A Comprehensive Overview Of Data Quality

In 2018,Experian's Global Data Management Benchmark Report表明95％的C级高管认为数据是其业务战略不可或缺的一部分。毫无疑问，数据是每个公司的重要资产。bob全站app但是所有数据都同样有价值吗？最简洁的答案是不。

尽管收集数据似乎是一半的战斗，但真正的挑战是在整个生命周期中保持高标准的数据质量标准。

数据质量

在这篇文章中，我们将介绍数据质量的定义和重要性，以及哪些工具可用于帮助数据质量管理和分析。

质量数据如何赋予良好的业务决策？

让我们退后一步，回顾一个质量数据如何赋予最佳业务决策能力的示例。

1. You have data, but it's not usable yet.

At this point, you just have values in a database or an Excel sheet. This raw data doesn't have practical use. For instance, you have thousands of email addresses from your customers and their topics of interest in a CSV.

2. You transform data into information.

You take that data to a tool where you can visualize it clearly in the right context. For example, an emailing list inside your marketing app. Now you can filter those email addresses according to their interests.

3. You obtain knowledge.

You analyze the information you've gathered and gain important insights from it. You might learn, for example, that 80% of your customers want to be contacted via email to get information about CRMs.

4.您做出明智的决定。

With that knowledge, you can make a data-driven decision, such as deciding to create a newsletter with content aboutCRMs. When you have quality data, you have the necessary knowledge to make the right decisions for your business.

数据质量的特征

Since data comes in all shapes and sizes, it's not always easy to determine its quality. However, there are some characteristics typically attributed to high-quality data.

在您自己的数据中寻找这些特征将为您提供数据质量的概念：

1.准确性

Is your data correct?它是否反映了您正在寻找的现实情况？

To guarantee accuracy and precision, you need to constantly optimize your数据管理策略.数据准确性与数据的完整性.

2. Completeness

您的数据全面吗？不完整的信息可能无法使用。

Though it's not advisable to collect more than the strictly necessary, make sure your must-have values are mandatory when storing new entries in your database. Otherwise, you'll end up with names without last names, or incomplete phone numbers you can't use.

3. Relevance

这是您需要的数据吗？Let's face it, not all the data you collect is going to be a game-changer. But if there's a reason why you are collecting data and the values you obtained can serve that purpose, then you have quality data.

For example, if you ask your customers what their birth year is when they're signing up for a trial with your product, but their age is not actually useful information to you, it's data without a purpose. Therefore, even if it's correct, data is not effective.

4. Consistency

Does your data contradict other sources?High-quality data shouldn't contradict the data stored in other databases. Otherwise, you would have to assume one of them is wrong — but which one?

当数据库之间存在不一致时，确定准确性是一种麻烦。相反，请确保在您的数据方面确保有一个真实的来源 - 这意味着将每个人都使用相同的数据软件或将数据工具集成到您的数据中bob电竞官方下载CRM.

This way, everyone within your organization can access your data via a single tool, no matter where they are or when they need access.

5. Accessibility

Is the information accessible to the right people?Similar to the previous point we just covered, many companies interact with customers, prospects, partners, and employees via different applications.

As a result, data is scattered throughout different tools, and if there's nosoftware integrationin place, you have a data silos problem.

Data silos是数据质量差的主要原因之一。即使有准确，一致且相关的数据，如果应该利用该信息访问该信息的团队，它也无法实现其目的。为了保证可访问性，integrate your business systems.

6. Timeliness

您的数据是最新的吗？数据正在不断变化，过时的数据的问题在于它可能无法代表当前情况。跟踪历史数据是很棒的，但是有清晰的时间感。

Ensure you're keeping your data records but you'll also want real-time data and reports so you're aware of any changes as they're happening. This way you can either capitalize on those changes or work to mitigate any issues as needed.

数据质量Analysis

Data quality analysis is how you ensure your data is... well, high quality.

In other words,it allows you to make sure your data is: accurate, relevant, up-to-date, and suited for its intended use and application.

数据质量分析往往是过程的一部分of data quality management.

数据质量管理

Data quality management is the process of ensuring your team has access to high-quality data — it entails pulling insights about the health of your data in order to improve upon that health. This leads to the application of accurate data and the creation of larger data sets.

