What does data cleansing mean?
Data cleansing is the practice of continuous management of data throughout its cycle of usefulness. Data cleansing is essential for ensuring high-quality data that is accessible, accurate, secure, and has high validity. Organizations invest in automated data cleansing to ensure compliance to standards and to ensure the usefulness of data, such that it can be retrieved for re-use.
Why data cleansing?
According to Gartner, in the year 2014, businesses worldwide invested $44 billion in data analytics. Be that as it may, these businesses do not use much of the data they have and the one they collect daily. Different studies show that businesses collectively use just a fraction of the data they have. The rest of the data, therefore, remain unused in their respective repositories and warehouses.
Data volumes continue to grow rapidly with the International Data Corporation estimating the world’s data to grow to 175 Zettabytes of data in the next five years. Most of this data today has evolved and increased in its heterogeneity and variety, making it a very tedious process for businesses to draw sense out of it. Data analysis has thereby become a time consuming and expensive process that is necessary for every business.
Data cleansing comes in handy to manage this data in such a manner that it is managed to make sense; storage is minimized and is secured. This is done by taking measures such as removing duplicates in the raw data, blank fields, misspelled words, adding or removing rows and columns as may be necessary, and introducing third-party tools and information necessary to curate the data.
What do you need to have clean data?
Having accurate data is the foundation of the usefulness of data in all its stages of use. Data develops inaccuracies at creation, collection, collation, during clean-up, or when being stored.
As data volumes continue to grow, instances where the data gets compromised also continue to rise. Infringements to data privacy and hacking cases are reported every day and continue to increase in volume and intensity. One of the best ways to ensure the security and privacy of data is ensuring that an organization has a useful data governance model.
A good data pipeline engine is sufficiently and efficiently scalable as well as robust. It processes data close to real-time and does not get overwhelmed. A scalable data pipeline is one with a good architecture that is built to anticipate changes in the volumes and diversity of data and data types over time. The latest solutions, such as DQLabs, employ such possibilities and have a highly scalable data pipeline engine.
Data with a high-performance score
Data storage can be optimized using a Hierarchical Storage Management system. This means that the frequently used data is stored in high-performance storage where retrieval is easy and very fast. On the other hand, data that is less frequently used is stored in slower storage. An organization may also classify this data, and if it is not very sensitive, it may be stored in less expensive storage.
Data with a high Data Quality score
DQLabs AI/ML-based smart curation modules identify the optimal data preprocessing strategies and automates data curation with controls on data quality thresholds. A Data Quality score is then given to the data. The higher the score, the more clean your data is.
Data with a good governance structure
Data Governance, which is the continuous management of data with regards to data ownership, accessibility, accuracy, usability, consistency, data quality, and data security in an organization. Data governance enhances data integrity and quality. This is through identifying and solving data issues such as errors, inaccuracies, and inconsistencies that may exist between various data sets.
Data encryption involves encoding information (changing the form e.g. scrambling it) so that only the intended recipients are able to decrypt it into a readable format. Encryption can be done on stored data files, servers, internet connection, emails, texts, websites, data files in transit, etc. While encryption is one of the best practices in data cleansing and will more than often to some extent be a mandatory requirement by law, it can also be used wrongfully.
Data with the right architecture
The right data architecture is the blueprint that informs data collection, enhancement, usage, and storage. Through this, organizational data is harmonized with the overall organizational strategies with minimal efforts.
Properly labelled data
Labelling of data comprises highlighting and adding metadata to keywords to incorporate them in the data processing. For example, adding tags.
A good data storage mechanism
Data needs to have an effective storage method that is secure and effective. Most of the time data input happens once, but its retrieval and usage will be multiple times and for a wide range.
The need for a data curator in an organization has grown over time. A data curator is now needed more than ever before. Their role has been clearly cut by the need to curate the ever-increasing data in any setting. Data cleansing has been in the past very haphazard and not much effort was put into it. Organizations have recently found themselves with large volumes of data that they do not use, creating the need to curate the data they already have for better and more economic use. This article discusses some of the challenges, which also explain the best practices organizations can use to enhance data cleansing. Our DQLabs ML data curation tool identifies the optimal data preprocessing strategies and automates data cleansing with controls on data quality thresholds. This is further enhanced with the help of reinforcement learning and predicts the type of repair needed to resolve an inconsistency and applies to improve quality.