The dissonance between the rapid evolution of technology and enterprises’ ongoing, foundational data struggles is difficult to ignore. Whether it’s maximizing the value of data, managing both legacy and new technologies, or grappling with the growing complexity of data and analytics systems while battling data silo, governance, and security challenges, organizations must consider new approaches to stay ahead.
To help shed light on the ways organizations can overcome these continuing obstacles, experts joined DBTA’s webinar, Achieving Unified Analytics: Next-Generation Data Platforms, to examine the latest and greatest solutions that promise to effectively modernize and unify enterprise analytics.
Conor Jensen, field chief data officer at Dataiku, explained that Dataiku is an AI analytics development platform “enabling everyday AI for all the people at your organization who are the ones that really do work with the data.”
According to a Dataiku customer—a director of data science at a manufacturing company—Dataiku “serves as a versatile orchestrator within our data ecosystem, empowering us to strategically allocate processing tasks across our diverse infrastructure. [...] Dataiku provides the flexibility to optimize for cost-efficiency and performance.”
Dataiku achieves this unification by enabling everyone across the organization—from the business leader to the data scientist, operator, data expert, and engineer—to benefit from an end-to-end, single platform that systematizes analytics and AI, according to Jensen. By looking at your existing landscape, Dataiku aims to fit seamlessly into heterogeneous environments, whether they’re cloud, multi-cloud, or hybrid.
Dataiku’s general purpose platform suits a myriad of use cases, noted Jensen, allowing users to leverage a full spectrum of analytics solutions to solve any data-driven challenge. It’s about “bringing the right analytics approach and technology to bear for all those people to access that data,” without sacrificing security, governance, or scalability.
“Data analytics has a last-mile problem,” according to Alex Gnibus, technical product marketing manager, architecture at Alteryx. “In shipping and transportation, you often think of the last-mile problem as that final stage of getting the passenger or the delivery to its final destination. And it’s often the most expensive and time-consuming part.”
For data, there is a similar problem; when putting together a data stack, enabling the business at large to derive value from the data is a key enabler—and challenge—of a modern enterprise. Achieving business value from data is the last mile, which is made difficult by complex, numerous technologies that are inaccessible to the final business user.
Gnibus explained that Alteryx solves this problem by acting as the “truck” that delivers tangible business value from proprietary data, offering data discovery, use case identification, preparation and analysis, insight-sharing, and AI-powered capabilities. Acting as the easy-to-use interface for a business’ data infrastructure, Alteryx is the AI platform for large-scale enterprise analytics that offers no-code, drag-and-drop functionality that works with your unique data framework configuration as it evolves.
Jesse Summan, senior product marketing manager at Sisense, examined the build versus buy problem that pervades enterprise strategies, where some organizations consider procuring new technologies, while others lean toward building in-house solutions to solve their problems. Ultimately, Summan urged webinar viewers to consider these components in their decisions:
- Time-to-market
- Features and functionality
- Ongoing maintenance
- Knowledge and expertise
- Total cost of ownership
TCO, Summan emphasized, is arguably the most important point to consider, as it may be what determines successful implementation and value delivery. When building your own solution, the cost—as well as risk—can quickly rise, where enterprises may be looking at a $75,000-$150,000 investment of a team of only two full-time engineers, according to Summan.
Speaking of the risk of building in-house, an enterprise’s given talent may leave the organization, resulting in a complete overhaul of the solution. This is, “from a resource and talent perspective, a challenge that many organizations don’t anticipate until they experience it first-hand.”
Properly examining your organization’s needs, as well as the potential—and large—resource costs is vital in determining how you unify your analytics stack. Sisense, Summan explained, helps “you better understand how you can unlock the value of your data with a predictable pricing model, so you don’t have to take on hidden costs or additional overhead.”
To view the full, in-depth webinar—featuring a roundtable discussion that followed the experts’ presentations—you can view an archived version of the webinar here.