Data Privacy Insight of the Week is part of our ongoing efforts to share and discuss important data privacy trends and information. Alexandra Mitchell, Privitar’s Customer Insight and Business Intelligence Lead, will examine and comment on key findings from Privitar Privacy Pulse 2019, a survey conducted by Edelman Intelligence that comprised nearly 6,000 consumer and business respondents in the US, UK and France.
It’s hardly surprising that nearly 90% of data leaders put such a high premium data privacy. Respecting customer privacy is integral to respecting the customer. Those same data leaders also know that while safeguarding customers’ sensitive personal data is vital, making use of data to improve decision-making, and to gain the insights that lead to better products, services and customer experience is essential.
Finding the right balance between data privacy and data utility is the challenge.
As with many of today’s toughest business business challenges, the right mix of people, process and technology can show the way forward.
The people affected by the problem – data privacy and data use stakeholders – need to work together to negotiate a solution. The processes that result from their collaboration combined with relevant technologies that automate the processes can achieve the intended outcome – delivering safe and useful data, quickly. This is doable today. It’s time to start embedding privacy into your data processes.
Doing so requires changing the status quo.
Today most data privacy processes aren’t working for data scientists. An understandable fear of the economic and brand-erosion damage that accompany a data breach – all too frequent these days – causes many data privacy managers to go into ‘lock-down’ mode. That makes it very difficult for data scientists to access quality data they need for insights that will result in new products and services that accelerate business growth and success. When data scientists can’t do their jobs, businesses risk losing out to competitors who are making the most of their data.
This is the scenario we see all too often when privacy is not fully embedded in the data provisioning process. When privacy is something that happens in a custom script, rather than as an integral part of the data pipeline, data engineers spend their valuable time managing exceptions in the data. It’s a slow, painful and dangerously ineffective process ‘ prone to error and certainly not working for the data scientists who need the data.
Automation, then, is crucial. Businesses that put a high priority on protecting customer privacy (as we’ve seen that’s nearly 9 in 10 in the Privitar Privacy Pulse Report) need to automate that protection, making it an integral part of the data pipeline. Unless that happens there’s a very real risk that the teams requiring data to do their jobs successfully will circumvent the processes. This creates obvious ‘ and unacceptable ‘ risk to your customers’ data privacy. Avoid that risk and make the most of your data with skillful deployment people, processes and technology.
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