What Is Data Cleansing And Why Is It Important
Data cleansing, also known as data scrubbing or data cleaning, is the first step in the data preparation process. It involves identifying errors in a dataset and correcting them to ensure only high-quality and clean data is transferred to the target systems. When data is coming from multiple sources, such as in a data warehouse, the need for cleansing data increases as the sources might have redundant data or incompatible data formats. For instance, many organizations collect data directly from customers through surveys and forms. Data processing and data cleansing may be important to sort the data into a single format. In this case, lead cleansing software and Salesforce data cleansing tools can be extremely helpful for general information cleansing and sorting. Another relevant example could be data cleansing and profiling in data analysis, which could help an analyst find meaningful patterns in clean, validated data to support business decisions.
Moreover, given the increasing reliance on information systems and technology for deriving strategic business insights, poor data quality increases an organizations exposure to risk. Hence, to remain competitive in todays dynamic business environment, it is essential to get rid of data inconsistencies. Therefore, enterprises must employ a rigorous data cleansing process to ensure that their data assets are accurate and complete.
Schedule Baseline And Work Breakdown Structure
This section of the Project Management Plan should discuss the WBS, WBS Dictionary, and Schedule baseline and how they will be used in managing the projects scope. The WBS provides the work packages to be performed for the completion of the project. The WBS Dictionary defines the work packages. The schedule baseline provides a reference point for managing project progress as it pertains to schedule and timeline. The schedule baseline and work breakdown structure should be created in Microsoft Project. The WBS can be exported from the MS Project file. Be sure to consult our Work Breakdown Structure Template.
The WBS for the SmartVoice Project is comprised of work packages which do not exceed 40 hours of work but are at least 4 hours of work. Work packages were developed through close collaboration among project team members and stakeholders with input from functional managers and research from past projects.
The WBS Dictionary defines all work packages for the SmartVoice Project. These definitions include all tasks, resources, and deliverables. Every work package in the WBS is defined in the WBS Dictionary and will aid in resource planning, task completion, and ensuring deliverables meet project requirements.
CPI less than 0.8 or greater than 1.2 SPI less than 0.8 or greater than 1.2
The Project Schedule Baseline and Work Breakdown Structure are provided in Appendix A, Project Schedule and Appendix B, Work Breakdown Structure.
Monitor The Data Cleaning System
Once automation has been achieved, it is important to monitor the entire process. Identify some key metrics to assess the health and effectiveness of the system.
Also identify ways to sample test data randomly to ensure that it is meeting the standards that have been established. Finally, you can also implement some test cases to see what decisions would be derived from various sample data sets to ensure that they are correct. Back testing is a great way to achieve this.
Data cleaning should be an endless loop. Consistent monitoring keeps this loop stabilized.
Implement periodic checks on your data cleaning process based on the situation. These can be weekly, monthly or even daily, depending on your needs and the availability of resources.
Finally, watch for changing situations in the process that require adjustments in processes or automation.
How to Measure the Success of a Data Cleaning System
Here are some ways to measure the success of a data cleaning system:
- Does the system detect/identify and remove or even correct major errors and inconsistencies?
- Does the system successfully use tools, scripts, and automation to reduce manual inspection of data?
- Is the system improving the overall quality of data?
- Are better decisions being made since the system was introduced?
- Is the system saving time and money, while improving data quality?
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How To Craft A Business Case For Cleansing Data
You know that you have dirty data. You know that there are valid reasons for cleansing it, such as a pending Salesforce implementation. Now all you need is to obtain approval for the project. A well-crafted business case can help you receive the authorizations you need to proceed. However, the first step is to understand thoroughly just what a business case really is.
Planning A Data Migration Successfully
Data migration is a complex process, requiring a robust methodology. The process in this data migration planning guide will help to minimise the risks inherent in a data migration project. It also dovetails neatly into the structure and requirements of most organisations.
1. Scope the project thoroughly
At the start of the project, scoping identifies potential issues that may occur later on. This enables the migration team to plan for any risks.
The aim of scoping is to thoroughly review the project before it starts. Our consultants divide the review into two parts: the projects structure and its technical aspects.
The project review should evaluate the following areas:
- Are the deadlines and objectives clearly defined?
- Is the budget large enough?
- Have the requirements of all potential stakeholders been included in the plan?
- Are there communication plans in place, and do they include all stakeholders?
- Are there enough team members and do they have the right skills? If theyre consultants, will they be available for the duration of the project?
The technical review is used to check the quality and appropriateness of:
- The proposed migration methodology
- The technical features of the proposed data migration tool
- The softwares fit with the skills of the people working on the project.
