When margins are thin, it’s natural to start cutting expenses and restricting spending. But, businesses that limit investments in the wrong areas can end up costing themselves more in the long run. One area that is well worth its weight in gold is the data department. While many companies can get away with cuts in certain departments, very few will increase their margins with cuts or limitations to data talent. Instead, problems pile up, creating operational drag, and eventually, margins get thinner.
Businesses are quickly realizing the value of people with good data engineering skills. In fact, many companies are having trouble finding and retaining good data talent due to high demand. According to a survey from McKinsey , 77 percent of companies report a lack of the necessary data talent and skill sets to perform the required tasks in mission-critical areas. When data talent is lacking, operations, efficiency, compliance, and margins suffer. It’s becoming clearer and clearer that a good data team is a critical part of the infrastructure needed for growth and risk management.
The High Cost of Data Talent Gaps
Even when businesses have systems in place to collect valuable data, without an appropriate analytics workforce to transform raw data into actionable business insights, the data can be worthless.
Those clean and insightful data dashboards don’t make themselves—skilled data talent, like engineers, analysts, and architects, must first collect, organize, clean, and analyze that data. When businesses fail to hire the necessary data talent—or worse, get rid of them—the resulting data talent gap leads to a series of issues that compound over time, including:
1. Poor data quality
This one might be the most obvious—a lean data team means less work goes into cleaning, organizing, and analyzing the data. This can lead to more frequent inaccuracies or incomplete data. For example, data on first-quarter earnings might get mixed up with second-quarter earnings in visualizations, leading to errant interpretations by business leaders. In this case, a loss of revenue might be associated with something that happened in the first quarter, but poor data quality places this loss in a later time period in the resulting flawed dashboards that led to bad decisions.
Ultimately, poor data quality causes decisions to be made based on inaccurate or incomplete data, leading to financial losses, missed opportunities, poor customer experiences, and operational inefficiencies.
2. Technical debt accumulation
Eventually, bad data and poor decision-making leads to technical debt. Without a proper data team, suboptimal decisions or changes might be made in data pipeline development, modeling, or architecture in order to meet immediate business needs. Because the team lacks the talent or bandwidth to implement proper fixes, technical debt accumulates.
In 2022 , Southwest Airlines internal systems incurred so much technical debt that the system failed. The company reportedly updated their customer-facing apps, but not the antiquated crew-scheduling system. Despite many warnings of impending failure, the company continued using the system, opting to revert to manual, human-driven processes when issues arose. The system could not handle the volume of real-time data changes during a particularly busy holiday season, causing it to fail entirely. This led to over 16,700 canceled flights, 2 million stranded passengers, and costing the airline over $800 million in losses and fines.
3. More time spent on rework
With increasingly bad data quality and technical debt, comes more time spent on trying to fix mistakes. Eventually, poor data quality is noticed, and technical debt causes issues. Both of these are sent back to the existing data team to rework, taking up even more of their limited time and resulting in compounding problems—even less time to spend on data analytics and data cleaning, the accumulation of additional technical debt, and overstressed data talent.
4. Burnout and turnover for existing staff
As technical debt requires more and more time spent correcting problems, understaffed data teams can quickly become overwhelmed and overworked. Without the time and talent to devote to quality data analytics, the team cannot put out quality work, and a constant stream of requests to fix prior datasets and dashboards doesn’t make it any easier. The resulting burden on these valuable team members will ultimately lead to burnout. And when many other companies are investing heavily in their data talent, it can be tempting for these employees to look elsewhere—leaving an even bigger data talent gap.
5. Compliance issues
For companies operating under regulatory landscapes, cutting or failing to hire or train professionals with data analytics and governance expertise eventually puts compliance on the line. A lack of expertise and the accumulation of technical debt and bad data often mean that audit teams cannot effectively validate the accuracy and completeness of the required data for audits. When audit time rolls around, the lack of data compliance, or proof thereof, can result in hefty fines, reputational damage, and in some cases, a suspension of business operations.
How Addressing Data Talent Gaps Mitigates Risk and Enables Growth
A lack of data talent can inhibit growth and increase risk, and can compound over time, leading to serious fines, burnt-out teams, and a decrease in revenue. But what happens when the data talent gap is addressed, and a good data team strategy is implemented?
Productivity Flourishes
When your data team has the right mix of talent and number of people to function properly, mistakes become less common, data quality improves, and the team has the time to update systems in ways that lead to accurate forecasting and strategic agility. Fewer mistakes and reduced technical debt mean less time spent fixing problems and more time spent on productive activities, like proper data cleaning, organizing, and analysis. More time spent on data cleaning and organizing results in more dashboards with reliable insights. The whole company starts to run more efficiently. It starts to feel like tasks are getting done rather than stalling and piling up.
The Path to Growth is Clearer
With improved data quality and more time spent on data projects, other teams can start to put more trust in their data. As data teams create more reliable dashboards and transparent KPIs that help other teams create and reach their goals, the non-data teams start to have a better understanding of how data relates to growth.
For example, dashboards made with accurate and comprehensive datasets and data consolidated from multiple sources allows non-data teams to spot trends, anomalies, and opportunities with ease. For example, reliable dashboards can help business teams identify real-time conversion rates to help them assess the effectiveness of marketing campaigns, or help teams determine customer acquisition cost to see if efficiency growth strategies are working. Overall, teams can make better business decisions that attract more customers, keep existing customers, or identify areas that lead to revenue loss.
Compliance is (Almost) Effortless
A data team strategy implemented by a good data team will naturally define and adhere to clear data governance policies, while implementing "privacy by design" to stay ahead of regulations. These data policies, along with practices like data encryption and access controls for sensitive data, can help align normal business practices with regulatory requirements. This ensures data is accurately classified and stored according to regulatory protocol as it is processed, instead of scrambling to make sure compliance is in order before an audit.
It Becomes Easier to Create a Data-driven Culture
Data literacy shouldn’t stop at the door to the data team office. Real growth requires a fully data-driven culture —where all divisions appreciate the power of data and know how to gather insights from what the data team provides.
When it becomes clear that leaders are investing in their data teams and infrastructure, the rest of the company will pick up on the value data can wield (of course, openly advocating for data literacy always helps too). This, in turn, can lead to more employees engaging with data, learning to use data tools such as dashboards, and applying their insights to drive growth and improvement in their departments.
Combining the Data Engineering Skills with Data Tools and Strategy
At the end of the day, neither data talent nor data tools alone will accomplish business goals. Your data team, their data engineering skills, and custom data tools and software combine to create a powerful data infrastructure capable of driving growth, mitigating risk, and ensuring your business excels in its goals.
CSG partners with businesses to design business intelligence strategies that foster growth and streamline processes. This involves aligning technology investments with business goals, designing people-centered and data-driven solutions, and empowering teams with ongoing coaching and partnerships.
If you’re ready to put together a data dream team and implement a data-driven culture with software that the whole company can find value in, contact us to get started.
