When looking for data insights, majority of people concur that your insights and analysis are usually as good as the data utilized.
Largely, the rule follows, bad data 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.
So, what is data cleaning?
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 analyzing and may hinder the 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.
But where does data wrangling fit in?
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.
How do you actually clean data?
The methods employed to clean data will vary according to the types of data you store, we have given you some helping step below to map out a framework for data cleansing.
Step 1: Get rid of duplicate or irrelevant observations
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 in to 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.
Step 2: Fix structural errors
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!
Step 3: Filter outliers that are not wanted
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.
Step 4: Manage the plague of missing data
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? Gold standard is no missing data - but in an imperfect world the following can be considered:
- Drop the entries with missing values. This will mean you lose information so approach this option with caution.
- Input missing values based on you observations. there is risk of losing data integrity, and influencing outcome from assumptions not observations.
- Alter the way the data is utilized to navigate null values.
- Data missingness is informative in itself. Even if you can impute the values, it does not add any real information, and will be simply reinforcing the patterns that are already provided by other features.
Step 5: Validate and QA
In the conclusion of the data cleaning process, you should be able to answer these questions:
- Does the data make sense?
- Does the data follow the appropriate rules with regard to its field?
- Will it prove or disprove your operating theory, or bring any insight to light?
- Can you find trends in the data to develop your next theory?
- If the answer is no, is that as a result of a data quality issue?
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.
Elements of quality data
Identifying the standard of data demands a review of its characteristics, and considering those characteristics against the goals and application of the data.
There are 7 characteristics of quality data to understand:
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
Benefits of data cleaning
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:
- Removal of errors when multiple sources of data are at play.
- Fewer errors make for happier clients and less-frustrated employees.
- Ability to map the different functions and what your data is intended to do.
- Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
- Using tools for data cleaning will make for more efficient business practices and quicker decision-making.
Data cleaning tools for efficiency
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 any one tasked with getting data prepared for its end use a significant amount of time with data issues instantly detected, and solutions recommended. These recommendation 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 simple use the clean data - its 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.
Want to try it for yourself? We have a free trial here.