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Is ‘dirty data’ really that problematic for data analysis?
As a company expands, the ever-growing mountain of data quite quickly breaks the banks of a standard spreadsheet or database. Trying to locate meaningful data insights or observations within this overwhelming mass of information becomes a far more complex task than any business leader can manage.
What is ‘dirty data’?
‘Dirty data’ is the raw data direct from the source, that may be compiled and is yet to be sorted to seamlessly engage with any business intelligence tools constructed for analysis.
It is no secret that data cleaning has now become a fundamental aspect of the data analysis process but there is benefit in examining the flaws that exist in raw data. Before filtering out anomalies, irrelevant data or incorrect formatting, this data will highlight where the approach to collecting information might need altering for future usage. The ‘no value’ response is telling and may showcase specific issues that a client might be facing and unearth further potential research areas.
There is an endless range of different data sets that companies can source. These can cover vast potions of their organisation, for example;
- Operational processes
- Supply chain
- Customer profiles
- Financial data
- Marketing information
It can be extremely difficult to gather this in a compatible format. While there are always nuggets of knowledge within the raw data, data cleaning can greatly expand its’ potential. The raw data that is collected from these different sources regularly have sparseness or formatting inconsistences which can hinder effective analysis. This is where data cleaning comes in.
How it works…
Also known as data cleansing, scrubbing or repairing, data cleaning looks to identify incorrect, incomplete, inaccurate or irrelevant data. With the continuously increasing automation in data science, there is now a best practice focus on using software, algorithms and models when handling data. By using business intelligence tools that can investigate your data, there is a far greater opportunity to identify issues earlier, simplify this process and observe analysis within an intuitive dashboard. These analysis tools can identify data that does not conform to the expected format. The ‘dirty data’ will likely fail at the first round of analysis simply due to a handful basic errors.
Throughout the data cleaning, any missing numeric values or structural faults will be flagged to ensure the data set is recognisable for analysis. Any incomplete values will need to be populated to ensure the data set does not have gaps or inconsistent units of measurements which would limit the direct comparability of the data.
Poor data management can cause structural errors such as typos, duplicate observations or inconsistent punctuation can further complicate the usability of data. Streamlining data from the start to remove unwanted observations will focus the construction of the data models moving forward.
This initial sweep by analysts can sort these issues and flag potential problems that could reoccur in the future. With this comprehensive exploration of the raw data, a rule can be implemented and automatically filter out these complications in future data sets.
For example, experts like Cast Solutions can enable a business to get the most from their raw data in their uniquely designed dashboards. By creating an exception reporting dashboard to highlight imperfections or training the business user in their existing business software and processes, Cast Solutions can find a wealth of knowledge within the current data analysis method. They can update the current processes or technologies a business has and include a hard coding that prompts the system to self-check and clean as it collects information.
So why get the scrubbing brushes out?
Although data cleaning is not a mandatory part of data analysis, it does have some key benefits which ensures one is getting the most out of the data available.
This process will reduce the costly impact that manifest with incomplete data and reduce the manpower dedicated to correcting inexact data, processing errors and troubleshooting.
Eliminate Incorrect Reporting
Companies need to complete various forms of regular reporting to a relevant governing body to ensure the business is legally compliant. These requirements cover various aspects of the operation as safety regulatory compliance, company taxation laws, or legislative requirements, for example. It is a non-negotiable obligation that companies avoid submitting inaccurate data as there is likely large financial or legal implications in providing erroneous reports. Data cleansing can assist in identifying any weaknesses in the data pipeline when building the final report and ensures the data is entirely accurate.
Comprehensive analysis allows companies to make sense of data from across different channels that might not harmoniously fit together. Seamlessly managing multichannel customer data provides greater prospects of sourcing noteworthy answers for customers. This can then be used in building successful operational strategies or identifying opportunities for growth or new service opportunities.
Integrity of the Observations
Through engaging business intelligence tools, this clean data can be a huge helping hand for customer planning processes. Seeing data in a continuously updating, clear format such as a dashboard, customers can comprehend and investigate the results more deeply and take key learnings with them moving forward. These insightful readings will solidify the integrity of the observations found in the data and further the trust between the customer and their data analytics and intelligence provider. In tandem with business expertise and auxiliary analytics, updated and accurate data provides a powerful resource which can only improve a company’s execution of successful strategic decision-making.
Instead of taking the wide range of accessible information available and wasting considerable time fixing data integrity issues, a company can use a dashboard to increase overall team productivity. When the active improvement of data sets’ consistency and accuracy is prioritised within an organisation, there is a strong likelihood their response rate and revenue capabilities will follow suit. In this regard, data cleaning is exceptionally beneficial to a company and ensures either an internal business user or clients can effectively observe the data to get the most out of it. A single source of data analysis allows a team to be aligned and make better communal decisions for the business.
Does complete data cleaning need to be completed before any analysis can take place?
In short, the answer to this question is no. There are several benefits to running analysis on ‘dirty data’. Primarily, it can showcase the shortcomings in the existing data collection, analysis and processing methods that require attention. Cleaning data provides a far more streamlined and palatable view of a company and could conceal some fundamental issues that will limit the overall usefulness of data analysis.
Engaging experienced companies such as Cast Solutions, who can take raw data and immediately begin analysis will avoid the costly and time-consuming task of scrutinising the unrelenting pile of raw data. Instead of adhering to the antiquated procedure where effective analysis can only commence once the data is fully cleaned, Cast Solutions can develop an automated data cleaning and analysis service that reports directly to the customer (and stakeholders) in a cloud-based product.
Enlisting experts that scrub and interpret data at the same time can provide a benefit to their client much quicker without disrupting business activity. a company far faster access to valuable information that can be used to get the most from operational or marketing plans.
When an experienced organisation concurrently evaluates and analyses a data source, their clients can gain a substantial competitive advantage by keeping critical business information contemporary and usable, no matter the changes in their market or industry. Data becomes your secret weapon to adjust to changing circumstances quickly and provide more effective analysis for clients without waiting for the time-consuming task of data analysis.
Join Cast Solutions for a chat about the current system of concurrently cleaning and analysing data and their real-time, intuitive dashboards that simplifies examining data.