Whether manufacturing, purchasing, marketing or sales, digitizing business processes is becoming increasingly important today. High competitive pressure, increasing customer demands and ever more diverse technical possibilities – all this is causing companies in every industry to undergo a fundamental process of change i.e. digital transformation. Networked systems lead to faster processes, save costs and open up a detail optimization that would not be possible manually. All digital processes are based on complete, valid and uniformly structured data. Because without good data, the best systems bring nothing. So, as you modernize your business, you’re ensuring optimized data quality management from the beginning – continuously and fully integrated.
The digital transformation of and between systems such as CRM, ERP, supply chain management, online shop and service portal is made possible. But these applications are only as good as the information they work with. Whether customer or supplier data, whether material or product data, only when your master data is available company-wide, valid and uniformly structured, your systems will have the maximum benefit. Only then will you be able to carry out well-founded analyses and effective measures. Here comes the utility of using a data quality software.
Data Quality Software enables profiling, purging and masking of data as well as long-term data quality monitoring regardless of format or size. It deduplicates, validate, and standardize the data to provide clean assets for access, reporting, analysis, and operational tasks. You can enrich data with external sources for address validation, enterprise identification, credit assessment, and more. It simplifies cleanup so you can easily gain valuable Big Data insights.
The most important features of a data quality software
- Holistic data management console – With this easy-to-use tool, the steps in data management workflows are modeled on a fully customizable interface.
- Discovery, search and profiling capabilities for businesses – Make sure you understand the nature of your data and can identify the relationships between different data objects.
- Rule creation for business analysts – With the Rule Builder, business analysts can create and test logical business rules without having to use the services of the IT department.
- The Comprehensive set of data quality transformations – Standardizing, verifying, enriching, deduplicating, and aggregating data ensures the delivery of quality information.
- Treatment of exceptions – With the integration of manual tasks into the workflow, business users can review, correct, and approve exceptions during the automated process.
- Role-based functions – Support business users and promote collaboration between IT and business stakeholders.
- Integrated business glossary – Enables collaboration between stakeholders, as well as building and managing a common business vocabulary used throughout the organization.
- Metadata Management – Gain transparency throughout the data transformation, giving you a cross-linked view of everything from sources to targets.
- Assign once, use everywhere – With the virtual machine, you can create the data quality rules once, and then run them directly on the software platform, in Hadoop, in the cloud, or even embedded in your applications.
Five important aspects of data quality software
1) Consistency: Data must be consistent and double-free.
2) Completeness: The amount of data must be exactly right.
3) Validity: Data must come from credible sources.
4) Accuracy: Data must be in the appropriate format with the required number of decimal places
5) Timeliness: Data must be delivered on time according to expectations.
Data Quality Software Provides The Following Features To Address Data Quality Issues.
1)Data Cleansing: The modification, removal, or expansion of data that is incorrect or incomplete, both with computer-aided and interactive processes.
2)Matching: Identifying semantic duplicates in a rule-based process that allows you to determine what matches and performs deduplication.
3)Profiling:Analyzing a data source to provide insight into the quality of the data at each level of knowledge discovery, domain management, reconciliation, and data cleansing processes.
4) Monitor: Track and determine the status of data quality activities.
Data Quality Challenges
1)Unknown state of the data quality of all business data
A fundamental prerequisite for the success of data quality initiatives is an exact knowledge of the state of the business data in relation to the data quality. Completeness, correctness and consistency are just some of the criteria that must be used in the assessment of data quality. As customer-centric data continues to be at the heart of data quality initiatives, data domains such as product and material master data, financial data or contract data are increasingly the subject of corporate data governance programs and organizations.
2) Initial basic cleaning of business data
Once the current data quality has been determined and target values for the quality improvement measures have been determined, the task is to initial clean up all master data. Different master data domains pose their own challenges to the functionality of the tools used in cleaning processes. If one can rely on fixed specifications of the national postal companies in well-defined countries and use extensive reference data directories, the considerably more pronounced peculiarities in other data domains require greater individual project- or sector-specific efforts to comply with defined business rules in the databases.
The Data Quality software offers the correction of address information according to international standards, based on excellent reference data. You can also check e-mail addresses, telephone numbers or bank details at various levels. These can be largely automatically consolidated on a rule-based basis or controlled for manual post-processing. Without suitable tools, data quality becomes a challenge. By enriching with business-relevant information, the tools enhance your master data – for example, by supplementing the addresses with geo-coordinates or industry keys. Depending on your individual needs, you integrate the data quality solutions into your business processes in real-time or in batch mode. You can also use a variety of data quality functions flexibly in the cloud. With software, you have a high-quality database that meets your needs. This not only increases the efficiency of your employees, but also the business processes run smoother and you are more successful in the market. And that finally shows in your corporate figures.