Quality of data is a pervasive issue for businesses and is now considered to be among the barriers to their success. Additionally, poor data quality costs companies huge amounts of money even as they increase their efforts to the shift of computing infrastructure to the cloud, including investments in cutting-edge machine learning and AI initiatives.
According to Gartner’s Data Quality Market Survey, poor data quality gave organizations an average of $15 million in financial costs last 2017. Also, IBM estimated that poor data quality costs the US economy $3.1 trillion per year. That's clearly not a small amount that could have been used for other operational investments.
When does poor data quality happen?
Poor quality in data is often identified as the source of operational mess, inaccurate analytics, and ludicrous business strategies. It is challenging to prevent it from happening as it occurs between applications and organizations, as well as between providers and consumers. This also causes economic damage from added expenses of wrong product shipments, lost sales opportunities, and fines for improper financial or regulatory compliance reporting. These issues happen because of the following:
Duplicated data is often the result of siloed processes and many systems that record the same information. Such as records for customers, suppliers, and products that are found in many systems. Particularly when no mechanism exists to identify each record across systems, and no proactive steps are taken to prevent duplicated data. This causes further issues like when customers receive several identical email campaigns/materials (which can annoy them and lead to lost opportunities), or difficulty in catering to their queries.
Among the blocking struggles for systems is identifying similar information but in different formats. This creates uncategorized and inaccurate results. Dates are a common example that confuses many systems, as there are many potential ways to input these, like DD/MM/YY and MM/DD/YY formats. Another difficulty that may arise is phone numbers, especially when some have area codes and others don't.
3. Incomplete information
CRM and marketing related software get this as a major issue. Incomplete or blank entries (i.e. ZIP codes, or street numbers) can make geographical analytics useless, or trouble in contacting customers. This also lets the organization miss to spot trends and make appropriate decisions. Unless all essential and correct information is ensured to be recorded, systems will face quite a struggle in excluding incomplete entries to reduce other concerns.
While formatting issues are amendable, and incomplete information can be acceptable at times, incorrect information on the other hand is mostly the hardest data quality issue to fix and spot. This is often caused by typographic errors, or when customers give the wrong information. By not having all the right data, it can limit decision-makers from making the right decisions or makes big data analytics simply wrong.
What are the effects of poor quality data?
Consequently, any poor quality data collected by any business can be as good as wasting resources, especially financial and time resources. It also generally affects the efficiency, productivity, and credibility that data is supposed to give. From a major perspective, poor quality in data affects an organization through:
With further advancement in today's era, cloud technology has developed solutions to these problems, though not completely. There are still certain challenges that arise when data move around in the cloud, as well as between cloud and on-premise. But, the proliferation of cloud-native systems, services, and platforms has made it easier to access data, including the challenge of unifying and consolidating a wide range of data formats.
Furthermore, with cloud integration, data quality tools can be used to make sure the data that is being delivered has value. It is also easier to manage multiple data streams, create a single version of the correct data, and take advantage of cloud-native applications and analytics tools.
Aside from the things mentioned above, cloud technology is able to improve the quality of data through the following ways:
The cloud can solve the aggregation problems of data collected from multiple sources, which provide a more efficient process once data collection systems work in sync with the cloud solution.
2. Data discovery
Data can be profiled along with iterative data analysis that enables a better understanding of the nature of data and the detection of problems.
3. Rich set of data quality transformations
High-quality information can be delivered with data cleansing, standardization, verification, parsing, and enrichment capabilities.
4. Pre-built, reusable rules and accelerators
There's a comprehensive set of pre-built business rules and accelerators that can be applied to reuse common data quality rules across any data, and from any source to save time as well as resources.
5. De-duplicate records
The level of duplication across all records in a data set can be analyzed and consolidated into a single preferred record.
Good quality data is becoming a crucial commodity that is not only desirable but necessary for managing projects, performing the right strategies, reducing waste in finances, and delivering business products and/or services efficiently. As organizations are creating, storing, gathering, and managing more data than ever before, data-quality issues are going to become increasingly evident. Including the use of data that extends from ordinary business transactions to the support of business-intelligence initiatives.