As I’ve been looking at where the data industry has been and where it’s going, I see three major data-centric opportunities for U.S. businesses, public officials, and advocacy groups as we cross the doorstep of 2019: Next generation data privacy, the expansion of data ecosystems, and increasingly enabling business through data science.
This is a perfect time to take a fresh look at the opportunities in data. Existing and new legislation, along with innovators large and small, continue to accelerate efforts to find new ways to use data to create solutions which drive measurable business results and deliver value to consumers. The appetite for data is insatiable, and the tools we can apply to find value in it are steadily growing in power.
As the breadth, quality, and availability of data increases, the opportunity for data science to create better and new consumer experiences and generate business results will also increase
We are seeing powerful data companies, such as Amazon, Google and others, leverage data to move into new industries. The 2018 announcement of an Amazon partnership creating a new healthcare initiative is an example of what is likely to be continuing expansion by the digital giants into new and more disparate industries. This will—and must—trigger innovative competitive responses from traditional players.
This confluence of how companies steward consumer data while enabling data-driven technology and innovation is the big-picture backdrop I see for opportunities in the year ahead.
Embrace Next Generation Data Privacy
Data protection and data privacy have long been key pillars of data strategy and execution. However, high profile examples of inconsistency in how consumer data has been used and protected have clearly increased the scrutiny around these practices. As new laws and regulations continue to be rolled out, U.S. companies have significant compliance obligations as well as a generational opportunity to evolve and elevate their policies, standards, and operational practices in the data privacy and protection space.
Examination and enhancement of end-to-end data management strategies, technology, and operational practices can have impact ranging from the front-office to the back-office. But the rewards can be great. Companies that get it ‘right’ will retire technical debt, while creating modern and integrated data management operations.
Bringing Data Ecosystems to Life
Data ecosystems and how companies participate through data to offer new products or services to consumers is the second pillar of this 2019 confluence. Many companies already have examples of these relationships, and this will only accelerate in 2019 and the years ahead. Data relationships and ecosystems will necessarily become a key part of growth plans in a fiercely competitive marketplace and some key catalysts will spur these relationships forward.
First, the importance of the 5G rollout in the US should not be underestimated. With anticipated speeds of up to 20 times faster than 4G, 5G will further open new ways to engage the connected consumer. The dramatically increased speeds will be an important spark for new innovation at the periphery as well as for helping to propel promising innovations, such as autonomous vehicles, virtual reality, and IoT, to the mainstream.
Second, we are seeing powerful data companies leverage data to move into new industries. The space is evolving quickly with new entrants partnering with established entities. This will—and must—trigger innovative competitive responses from traditional players.
Finally, data modernization, driven in part by changing consumer expectations and compliance efforts, as well as new laws and regulations could remove many of the technical barriers for data experimentation, data innovation, and exchange. In other words, the massive silver lining of compliance is better data that further accelerates the business execution of data ecosystems.
Enabling Business with AI, ML, and Data Science
Increasingly, effective use of data science and analytics starts with maintaining a constant focus on operational excellence in data practices. This includes the basic hygiene factors of data quality, master data management, data retention, correct metadata attribution, and data lineage and data location. These principles, when put into practice at scale, create the data foundation for both operational efficiency and new business opportunities.
As the breadth, quality, and availability of data increases, the opportunity for data science to create better and new consumer experiences and generate business results will also increase. I believe this year will bring an increasing rate of adoption and comfort across all levels of organizations—from the c-suite to analysts—in the use of data to identify trusted opportunities, evolve business processes, increase the speed and quality of transaction execution, and improve any number of operational KPIs.
As we proceed through the details of harmonizing data privacy and innovation, there are many views. For example, Apple CEO Tim Cook gave a high profile speech on the topic to a conference of European privacy commissioners in October 2018. Of particular note, Cook stated:
“Technology’s potential is, and always must be, rooted in the faith people have in it…In the optimism and creativity that it stirs in the hearts of individuals…In its promise and capacity to make the world a better place. It’s time to face facts. We will never achieve technology’s true potential without the full faith and confidence of the people who use it.”
There is little doubt that 2019 will be another year in which new horizons of technology’s true potential are fueled by innovative uses of data. But this innovation cannot come at any cost. It must be coupled with demonstrable philosophical and operational adherence to consumer data privacy principles. Increasingly, the competitive winners will be determined by their ability to generate consumer trust and retention through the harmonization of their data innovation with their privacy practices.
As the rules of the game are written and rewritten, sometimes it’s the data we don’t use or the algorithm we don’t write that makes all the difference.