We have read and learnt a lot about data analysis and data processing. The power of both is not hidden from the giant tech world. But how well do we know that data processing and data analysis cannot happen without data profiling?
One thing as an initial introduction statement to data profiling is: As data gets bigger and infrastructure goes into the cloud, data profiling becomes more important. Data profiling is an act of monitoring data and cleansing data. It is also a great tool for organizations to make better decisions.
In today’s connected world, the amount of data being generated as also its sources continue to grow. Often a visual assessment, data profiling uses a mix of business rules and analytical algorithms to find, understand and lay bare inconsistencies in data. The knowledge is then used to enhance data quality. It is an important aspect to monitor and improve the health of these newer, bigger data sets.
The scope of data profiling will only grow in the future. Data profiling standard is achieved well when two conditions are met, one is quantity of data, and the other is the quality of data. Data that isn’t formatted in the right manner or rightly integrated with the rest of the database can cause problems, delays, missed opportunities, misleading customers, and poor decisions. And to solve these problems, data profiling is very helpful.
Let us now dig a little deeper and know more about ‘data profiling’.
Types of data profiling
1. Structure discovery
It is a process to validate that the collected data is in the right format and is consistent. It is required to perform mathematical checks on the data. With the help of structure discovery one can understand how well the data is structured. For example, if you have a data set of phone numbers, pattern matching can help find the valid set of formats within the data sets.
2. Content discovery
It is the process of looking closely into all the elements of the database to do a quality check of the data and spot the errors. It helps find the places with null values or values that are incorrect or ambiguous.
The standardization process in content discovery plays a major role to fix these problems. For example, finding and correcting data to fit street addresses into the correct format here is an essential part of this step. The probable issues that non-standard data may give rise to, like the inability to reach customers through mail because the data set has incorrect formatted addresses, are costly. This is something that can be addressed early in the data management process.
3. Relationship discovery
It is about finding out what data is in use and trying to gain a good understanding of the connections between the data sets. The process starts with metadata analysis to find key the relationship between data and narrows down to the connections between specific fields, it works well specially where the data overlaps. Probably, it reduces the problems coming from the data warehouse or other places where data is not aligned.
Now, there are techniques to perform the above. The following methods can help achieve better data quality:
- Column profiling
- Cross-column profiling
- Cross-table profiling
- Data rule validation
A lot many organizations apply these techniques to manage their datasets. There can be complex databases, and confusing data bundles as well which might seem out of scope for employees and even for the highly skilled team members. Data profiling is used to troubleshoot problems within even biggest data sets by first examining metadata.
To learn how they apply and gain maximum out of their valuable data, we are putting down a few examples of data profiling.
Real life use-cases for data profiling
One can troubleshoot and fix problems within the data using SAS metadata and data profiling tools with Hadoop. This can help identify data types that can best contribute to new business ideas.
In SAS Data Loader for Hadoop, one can profile Hadoop data sets using a visual interface and store the results in a report.
The capabilities of data profiling can provide data quality metrics, descriptive measures, metadata measures and other charts, which help get an understanding of data and improve data quality.
For example, the Texas Parks and Wildlife Department took the help of the SAS Data Management, and its data profiling features to enhance the customer experience. But how?
The data profiling tools helped identify incorrect spellings and address standardization or other problems within the data sets. Such information was used to improve the quality of customer data and resolve ongoing issues using millions of acres of park lands and waterways available for them.
Open-source data profiling tools and their key features
- Aggregate Profiler
- Data enrichment
- Single customer view
- Dummy data creation
- Basket analysis
- Metadata discovery
- Data profiling, and governance
- Hadoop integration
- Real time alerting for data issues
- Quadient Data Cleaner
- Date gap analysis
- Detect and merge duplicates
- Completeness analysis
- Character set distribution
- Reference data matching
- Data quality, data wrangling
- Talend Open Studio
- Analytics with graphical charts
- Column set analysis
- Time column correlation
- A pattern library
- Customizable data assessment
- Fraud pattern detection
- Advanced matching
Commercial data profiling tools
- Oracle Enterprise Data Quality
- Data profiling, auditing, and dashboards
- Automated match and merge
- Address verification
- Product data verification
- Case management by human operator
- Integration with Oracle Master Data Management
- SAS Data Flux
- User-friendly semantic reference data layer
- Real-time master data management
- Visibility of data originated and how it got transformed
- Optional enrichment components
- Cleanses, transforms, loads, and manages data
- Data Profiling in Informatica
- Enterprise data governance
- Metadata management
- Exception handling interface for business users
- Data enrichment, standardization, consolidation, and de-duplication
- Data stewardship console that mimics data management workflow
How data profiling contributes to sales and marketing
Data profiling helps sales and marketing teams by making the campaigns more efficient and, thus, enhances the B2B lead generation process. Following are a few ways and practices that can help organizations achieve business growth:
1. Helps in decision making
Data profiling builds a systematic process. It fills the pipeline with accurate data into the database and helps eliminate redundancies in the sales cycle, plug leakages, and reduces wasted efforts. Obsolete or poor quality of data can be a costly affair. Business can make good projections and improve their performance with the identification of errors and implementation of corrective methods. Data profiling integrates accurate predictive analytics in everyday marketing processes and decisions. This makes decision-making in sync with other internal or external changes, thus giving an edge over the competition and increasing agility.
2. It enhance marketing ROIs for businesses
Marketing efforts are guided by reliable data. Data-driven campaigns allow companies to effectively capture end-users. Better access, engagement, and measurement tools can allow marketing teams to create a personalized journey for every customer.
B2B companies that use demographics and occupational data can level up their marketing ROI in a better way.
Marketing campaigns can be customized based on relevant industry data, title, role, geography, and even down to an individual. Hence, a full-proof customer data profiling process ensures the effort and investment is directed to the right source.
Poor quality data may hamper the marketing campaigns and bring in irrelevant audiences. Engaging with not-so-relevant audience can lead to incurring high costs.
3. It improves customer relation, leading to customer retention
These days, retaining customers is a tough job due to the changing preferences, and the cost factor. But the same can be achieved with the help of well-defined metrics that can track engagement and activity levels. Businesses can remedy challenges and prevent obstacles only when they understand what their customers are experiencing,
Here, data profiling helps ensure that databases are updated and maintained well. It helps identify the pain points, collect feedback, and bring customer solutions. These processes can also classify customers better and target them with the most appropriate messaging, offers, and support to sustain their trust and engagement. Data profiling also improves the efficiency of the sales and marketing teams by identifying customer challenges, analyzing trends, suggesting remedial measures.
Conclusion
Data profiling is more crucial today than ever. It helps businesses tap untapped growth with enhanced decision-making, higher optimization, and improved efficiency of the sales and marketing function. This is particularly true for B2B organizations where companies strike bigger deals and the customer journey, too, is longer. High-quality data when infused in the decision-making process can increase the scope of sales and marketing campaigns. It also eliminates estimation and guesswork, keeping cost under check and gives optimum returns.
Hope the insight answers the basic of data profiling for you. You can also download the whitepaper to learn more on data management.
