Data Cleansing: 7 Reasons Why the Quality of Data Matters

Data cleaning is most important in any industry or technology. For the business is very important to drive value from the data. As the organizations receive data from different sources, this may occur duplicate data, incomplete, invalid, etc. To clean this raw or unstructured data, companies must also have an effective data-cleaning procedure to prepare this for analysis.

The fact is that data also gets changed as time moves, just like people change their addresses, car, phone numbers, etc. With all these changes, the previous data available will be of no worth or useless. Therefore, your organization requires data cleaning that gives value to your business, whereas the unclean data may indicate a wide range of consequences and issues.

Reasons why Data Cleansing is Important

 

Reduce Overall Cost.

For firms, operations must be streamlined as effectively as is practical. Lower total costs lead to higher earnings. Businesses will be better able to spot new possibilities if they integrate the right analytics and cleansing technologies.

Improved Decision-Making.

It is essential to have correct data for effective decision-making. Every firm receives or produces numerous data every year.  However, putting less importance on data quality management could result in bad decisions, ultimately resulting in economic loss. On the plus side, having clean and correct data serves as a catalyst for improved business intelligence and better analytics.

Remove Duplicate or Irrelevant Data.

Most of the time, Duplicate data are received, which increases the time to make decisions. Most of the time, duplicate data reduces the quality obtained during data collection. To remove this data, you require experts and help to streamline business operations and increase efficiency.
 

Streamlined Workflows

Data must be fed into several business actions, decisions, and workflows. To manage your data, glean valuable insights from it, and make the most of it, you need this data for tasks like data analytics, data processing, data entry, etc. If the data is unreliable, inaccurate, or out-of-date, the results could vary and the attempts would be unsuccessful.
 

Maximized Productivity
Employees can use firm data to its fullest potential when it is accurate and current, which boosts output and promotes expansion. They don't need to waste time seeking up working phone numbers, legitimate email addresses, or completing the blanks. Furthermore, when professionals look after the quality of your data, you get an advantage. As a result, management may concentrate on other elements of the business while quickly increasing the productivity of their resources.

Avoid wasting on data investment.

Data intelligence requires a substantial investment, thus it needs to be safeguarded. If you don't secure it, all of your money will be wasted. Your reports will become erroneous and useless if you don't have a good data cleansing strategy. 

Don't throw your money away. Create a sound data cleansing strategy to prevent asset loss.If a business doesn't have a data analytics plan, it will fail. Data cleansing is an important part of this comprehensive process.

Optimized Marketing Campaigns

Marketing campaigns are effective and are reaching the proper target market. Because consumer preferences don't last very long and change frequently, markets are very unstable. Therefore, all of the previously saved data might not be obsolete at this time. Updating databases frequently is necessary to stay current with changing client preferences and stay ahead of the curve.

 

Summary

I hope the article was effective, and how essential data cleansing can be for businesses. The importance of data cleansing refers to the ticket for business growth. However, if you are looking to improve the data of your business, take data cleaning services from the best services provider company.

 

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

The Essential Data Cleansing Checklist

This article covers data quality issues, such as missing, duplicate, or inaccurate values, which cause headaches. Creating a suitable data cleansing checklist makes it ideal to use in systems.

Data quality issues, such as missing, duplicate, inaccurate, valid, and inconsistent values, cause headaches in finding and using data sets. Having a suitable data cleansing procedure handles this bad data and makes it suitable for other people and systems.

A helpful data cleansing process standardizes data, fixes, or removes erroneous values, and formats records to be readable. You get these adequate results from data cleansing when you know your data’s original purpose and visualize the good data you require to meet new goals. You need to create a good foundation and run through the essential data cleansing checklist in this article to achieve your objectives.

Recognize When Your Data Needs Change From Its Original Purpose

Clean your data sets any time you use them for a different business purpose or context than from the data’s creation. At the start of the data lifecycle, you create and obtain data for some reason and within explicit and implicit circumstances, such as customer preferences for online shopping or technologies available at that time. Recognize this data’s original purpose.

Expect that over time and with a better understanding of a problem, your needs with this data set will change from its original purpose. To adapt, you may need to migrate data from one system to another or integrate data from multiple systems to achieve our new business objective. Perhaps you will end up transforming that data to fit a new business problem and its situation. We need to revisit our structures supporting data cleansing and rerun through our essential data cleansing checklist in any of these cases.

Create a Good Foundation

Any data cleaning project succeeds with a good foundation. Like cleaning old files from a system or comments from code you wish to commit, you need an underlying plan, processes, and tools to tackle data cleaning.

To get this strong foundation for your essential data cleansing checklist:

• Form a Data Cleansing Strategy: A data cleansing strategy, based on a larger holistic data strategy, informs what data sets to clean and prioritize. You can develop such a plan from your user stories or requirements documentation.
• Follow Company-Wide Data Governance Directives When Cleaning Data: Data governance policies and practices formally guide data cleansing activities. Follow data governance guidance in determining your role in cleaning data sets and cleansing outcomes.
• Tailor Your Data Cleansing Activities to Your Data Architecture: Data cleansing activities will differ with data technologies. For example, to move data into a data warehouse, you need to massage the migrating data to the data warehouse schema. On the other hand, if you load data from a data lake, you do many different data cleansing iterations. Be sure you know what data technologies require

#big data #analysis #data quality #data cleaning #data cleansing #data quality management

How to Fix Your Data Quality Problem

Introducing a better way to prevent bad data.

Data quality is top of mind for every data professional — and for good reason. Bad data****costs companies valuable time, resources, and most of all, revenue. So why are so many of us struggling with trusting our data? Isn’t there a better way?

The data landscape is constantly evolving, creating new opportunities for richer insights at every turn. Data sources old and new mingle in the same data lakes and warehouses, and there are vendors to serve your every need, from helping you build better data catalogs to generating mouthwatering visualizations (leave it to the NYT to make mortgages look sexy).

Not surprisingly, one of the most common questions customers ask me is “what data tools do you recommend?”

More data means more insight into your business. At the same time, more data introduces a heightened risk of errors and uncertainty. It’s no wonder data leaders are scrambling to purchase solutions and build teams that both empower smarter decision making and manage data’s inherent complexities.

But I think it’s worth asking ourselves a slightly different question. Instead, consider: **“what is required for our organization to make the best use of — and trust — our data?”**

Data quality does not always solve for bad data

It’s a scary prospect to make decisions with data you can’t trust, and yet it’s an all-too-common practice of even the most competent and experienced data teams. Many teams first look to data quality as an anecdote for data health and reliability. We like to say “garbage in, garbage out.” It’s a true statement — but in today’s world, is that sufficient?

Businesses spend time, money, and resources buying solutions and building teams to manage all this infrastructure with the pipe(line) dream of one day being a well-oiled, data-driven machine — but data issues can occur at any stage of the pipeline, from ingestion to insights. And simple row counts, ad hoc scripts, and even standard data quality conventions at ingestion just won’t cut it.

#data-science #data-analysis #data-quality #towards-data-science #data #data analysis

Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations