Data: A double-edged sword

No matter the goal, data can be a double-edged sword for technology companies. If wielded with skill, it can cut through competition. If managed poorly, operational and technical challenges can lead to self-inflicted wounds. In a candid discussion with a group of technology industry leaders with data management expertise and influence, we explored their operational maturity, challenges, thoughts on regulation, use of emerging technology, and organizational leading practices.¹

Most leaders told us that they had a high degree of confidence in their ability to handle any data management issue, but admitted that challenges still exist around governance, privacy, security, and architecture. Those that felt they were ahead of their peers credited modern applications and infrastructure, multiyear transformation initiatives, data loss prevention solutions, strong organizational collaboration, and robust policies around governance, privacy, and security. The few that felt they were lagging said that they lacked resources to enforce their governance, privacy, and security policies and had struggles with data quality. One leader admitted, “In the spirit of ‘the cobbler's son wears no shoes,’ we are definitely underfunded in IT, with strained resources that are challenged by being leading-edge in our capabilities.”

Three key challenges to achieving leaders’ data management goals

Collecting and protecting ever-growing volumes of data ranked as the top barrier (figure). One leader told us, “Volumes continue to rise across all functions, without much regard for prioritizing what data is required to be maintained.” The data leaders highlighted a need for better technologies to collect data, make sense of it, and make it meaningful. They also said that achieving a holistic view of their entire enterprise’s data landscape and identifying sensitive data were significant challenges. Finally, with increasing amounts of data to handle, managing policy implementation and audit was getting harder.

Shifting regulations garnered the next-highest ranking. “Regulations changing all the time is a giant pain on top of an already complex problem,” one leader said. Most were resigned to the fact that inconsistencies will arise when trying to satisfy multiple regulators around the world. One said, “All regulations are a cost of doing business; we adapt to them because we don’t have a choice.” Many pointed out that new data-related regulations could lead to higher costs, increased complexity (e.g., network segmentation because of data sovereignty), and challenges to software development.

As part of the broader regulatory landscape, cross-border transfers and data localization issues also worried our data leaders. Some were trying to avoid or minimize having to transfer data out of country. A few had to establish additional cloud data centers around the world and develop new preferred partners. Generally, there was a fear of unintentionally “tripping up,” because they didn’t know something regarding the dynamic regulatory landscape.

The third major barrier, cost and complexity of data privacy, was selected by 18% as number one but by 50% as number two, indicating a high level of concern. Data leaders are dealing with a greater focus on data privacy due to customer requests and requirements, regulations, and internal improvements. This is creating a ripple effect, leading to increased costs for leaders, “The cost of everything is rising but our budgets aren't rising with it.”

How leaders are addressing their data management challenges

The amount of data collected and generated isn’t going to decrease. This means data leaders should anticipate and prepare for potential challenges. Our panelists told us they were focused on four areas:

Improving internal, ecosystem, and industry-wide collaboration. Collaboration is essential for planning data management strategies and facing challenges head-on. Data leaders suggested that clear roles and responsibilities, aligned priorities, and transparent communication and policies were important. Whether collaboration is more structured or ad-hoc, a variety of functional viewpoints is critical: The CIO, research, security, legal, data management, business leadership, application development, infrastructure, and compliance should work together.

Staying on top of rapid regulatory change. It is important to make strategic investments and choices with potential regulatory developments in mind. Having strong, cross-functional collaboration and well-established workflows can help organizations react quickly to regulatory challenges. Some of our data leaders had dedicated teams or roles to monitor and evaluate the impact of global regulations. Modern infrastructure can help as well, one leader told us: “Most of our critical infrastructure is new, automated, and in public cloud, which gives us an advantage in terms of agility. We can redesign and redeploy much of our infrastructure confidently.”

Improving training to increase employee awareness and compliance. Leaders told us that, across the board, there needs to be better user education and stricter enforcement of rules when it comes to data management and cybersecurity. This includes both methodologies and technologies. Data leaders should share in the responsibility as well, by implementing more automated tools. “The average user is not suited to be responsible for understanding the full scope of data they handle and shouldn't be saddled with this level of accountability,” one leader said. “We have to provide more intelligent systems and processes.”

Modernizing infrastructure, deploying automated solutions, and experimenting with emerging tech. Many of the data leaders we spoke with wanted to continue to streamline their systems and create a “lighter footprint.” This means moving to a fully SaaS-native cloud and standardizing their tools across all clouds. They also want to integrate new data sources and improve their ability to process real-time information. Some were beginning to experiment with automated data classification, including deployment of homegrown and commercial artificial intelligence/machine learning tools.

It can be difficult for tech industry leaders to weather the many challenges of increasing data volume, shifting regulations, higher costs, and more complexity. However, by focusing consistently on collaboration, flexibility, modernization, and automation, they may enhance operations and anticipate regulatory requirements.