Self-service Business Intelligence Tools For Data Governance And Compliance – In the age of the customer, real-time insight and action are key. The challenge is that users interact with organizations through multiple channels, so analytics users must be able to aggregate data from multiple systems and ensure that the data is in a common format so that meaningful insights can be derived from it. This challenge is not separate from customer data; the same is true for supply chain data, order-to-cash analytics, and other data-driven processes. Data is locked in many silos. For the average business user, integrating this data for analysis is too complex and requires the help of IT resources.
To become truly data-driven and enable self-service business intelligence (BI) and analytics, organizations must be able to democratize access to data so that everyone in the organization can use the data to make informed decisions. Organizations need a self-service analytics platform that allows anyone to find, access, integrate and securely share data in real-time, no matter where that data resides. This is exactly what the Platform provides.
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“Best suited for customers seeking an enterprise-wide data architecture strategy to support BI, data collaboration, user intelligence, data engineering, data science, IoT analytics, operational insights, and predictive analytics use cases…” Forrester Wave™: Enterprise Data Fabric, 2022 II quarter
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A logical data management, data integration and data delivery solution that provides real-time access to selected high-quality data. Powered by data virtualization, the platform creates a logical abstraction layer across all business data resources, allowing immediate access to any data set without first copying or replicating it. When a user logs in and requests data, the platform retrieves that data from one or more back-end systems in real-time, integrates it into business views, and presents it to the user.
This logical data access layer simplifies the data management process for IT and security teams with a centralized point from which all data access can be monitored and controlled.
A centralized level of logical data access allows everyone to access data quickly and easily, regardless of where it is located, without the help of IT.
A data catalog allows users to browse, discover and use data resources located anywhere in the enterprise. With an artificial intelligence (AI)-powered recommendation engine, collaboration tools, and smart search features, users know they can trust the data they discover.
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With the broadest data presentation options to meet your needs, including JDBC, ODBC, ADO.NET, SOAP, RESTful web services, OData, GraphQL, GeoJSON, export to Microsoft Excel/SQL, Tableau data extracts, and JMS message queues. access data in desired ways.
Free Trial 30-day free cloud trial to fully test the pro START FREE TRIAL In our last blog, we explored seven common data problems that can be solved with effective data management. Today, we’ll share our approach to building data governance programs to drive data transformation and foster a data-driven culture.
Data governance is a key aspect of managing an organization’s data assets. The primary goal of any data governance program is to achieve prioritized business goals and unlock the value of your data across the organization.
Understand that a data management program cannot exist by itself – it must solve business problems and deliver results. Start by identifying your business goals, desired outcomes, key stakeholders, and the data needed to achieve those goals. Technology and data architecture play a key role in enabling data governance and achieving these goals.
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For example, if the goal is to improve customer retention, a data governance program should focus on where customer data is produced and used throughout the organization, ensuring that the organization’s customer data is accurate, complete, secure, and accessible to those who need it. . solutions that will improve customer retention.
It is important to coordinate and standardize data governance policies, roles and processes to align with business objectives. This will ensure that data is used effectively and that all stakeholders are working towards the same goal.
Starting a data management program may seem like a daunting task, but if you start small and focus on your priority business outcomes, data management can become a natural extension of your day-to-day operations.
Developing a data management program is an iterative and incremental process. Step 1: Define data strategy and data management goals and objectives
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What are your organization’s business goals and desired outcomes? You should consider long-term strategic goals and short-term tactical goals, and remember that goals can be affected by external factors such as regulations and compliance.
Data strategy identifies, prioritizes, and aligns business goals across the organization and across its functional areas. Based on multiple business objectives, a data strategy will identify data needs, tools and key performance indicators, stakeholders and required data management processes, technology priorities and capabilities.
It’s important to regularly review and update your data strategy as your business and priorities change. If you don’t have a data strategy, you should create one—it doesn’t take long, but it does require input from the right stakeholders.
Once you have a clear understanding of your business goals and data needs, set data management goals and priorities. For example, an effective data management program can:
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Clearly defining your goals will guide your prioritization and development of your data management program, which will ultimately lead to revenue savings, cost savings, and customer satisfaction.
Identify the key stakeholders and roles of the data governance program and who will be involved in its implementation. This should include employees, managers, IT staff, data architects and business owners, and data custodians inside and outside your organization.
The key is an executive sponsor—someone who understands the importance and goals of data governance, recognizes the business value that data governance provides, and supports the investment required to achieve these outcomes.
With core sponsorship in place, build a team to understand a compelling story, define what needs to be accomplished, how to raise awareness, and how to create a funding model that will be used to implement the data management program.
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By effectively engaging key stakeholders, identifying and delivering clear business value, implementing a data governance program can become a strategic asset for your organization.
Having clear business goals and data management supporters and stakeholders, it is important to align these goals with the human, process and technology capabilities available to achieve these goals.
Data management frameworks such as the EDM Council’s DCAM and CDMC offer a structured way to measure the maturity of your data against industry benchmarks using a common language and set of data best practices.
Find out how data is currently handled and managed in your organization. What are the strengths and weaknesses of your current approach? What is needed to achieve key business goals?
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Remember that you don’t (and shouldn’t) do everything at once. Identify areas for improvement based on business objectives to prioritize your efforts and focus on the most important areas for meaningful business results. An efficient and effective data management program will support your organization’s growth and competitive advantage.
A data policy is a set of documented guidelines for the consistent management, control, protection and use of an organization’s data assets. Data policies are driven by your organization’s data strategy, aligned with business goals and desired outcomes, and may be influenced by internal and external regulatory factors. A data policy may cover topics such as data collection, storage and use, data quality and security:
A data policy ensures that your data is used in a way that supports the overall objectives of your organization and complies with relevant laws and regulations. This can improve data quality, lead to better decisions, and increase confidence in an organization’s data assets, ultimately leading to a more successful and sustainable organization.
Define clear roles and responsibilities for those involved in data management, including those responsible for data collection, storage and use. This will help ensure that everyone understands their role and can effectively contribute to data management efforts.
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The structure of data management can vary from organization to organization. In a large company, data management may have a separate team that oversees it (as in the table above), while in a small company, data management may be part of existing roles and responsibilities. A mixed approach may also be appropriate for some organizations. The key is to consider the company culture and create a data management system that promotes data-driven practices. The key to success is to start small, learn and adapt, focusing on achieving and measuring business results.
A clear understanding of the roles and responsibilities of those involved in data processing can ensure that they have the necessary skills and knowledge to carry out their duties.
Data governance processes ensure effective decision-making and enable consistent data management practices by coordinating teams within (and outside of) your organization. In addition, data governance processes can also ensure compliance with regulatory standards and protect sensitive data.
Data processes provide formal channels for routing, escalation, and resolution. Data management processes need to be easy to achieve your business goals without creating unnecessary burdens or stifling innovation.
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