by Ben Cutler
In the world of big data, our clients deal with customer, vendor, asset, product, location, prospect, and many other types of large data sets. These data sets are imperfect, disparate, differing in size, structure, format, and for various other reasons, they’re difficult to use the way that we need to.
The power of this data lies in the quality, and the overall value to the organization. Data quality affects customers, vendors, employees, and stakeholders, ultimately reaching every corner of the organization.
Clean, consistent, relevant, accurate, and complete data has the power to significantly increase efficiencies, productivity, and success of strategic initiatives, driving revenue, and reducing things such as risk, cost, and other consequences.
According to Gartner, 90 percent of large organizations will have a chief data officer by 2019, as “business leaders are starting to grasp the huge potential of digital business, and demanding a better return on their organizations’ information assets and use of analytics.”
The impacts of bad data quality result in higher customer turnover, increased cost of customer contact processes, and affects budgeting, policy, compliance, security and fraud prevention / detection, manufacturing, distribution, and much more.
Bad quality data has been shown to be the primary cause of 40% of all business initiatives failing to achieve their targeted benefits.
Primary data quality issues include inaccurate data, incomplete data, and duplicate data. More in detail, data quality metrics include the following criteria:
a] Existence: whether the organization has the data
b] Validity: whether the data values fall within an acceptable range or domain
c] Consistency: whether same data in different locations have same values
d] Integrity: the completeness of relationships between data elements and across data sets
e] Accuracy: whether the data describes the properties of the object it is meant to model
f] Relevance: whether the data is the appropriate data to support the business objectives
The act of comparison is the root of all data-oriented activities. This includes cleaning, analyzing, migrating, merging, enriching, reporting, searching, and more. Examples include: comparing a customer name to a master set of customer data; comparing large and disparate data sets prior to a migration; comparing internal and external data sets; comparing historical data sets, and much more.
Due to all the inherent challenges working with large and disparate data sets, the act of comparison can be one of the most difficult, but also one of the most important activities.
This is where we can help our clients. We can empower business users to overcome the most common challenges in making these comparisons, link imperfect records between very large and disparate data sets, and to realize the full potential of their data.
With 10 years of R&D in the data matching and record linkage space, and clients in different industries all over the world, Data Ladder can put the power of simplicity and sophistication in your hands to accomplish even the most complex comparisons. Contact one of our data cleansing specialists today!