How well you profile your data will determine the health of the data. Only 3% of data meet quality standards according to data quality assessments. Companies that do not manage their data well can lose millions of dollars in lost time, money, and untapped potential. Data profiling is a process that analyzes data to identify its authenticity and quality.
Healthy data is easy to find, and understand, and provides value to those who use it. This is something that every organization should strive to achieve. Data profiling is a tool that allows you and your team to analyze and organize data in a way that maximizes their value and gives you an advantage in the market. This article will discuss data profiling and the many ways it can be used to transform raw data into actionable insights and business intelligence.
What is Data profiling?
Data profiling refers to the process of analyzing, summarizing, and creating useful summaries. This process provides a high-level overview that aids in the identification of data quality problems, risks, and overall trends. Data profiling provides critical insights that companies can use to their advantage.
In particular, Data profiling is a process that analyzes data to identify its authenticity and quality. Analytical algorithms identify data characteristics like mean, minimum, maximum, and percentile. They also provide detailed information about the frequency of data examination. The algorithm then analyzes the data to discover metadata. This includes frequency distributions and key relationships. Foreign key candidates are also included. Functional dependencies are also revealed. It then uses this information to determine how these factors are aligned with your business’s standards.
Data profiling can help eliminate costly errors in customer databases. These errors can include missing or unknown values, null values, values that should not be included, unusually high-frequency or low-frequency values, or values that aren’t consistent with the expected range.
Also read: What is Data Interpretation and How to Collect Accurate Data?
Benefits of data profiling
Bad data can cause businesses to lose 30% of their revenue or more. This can lead to millions of dollars in lost revenue, a strategy that needs to be recalculated, reputations that are damaged, and even a loss of reputation. So how do data quality problems arise?
Sometimes oversight is the problem. Sometimes companies can get so involved in collecting data and managing operations that they lose sight of the quality and efficacy of their data. This could lead to lost productivity, missed sales opportunities, and missed opportunities to improve the bottom line. This is where data profiling tools come in.
Data profiling applications are able to continuously analyze, clean, and update data from your laptop. This allows you to gain critical insights. Data profiling is a method that provides data profiling.
Higher data quality and credibility
After data is analyzed, the application will help to eliminate duplicates and anomalies. It can help you make better business decisions, spot quality problems in your organization’s systems, or draw conclusions about your company’s future health.
Predictive decision making
Profiling information can be used in order to prevent small errors from turning into big problems. It can also help you to envision possible outcomes in new situations. Data profiling provides a snapshot of the health of a company to help with decision-making.
Proactive crisis management
Data profiling is a quick way to identify and fix problems quickly, often before they occur.
Organized sorting
Many databases can interact with diverse data, including blogs, social media, or other big data markets. Profiling allows you to trace back to the source of your data and can ensure that it is encrypted for security. Data profilers can analyze these different sources, databases, or tables and determine if the data conforms to standard statistical measures and business rules.
Understanding the relationships between data available, missing, and required data can help an organization plan its future strategy and set long-term goals. These efforts can be streamlined by having access to a data profiling tool.
Types of data profiling
Data profiling programs analyze databases by organizing and collecting data about them. This covers data profiling techniques such as column profiling, cross–column profiling, and cross–table profiling. These profiling techniques can all be classified in one of the following three ways:
- Structure Discovery — This is a method of determining if your data are consistent and properly formatted. This tool uses basic statistics to give information about the data’s validity.
- Content discovery — Content discovery focuses on data quality. Data must be formatted and standardized, then integrated into existing data in an efficient and timely manner. If a street address, phone number, or delivery address is not correctly formatted, it can mean that customers cannot be reached or that the data is missing.
- Relationship Discovery — This tool identifies the relationship between data sets.
Also read: Data Storytelling: How to Tell a Story with Data
Data profiling put into practice
Companies can sometimes feel overwhelmed by the sheer volume of data they have today. Companies often fail to make the most of their data and lose valuable and usable information. Data profiling is a way to organize and manage big data in order to unlock its potential and provide powerful insights.
Domino’s data avalanche
Domino’s had almost 14,000 locations and was the largest pizza business in the world as of 2015. The company’s AnyWare ordering platform was faced with a flood of data when it launched. Now, users could place orders via any app or device, including smartwatches and TVs and entertainment systems, and social media platforms.
Domino’s was able to access data from all angles. Domino’s has now put reliable data profiling into practice. It collects and analyses data from all company point-of-sale systems to simplify analysis and improve data quality. Domino’s gained greater insight into its customer base and has improved its fraud detection processes. This has resulted in increased sales.
Data quality for customer loyalty
Office Depot has an online presence and continues to implement offline strategies. Integrating data is essential, as it combines information from three channels: the online catalog, the website, and customer service call centers.
Office Depot uses a data profiler among other things to check and control data before it’s entered into its data lake. Integrating online and offline data gives customers a 360-degree view. It provides high-quality data for back-office functions across the company.
Higher customer lifetime value with healthy data
Globe Telecom offers connectivity services to over 94.2 million Filipino mobile subscribers. There are few opportunities to increase market share, so Globe Telecom needed to gain a better understanding of its existing customers so that it could increase the lifetime value of each relationship.
Globe required data that was suitable for data analytics and healthy to deliver customer insight. This proved difficult in areas such as data scoring. At that time, Globe used spreadsheets and offline databases for validation and data quality rules.
Globe has a center for excellence in data management that includes data quality, data engineering, and data governance. Globe achieved a higher ROI per marketing campaign due to healthy data. This included a 30% reduction in lead costs, 13% better conversion rates, and an 80% increase in click-through rates.
Data profiling using data lakes and cloud
Effective data profiling becomes more crucial as more companies store large amounts of data in cloud storage. Cloud-based data pools allow companies to store petabytes of information. The Internet of Things expands our data storage capacity by collecting massive amounts of information from a wide range of sources, including our homes, our clothes, and our use of technology.
Being able to harness all the data is key to staying competitive in today’s market, which is increasingly driven by cloud-native big information capabilities. Data profiling can make the difference between success or failure in managing your data stores, from maintaining compliance standards to creating a brand that is known for exceptional customer service.
Last Line
Data profiling does not have to be done manually. In fact, automating the process of profiling with data management software is the best way to make it efficient. Data profiling tools improve data integrity by eliminating errors and applying consistency to data profiling processes.
