Self-service Business Intelligence Tools For Data Quality Management – Last month, Innive partnered with Tableau to host a webinar on supporting self-service analytics using curated datasets. In a one-hour live webinar, John Parker, vice president of data science and research at Inniv, provided a detailed overview of the key steps involved in building a self-service analytics program for school districts and similar organizations.
Self-service analytics is a data analysis technique that allows business users to access, analyze, and visualize data without relying on IT or data experts. It provides easy-to-use tools and platforms that enable users to explore and understand data, generate insights, and make data-driven decisions.
Self-service Business Intelligence Tools For Data Quality Management
The main goal of self-service analytics is to promote a data-driven culture within an organization by making information accessible to all employees, regardless of technical knowledge. This approach helps reduce the time and resources spent analyzing data by IT teams and data specialists, allowing them to focus on more strategic tasks.
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In the webinar, Parker addressed some of the biggest challenges often encountered in strategizing and implementing self-service analytics tools and processes:
Parker covered a wide range of topics in the webinar, including why it is so difficult to develop some reports when all the data appears to be available and how to strategically solve the problem of data integration and centralization. Parker also discussed the importance of developing an “information culture” with employees who have enough information so that they can successfully and appropriately use the information available.
“It’s important that we educate our users and provide good tools so they know how to use this data to improve outcomes,” said Parker, discussing the need to support data literacy.
To learn about successful strategies for solving these problems, watch the Self-Service Analytics Support with Curated Data Using Tableau webinar recording. Additionally, learn about the policies, people, processes, and technologies involved in developing a self-service program in your district.
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Contact us to discuss ways Innive can help your K-12 district or organization develop a culture of self-service analytics to support student success.
Jenny Boroniak has worked in the Software as a Service (SaaS) space for over 10 years as a marketing and training content specialist. Innive K12 360° helps school districts develop content to support student success through informed decision-making. What is data quality? Benefits of Data Quality Core Components of Data Quality Avoiding Data Quality Issues Data Quality Indicators Impact of Data Governance on Data Governance Four Steps to Improve Data Quality
Data quality refers to the overall usefulness of the dataset and its ability to be easily processed and analyzed for other purposes. It is an integral part of data governance that ensures your organization’s data is fit for purpose. The dimensions of data quality include completeness, consistency, consistency, accuracy, and completeness. Managing them will help your data management, analytics, and artificial intelligence (AI)/machine learning (ML) initiatives deliver reliable results.
Quality information is useful information. And poor quality data can have negative consequences. These include potential commercial risks, financial impacts and reputational damage. To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database integration or systems/cloud integration processes. In this case, the data fields that must be compatible are not due to schema or format incompatibilities. But more broadly, data quality issues can also be caused by human error, system errors, and data corruption.
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Data quality issues can arise due to incompleteness, inaccuracy, inconsistency, or duplication of data. This happens when there are multiple copies of the same data, leading to inconsistencies. Any of these issues can cause the data to undergo cleaning to improve its quality. Effective data validation and management processes also help ensure data quality. It also helps ensure data quality.
Data quality activities include data rationalization and validation. Data quality efforts are often necessary when integrating disparate applications. This may occur during merger and acquisition activities. It is also used when siled data systems in an organization are first combined into a cloud data warehouse or data lake. Data quality is also critical to the effectiveness of horizontal enterprise applications. This includes enterprise resource planning (ERP) and customer relationship management (CRM).
When data is of good quality, it can be easily processed and analyzed. This leads to insights that help the organization make better decisions. High-quality data is essential for analytics, AI initiatives, and business intelligence efforts. Thus, maintaining high data quality standards helps organizations ensure regulatory compliance, improve customer experience, enhance data-driven innovation, and enhance decision-making capabilities. This benefits companies across all sectors, from improving patient care in healthcare to optimizing supply chain operations in retail and personalizing banking offerings in financial services.
In addition to helping your organization get more value from its data, a data quality management process can improve organizational efficiency and productivity. It also reduces the risks and costs associated with poor quality data. In short, data quality is the foundation of reliable data that enables digital transformation. A strategic investment in data quality pays off in multiple uses across the enterprise.
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The success of data quality management is measured by several factors. This includes how confident you are in the accuracy of your analysis, how well the data supports various initiatives, and how quickly these initiatives become of real strategic value.
To avoid problems, it is important to understand the key attributes of data quality. Data quality operates across seven main dimensions:
All seven of these dimensions of data quality are important, but your organization may need more emphasis than others. It depends on the specific use cases you support. For example, the pharmaceutical industry demands accuracy, while financial services companies must prioritize accuracy.
Some data quality metrics are consistent across organizations and industries. For example, they must ensure that customer billing and shipping information is accurate, that the website contains all relevant information about products and services, and that employee records are up to date and correct.
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The potential consequences of poor data quality range from minor inconveniences to business disruptions. Poor data quality wastes time, reduces productivity and increases costs. They can also compromise customer satisfaction, damage brand reputation, and force an organization to pay heavy penalties for non-compliance. They can put the safety of customers or the public at risk.
Here are some examples of companies that have faced the consequences of data quality issues and found solutions:
Data governance serves as the overall framework that guides and manages data-related activities in an organization. Data quality is an important aspect of data management as it also aims to ensure the dimensions of data quality listed above. Although governance and data quality are interdependent, they are mutually reinforcing. Effective data management provides the structure needed to establish high-quality standards.
Data governance supports improving data quality by establishing data quality responsibilities across the organization. These individuals or teams are responsible for data quality. Data governance also provides the authority necessary to enforce data quality standards. Through data governance, organizations can allocate resources, define workflows, and implement data quality tools to support data quality improvement initiatives.
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Data governance facilitates collaboration between multiple stakeholders involved in data management and quality. Promotes a culture of awareness and improvement of data quality. When it comes to creating data quality standards and policies, data governance defines and establishes them based on industry best practices and regulatory requirements. Data governance also ensures that data quality standards are aligned with business objectives and stakeholder needs. Data governance also establishes policies and procedures for evaluating, measuring, and monitoring data quality, enabling organizations to continually monitor and improve data quality.
Data governance ensures the implementation of data validation rules and data integrity mechanisms. Through data governance, organizations can establish data quality control, data quality testing, and data profiling techniques to identify and resolve data inaccuracies. Data governance also implements data quality improvement processes, such as data cleansing, enrichment, and correction, to improve data accuracy and resolve data quality issues. By implementing data governance practices, organizations can establish data quality frameworks and metrics to measure and monitor data integrity and accuracy.
Only after you understand your starting point can you plan your data quality journey. To do this, you need to assess the current state of your data. Determine what you have, where it resides, its sensitivity, data relationships, and quality issues.
The information collected in the discovery phase will inform the decisions you need to make about data quality and the rules you can create to achieve your desired end state. For example, you may need to clean and deduplicate data, standardize its format, or discard data before a certain date. Note that this is a collaborative process between the business and IT.
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After you define the rules, you integrate them into your data pipelines. don’t get stuck in a silo; Your data quality tools must be integrated across all data sources and objectives
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