- The structure, volume and quality of the data.
2. Choose a robust data migration methodology
Read our example of a data migration methodology: .
3. Prepare the data meticulously
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The Nuts And Bolts Of Data Governance: Tools And Frameworks
Data governance is not in and of itself a technology, but it can be aided by the use of technology tools. A governance committee sets the guidelines, policies, and focus, and this team should be a prime driver for deploying appropriate data governance tools. Tools that support security and compliance will work differently than those that value storage and retrieval. However, as with most initiatives that involve changing roles or processes, transparent and continual communication should be a high priority. The right tool can also foster this function.
When researching appropriate tools, look for one that strikes the appropriate balance between the role of governance and the functional framework that supports data management.
As with other software solutions and tools, your choice will depend on your organizations needs. Some tools may be labeled as data governance solutions, while others may be primarily used for different purposes, but are able to address governance needs.
The following are vendor tools for data governance:
- Collibra
For more information on these and other processes, read this article.
What Is Data Governance
Data governance defines the rules, influence, and regulations for data in order to set and oversee appropriate policy. These rules and policies establish decision rights, as well as the controls that ensure security, accountability, and trustworthiness. Governance is not active day-to-day oversight, but rather a strong foundation for a viable data management system. Like any governance structure, data governance is in place to foster good use of information through sound policy, clarity of controls, and consistent processes.
Data governance stems in part from the collapse of Enron, Adelphia, and other businesses in the early 2000s, which forced companies to take another look at their data and the U.S. government to pass laws ensuring that corporations post accurate financial reports. Data governance formed the core of this work to satisfy requirements of the Sarbanes-Oxley Act and other regulations. But data governance has evolved over its relatively short life, and today, even small businesses need available, accurate, and comprehensive information to lead their decision-making and growth.
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Difference Between Data Cleansing And Etl
Although data transformation and data cleansing are two separate terms, many ETL tools offer advanced data profiling and cleansing capabilities along with data transformation functionality to cater to complex data management scenarios, such as data migration and master data management.
Astera Centerprise is an enterprise-grade data management solution that enables users to evaluate the integrity of critical business data with its flexible data quality and validation features, which enhance the data processing and cleaning during the ETL process, and provides accurate data for business intelligence.
Break Down Data Silos With ETL
Simplify Complex ETL Processes in a Codeless Environment to Speed up the Data-to-Insight Journey
How To Assess The Quality Of Your Data To Determine If Its Clean Data
Before you begin the data cleaning process, assess the quality of your data. Doing so will help you determine which data sources deliver quality data and where most of the bad data is coming in. You can use your assessment to put data collection processes in place that limit the number of bad data points that end up in your CRM.
There are five criteria to use when analyzing the quality of your data.
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Data Migration Checklist: The Definitive Guide To Planning Your Next Data Migration
Coming up with a data migration checklist for your data migration project is one of the most challenging tasks, particularly for the uninitiated.
To help you, we’ve compiled a list of ‘must-do’ activities below that have been found to be essential to successful data migration planning activities. It’s not a definitive list, you will almost certainly need to add more points, but it’s a great starting point.
Please critique and suggest any additions using the form at the end of the guide .
Use the data migration checklist below to ensure that you are fully prepared for the data migration challenges ahead.
Please note, if you prefer to have a spreadsheet and mindmap planner, instead of the text-only format on this page, simply enter your details below and well email it straight to you.
Why Does Data Cleaning Matter
Data analytics is a complex, time-consuming, and expensive effort.
If youre working with large sets of data, chances are there are significant business consequences involved. Like deciding where to allocate your funds best or how to reach peak productivity.
Thats why its imperative to minimize the risk of a fiasco.
How do you do that?
Data cleaning.
Without proper data cleaning procedures, one cant be sure if their data analytics results will provide real insights. IBMs study shows that low data quality costs 3.1 trillion dollars every year in the U.S. alone.
To demonstrate the gravity of the situation, lets analyze two data analysis scenarios one with data cleaning and the other without it.
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Analyze A Patient Walks Into The Er
People dont just show up to the ER to hang out or get a cup of coffee. They are there for a reason. The context of the visit is unmistakable even if the cause of their ailment is yet to be diagnosed. The same is true for data cleaning.
One of the difficulties with the more traditional linear approach to data cleaning is the abstract nature of the situation. Lets think about this for a minute. If were looking at data in our database, but not actually trying to analyze anything for the company dirty data may or maybe not be obvious. But once we begin calculating revenue, profit, churn, and the other typical items our business is asking to know, dirty data rears its ugly head. Data, out of context, can easily mask itself as clean data. So, in the linear approach, we often miss many data fields that actually contain dirty data. The resulting clean data at the end of a linear cleaning project will need to be revisited the first time an analyst discovers the numbers in the profit column simply do not add up.
