Businesses today collect and store an astonishing amount of data. According to estimates from IDC, 163 zettabytes of data will have been created worldwide by 2025. However, this data is not always useful to business leaders until it is organized to be of higher quality and reliability. Despite its importance to effective data analysis, most business leaders simply do not know how to improve the insights they extract from collected data. Here is what businesses are doing right now to maintain high levels of data reliability. 

Challenges Businesses Face When Trying to Improve Reliability

Business data is divided into silos that do not allow information to flow smoothly across data pipelines. As more businesses adopt technology to help them optimize their daily operations, software vendors have begun to develop highly specialized tools as a means of differentiating themselves from their competitors. The adoption of these specialized tools means that it is not uncommon for each business division to use a Data Management tool that is different from other departments. This further entrenches data silos and forces business leaders to piece together incomplete and unreliable data sets to achieve a true overview of their operation. 

Companies generate data faster than they can collect and organize it. Since data processes often occur alongside regular business operations, it can be challenging for data leaders to build and maintain systems that can collect, organize, and analyze data at the same pace it is generated. This creates a backlog that data teams then scramble to clear. It also increases the likelihood of human error, which erodes the reliability and quality of the data. Moreover, it means that data analysis provided to business leaders is almost always outdated and incomplete.

Data professionals are in short supply. A shortage of talent has been a widespread concern for U.S. businesses across all disciplines and industries, and this shortage extends to data professionals. Experienced and knowledgeable data leaders are often in high demand, but short supply. This has led to CEOs shouldering responsibilities for more data initiatives.

Recent research has shown that over half of surveyed companies still rely on their CEOs to set a data and analytics agenda. CEOs are usually stretched by their jobs and are often unable to set aside enough time to develop and maintain reliable and efficient data processes across an organization. 

5 Best Practices for Improving Data Reliability

Here are five things data leaders can do to overcome challenges and increase data quality and reliability.

1. Develop effective data collection channels and strategies

The first step to building an effective and efficient foundation for data-based decision-making is ensuring that business data is collected in an organized manner. Business leaders must know where each piece of data is coming from and where it should be stored for future use. This allows businesses to ensure that data is collected seamlessly and that data processes are properly embedded in a team’s regular workload. 

2. Bridge the information gap between disparate business departments

Information silos have always been a barrier to data reliability and the use of specialized tools for each business division has only made this problem worse. Therefore, business leaders should pay special attention to the gaps between business silos and close them as quickly as possible.

This can be done through application integration and Data Management systems that span the entire organization instead of specific operational departments. This also means that standardized data collection and organization processes must be put in place across all departments in a business. 

3. Ensure that business data is properly segmented and organized

Simply having a complete data warehouse does not guarantee high levels of data reliability for business leaders. It’s not uncommon for data lakes to turn into data swamps. Databases must be organized in a way that is easy to interpret. Business leaders should be able to pick out the information needed at any given moment.

This can be done through the use of tags, labels, lists, groups, and a variety of other properties. This segmentation allows business leaders to highlight important pieces of information while still retaining an overarching view of all their data. Acceldata’s data observability platform allows business leaders to use consistent organizational labels to organize their data and be informed of and resolve inconsistencies early.

4. Shift left with data quality and reliability 

From an operational perspective, data quality and reliability require a significant effort. It’s common for many businesses to ingest information from multiple streaming sources. The earlier that data is monitored and observed the less likely bad data will affect downstream analytics. 

Modern software solutions allow business leaders to be alerted to major data issues. However,  businesses must go beyond simple alerts if they wish to optimize their investments in data and their modern data stack. 

5. Utilize user-friendly applications to display and report the results of data analysis

Ultimately, business leaders can and usually do put data collection processes in place. However, these processes are limited in their ability to deliver value if only highly knowledgeable data experts can interpret them. Decision-making and data analysis are not always limited to data teams and insight generated by data must be made available to employees at every level of the organizational hierarchy.

This not only empowers employees to use business data to improve efficiency in their daily tasks but also increases the likelihood of data discrepancies being identified early. This accessibility can also help business leaders develop a data-driven culture that encourages employees to share the responsibility of data collection and management. 

Data can give business leaders deeper insight into their operations. This insight can lead to higher profitability, increased productivity, and greater customer satisfaction. Before businesses can do this, however, they must first ensure that business decisions have a solid data-based foundation. With data systems and pipelines that are fully observable across the data stack, businesses can be sure that the information they use to make crucial decisions is completely reliable.