Editorial
The Gist
- Unified platforms essential. Data governance requires unified platforms for effective data analysis and decision-making.
- Collaborative efforts amplified. Tableau and Databricks are intensifying integrations for better data collaboration and governance.
- Generative AI integration. Embracing generative AI helps drive deeper insights and smarter data science models.
SAN DIEGO — This follow-up to my previous article encapsulates my remaining highlights from the Tableau Conference. Building upon Tuesday's announcements, today's focus extends to areas that should be of keen interest to data and customer experience aficionados.
From discussions on democratizing data to optimal structuring, these insights cater to those who champion the belief that data is truly everyone's business. Additionally, I will share two compelling customer studies that resonate beyond their specialized fields, underlining lessons to data mastery.
Related Article: Salesforce Tableau Champions Data-Driven Decision Making at Annual Event
Databricks and Tableau Forge Strong Data Partnership
In a candid exchange with Ken Wong, senior director of product management at Databricks, the symbiotic relationship between Databricks and Tableau was made clear. With a combined clientele numbering in the thousands, their partnership underscores the crucial interplay between data management and data visualization.
Wong's insights shed light upon Databricks' distinctive focus on the "lakehouse" concept, emphasizing the pivotal role of behind-the-scenes experts — data engineering and data science. While Tableau caters to data analysts and data consumers — CMOs and customer experience — Databricks champions data engineering and data scientists, shaping the landscape of data utilization.
Collaboration Key in Tableau-Databricks Integration
Navigating the complex terrain of data management and analytics, Tableau and Databricks cater to distinct yet complementary audiences; often both are typically overseen by the chief data and analytics officer (CDAO) or the chief information officer (CIO). While Tableau fluently communicates in the language of business — reports and analytics, Databricks boasts SQL as its lingua franca, empowering data engineers and data scientists.
However, amidst their differences, the necessity of business collaboration becomes evident. Recognizing this, both companies are diligently enhancing their integrations, with initiatives like "Delta Sharing." As Wong emphasized, the rising significance of the data scientist audience underscores the evolving landscape that both Tableau and Databricks are navigating together and separately.
Wong Advocates for Unified Data Governance
Wong unveiled in our conversation his company's aspiration to emerge as the quintessential platform for data management — all the stuff that makes data available and ready for analysis or data modeling. Databricks last year interestingly declared that "English is the new API." Against this backdrop, Wong delved into the intricacy of data catalogs and the respective markets different flavors address. While Tableau caters with its catalog to data consumers and Alation and Collibra do so for data governance, Databricks' Unity catalog stands poised to serve data engineers.
However, Wong is clear that they are transitioning toward a more comprehensive data governance approach. Wong's clarion call for seamless integration underscores the imperative of harmonizing diverse environments, emphasizing the pivotal role of metadata integration in this transformative journey.
Databricks Enhances Data Control with Unified Catalog
Databricks envisions a unified technical catalog capable of intricate policy management to automatically creating code from them. Last year's emphasis on SQL showcased the company's adaptability to audience needs, yet its focus now lies in integrating with policy-setting realms to establish themselves as the authoritative source of record. Prioritizing bilateral synchronization across diverse environments, the imperative to translate policies into actionable code emerges as a central theme. In essence, collaboration is paramount in this quest for unified, policy-driven data governance.
Databricks, Tableau Foster Alignment and Insight
Wong also underscores the imperative for data scientists to craft metrics that illuminate the broader implications of their models, fostering alignment between technical innovation and business outcomes. In parallel with Tableau's ambitions, Databricks aspires to cultivate a semantic layer, facilitating seamless translation of language to empower deeper insights and understanding.
Databricks Aims for Smarter, Self-Service AI Models
Last, in the realm of machine learning, Databricks seeks to empower self-service capabilities while grappling with the transformative potential of generative AI. While the synergy between the two remains a puzzle, Databricks sets its sights on facilitating complex data science endeavors, contrasting Tableau's focus on simpler models like time series. Embracing their evolving identity as a data intelligence platform, Databricks endeavors to shepherd data through a journey of enlightenment, culminating in the deployment of smarter, more insightful models.
Related Article: Databricks Takes on AI Goliath, Challenges OpenAI's Reign With Dolly 2.0
Tableau Engineers on Steering Data Governance
Data governance is about many things, but at its core, it is about making data that is ready for analysis, findable and protected. The authors of the McKinsey Guide “Rewired” call this data trust.
Tableau Engineers Advocate Balanced Data Governance
Principal Solution Engineer Tom Kern and Senior Solution Engineer Tugba Zengin, both of Tableau, discussed in their presentation the process for leading the steering data governance, advocating for a nuanced approach that transcends the binary of overcontrol and self-service. Emphasizing the necessity of establishing clear control mechanisms, defined roles, and robust policies, they underscore the delicate equilibrium required to govern data and content effectively.
Vision for a Trusted, Balanced Data Platform
Central to this balance is the need to harmonize security measures with the imperative of enabling data exploration, ensuring that decisions are underpinned by accurate, trusted and secure data. At the heart of their vision lies the concept of a trusted data platform, where the triad of security, data democracy and business agility converge without compromise.
Tableau Champions Flexible Data Governance Models
Recognizing the multifaceted nature of governance, Kern and Zengin advocate for a spectrum of approaches, ranging from centralized to delegated and distributed governance models. Centralized access, managed by a centralized group, offers stringent control, while delegated access, restricted to trained individuals, strikes a balance between control and flexibility.
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On the other hand, self-governed open access, facilitated through a rigorous certification process, champions data democratization. The optimal balance among these approaches may vary across departments such as finance, sales, and marketing, reflecting the diverse needs and priorities within these organizations.