Using the cyclical process of data cleaning, we begin with analysis. Just go ahead and turn the analysts loose in the data. Tell them to make requests of the data engineers, work their analytical magic, and not be bashful about raising questions when something in the data doesnt look correct. This is help and context our cleaning process desperately needs in order to correctly and holistically clean the data we currently have.
Its Never My Way Or The Highway
It takes data to clean data. The Openprise Open Data Catalog provides you with the data sets you need to clean your data. You can even add your own custom data sets unique to your business.
There are no black boxes in Openprise. You, not a data provider or software vendor, decide how to clean and standardize your data. Openprise bots reference dozens of open data sets that you can tweak to fit your needs.
More
- Create your own buyer personas to map to job titles, job functions, and job levels.
- Define how geographies roll upput places like Alaska, Hawaii, and Puerto Rico in whatever regions align with your sales territories.
- No more spreadsheets, macros, or error-prone filters to normalize your data.
With cleaner data, better segmentation, scoring, routing, and attribution are right around the corner.
It takes data to clean data. The Openprise Open Data Catalog provides you with the data sets you need to clean your data. You can even add your own custom data sets unique to your business.
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Why Data Cleansing Matters To You
Once you understand the 3 Cs of contact data that affect data quality, there will likely be some cleaning to do. No worries youre not alone: 94% of B2B companies face the same challenge.
Before taking any action, you need a data cleanup strategy. Why?
As Dr. Stephen Covey said in his bestseller The 7 Habits of Highly Effective People you must start with the end in mind.
Data cleansing best practices suggest that you ask yourself the following questions:
- What are our goals and expectations for data cleansing?
- How do we plan to execute our data cleansing plan?
Answering these questions for the first time can be a daunting task. If youre just getting started and havent yet thought through your plan that much, this article will help.
Dont panic Well help you! Simply follow the data cleaning techniques below.
Data Cleansing In 5 Steps
Different data types require a different approach, so the techniques used to clean up data may differ slightly depending on the database you are dealing with. Nevertheless, usually the business customer databases are quite similar . Hence, in the remainder of this article, we will primarily focus on data cleansing these types of records.B2B data cleansing is a process that usually consists of at least five steps. Those are:
Below we describe how data cleaning looks like in each of the stage, together with simple examples of implementation.
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Inconsistent Type #: Capitalization
Inconsistentusage of upper and lower cases in categorical values is a commonmistake. It could cause issues since analyses in Python is casesensitive.
How to find out?
Lets look at the sub_area feature.
It stores the name of different areas and looks very standardized.
Butsometimes there is inconsistent capitalization usage within the samefeature. The Poselenie Sosenskoe and pOseleNie sosenskeo could referto the same area.
What to do?
To avoid this, we can put all letters to lower cases .
Determine The Necessary Steps To Clean The Data
With the proper team in place, the treatment of the patient can now begin. The assembled team will need to decide how to best clean the data for use and storage in the database used by the analysts. Of course, this could be a fast process or it could take some time depending on the complexity of existing ETL or the dirty nature of the source systems involved.
Once the necessary steps have been identified, tested, and agreed upon, they need to be documented and, of course, implemented. Its always best to use a development, test, production environment architecture and first implement the change into development. Then promote to the testing environment and only once everything is verified as correct, promote the final solution to the production environment. But these environments and steps to deployment differ from company to company and youll need to follow your organizations outlined process.
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Benefits Of Data Cleaning
So you know what happens when you neglect the data cleaning stage.
But what are the upsides of cleaning your data?
- Saved time and money Inaccurate data leads to business strategies based on false assumptions. Data cleaning saves your company from potentially wasting both time and money, developing an ineffective strategy.
- Increased productivity Effective data cleansing leads to consistent and highly functional databases. No errors mean faster, more effective workflows, which directly impacts productivity.
- Improved business results Data cleaning is the key to a properly functioning data analytics solution. Whenever these two things occur, you can expect
- Better decision-making There is a direct correlation between clean, quality data and reliable business insights: the cleaner the former, the more abundant the latter.
- Maintained reputation Bad business decisions cost more than money. If you make a decision based on inaccurate data, it makes you look bad and unprofessional. But when your insights are useful, people will notice, and your reputation will grow.
Interested in other benefits that big data analytics can generate for your business? Make sure you check out our article: Big Data and Its Business Impacts