Challenges and best practices of data cleansingChallenges and best practices of data cleansing DQLabs DQLabs
This article will detail the challenges and the best practices of data cleansing in data quality management.
Maintaining Data Accuracy
Data accuracy is the biggest challenge encountered by many businesses in their quest to cleanse 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. The inconsistencies arising from any of the sources render the data useless or of less value. The discrepancies in many instances make it difficult for organizations to correct them in later stages of data, for it becomes expensive and very tedious. Data cleansing aims at removing these inaccuracies at every step. Data cleansing makes data useful in all its stages and even when stored for future use or re-use. One of our articles on understanding the significance of data cleansing in data quality management helps you know more about what is data cleansing and why it is important for organizations.
The first step in having accurate data is validating it at its creation stage. Validation of data is as easy as it can be done by any user who gets involved first in its creation. The user can be given measures to follow to validate the data acquired before it is moved to any other stage. Organizations can further enhance this process by auditing either all the data at their acquisition or random sampling.
Further measures at this stage include tagging of the data, which involves data labeling. Duplications are also checked at this stage and removed before the data going to the processing stage.
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. Such a model, such as the European Union’s General Data Privacy Regulation, defines how and who accesses data meant to be kept private. For the sake of organizations with large volumes of personal data, the fewer the people accessing the data, the more secure the data is likely to be.
A good data governance model also defines how data will be used and moved from one stage to another.
Further, in ensuring data security, encryption can be done to prevent a breach. A secure encryption key is important to be used, for it, too, can get compromised by hackers. When encryption is implemented together with other measures, such as a strong firewall, it keeps the data safe and secure.
Data Performance and Scalability
With the rapid growth in the volume of data, a data pipeline experiences a challenge in scalability. 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.
Organizations pledge to improve the customer experience every day. Chief among ways to improve customer experience is to avail services and data upon the customer’s request. A good data pipeline avails data in real-time. It does not get overwhelmed by requests and the transfer of data within a system.
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 data cleansing platforms, such as DQLabs, employ such possibilities and have a highly scalable data pipeline engine.
This data is then stored in a manner that can be easily retrieved at any time in the future. Data storage is highly 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 Governance 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.
With data governance, an organization is able to remain compliant with applicable data regulations and laws. We have seen the increasing need to protect organization data from falling into wrongful hands through cyber attacks. This has led to enhanced data privacy laws and regulations. To ensure compliance with these laws, an organization must have an elaborate data governance team and process.
A good data governance team should continually manage any challenges that may arise with an effect on data. This includes; creating definitions, outlining standard data formats, ensuring appropriate accessibility and usage, enforcing and implementing data procedures that are laid out, etc.
There is an ever-present need to access real-time data and to share the same data across different organizational functions. Data governance helps achieve this by ensuring that there are policies and well-laid out processes to enable this. Without this, it is not unusual to find data silos among different organization segments. This can significantly contribute to inefficiencies such as repetitive data and errors, which in effect compromises the integrity of the output generated from data analysis and can be very costly to an organization.
One of the biggest challenges with data is security. In the past, this was a major concern within governments mostly. However, today there is so much confidential data in the possession of many organizations. This poses a high risk if the data can be accessed maliciously. 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.
The Advanced Encryption Standard as developed by the National Institute of Standards (USA) is what provides the basis of a majority of most encryption types. This is through availing a set of keys to be used when encrypting data. The longer the key, the stronger the encryption is. The keys are available either as 128 bit, 192 bit, or 256 bit.
Encryption can be done on stored data files, servers, internet connections, emails, texts, websites, data files in transit, etc.
While encryption is a best practice in data cleansing and will more than often to some extent be a mandatory requirement by law, it can also be used wrongfully. Cyber attackers can maliciously target to encrypt an organization’s devices and servers without any interest in the data therein.
This indicates that encryption in itself is not sufficient enough. Good organizational practices such as only installing trusted software and always backing up data can help counter this.
Also, avoiding clicking suspicious links, downloading suspicious email attachments, and visiting insecure sites.
Annotation & Labelling
Since data from input sources may take different forms, it is good practice to put it in order for the purposes of minimizing the cycle time, improving accuracy, and cost optimization.
One way of doing this is through annotation and labelling. Annotation involves correcting, aligning, and grouping data for machine vision. This is critical in machine learning and helps the machine to understand and recognize similar input trends. The data can be in the form of text, image, or video. Labelling then comprises highlighting and adding metadata to keywords to incorporate them in the data processing. For example, adding tags.
Annotation and labelling greatly contribute to improving the user experience which is desirable for any organization. It also leads to improved output results making data efficient.
Today, data is the impetus of all organizations. Since it is spread out almost everywhere in the organization, there is a high likelihood to have data chaos in the form of inconsistent, outdated, aging, unclean, and incomprehensive data. This creates the need for the right data architecture. Consider the right data architecture as the blueprint that informs data collection, enhancement, usage, and storage. Through this, organizational data is harmonized with the overall organizational strategies with minimal effort.
The right data architecture should inform the standards set with regard to data across all the data systems in an organization. This is through defining procedures on how to collect, process, store, and share data from the organization data warehouse(s). It follows that the architecture is responsible for establishing and controlling the flow of data within systems. This brings about integration which is crucial in saving time and resources needed to share data across different organizational functions. The time saved can be spent on analyzing data in real-time to inform important business decisions.
The importance of the right data architecture should not be underestimated. It helps to understand existing data and make sense of it. It is also the backbone in the management of data throughout its life cycle in an organization.
The right data architecture lays out the foundation for a good data governance structure which as mentioned earlier in this article is not only needful but also a requirement by law in most instances.
Lastly, data architecture assists an organization’s data warehouse or Big Data with its Business and (or) Artificial Intelligence.
It is therefore worthwhile to invest in the right architecture as early as possible.
The more the data collected, the higher the need 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.
Modern-day today has seen the evolution of storage methods and capabilities. As a best practice, good data storage should not limit retrieval and processing time. This can be through the use of high-performance storage for frequently retrieved data. A commonly used system on this is the Hierarchical Storage Management (HSM) which toggles data between high-speed (hence high cost) and low-speed (low cost) storage devices.
The desired situation would be to have all data in high-speed storage but it is so expensive. HSM determines which data is most appropriately stored in high speed while stores for example long-term archive data in low-speed storage.
Data storage should also take very keen consideration of data security by utilizing practices such as encryption as mentioned before.
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