7 Ways Poor Data Quality is Costing Your Business
November 14, 2021
Organizations continuously strive to make the best decisions for their company to satisfy stakeholders, upkeep client relationships, and maintain an established reputation. To make the most informed decisions, executives rely on data analytics. However, what happens if the quality of the data, that these vital decisions are being based on, is ‘poor’? More specifically, what are the consequences of accepting inaccurate, incomplete, and invalid data that does not follow any form of standardization?
In this article, we break down the top 6 costs that organizations are currently enduring whilst they have no action plan to clean their data quality.
Higher Costs to Organization
Firstly, the most notable consequence that poor data quality can have on an organization is higher costs. According to IBM, poor data cost companies three trillion dollars per year. Although financial costs are the more prominent due to its quantifiable property, there are also intangible impacts such as decreased worker productivity and extended project deadlines. The presence of poor data can result in an increase of time spent on a project than initially allocated. The allotted time will then be spent on fixing and standardizing the dataset rather than performing data analyses. This revised process will lead to additional hourly wages to be paid to data scientists and analysts. Possessing poor data quality means having data scientists cleaning up data rather than analyzing it, which isn’t a good utilization of their time, abilities, and skills. Additionally, storage costs of data can also accumulate overtime, especially if there are duplications of information within an organization’s database.
Poor Prediction Data Models
In the current environment, technological solutions involving machine learning and artificial intelligence are becoming extremely popular. Using an algorithm, one can autonomously analyze data and predict specified results with little manpower. However, the algorithm’s success is heavily dependent on the training dataset. Utilizing inaccurate data in this case, can lead to poor analyses on an organization’s data and false predictions. The poor data can influence the algorithm to misrepresent the information and produce inaccurate prediction models. Resulting to poor decision making as decisions are based on incorrect data. Additionally, further time will be incurred by the organization to fix and retrain the prediction model using correct data.
A common characteristic of poor data quality is incompleteness, along with invalidity and inaccuracy. Poor data quality can result in biased and unethical conduct if the accumulated data is not complete or following a standard. For example, if information was required for healthcare, and only patients with specific health problems, such as high cholesterol, filled out all the data accurately and completely, then it would appear as though that health problem is the most common when that may not be the case. It could also seem as though that specific demographic of patients filling out the information predominantly suffer from that problem. Possessing incomplete data can lead to an inaccurate representation of the sample size and demographic, which can result in decision making based on biased data and ultimately, unethical conduct.
Over the years, governments have become proactive in creating personal information regulations and privacy acts to protect information that their citizens may provide to organizations. Different geographical locations have their own privacy acts such as Canada with the Personal Information Protection and Electronic Documents Act (PIPEDA) and California with the California Consumer Privacy Act (CCPA). Each act allows consumers to access the personal data that they have given organizations and request its deletion, usage, and lineage. Tolerating and accepting poor data quality can cause non-compliance with government regulations and legal issues for an organization.
For instance, if a person provides information twice to an organization, this can lead to duplicate entries within an organization’s database if no data standard is in place. Although this may not seem like a significant issue, should this person request to remove their personal information, the organization can potentially face compliance issues. Both records would need to be removed to abide by the wishes of the consumer, however if the data analyst is unaware of the duplicate record, the consumer’s personal information will still exist. This can lead to large fines and a penalty and in some cases, a tarnished reputation for the organization.
Lack of Trust
Performing analyses on poor quality data can yield inaccurate models, predictions, and representations of data which can be more than just financially costly – it can impact the relationship between the organization and their clients. The false representations and profiles can cause the organization to cast assumptions, take actions based on the inaccurate information, and make suggestions to a client that does not necessarily align with their needs. This can cause clients to feel like the organization does not truly know them, care for them, or have their best interests as a priority. A strained relationship with the client may occur and ultimately a loss of business as they will pursue another organization who better understands them.
Miscommunication within Organization
The success of a company can solely be attributed to how well the different departments within an organization can interact with each other to achieve a common goal. As such, it is beneficial to have standards and procedures in place to ensure conformity and a common understanding of business terms and practices. Accepting poor data quality can cause a hindrance to relationships amongst employees and worker productivity. If there is no standard followed with regards to data retrieval and storage, the information can be misconstrued and understood uniquely from various people within a team or the company. Furthermore, neglecting the importance of data quality leaves room for the repetition of issues, as one may be able to clarify data definitions today, and need to do the same for another employee later.
According to Gartner, poor quality data can cost an organization $9.7 million annually and cause a 20% decrease in employee productivity. It can also be viewed as primary factor as to why 40% of businesses fail to accomplish their goals. By improving data quality via data regulations, an organization can eliminate financial costs, avoid legal issues brought by non-compliance and unethical conduct, and maintain healthy relationships with clients. Although creating these standards for data collection and storage can initially be costly, it offers an organization complete, valid, accurate and consistent to base future actions and decisions on.
For over 20+ years, Adastra has continued to innovate, creating new methodologies and best practices centered around Machine Learning capabilities for self-healing and improved data, setting the foundation for enterprise data strategies. With these foundations in place, organizations can accelerate analytics, extract valuable insights, and deploy advanced models with their information.
Adastra offers a wide variety of MDM capabilities and accelerators across all industries and platforms. Including but not limited to MDM Technology Evaluation, MDM Business Case, Strategy and Roadmap, Governance for MDM, and Managed Services for MDM on Cloud. Adastra has developed domain-specific MDM model accelerators and MDM templates for financial, retail, automotive, and healthcare industries, among others. They also continue to develop and provide methodologies for iterative, extensive, and scalable MDM solution planning and implementation, as well as accelerators for data quality, matching and merging rules, and AI and ML Augmented Stewardship.