When looking for data insights, the majority of people concur that your insights and analysis are usually as good as the data utilized.
Largely, the rule follows, bad data quality in - bad analysis out. Incorrect or inconsistent data leads to false conclusions. And, false conclusions drain resources, this is true whether you are a researcher, small business owner or a large enterprise. Data cleaning, commonly referred to as data cleansing and data scrubbing, is one of the most crucial and, frustratingly, most time-consuming steps for quality data-driven decision-making.
Data cleaning is the steps taken to prepare data for analysis. This is accomplished by removing or modifying data that is incomplete, incorrect, irrelevant, duplicated, or improperly formatted. The data is usually not useful when it comes to analysis and may hinder the data science process or worse, provide inaccurate results.
When combining multiple data sources, opportunities for data to be duplicated or mislabeled increase. Incorrect data means outcomes and algorithms are unreliable, although often they may look correct. Prescribing the precise steps in the data cleaning process is difficult as it will inevitably vary from dataset to dataset. However, establishing a process and template for your data cleansing process gives greater assurance that you are employing the right method every time.
Data wrangling or data munging is the data transformation process where data is converted from one format or structure to another. This is different from data cleansing which is removing the data that does not belong in your dataset.
The methods employed to clean data will vary according to the types of data you store, we have given you some helping steps below to map out a framework for data cleansing.
Remove unnecessary observations from your dataset, these include duplication and irrelevant observations. Duplication is common during the data collection phase, whenever you incorporate data from several places or scrape information.
The irrelevant observations are findings that do not fit into the specific problem you might be trying to analyze. This could be where you need to analyze data regarding mobile phone users, but your dataset includes landline users. Removing irrelevant observations can make analysis more efficient, minimize diversion from the primary target and create a more powerful data set.
Structural errors usually arise during data transfer, measurement, and poor data keeping, and can include mislabelled classes, feature name typos, and use of the same attribute with a different name to name but a few!
It is important to note that just because an outlier exists, does not mean it is incorrect. However, if an outlier proves to be irrelevant for analysis or proves to be a mistake, it should be removed. In doing so, you can help the performance of the data set.
You may want to ignore missing data - but you shouldn't. Often algorithms will not accept missing values. So, how do you deal with missing data? The gold standard is no missing data - but in an imperfect world the following can be considered:
At the conclusion of the data cleaning process, you should be able to answer these questions:
False results as a consequence of incorrect or dirty data may inform poor strategy and decision-making. Conversely, data cleansing can help achieve a long list of benefits which can lead to maximizing profits with less operational costs.
It is important to generate a culture of quality data. To do this you should document the tools required to create this culture and confirm the directive and a roadmap to get there.
Identifying the standard of data demands a review of its characteristics, and considering those characteristics against the goals and application of the data.
1. Accuracy and Precision
2. Legitimacy and Validity
3. Reliability and Consistency
4. Timeliness and Relevance
5. Completeness and Comprehensiveness
6. Availability and Accessibility
7. Granularity and Uniqueness
Dirty data is costing you.
Having clean data will ultimately increase overall productivity and allow for the highest quality information in your decision-making. Benefits include:
The AI & Analytics Engine can assist in providing a fast AI-powered platform to guide you through cleaning and transforming your data. Using the Smart Data Preparation feature, you can save a database administrator, academic researcher, or anyone tasked with getting data prepared for its end users a significant amount of time with data issues instantly detected, and solutions recommended. These recommendations can be considered by a human, before being committed immediately with one click. Moreover, this feature builds directly into an end-to-end machine learning pipeline so that data science users, within the same platform can engineer features, select models for their newly cleaned and conformed data set, and then train and deploy models seamlessly (or simply use the clean data - it's up to you!)
By understanding the importance of data quality and the tools and methods you need to create, manage, and transform data you are one step closer to making efficient and effective data-driven decisions.
Not sure where to start with machine learning? Reach out to us with your business problem, and we’ll get in touch with how the Engine can help you specifically.