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Data AI

Leveraging data (internal and external) and customer analytics to innovate and create competitive advantages is more powerful than it has ever been. This popular practice is fueled by the growing volume of operational and customer data and technological advancements that make extracting value from data even faster and more accessible. Driving more value from all data feels even more urgent when considering that experts predict that analytics and AI will create $15.4 trillion by 2030, as reported by McKinsey & Company. Yes, that’s right, $15.4 TRILLION!

When it comes to their data and AI strategy, every CXO aims to accomplish three things: get better insights from data, reduce risks, and control costs. Ultimately, this focus is the key to becoming part of the 13% of organizations that are succeeding on their data strategy (MIT Tech Review, 2021).

So, how are they delivering on this goal? In this blog post, we’ll dive deeper into the three focus areas for CXOs and how the lakehouse architecture can help drive an enterprise data and AI strategy that enables transformation.

Top strategic goals of the modern CXO

Better insights to increase business impact

Organizations are now collecting more data across more data types than ever before with the goal to leverage all data sets and data assets to generate better actionable insights within the organization and  make better business decisions. The typical CXO is now not only looking at traditional structured information, such as purchase and CRM data, but also semistructured data, like customer interactions from web and mobile properties, and more increasingly unstructured data, such as social media posts or customer service chat or phone logs. The application of the data now extends beyond traditional SQL and business intelligence (BI) reporting but increasingly shifting to do more with artificial intelligence and machine learning (AI/ML).  Within enterprises, there’s a push to move away from very complex and expensive on-premises architectures  (e.g., Hadoop) and disparate tools to a more streamlined, lower-cost approach focused on improving the user experience and increasing collaboration across data personas.

Reduce risks from weak data management

Organizations need to be able to reduce the risks associated with data management to minimize the threat of cyber attacks by having a consistent way to store, process, manage and secure data. But they also need to adhere to the growing data privacy regulations like GDPR and CCPA, as well as contend with new privacy directives, like those issued by Google and Apple, which effectively eliminate third-party reporting sources. Ultimately, CXOs need a consistent way to store, process, manage, secure and leverage ALL their data, to not only mitigate risk but also take advantage of new and unexplored data sources that can replace traditional customer behavioral, demographic, and interaction intel.

Control costs

The on-premise data architectures that many organizations currently rely on are expensive. There are a lot of moving parts, overhead from operations and maintenance and an overwhelming number of vendor agreements locked in. What’s the alternative? To truly drive modern data and AI initiatives, data leaders need a simplified cloud architecture that executes more of the data workloads with a less complex environment. In turn, CXOs have stronger control over their costs as they move forward with their transformation. Simpler architectures also mean more agility for CXOs and their teams to iterate and produce actionable insights without delay or IT intervention.

Driving enterprise data initiatives with a lakehouse architecture

Legacy architectures have done a great job at serving the needs of enterprises when data came in batches and lacked data types and complexity. The lakehouse architecture delivers on the shortcomings of legacy architectures while reducing complexity. Lakehouse architecture combines the best elements of data lakes and data warehouses — delivering data management and performance typically found in data warehouses with the low-cost, flexible object stores offered by data lakes— enabling the full range of analytics and ML workloads. This helps to not only control costs but also increase the performance of the architecture to do more faster.

By having a simplified data architecture, CXOs can operate their organizations with more confidence in reduced risk by enabling fine-grained access controls for data governance across clouds, functionality typically not possible with siloed data across data warehouses and data lakes. Furthermore, organizations can quickly and accurately update data in your data lake to comply with regulations like GDPR and maintain better data governance through audit logging, automatic data testing, and deep visibility into the ETL process for monitoring and recovery.

The unified approach also eliminates data silos that traditionally separated analytics, data science and machine learning. When brought to life in platforms like the Databricks Lakehouse Platform, native collaborative capabilities across clouds accelerate the ability to work across teams and innovate faster in a highly secure and scalable data and AI infrastructure.

A modern architecture alone isn’t enough - the data and AI strategy matters

The most critical step for CXOs to enable data and AI at scale is to develop a comprehensive strategy for how their organization will leverage people, processes, data and technology to drive measurable business results, such as increased sales or customer loyalty and business priorities. The strategy serves as a set of principles that every member of your customer experience team can refer to when making decisions. The strategy should cover the responsibilities of roles within your team for how you capture, store, curate and process data to run your unit— including the resources (labor and budget) needed to be successful. The strategy should clearly answer these questions and more and should be captured in a living document, owned and governed by the CXOs, and made available for everyone on the team to review and provide feedback. The strategy will evolve based on the changing business and/or technology landscape -- but it should serve as the North Star for how the team will navigate the many decisions and tradeoffs that you will need to make over the course of the transformation. Download the eBook,” Enable Data and AI at Scale to Transform Your Organization” to get comprehensive guidance on building out an effective and executable strategy.