves that data illiteracy is present. 

“If it’s an issue that is recognized by leadership and they are willing to admit the need in their organization, that’s a good start”, he said. 

“You should start with educating people on the possibilities of data and its role as an economic asset. Its role as a fifth factor of production”, said Doug. He reiterated that in this information age, it is important to treat data as an asset and measure it with the same discipline as any other equally important asset. 

While these views center around critical thinking, Russ simplifies the argument down to a couple of questions: What does the data tell us and what data are we missing? “I don’t want to be data-led, I want to be data informed”, he said. 

This roundtable debate was recorded at The Studio. To find out more about The Studio and how you can apply to Speak alongside these leaders, click here.

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