Cybersecurity Companies Must Practice What They Preach to Avoid the Data Paradox
Cybersecurity companies — traditionally considered pioneers of data innovation — are often the ones struggling to unlock the full potential of the data they collect within their own organizations. They build solutions that harness vast amounts of data in real-time to predict, detect and neutralize threats for their clients, yet they fall behind in using their own expert capabilities to generate insights that improve their operations.
Data-driven insights are the backbone of most modern businesses, especially in the cybersecurity industry, where the stakes are high and the competition is fierce. However, a recent Forrester study revealed a startling truth. Only 12% of data collected is used for analysis, and fewer than 30% of companies report being able to translate this data into actionable outcomes.
It’s a tricky issue, but if cybersecurity companies follow their own lead, the solution is relatively straightforward. Leveraging artificial intelligence (AI) and machine learning (ML) technologies, which cybersecurity organizations have long championed, can optimize customer data to drive business growth, improve customer experiences and maintain a competitive edge.
Poor Data Analysis Results in a Multitude of Missed Opportunities
The data paradox is backed by hard numbers. Forrester’s study revealed an alarming underutilization of organizational data across industries, including cybersecurity. Despite their technological prowess, many cybersecurity companies are leaving mountains of valuable data untouched and under-utilized. Additional studies underscore the problem. Gartner reports that 87% of organizations lack advanced analytics capabilities, even though nearly all acknowledge the importance of data-driven decision-making. Businesses collecting massive amounts of data but failing to harness its potential is a disconnect that signals a fundamental issue with serious consequences.
For cybersecurity companies, unused data represents missed opportunities for revenue generation. Each lost insight could have been a pathway to identifying cross-selling opportunities, improving customer retention or establishing a competitive advantage. In addition, untapped customer data leads to subpar experiences. Those effects are especially significant in the cybersecurity space, where trust and customer satisfaction are critical to maintaining long-term relationships. Customers who feel misunderstood or underserved due to a lack of personalization or insights-driven engagement may take their business elsewhere.
Tackle Historical Challenges With New Technology to Break Down Barriers
Historically, there has been an “external-first” mentality in tech-driven industries, which partially explains why high-tech companies, including cybersecurity firms, lag in adopting the solutions they create. Companies focus on innovating to serve clients and external markets while neglecting to implement similar strategies internally, which stems from prioritizing revenue-driving activities. While customer-facing technologies often receive attention and funding, internal adoption of these tools frequently takes a backseat. Additionally, organizations may believe their existing processes are sufficient, unaware of the untapped potential within their data.
Several factors hinder the internal adoption of better customer data analysis practices:
- Data silos: Information is often fragmented across departments, making it challenging to generate a holistic view of customers.
- Resource constraints: Many organizations lack the time, budget or personnel to implement and scale processes for internal use.
- Expertise gaps: While these companies lead in building innovative technology solutions, integrating and applying these cutting-edge tools internally to non-engineering functions often requires new skill sets.
When it comes to dealing with these challenges, cybersecurity companies have a unique advantage — they’re already experts in the best-emerging technologies, including AI and ML. Companies can extract actionable insights from customer data by applying their expertise to leverage AI and ML internally for a variety of purposes, including predictive analytics, segmentation and feedback analysis.
Predictive analytics help organizations anticipate customers’ behavior and need to proactively address pain points or offer timely solutions. This strategy can be targeted even further by using segmentation to provide tailored services or messaging to distinct groups identified within the customer base. Analyzing customer feedback at scale can uncover recurring themes and areas for improvement so reacting to customers becomes a faster, less labor-intensive process.
Optimizing Data Analysis to Unlock New Advantages Requires Solid Strategy
The same customer insights that drive satisfaction can also propel businesses forward, unlocking new avenues for growth, profitability and competitiveness. By tapping into data-driven intelligence, companies can identify fresh revenue streams, accelerate upselling and cross-selling, and foster lasting customer relationships. Meanwhile, AI/ML-powered analytics can help organizations leave competitors in the dust, abandoning manual data analysis for more agile, informed decision-making.
For cybersecurity companies ready to start maximizing the potential of the data collected within their own organizations, it’s relatively easy to get started. However, a well-developed plan is crucial, starting with a thorough audit to identify where customer data lives and evaluate its current usage — or lack thereof. Utilizing processes and platforms that centralize data across departments will help to break down siloes and pave the way for the implementation of cross-functional teams. The combination of expertise from different verticals and departments – such as engineering, science and operations – is the most effective way to successfully implement the use of AI/ML tools.
It’s also wise to start small by beginning with a pilot project, for example, and focusing on one aspect of customer data, such as segmentation or churn analysis. The final touch – the use of clearly defined metrics to evaluate the impact of data-driven initiatives – is among the most important implementation components. Result measurements can then be used to refine and improve a company’s AI/ML strategy accordingly.
The data paradox facing cybersecurity companies presents a unique opportunity for growth and transformation. By acknowledging the disconnect between their external innovation and internal operations, these organizations are progressing toward the extraction of meaningful insights from customer data. Cybersecurity leaders willing to practice what they preach can unlock the full potential of their data to drive business success.