数据质量Metrics

Data quality metrics are how you actually determine the quality of your data — they're the unique measurements that you put in place to analyze your data.

数据质量指标实际上决定了数据的准确性，相关性，应用程序等的水平。因此，您将知道数据的高度（或低）质量。

数据质量管理Best Practices

Here are some data quality management best practices to keep in mind while analyzing the quality of your data.

• 确定您的团队's most important metrics.
• Get data quality buy-in across your business so everyone understands its importance.
• Perform data quality audits regularly.
• Invest in the necessary resources for data reporting, analysis, and quality training.
• Dive into the reasons for any data quality failures or notable successes that your team experiences.
• Use a data quality management tool (read more belowon data quality tools — these will help with data quality management).
• Ensure there's a single source of truth across your organization when it comes to your data (whether in yourCRM,销售软件bob电竞官方下载, etc.).

数据质量工具

Here are some powerful data quality tools to help you accomplish everything we mentioned above and more.

1.Operations Hub

操作中心使您可以轻松同步客户数据并自动化业务流程。您的团队将与客户数据的干净，连接的真实来源保持一致，并且您的业务将有权适应客户的不断变化的需求。

Operations Hub automates the process of data quality analysis — rather than programmable automation (a.k.a. choose-your-own-adventure), the data quality actions in HubSpot are pre-made and out-of-the-box.

HubSpot's Ops Hub includes three unique programmable automation features: 1) custom coded workflow actions, 2) custom coded bot actions, and 3) webhooks in workflows. Speaking of workflows, you can use them to automate and solve common data issues. For instance, you might set up a workflow that capitalizes the first name property whenever a new contact fills out a demo form.

Pro Tip:使用HubSpot操作枢纽轻松同步客户数据并自动化业务流程，因此您的团队通过单个平台保持一致在所有客户数据上。

2.Insycle

Insycle— aHubSpot App Partner and integration— is a complete customer data management solution. It helps you manage, automate, and maintain your customer data. Insycle improves efficiency, reporting accuracy, and team alignment.

3.刺激

刺激finds and merges duplicate data automatically, saving you time and headaches and improving confidence and alignment across your company.

4.SAS

SASis an enterprise software suite with products that manage, improve, integrate, and govern your data. One of its best-reviewed products is SAS Data Management — it's designed to manage data integration and cleansing. The tool also provides powerful ways to implement data governance.

SAS还提供SAS数据质量作为解决数据质量问题的解决方案，而无需移动数据。

5.Talend Open Studio

Talend Open Studio是中型市场业务理想的开源套件的一部分。拖放构建器使其灵活且易于使用。塔伦德（Talend）附带了几个功能，旨在帮助您解决集成问题。

6.OpenRefine

OpenRefine(formerly Google Refine)是适用于各种尺寸的企业的免费开源工具 - 它用于管理和清洁数据。

OpenRefine focuses on transforming and reformatting disparate data to standardize it. This software allows you to add countless extensions and plugins so you can work with multiple data sources and formats.

7.Datawarehouse.io(Ultimate Data Export)

The Datawarehouse.io, also known asUltimate Data Export, is a data warehousing middleware solution for your HubSpot data — by syncing the software, your HubSpot platform and data will be integrated without the need for code.

无缝导出所有HubSpot数据（例如门票，产品，电子邮件和Web分析），以表现出色，并将您的数据与诸如Tableau之类的商业智能工具集成在一起。

8.Ataccama

ATACCAMA是一个数据管理和治理平台，具有用于数据质量，数据管理，数据目录，参考数据管理，数据集成和数据分析的工具。

The tool's data analysis and management features provide insight into the quality of your data. They also help you validate your data, improve upon it, filter out any low-quality or incorrect data, and monitor quality over time.

Get a Comprehensive Overview of Data Quality

确保数据质量并不总是那么容易，但是您投入的时间和精力在业务的长期成功中得到了回报。它使团队领导者可以做出知情和数据驱动的决策。

Not everyone can be a data expert, but there are some key concepts, techniques, and tools that make it possible for every professional to improve their data quality.

Editor's note: This post was originally published in October 2020 and has been updated for comprehensiveness.