How to Evaluate Data Quality Using 6 Key Traits

By Loretta Jones   

Most business leaders agree that high-quality data can transform business operations. However, recent research from Gartner revealed that almost a third of business leaders listed poor data quality as a major reason for not using data analytics in decision-making processes. This has led to data and analytics leaders taking a more active role in designing and implementing digital transformation initiatives.   

However, it is not always clear to company leaders how to effectively assess data quality. Here are six key traits leaders can use to evaluate the quality of their businesses’ data and identify areas that are impeding true data-driven decision making.  

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6 key traits that data leaders must consider to properly evaluate data quality

    1. Accuracy
      Businesses rely on data to conduct even the most basic of business functions, and data is useless if it is not accurate. The output is only as precise as the data that goes in, and careless input, miscalculations, duplications, omissions, and oversights all make data-driven decision-making impossible because the data cannot be trusted. Data must reflect the real-life situation of the organization in all respects and be orchestrated to deliver accurate, valid results.
    2. Completeness
      Effective decisions cannot be made using half-truths, and information is not useful unless it contains all the contextual information needed to make sense of it. Letters with a name but not an address, for example, will not find their way to the intended recipient. This is why data teams need insight into their data pipelines to ensure that issues like data reconciliation, data drift, or schema drift have not occurred as data moves through the pipeline.
    3. Consistency
      Multiple data sources can cause their own chaos. Needs change as businesses grow, and those that began by subscribing to single data storage systems must eventually expand. More enterprises are embracing multi-cloud, with 92% of them adopting a multi-cloud strategy to store and manage their data. To maintain high data quality across cloud services and on-site servers, data leaders should always ensure that all data stored on individual servers is consistent with related data stored elsewhere. This principle can also be applied in large, singular data banks with related data streams or data strings.
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    1. Uniqueness
      Another byproduct of having multiple sources of data is data duplication. This is a particularly important data trait that researchers must evaluate thoroughly. When solving equations or calculating solutions based on large pools of data, repeated data items or strings can lead to researchers or data leaders reaching the wrong conclusions. When moving data from one location to another, data teams need to ensure that each data piece exists in the right place and is not repeated in other repositories by accident.
    2. Freshness
      The last couple of years have reiterated how quickly business operations can change. The implication for data leaders is that the data collected in a specific business environment can only be applied to that environment. As such, ensuring that data is current is crucial for ensuring high-quality output that is appropriate for real-time decision making.
    3. Validity
      The final trait that data leaders should use to evaluate data quality refers to the format in which data is stored. For data to be valid, each piece of information must be presented in the format defined by the organization. When business leaders ensure that data pieces are correct and in the right format, they can prevent inadvertent duplication and incomplete data pools. Businesses can choose to highlight particular parts of the data for decision making. Ensuring that each piece of information is input with the appropriate format can make identifying the correct information strings significantly easier.

These six dimensions of data quality can help data leaders proceed with advanced information processing with the assurance that the basis of their research or analysis is sound. Business leaders can also identify areas for improvement once data teams have completed an evaluation based on each of the stated metrics. Ultimately, all business leaders want their decisions to be founded on high-quality data. By ensuring their data meets these six standards, they can be sure that they are.

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Loretta Jones is an established growth marketer with extensive experience marketing to SMBs, mid market companies and enterprise organizations. She is a self proclaimed ‘startup junkie’ and enjoys growing early stage startups. She studied Psychology at Brown University and credits this major to successful marketing as well as navigating a career in Silicon Valley. She’s a nature lover and typically schedules her vacations around the migratory patterns of whales and large ocean creatures.

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