Today we are going to discuss Data Quality and its impact on data analytics. Before starting with analytics and more towards data, you must know the source of your data.
I have been researching for long and what I observe is the data source. For any further process on your collected data, a resource of data is to be verified so you can take the further process for your gathered data.
So, where your information is coming from is the most important thing to know and how to collect high-quality data?
In my recent experience with data, it comes from a handful of places. There are so many opportunities for data acquisition layers to solve its problems. Without much due, lets’ see how.
“When it comes to analytics, it is heard that 59% of businesses are using analytics as their capacity”. So, it is not limited to only large organizations; anyone can collect High-Quality Data and utilize the information for better analytics as per the business technology and further needs.
Data is available in bulk for any of the businesses irrelevant to the field. Now, to utilize such a huge amount of information for better business insights, the quality of the data must be achieved.
One study from the Harvard Business Review for quality data shows merely 3% of the data quality management scores are achieved when it comes to analytics.
You must be aware of the quality of less information or any such useful document that will be worthless if not superior with quality. And no business wants to affect the performance at the end. Right?
And to achieve the standard data-quality, a business must follow documented agreement or a pre-planned format. It includes:
- Data format
- Data characteristics
- Pre-planned business standards
Your customer may not be satisfied, or a product may not be able to compete in the market if your information is invalid. And it is ultimately going to affect the whole business cycle.
Data Quality Dimensions
The above image shows the dimensions of the data to be followed by the organization. You must be thinking of how the quality of the information can be measured. So, as data quality a top priority for any business, we have researched a few ways to achieve quality by web data integration (WDI).
A stored and structured data from websites by a process that aggregates and organizes whole data into a workflow from various website sources is WDI. In nutshell, a process that includes transformation, data access, data mapping, quality assurance, and much more.
Assessing Data Quality
As shown in the image, data gets divided into sections to identify its usefulness and to move further with the process.
Now a question may arise, how to identify the information with low-quality?
For the same, one article for data by Harvard Business Review came up with the following crucial steps to be followed to identify the value of your data:
- List of used or collected data.
- Look out for the most crucial business data elements for functioning.
- Ask your data teams to identify and look over each error from the data record.
- Measure the results from the process.
Here comes the process of data management, as businesses facing difficulty to manage the vast amount of data. But at the same time, it is very important to solve quality problems.
How to improve data quality is the biggest question. Data management and solving quality problems are a continuous process. With every single day, data should be checked and processed well.
IBM, in the year 2016, faced the data quality issue where they paid a high cost to fix it, and it turns out to be $3.1 trillion across the U.S economy. So, imagine the value of data if it has not been qualified well.
With research, we can say, approx 30 percent of data analysts spend 40 percent of the time to validate the data before it is used for business functioning and better decision making. It clearly shows the scale of the data issues.
How to Ensure Data Quality?
Monitoring your information is the key aspect to go for better quality and to clean all your business data for its better use. To get your information as per the standards for quality based results, validation of information is the further do to unlock new opportunities and utilize qualified information.
How quality information helps business
Good quality data helps businesses to achieve the desired results and brings customers’ trust for the organization providing quality products. It further facilitates by combining data, technology, and organizational culture to deliver meaningful results.
- First, check the uniqueness of data and analyze the data.
- Management of metadata: Data quality has been checked in various ways by multiple people.
- The next in line is to assist the documentation for data processors and data providers for proper data measurement availability.
- Now, policies require to manage the collected data as people in different parts of a company may misinterpret specific data terms.
- Centralized management of metadata helps to solve the issues by reducing inconsistency and guide to achieve quality standards.
In the end, you have to make some specifications as per business standards that offer a data dictionary so all the upcoming data goes with the same cycle for qualification.
Quality of your information will make your service/product more competent and helps you to reduce the costs associated with the quality of fewer statistics. i.e., decisions made using incorrect analytics.
Choosing The Right Tools
The procedure to know your data value and to correct flaws from your data that supports adequate information for operational business processes and decision making is all about data tools.
Demo for any of the data-quality management tools is a wise decision to get hands-on tools before performing data quality tools for better end-results. Here are successful data quality tools in the cloud:
-> Data Profiling
-> Data Stewardship
-> Data Preparation
It is essential to choose the right tools and technologies that hold all available data to make it precise. There are 4 major aspects to be considered before using data quality tools and techniques to get valid information analytics:
• Data management
• Third-party integration
• Fully mobile support for end-users
• Shareable dashboards for streamlined communication
Why Is Data Quality Important
It is important to know what your data represents, i.e., type of data. So, data resources are equally important to identify and modify your data based on organizations’ needs.
To this, we came to know that high-quality information guarantee more efficiency in driving a company’s success. That is based on data dependency and facts-based decisions, instead of following legacy systems.
Lets’ see five significant components that show the importance of data quality:
- Completeness: Incomplete data leads to wastage of time and resources while no gaps in the data show the validity and better usage.
- Accuracy: Pure data and data collected from the base shows its relevancy and accurately represents its value.
- Consistency: Consistency is the key. Data must align with the expected type once collected for its easy utilization.
- Validity: For better insight, the initial process matters that derives data validity to the final result.
- Timeliness: information shows its value that is used for business efficiency. And to achieve the same, the data must be received at the expected time in order of its prompt usage.
Each of the above components should be properly executed to get high-quality information.
Yes, the inadequacy of any of the components or aspects may fail the process of qualifying your data. With real-time data and analytics, business is better equipped to make customers aware of more effective and informed decisions.
One project can be achieved with ease, but when it comes to managing a large table, a continuous process is done to make your data more focused and result-driven. It takes effort and planning to make it reliable and accurate. And that’s what entrepreneurs are looking for.
Confidence in your data leads you to achieve better decision-making and you can rely upon it. Above mentioned aspects help you to ensure a high level of data quality or contact us for data quality in business analytics.