Related Article: The Imperative of Data Literacy in Business Decision-Making
Salesforce Enhances Security for Generative AI Systems
Sri Srinivasan, Salesforce senior director of information security, delved into the intricate landscape of securing LLMs (large language models), unraveling the structural intricacies and associated risks within generative AI systems. Introducing the Einstein Trust Layer, a fortified gateway interlinked with hosted models within the Salesforce Trust Boundary, Srinivasan illuminates the pivotal role of safeguarding against prompt injections and ensuring data integrity.
With capabilities ranging from audit trail provisioning to toxicity detection and secure data retrieval evaluation, this layer emerges as a bulwark against potential vulnerabilities. The latter involves the notion of detecting a sensitive data request, and masking on the fly when personally identifiable information (PII) is effectively being requested. Emphasizing the necessity of system guardrails in the form of policies and security measures, Srinivasan champions the concept of secure retrieval and dynamic grounding as essential components in fortifying generative AI systems against external threats.
Related Article: Salesforce Introduces AI Cloud: Generative AI Infusion Into CRM
USAA Champions Transformative Data Insights
To cultivate a Center of Excellence for Data, like Lead Business Intelligence Analyst Michael Sandberg's initiative at USAA, is to sow the seeds of transformative insights within a community. A center of excellence data community thrives with champions who not only educate but also inspire others, fostering a culture of literacy and adoption. Doing this well requires a delicate balance of collaboration and standardization, where governance and best practices guide the journey toward building a data-driven organization.
Driving Business Agility Through Data Governance
Whether adopting a centralized, decentralized, or hybrid approach, the aim remains consistent: to drive assessment, consistency, and ultimately, business agility. Executive sponsorship is crucial, as it articulates the purpose and desired outcomes, anchoring the efforts in tangible business goals. With trust, governance, and a focus on empirical data over intuition, the path to a world-class service for customers becomes clearer, guided by the beacon of actionable insights.
Related Article: Mastering Customer Insights Through Data Segmentation
Tableau Engineers Push for Customer-Centric Data Products
In their discussion, Solution Engineers Anthonia Kluess and Olga Kuzmina of Tableau shed light on the evolving landscape of data products, advocating for a shift toward a data mesh framework. Emphasizing the significance of focusing data products on customer-facing outcomes and monetization, they highlighted key steps including enrichment, productization and strategic market selection. Their approach marks a crucial stride toward leveraging data as an asset in driving tangible results and enhancing user experience.
Goosehead Insurance Innovates With Salesforce Einstein
Brim Basom, managing director of technology and innovation at Goosehead Insurance, is harnessing the Salesforce Einstein Platform to drive innovation and increased customer-centricity for his business. Since going public in 2018, Goosehead has reshaped client relationships and operational efficiency, operating with 150 carriers and processing $3.8 billion in policies annually. By integrating the Einstein platform, Goosehead's Aviator platform empowers agents to offer comprehensive insurance solutions with lightning-fast quoting capabilities.
Goosehead Enhances CX with AI Insights
Through advanced analytics and AI-driven insights, Goosehead ensures agents deliver personalized experiences and better business outcomes. Leveraging data from Salesforce CRM and Snowflake, they utilize generative AI, like ChatGPT, to analyze policies and provide actionable insights internally and for customers. Additionally, they employ machine learning to discern market trends and make carrier recommendations to enhance customer experience and drive sales.
Goosehead Boosts Data Governance for Innovation
As Goosehead innovates, they are increasingly prioritizing data governance to ensure accuracy and reliability in their analytics endeavors, supporting the needs of their dynamic business environment. With the forthcoming integration of Tableau Pulse, Goosehead aims to elevate their insights further, driving strategic decision-making and enhancing customer satisfaction across the C-suite.
Leveraging Tableau for Global Data Insights
Merck KGaA, Darmstadt, Germany, a science and technology company, operates life science, healthcare, and electronics businesses. To expedite the journey toward life-saving therapeutics and new business opportunities, a unified data visualization platform is imperative. Merck KGaA, Darmstadt, Germany today has over 5,000 employees utilizing Tableau for daily insights, accessible data and actionable intelligence. The company's vision is to create a global data community, empowering colleagues to swiftly uncover insights crucial to new scientific breakthroughs and customer interactions.
Tailored Data Strategy Innovation
Walid Mehanna, Merck KGaA, Darmstadt, Germany's chief data and analytics officer, navigates a vast data landscape across its businesses. This has resulted in a broad technology and a sometimes disconnected foundation. Without question, serving varied business entities requires tailored data strategies, a challenge met with enthusiasm by Walid. Personally, Walid thrives in learning and innovation of data products. Orchestrating a unified platform and governance is paramount amidst AI's transformative impact on processes, exemplified by generative AI's role in hyperpersonalized marketing for electronics sales. For this reason, Mehanna encourages each business to experiment. Walid shared novel applications such as generative AI's utilization in genetics and semi-structured data analysis.
Generative AI Transforms Data and Service Management
Walid agrees with the potential to address issues like the "purple toes" phenomenon in patient records and analysis of unstructured patient data. Amidst legal and compliance complexities, data emerges as a business treasure, driving initiatives like precision medicine data products. As data readiness becomes imperative, emphasis is placed on quality and cleansing.
Meanwhile Walid shared inventive uses for leveraging generative AI. One is using generative AI to frontend a ticket system and workflow optimization promising to reshape service and customer experiences, easing business processes while enriching insights. Here, imagine generative AI asking questions to fill out a form and then with a tie to databases being able to limit questions to authenticating.
Parting Words
There were numerous sessions and customer stories I couldn't share here. However, the insights — particularly those from customers — should interest the CMSWire audience. Data undeniably affects everyone’s role, and it's essential for CMOs and customer experience teams to enhance their data proficiency.
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