From startups to mega-corporations, retail to healthcare, data is a fundamental part of the business model. But in the rush to become data-informed, are organizations failing to consider the costs of bad data quality?
Quality data is a mission-critical requirement. According to research from the University of Texas, if median Fortune 1000 businesses can increase the usability of their data by only 10%, it offers a potential increase of $2.01 billion in total revenue each year. Furthermore, IBM estimates the yearly cost of bad data for the U.S. alone at approximately $3 trillion per year.
There are a number of conditions that can impact data quality. These are the most common:
Incomplete or Inconsistent Data
Even the most comprehensive and in-depth data can’t provide insights if no one understands it. For data to be useful, it needs to be understood by the end users. If an organization has data capture methods that don’t have a clear system or standards, only a few people know how to interpret the data.
For example: two systems hold different tax identification numbers for the same customer. Which is the correct system of record? Is one EIN incorrect, or has the company reorganized, been sold, or for some other reason changed its EIN. Are there other systems or applications that use the EIN and need to be updated? If a new data import contains the new EIN, will it sync with the correct organization?
Last year’s data may be incredibly important for your business intelligence system. But for the frontline manager who needs to make decisions based on yesterday’s inventory volumes, it’s of little use. The same goes for the HR manager who needs to understand where there are shortages in staff today to make hiring decisions. Operational systems need access to real time data for business-critical insights.
The data businesses gather is for the use of the teams within the organization. If they can’t access it, or don’t know it exists, they can’t use it. In larger organizations, data may be siloed – isolated in a database where it is not available for use by other departments. This may be due to interdepartmental turf wars, but more likely is that the data source is one that cannot be accessed by other applications. For example, a company collects customer info in a CRM system and also maintains a database of customer support tickets. A sales manager can access the CRM, but because there’s no integration with the ticketing system, has no insight into the status of a problem a customer reported.
In some cases, data silos are a result of incompatible schemas, permission issues, or different data sources populating different environments. To bring it all together, many organizations push their data into data lakes or upload them into data warehouses. That still requires technical support to access the data.
Even in accessible databases, companies collect an enormous amount of dark data, valid and likely valuable data which is simply never used in any processing because no one has figured out a use for it.
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