Many companies find themselves in possession of a considerable amount of data without knowing exactly what value can be extracted from it or how it can be extracted.
Because they are scattered, have different structures and formats or are incomplete, they become very difficult to use. Difficult, but rarely impossible... With the mechanisms currently in place for conversion and validation, good preparatory work can make analysis of great value to the business possible.
Even in a
database, a data quality assessment process can find outliers, duplicate records or inconsistencies.
find outliers, duplicate records or inconsistencies. A detailed analysis
of these situations may identify opportunities for improvement in the
ETL (extrat, transform and load) process or even business-related opportunities.
business. The same process could include cross-referencing with external data sources, enriching the
sources, enriching the data and generating information of great potential.
A model is only
good as the data it learns from. Before investing in
application of Machine Learning or Artificial Intelligence, make sure you have
full control over the quality of your data, or your results are
doomed, no matter how good the model behind it.
The topic of "Data
quality" is very broad and can be addressed at different stages of the pipeline or with different
pipeline or with different approaches or technologies. Here are some of the
steps that we think are important for ensuring data quality:
Rigorous data profiling and data source control
A good
Data profiling tool will be important to examine the following
aspects of the data: format, patterns, consistency of records, distributions
of values and outliers and whether the records are complete. Data profiling will
help you identify problems in your data that can be addressed
directly at the source or in the ETL process. To do this efficiently there must be
a dahsboard, with general KPIs that reflect the results of this profiling and that
allow you to monitor data behavior against expectations.
Detailed design of the data pipeline
The
of the data entry pipeline includes areas such as the definition of
fields, business rules and database connection rules. In
large organizations, it is often the case that several different areas
interact with the same record, for example a customer. Ensuring that there is a
common understanding of the input fields and that there are no duplicate entries are
some of the concerns to be taken into account when designing the pipeline. Communication between
different areas of the organization must ensure that rules are defined that are as
transposable to the systems in order to reduce human error as much as possible.
human error.
Precise collection of requirements
Validating the quality
must be seen in a well-defined context. The
objective of the analysis in order to define the requirements that the data must meet.
meet.
Data integrity
Data integrity is an essential aspect of data quality. If your database is relational, you can ensure this by using primary and foreign keys, validating additional conditions to the format (check constraint) and using mechanisms triggered by specific actions (triggers). The problem becomes more complex when the data is spread across different database systems, but its importance should never be ignored or downplayed.
Traceability of data origin
Another
of data quality is its traceability. Whenever a problem is
detected in a record, it is essential to be able to quickly identify its
origin and correct it without jeopardizing the project deadline. For
this is possible, traceability must be at the heart of the data pipeline design.
data pipeline.
Automatic regression tests
There will always be
time when you want to enter a new set of data or change something in the existing fields.
existing fields. To ensure that these changes don't impact
the services that are running, it's important to have automatic tests in place.
running. This will give you the ability to make migrations without postponing
without compromising business-critical data.
Because we have been
processing our clients' data since the last millennium, experience tells us that
experience tells us that this issue of data quality is often underestimated.
underestimated. On our side, we are learning and understanding its importance and
how it facilitates further processes. Count on us to invest with you in
preparatory data quality treatment processes and solid
processes so that, together, we can get the most out of your data.
data.
Source: https: //towardsdatascience.com/7-steps-to-ensure-and-sustain-data-quality-3c0040591366