The volume, variety, and velocity of available data continue to grow at an exponential rate. While collecting mass amounts of information can be beneficial to the growth of an organization, it is important to remember that data is only as valuable as the quality behind it. Financial firms that analyze and use low-quality data are no better off than a firm that makes decisions based on guesses. They risk operational inefficiencies, regulatory fines, and misinformed decision-making. As a result, more firms are seeking out tools that prevent and solve these challenges. It is vital that business and data analysts work together to understand current data quality trends before building a solid data management foundation and implementing an effective tool.
Business analysts have a wide variety of responsibilities surrounding managing a firm’s data and processes. As we look into 2021 and beyond, analysts should know these three data quality trends.
Approach data quality holistically and proactively.
Data quality initiatives have traditionally been reactive, or centered around fixing existing errors or inconsistencies within static, siloed data. However, resolving problems as they happen is nowhere near as effective as preventing them in the first place. As a result, data quality programs are quickly transforming into holistic and proactive practices.
By incorporating an active data governance model into daily data management, firms can set up rules and policies to continuously monitor data quality and foresee problems before they occur. Data quality can only be successful if firms monitor the entire data landscape as a whole, from the moment data enters the pipeline to the end of its lifecycle.
Unify silos for better visibility.
The second data quality trend is to unify fragmented silos within an organization’s landscape. Since banks have very complex IT architectures with a multitude of databases, applications, and external data sources. This results in scattered, outdated, and duplicate records, leading to poor data quality, availability, and accessibility.
Measuring data quality across data and information silos requires gaining an end-to-end view, standardizing the data, defining relationships between the data, and applying data quality rules at every point in the data’s life cycle (regardless of its location). This is a challenging endeavor and requires expensive investment.
To solve these challenges, firms must implement a data quality tool that both breaks down silos and unifies the data across workflows.
Embrace automation, through AI and ML.
Automation through artificial intelligence (AI) and machine learning (ML) is quickly gaining momentum, as more firms experiment and adopt more efficient technologies. Data scientists spend most of their time manually finding, cleansing, fixing, and organizing data? Specifically, ML and AI can expedite these tasks, setting up rules and processes that automatically eliminate human error and increase efficiency.
Data quality solutions are quickly transforming from static, reactive approaches to dynamic, self-adapting approaches. By implementing tools that can perform and even improve repetitive processes and tasks, organizations can save resources, avoid manual errors, and efficiently ensure their data’s quality.
These three data quality trends challenge organizations to assess and correct broken or inefficient processes, for better business performance and cost-savings.
PeerNova’s Cuneiform Platform is the only data quality platform on the market that provides complete end-to-end data quality metrics for business workflows. The solution provides a rich and flexible set of tools so customers can measure data quality across four characteristics: Correctness, Completeness, Consistency, and Timeliness. As a result, firms are empowered to make confident and timely decisions using high quality business contextual data.
Specifically, the Cuneiform Platform is a zero-code, self-serve platform that provides continuous data quality and simplifies exception resolution across internal and external data sources. The solution measures data quality metrics and allows users to resolve and prioritize data quality errors quickly. This enables financial firms to increase operational efficiency, improve business performance, and address executive, stakeholder, and regulatory inquiries confidently and promptly. If you are interested in finding out more, you can easily request a demo.
