Optimizing Data Quality to Navigate Economic Turbulence 

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Read more about author Asad Siddiqui.

Sustained economic volatility and a global recession have forced enterprise leaders to make difficult decisions. Some are reducing headcount and scaling down operational overhead to become more agile, while others have implemented cost-saving measures, such as cutting tech spend, to improve financial flexibility. These initiatives may provide companies with short-term reprieve during times of disruption, but it forces them to rebuild once the economic climate improves. Organizations are using the economic downturn to bolster areas of their business that deliver long-term value and allow them to accelerate once markets stabilize. To that end, now is the opportune time for enterprise decision-makers to invest in their greatest asset – data. 

Data is paramount to enterprise growth and success, but ensuring its quality and accuracy can be challenging. This requires a comprehensive strategy and careful planning to support informed business decisions, drive efficiency, and foster operational continuity. Let’s look at how organizations can utilize the right resources and people to gather trustworthy data. 

Identify Opportunities to Reduce Manual Processes 

It is imperative for business leaders to identify opportunities that improve their data supply chain and overall data quality. Research shows that nearly half of newly created data records have at least one critical error and just 3% of companies’ data meet basic quality standards. Companies that rely on manual tasks and processes often create data silos and experience greater rates of human error. The combination of manual data movement, proliferation of SaaS applications, and broken integrations reduces accessibility and leads to bad data, which impacts all levels of an organization. Forrester found that by eliminating these time and resource consuming steps and increasing data accessibility by just 10%, a typical Fortune 1000 company will generate more than $65 million in additional net income. 

Modern enterprises rely more on data to achieve business goals and objectives than ever before. Manual tasks and processes exacerbate employee burnout and the ongoing tech skills shortage, diminishing the quality and accuracy of the data necessary for better decision-making. It is impossible to connect siloed software, digitize the enterprise, and improve data quality without reducing manual business processes. Failure to address this issue will prevent an organization from implementing an effective data governance strategy. 

Develop a Sound Data Governance Strategy 

The most successful companies prioritize and take the necessary time to develop programs that drive strategic business decisions. This applies to data governance because it enables organizations to evaluate and improve the availability, quality, accuracy, and timeliness of their data. CIOs and other IT leaders must define clear goals and objectives for their data optimization initiatives to ensure these efforts are aligned with the overall business strategy and focused on achieving measurable outcomes. Next, they should map out all processes, which includes identifying roles, tasks, and objectives of each, to determine how they can be streamlined and automated. This should also include the personas involved because without understanding their experience level, the data will remain inconsistent and ineffective. Finally, assess the available technologies and select the most suitable tools for specific use cases based on the level of integration with existing systems. 

The CDC Foundation experienced this when it first migrated its entire operations into cloud-based solutions. The business was feeling the weight of manual processes, with staff members conducting manual CSV uploads and reconciliation from system to system. Given the uniqueness and highly customized nature of its processes and operations, they knew they would need to connect them all to properly manage them. Malcolm James, Director of Information Technology at the CDC Foundation, admitted that it was a challenge for the organization to not only determine what to connect, how to connect and when to connect, but also what the business required. By mapping out processes, defining goals and implementing a change management strategy simplified the transition for Malcolm and his staff, allowing them to become familiar with and take ownership of the initiative. 

A robust change management plan is essential for the successful implementation of enhanced data governance initiatives. It helps establish how an organization’s day-to-day operations will run while executing a new strategy and explains how they will run moving forward. This allows business leaders to communicate the benefits of optimizing data to their stakeholders, provide training and support for employees, and manage any potential resistance to change. Companies without a change management strategy tend to face pushback from employees because they are unaware of how a different approach will impact their core job functions. Focusing on the end-user experience and personas involved with an enterprise-wide change will simplify the transition and provide a great experience for the entire organization. 

Create a Data-Driven Culture 

The foundation of a successful data governance strategy is an organization’s willingness and capacity to create a data-driven culture. This shared vision is a fundamental shift for most companies because it requires a commitment from the entire organization – not just the IT department – to build an interconnected business that eliminates disjointed datasets. Business leaders must focus on this early in the process because success is contingent upon a unidirectional approach. 

Fostering an environment that prioritizes the quality and accuracy of an organization’s data is the key to improvement initiatives. Embracing this mindset and making it a core priority of the company helps establish a well-defined strategy, delivers greater ROI and drives long-term value. Generating buy-in across the company and cross-functional departments will aid in securing funding, soliciting feedback, and ensuring departments are committed to making data-enhancing initiatives successful. 

The organizational value return of being able to confidently trust data and inform business decisions is exponentially greater than undertaking cost-saving measures. However, simply committing to improving data quality is not enough. Business leaders must understand that data optimization is dependent on the right tools, people, and strategies to create a company-wide mentality. By reinforcing their approach to data during times of economic disruption, leading enterprises will be poised for accelerated revenue expansion when the market stabilizes.