In today’s data-driven world, organisations recognise data is no longer just an asset but a critical component of their overall business strategy. Data powers everything from strategic decision-making to operational efficiency.
As organisations increasingly rely on data to drive growth and stay ahead of the competition, effective data management has become essential. From data integration and quality, to analytics and governance, businesses must implement the right strategies and tools to manage their data effectively and leverage its full potential.
Data quality strikes at the heart of the data value chain – it is crucial; remember the old adage “garbage in, garbage out”. Poor data quality can result in incorrect insights and decisions, wasted resources and lost opportunities. However, it’s important to note that investing in data quality can ultimately save time and money.
Poor data quality can lead to costly errors and inefficiencies, while high-quality data can improve decision-making, increase productivity and drive innovation.
While ensuring high-quality data can be a time-consuming and expensive undertaking, it’s important to weigh the costs against benefits and look for strategies to use new developments in technology to save on this essential expenditure.
This involves a systematic approach to identifying, assessing and improving data quality. It includes processes for data: profiling, cleansing, enrichment and validation. However, it should be noted that manual data management tasks devour time, and businesses need to find ways to automate these processes.
Scaling operations by bringing in more staff can be challenging, as budget growth often doesn’t keep pace with greater demands from the business for data and insight. Data management specialists are in demand, leading to long and expensive recruitment processes that can end in frustration.
With the scalability of automated DQM tools, companies can manage increasing volumes of data while maintaining quality.
Fortunately, augmented data integration tools have allowed data pipeline projects to be streamlined through automation and thus are less manually-intensive. This really helps to speed up the process of getting meaningful and real-time information for the business to consume.
Augmented data quality management (DQM) tools that match the automation ones are thankfully available today.
Why automated DQM is essential
Managing data manually can be time-consuming and error-prone, and the manual tools of a decade ago can no longer keep pace with the increasing volume, variety, velocity and demand for data from systems and business users.
Automated DQM tools can help companies save time and resources, while ensuring data is accurate, complete, consistent and timely.
With high-quality data, businesses can make informed decisions, respond quickly to changes in the market and improve products, processes and servicesby infusing data into them.
Additionally, these tools can help businesses establish clear policies for data governance, ensuring it is managed in a compliant and secure manner. With the scalability of automated DQM tools, companies can manage increasing volumes of data while maintaining quality.
Benefits of automated DQM
Decision-making based on high-quality data leads to improved customer satisfaction, increased revenue and reduced risk. For example, a retail organisation could use automated DQM tools to ensure customer data is up-to-date and accurate, allowing for targeted marketing campaigns and personalised experiences.
It also results in increased efficiency, as staff are allowed to focus on more critical tasks. For instance, a healthcare organisation could use automated DQM tools to streamline the process of identifying and resolving errors in medical records, allowing healthcare professionals to focus on providing quality care to patients.
Better data governance is enabled, as businesses can avoid legal and regulatory issues and protect sensitive data. For example, a financial institution can ensure customer data is properly secured and complies with regulatory requirements.
Another example is a large retail chain that needed to guarantee compliance with data protection regulations such as GDPR.
It used automated DQM and master data management (MDM) tools to monitor data usage and track changes, which in turn guaranteed compliance, while at the same time protecting customer data. This helped to build trust with customers and stakeholders, enhancing its reputation as a responsible and trustworthy organisation.
The bottom line
Automated DQM and MDM tools are essential for modern businesses to manage data effectively and leverage its full potential.
As we move towards next-generation technologies with built-in artificial intelligence and machine learning, organisations must invest in their employees and provide them with opportunities to learn and develop new skills if they are to keep pace with the ever-evolving data landscape.
Effective data management is a critical component of informed decision-making and ultimately the ability to remain competitive.
With the right tools and strategies in place, organisations can successfully manage the increasing complexity of data sources and formats, while ensuring data quality and timely insights.
The key is to stay informed about the latest advances in data management and continuously evaluate and improve data management processes.
Written by: Carl Butler, Sales Executive, Qlik Data Integration
Originally published here.