The Consequences of Poor Data Quality
We talk a lot about data quality and data management. When consulting with wealth management firms, we frequently use the phrase “get your data in good order.” But what does that really mean? And why does it matter? The bottom line is this: poor data quality can cause inaccurate reporting, commission accounting errors, and misinformed decision-making.
Data quality is a crucial driver of business success for all institutions, especially those within the financial services industry. Yet, according to an IBM research study, 27% of respondents in one survey were unsure of how much of their data was inaccurate.
Research from IBM found that one in three business leaders do not trust the information they use to make decisions. As a leader, you need good quality data so that you can make the right decisions for your business. Lack of data insight makes it more challenging to act swiftly and make informed business decisions. Your decisions are only as good as the data they’re based on.
Recent Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses. A key reason poor data costs so much is that decision-makers, managers, data analysts, and others routinely make accommodations for it. When working with tight deadlines, workers make the corrections themselves rather than fixing the core, root cause.
According to Forrester, nearly one-third of analysts spend more than 40% of their time vetting and validating their analytics data before it can be used for strategic decision-making. How much time does your firm spend tracking down discrepencies in transactions or manually cleaning up files and spreadsheets?
Characteristics of Data Quality
How do you assess the quality of data? The following are six core principles data professionals use to measure data quality.
- Relevance: This is a characteristic that is external to the data. The same data may be used to provide information that is relevant in one situation but not in another without any change in the quality of the data.
- Accuracy: This characteristic refers to the exactness of the data. It cannot have any erroneous elements and must convey the correct message without being misleading.
- Timeliness: Data represents reality within a reasonable period of time, and the data should be available when it’s expected and needed by the user.
- Accessibility: This characteristic refers to the difficulty level for users to obtain data. The accessibility of data reflects how readily the data can be located and accessed from within the data holdings.
- Completeness: Incomplete data is as problematic as inaccurate data. Gaps in data collection lead to a partial view of the overall picture to be displayed. Without a complete picture of how operations are running, uninformed actions will occur.
- Coherence/Consistency: The coherence of data reflects the degree to which it is logically connected and mutually consistent. This implies that the data is based on common concepts, definitions, and methodology over time.
How to Improve Data Quality
Improving data quality is more than merely reactively cleaning up bad data when it occurs. It requires a concerted, proactive approach with the understanding that sustaining good data quality will be an on-going challenge.
Establish a Data Quality Management Framework
Data quality management (DQM) can be defined as “an administration type that incorporates the role establishment, role deployment, policies, responsibilities and processes with regard to the acquisition, maintenance, disposition and distribution of data.” In addition to establishing roles and responsibilities, there needs to be a standardized approach to data collection, definitions and validation rules, change management policies, and a single source of truth.
Invest in Technology
Data quality suffers when its compiled from numerous disparate sources via spreadsheets and only gets worse when transferred from one stage to the next. Data integration technology ensures that your data is in good order by aggregating multiple disparate sources and automates the processing of the data. Having all revenue aggregated into one database gives management the ability to set uniform data update rules. With all the data in one centralized place, your firm can expect accuracy and efficiency in your operations.
Build a Culture of Quality Data
Having a DQM framework and technology tools in place will only get you so far. Data is a business asset, and as such, it needs to be treated like any other business asset. Similar to how all employees are responsible for being good stewards of company resources (budgets, talent, equipment, etc.), maintaining data quality needs to be a shared responsibility by the entire firm. As with any business culture mindset, this commitment to data quality starts at the top. Leadership needs to set the tone by investing and adhering to a data quality strategy.