The ability of any business to accelerate their organization’s digital growth lies greatly in their leader’s ability to take pragmatic and targeted actions within the business. One of the best ways to ensure this is by managing your data better. Low data quality levels are a bane of every business’s existence. How do we get around this issue? By employing data analytics measures that’ll rigorously track data quality levels and drive better decision-making. We highlight the top tips in the following paragraphs.
Develop a thorough understanding of data quality.
One of the best tips for effective data management is to map out the links between your key performance indicators, business processes, and data assets. If possible, outline all quality-related issues you experience within the organization and clearly state how they affect your growth and profitability.
Once all stakeholders are aware of the current data quality situation within the company, everyone is in a better position to build a targeted data management plan that is geared at improving the quality of your data. Every strategy created will have a defined scope, a clear list of all stakeholders, and an estimated return on interest.
Set your data entry standards.
The process of adding raw data into a CRM software system or any other business system should come with an agreed set of guidelines. This is a great strategy every business can start with after forming their “diagnosis.” The specifics surrounding your data entry standards and requirements vary from business to business, so be sure to research what is defined as a “good” standard of data within your industry.
One way to set your standards is by holding periodic stakeholder meetings, where data and analytics leaders determine everyone’s expectations concerning your data and its quality. Once these standards are set, they should be followed within all departments in the organization.
Employ data profiling as often and early as possible.
Analyzing the information gathered from existing data sources and adequately creating detailed summaries is termed data quality. One of the many benefits of data profiling is that it provides valuable insight into data presented to help all stakeholders make informed improvement decisions. It also enables stakeholders to identify corrective actions to be employed.
For data profiling to be highly effective, it must be done often, provided you have all the necessary resources, data, and every other necessary element of its successful application. Data profiling can reveal certain errors affecting your customer service or poor communication between departments, affecting customer satisfaction. These issues may be termed high-priority activities, which need to be worked on urgently.
Design data quality dashboards for your master data.
The design and implementation of dashboards aren’t only for master data but also for monitoring all critical data. This dashboard aims to provide you with a detailed summary of your data sets, which is shared with all business users. The dashboard exhibits all past data to easily identify patterns and trends for better decision-making in the future. The dashboard is highly customizable to suit your business needs, and it’s a great way to track all improvement activities undertaken toward increasing the quality of your data.
Employ integration tools.
Many organizations rely on multiple data systems for their data analysis and information. One major disadvantage of this is that decisions are often made in isolation from other data systems, making these decisions woefully inadequate or inaccurate. In such situations, data might have to be rekeyed into different systems at certain times, which can be both time-consuming and confusing, especially when done manually. Your best solution? Using integration tools. Data integration tools enable creation and storage in record time. It is an automatic system that streamlines data from multiple sources, placing them into a single platform for easy retrieval and usage.
In short, bad quality data can impact your organization’s revenue over the long term, leading to poor decision-making and increased complexity in your data ecosystems. With the above tips, you can quickly improve the quality of data.
