The first step toward being able to use AI applications is assessing the current state of your enterprise reporting to make that data more accessible.
How Can Organizations Improve Data Access?
Data Access Powers Predictive AI
Predictive models thrive and provide reliable insights when based on consistent, trustworthy data about the past and present. Read below for industry-specific examples of how superior data access can unlock next-generation strategies.
How Can Leaders Get Started?
Ready to create an actionable data strategy—and better access your data so it can drive successful AI and ML use cases, at scale? Follow these steps.
Vice-President, Business And Product Solutions, Cloudera
If you can't look at your business operations on a daily basis and know how the business is running, you probably have data [access] challenges.”
Backward-looking data has value for year-over-year comparisons and plays an important role in AI model training. But enterprise data that can only provide retrospective insights is not accessible enough for the business challenges and techniques ahead.
Consider these questions to determine how robust and trustworthy your enterprise reporting is today—and whether it’s ready to unlock opportunities in augmented intelligence.
Has your organization streamlined the assimilation of both structured and unstructured data into a data fabric accessible by all relevant business users?
Is data governance both clearly documented and consistently applied across the entire data lifecycle?
Can you demonstrate value from data across multiple use cases? Or is data a net liability on the balance sheet?
Click to explore Industry use cases
By improving visibility and monitoring of data-driven activities at every stage of research, clinical trial and care delivery, healthcare and pharmaceutical organizations can further reduce the risk of regulatory action. Backward-looking reporting can only reveal whether an issue has occurred. Better access enables real-time data and predictive reporting to avert incidents. Quest Diagnostics, for example, uses Cloudera technology to keep the extensive contents of its data lake in HIPAA compliance, both in motion and at rest.
As 5G grows, and reliance on next-generation wireless tech increases, telecommunications companies need to optimize investment in new mast-and-antenna equipment. Usage patterns, population trends and customer churn data can all inform and improve these decisions. Broadband providers can also leverage network insights to identify potential misconfigurations and resolve them at lower expense than a field service call. Using the Cloudera platform, South Korean telecom provider LG Uplus, for example, now identifies network performance flaws in minutes rather than days after expanding its network analytics.
Retail stores can use historical and real-time customer traffic data (collected through Bluetooth beacons, on-site Wi-Fi and other sensors) for a range of use cases. Knowing the flow of traffic helps optimize merchandising and store layout. Combining proximity data with other information—like Cloudera customer Shoppermotion does—can also enable real-time applications, like offering location-specific digital promotions on a mobile app and dynamic staffing of check-outs to minimize customer wait times. “You can use proximity data and potentially combine it with loyalty data, purchase history and other digital data to predict customer behavior, inspire additional purchases and dramatically increase the average sale,” Stoop said.
Automatically flagging suspicious transactions as well as avoiding purchase-disrupting false negatives are crucial practices as electronic payments become increasingly diverse and widespread. Integrating data about cybercrime trends with customer purchase insights across all channels can shore up these risk-scoring models. Up-to-the-minute insights into risky transactions as well as data-driven pathways to help legitimate buyers complete anti-fraud validation steps can reduce costs and improve the buyer experience, alleviating customer churn. Partnering with Cloudera, TD Bank, for example, uses customer behavioral modeling to improve fraud detection for millions of customers while reducing data management costs.
Overhaul Data Management Practices
Data collected and curated with poor processes will lose its value or lead to poor decisions. “In getting results from ML and AI, data governance is very important,” said Maike. Leaders should ask themselves, “Is [my] data today an asset, or is it a liability? … If you have poor data quality, you don't know where your data's at or you don't know how it's being used, data's a liability to you.” For example, organizations operating globally are subject to regional privacy laws, which may restrict how data can be stored and used. To remain compliant, it’s important to have a data management platform that consistently secures and governs your data, wherever it resides.
Fold Silos Into A Broader Data Fabric
Siloed data and point solutions become part of an IT framework because they address a particular short-term need, often without careful examination of how they might impede data access. Establishing a data fabric that provides uniform data intake, processing, analysis and access control among silos can improve the depth of insights available, auditability and credibility with regulators. Data fabric does all this by unifying disparate data sources and applications in a secure fashion without changing where or how that data is stored. It also connects data with analytics and users wherever needed.
Evaluate Data Access Continuously
Measuring the health of data access is not a one-time event. Data sources and the applications that process data are subject to constant change, meaning that a well-integrated shop today may not be one by next quarter. Assumptions about data access and applications should be under constant examination.
Define Business Objectives
Don’t plan around the technology you have today, as you’ll be limited by its capabilities. Instead, ask what key questions your data can answer. “Then use technology to make that a reality,” said Stoop. “Use cases typically answer the question, ‘What is it that I can't do today that I wish I could for which data is such an important element?’” he explained. “For different industries, that’s different use cases, but typically they come in about four flavors: grow revenue, optimize expenses, minimize risk and transform business.”
To determine where your focus should be, consider the specific use cases that would move the needle at your organization—whether it’s better fraud detection for financial institutions or predictive maintenance for manufacturers—and examine how they are impeded by siloed data or disparate systems. Some businesses may have multiple objectives, but rather than attempting to tackle them all at once, Stoop said, define and communicate a clear hierarchy. By doing so, leaders can ensure that teams cross-organizationally are working toward common goals, ultimately accelerating outcomes. And once your first use case succeeds, not only will it demonstrate the benefits of transformation to key stakeholders, it’ll also allow you to apply the knowledge you’ve gained to your next use case, maximizing the return on your investment, time and experience.
By improving access to backward-looking records of repairs and failures with sensor data, manufacturers can both offer timely interventions for customers when hidden faults develop and revise maintenance schedules to keep valuable equipment in service for longer. Helicopter manufacturer Sikorsky worked with Cloudera to gain a competitive edge by demonstrating that its data-driven predictive maintenance solutions worked and presenting that data to aviation regulators who agreed to extend mandatory maintenance periods. These longer periods mean customers can reduce the time their expensive equipment is stuck on the ground.