We live in a data-driven business culture, where decisions are made based on insights and data predictions. Data is extracted by businesses to produce differentiating insight to guide their decision-making, however, what if the data that businesses rely on is inaccurate?
The exponential growth of data sources, from web, IoT, internal, external, poses a monumental challenge. Managing and controlling these assets is critical for the success of organizations. Businesses realize the opportunities presented by data are limitless and transformative, and they must be acted on with urgency. Some critical risk considerations include the quality, transparency, accessibility, integration, sharing, and use of data. Inaccurate data, ineffective data integration, and inaccessible data all disrupt the data driven ecosystem. To ensure these challenges are effectively addressed requires a robust data governance framework and culture.
What is Data Governance and its importance?
Data governance is the process of managing the availability, usability, integrity, and security of the data in enterprise systems, based on the internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent, trustworthy, and is serviceable. It’s increasingly critical as organizations face new data privacy regulations and rely on data analytics to help optimize operations and drive business decision-making.
As businesses turn to data analytics to optimize their performance, one needs to be working with well-organized, clean data sets that every member of your organization can trust. Without effective data governance, data inconsistencies in different systems across an organization might lead to multiple competing views of the business’s health, strengths, weaknesses, opportunities, and threats. For instance, customer information may be listed differently in multiple systems. This could complicate data integration efforts and create data integrity issues that affect the accuracy of BI, enterprise reporting, and analytics applications. Without unifying attributes, we are at risk of overestimating our number of customers and misjudging our most loyal or at-risk clients. Leading us to make data-driven decisions that lead us astray.
Why is Data Governance Important?
The importance of Data Governance lies within its ability to manage data and processes to ensure data can be used as a consistent, secure, and organized asset that meets policies and standards. Effective, enterprise-grade data governance does the following:
- Breaks down departmental silos
- Determines policies
- Supports the data stewardship process
- Organizes data
- Aligns terms across the organization
- Connects technical databases and those terms
Data governance goes beyond data management or master data management; it allows data citizens to access the right information and extract value from the data. Not all information is equal since the data ecosystem is dynamic and continuously evolving. Third-party data may be coming in from an external source, and because of this, the trustworthiness of that data is not on the same level as primary data that is captured directly from the user. This leads to users not trusting the data and shying away from using it to make decisions.
The Demand for Self-Service
Business professionals are learning to perform daily analytics work with less and less reliance on IT. Unsurprisingly, self-service tools like Power BI, Tableau, and Alteryx are on the rise. These solutions help individuals within an organization find insightful answers to critical business questions. The Self-service wave in 2018 and 2019, have matured into comprehensive Data Democratization Strategies. Yet using a self-service reporting system with little or no control over data governance means business users are likely to end up with duplicated efforts, less than accurate data, and potentially inaccurate conclusions all of which has real and significant costs to your bottom and top lines.
The role of IT has always been to enable the business to leverage the best suite of Information Technologies available to drive value. As business users mature into data citizens and demand access to more and more data, our goal becomes to enable and develop those capabilities in a sustainable way. Organizations are embracing self-service analytics and business intelligence (BI) to bring these capabilities to business users of all levels. These trends are likely to accelerate and push further up the value chain towards Citizen Data Science and ad hoc AI/ML. Which create amazing capabilities but, in the end, require foresight and proper governance and guidance in their development.
Adastra has partnered with select tools that provide advanced capabilities in these spaces and has developed a robust set of guidelines to drive Sustainable Data Democratization.
Adastra + Alteryx Solutions
In 2021, Adastra partnered with Alteryx, the Analytics Automation company to equip organizations paths to sustainable data democratization through self-service oriented modern architectures and robust governance practices.
Alteryx is low code/no-code analytics platforms empower analysts to prepare, blend, and analyze data faster with hundreds of building blocks. That enables users to develop sophisticated workflows and process automation with zero coding experience. Allowing users to access data wherever it resides and work with it in an isolated memory-based environment without impacting source systems. Allowing organizations to reduce risks to their systems while still enabling data driven decision making.
Alteryx has been a tool that many large organizations flocked to with the purpose of accelerating their digital transformations. As the platform and the data world continues to mature, we find the need to revisit some of our previous guiding principles in IT. How do we implement data governance if everyone has access to everything? How do we impose security? How do we ensure we are able to support our users?
Adastra has helped dozens of customers address these problems. By defining and implementing processes to accessing use-case complexity, implementation efforts, costs, and support paths our goals it to develop an enterprise strategy that works for the business and for IT. We work with the business users to enable them to become local experts and power users for their teams’ tackling projects and use-cases within their departments. This allows the organization to take full advantage of innovative self-service across their enterprise. Combining this with a Center of Excellence approach for governance and defining layers of governance rigor that fit the way businesses operate.
By allowing IT to focus on protecting the enterprise platform through rigorous vetting and documentation of use risk and control frameworks, development of Data Loss Policies and access privileges allows the organization to protect their corporate crown jewels. Bringing us back full circle to the goal of ensuring good governance of our data.
Future Success of BI Depends on Data Quality and Data Governance
Self-Service analytics, undoubtedly, has come a long way in adding value to daily business decision making. Nonetheless, much is still lacking in terms of Data Governance, Data Security, and Data Privacy. The primary objective of a Data Governance framework is the ability to add real value to an organization through the Data Management infrastructure, which is at a different level than issues of Data Security or access control. With clean and consistent data at their disposal, ordinary business users can singularly concentrate on their Analytics and BI tasks with embedded tools, without having to worry about wrong computations or bad results. If self-service technology really succeeds in overcoming the “technology confusion,” then it does have the promise of delivering actionable insights and market intelligence just when the users need it.
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