Today many businesses are fortunate to have an abundance of valuable data that can enable increased intelligence and drive new revenue when put to proper use. Whether improving customer loyalty programs, applying analytics to understand consumer and organizational behavior, developing new products and services from insights, or to simply innovate faster, data is the lifeblood that helps organizations unleash new opportunities. To fully unleash new value, the most relevant data needs to be made available to data consumers throughout the organization in order to empower them to find and use this data.
But this ever-increasing flood of data comes at a price: can sensitive, business-critical, information be trusted for appropriate value creation? Or—thanks to the growing tide of data privacy legislation intended to minimize risks, along with additional consumer rights to transparency—does it become a potential liability, subject to increased scrutiny for possible abuses? Organizations need a reliable strategy that is able to govern appropriate data use, align to legal and internal policy requirements, and bring business and technical stakeholders together to understand intended purpose.
The result? Organizations, unsure whether they can trust their data, limit access, instead of empowering the whole enterprise to achieve new insights for practical uses. To drive new value—such as expanded customer marketing and increasing operational efficiencies—democratizing data demands building out a trusted, governed data marketplace, enabling mastered and curated data to drive your innovations that leapfrog the competition. To do this, trust assurance has become the critical enabler. But how to accomplish trust assurance?
So, what is trust assurance, and how can data governance help accelerate it? If an organization is to convert data insights into value that drives new revenue, improves customer experience, and enables more efficient operations, the data needs controls to help ensure it’s both qualitative for reliable results as well as protected for appropriate, and compliant, use.
According to IDC, we’re seeing a 61 percent compound annual growth rate (CAGR) in worldwide data at this moment—a rate of increase that will result in 175 zettabytes of data worldwide by 2025. Not only is this a stunning number but having too much data to manage not only creates uncertainty about not only if it’s relevant to maintain (assuming you know where it all is), but whether it’s a liability if it’s not used appropriately in accordance with data governance policies.
Clearly a reliable method is needed to govern data for appropriate use, and this requires an effort to align the organization for success. Coordinating stakeholders and remediating issues that don’t reflect organizational policies for appropriate use is the job of responsible data stewardship, and you need the right tools to intelligently automate key processes.
Informatica has developed a 6-step approach to driving data empowerment through data governance best practices and—if you can master these steps—you will be on the road to democratize data for trusted use. You can build a thriving data marketplace and accelerate data-driven intelligent decision-making across your global organization. Moreover, you can reap the benefit of establishing the Chief Data Officer and data stewards as leaders who deliver measurable market-competitive value to the business.
The journey to democratize data with trust assurance consists of six important and equally critical steps. Let’s cover each one briefly:
The first step to democratizing data is to establish a foundation for data governance and documenting it. You then need to nurture a collaborative culture that builds on that documented foundation. Even the most carefully thought out plan won’t work without the right collaborative culture. The Boston Consulting Group (BCG) found 60 percent of businesses rated their data governance capabilities as “underdeveloped.” Much like building a skyscraper, if you don’t start with a trusted foundation, your top floors—like your goal of democratizing data—come crashing down. Where do you stand today?
2. Discover and Curate Data
Once you have your governance framework in place and documented—and a data-friendly, collaborative organizational culture that encourages users to follow it—it’s time to find the data. Some organizations flip Step 1 with this one, depending on needs; but according to McKinsey, if a clear inventory of data is not available, users can spend 30 to 40 percent of time searching, leaving less time to analyze or act on it. Discovering data is key, and curation makes it useable and useful for users, and is essential to helping users understand if a particular dataset meets their needs.
3. Cleanse and Master Data
These next two steps are about ensuring you can trust your data. Unfortunately, 60 percent of organizations say they are challenged by the quality and complexity of their data. Data cleansing is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. And once cleansed, mastering is all about having a single source of truth.
4. Protect and Monitor Data
While data quality is one key aspect of trust assurance covered in the last step, data privacy and its protection are the other. Ensuring that sensitive data is reliably protected against inadvertent misuse or an overt data security breach is an essential step for providing safe access to it. Democratizing data for use in value creation programs means avoiding the heavy fines and reputational damage if non-compliant with privacy policies. Monitoring data use is also critical for enabling transparency that allows organizations to respond to consumer rights for appropriate data use, such as fulfilling DSAR inquiries.
5. Provision and Consume
Steps 5 and 6 are about packaging up the data, getting it to the right place, the right people, in the right form, so it can be found and analyzed, and used quickly and efficiently. Organizations must have the ability to source, transform, blend, and then process data into high-quality, governed datasets before it can be shared and consumed by business users. This is data provisioning for enterprise consumption.
6. Set Up Shop and Deliver Value
Lastly, once you’ve created high-quality, trustworthy and protected datasets, you can publish and make them available in a data marketplace. What is a data marketplace? It’s like an ecommerce store but designed to be used by internal data consumers as they “shop” for data assets. It provides an easier end-to-end experience to intelligently and automatically enable data users to browse, discover, shop, and understand the context of data—and, if authorized, access it for responsible use that aligns to your governance policies, helping to unleash new value across the organization.
By building a new self-service model that supports data democratization, you can empower teams across your organization—from the executive suite to marketing to manufacturing—to drive productivity, efficiency, and the trusted use of data. But this worthy goal of data democratization can be achieved only with strong, scalable data governance driven by improved data quality and privacy. And the only way to get there is through AI- and machine learning-powered intelligent automated data governance in order to scale reliably.
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