Three ways to help everyone make fast, data-driven decisions with modern BI
This is the second post in a three-part series about transformational modern analytics. In case you missed it, read the first post to learn how governance and data management enable your digital business.
In our age of digital transformation, we are witnessing the transformational power of data to inform business decisions and drive change in real time. Tapping into organizational data can help your teams shape connected customer experiences, surface system constraints and improve operations, and align business leadership on shared metrics.
However, many organizations still struggle to increase the number of people using data in their daily routines and workflows. There are plenty of barriers between employees who rely on data and the sophisticated analysis required to make the best decisions with it.
Fortunately, evolving modern analytics platforms can help everyone make informed decisions with trusted data quickly and confidently, from power users to everyday front-line employees. Let’s explore this evolution, which is happening through a convergence of three paradigms:
- Data democratization
- User interfaces that equip people of all skills to get fast insights
- Artificial intelligence (AI) applications that make advanced analysis approachable
Democratize data to empower organizational agility
With millions in capex spent to capture, curate, clean, and store data, it’s surprising that many organizations are not also democratizing it. If you can get data into the hands of those who intrinsically know your business, rather than restrict access, that’s when the magic happens.
IT is often a gatekeeper and bottleneck to how businesspeople get insights. A dashboard or report may help answer some questions, but what happens if teams have follow-up questions? Do they go back to IT and wait in a queue of requests? Or do they export data to spreadsheets, sacrificing data governance? What’s the typical scenario at your business?
Expanding access to information with self-service analytics is just the beginning. By enabling data exploration through different user interfaces and interactions with data, you can help employees discover “unknown unknowns” and impactful insights beyond their initial, routine questions.
Give your organization more ways to interact with data and find insights
The transformative value of analytics lies in data exploration and interactivity, so anyone can answer question after question in an uninterrupted flow. Enterprise solutions often provide the advanced capabilities that hardcore users demand, but at the expense of a steep learning curve or limited use cases for other parts of the business. People are interested in asking questions about their data—not learning software—and you shouldn’t have to consider trade-offs or point solutions to equip everyone across your workforce with relevant insights.
Your employees do analysis differently, based on their job functions, analytics use cases, and comfort level in working with data. Drag-and-drop visual analysis makes it easy for people to perform analysis; no more antiquated, code-heavy processes and predetermined criteria needed to create reports and dashboards. Built-in visual best practices also help people see and understand their data more intuitively—from hand-selected color palettes to automatic suggestions for effective chart types based on the data that’s being analyzed.
Natural language (something we’re all familiar with) also helps people explore data easily. They can type questions or keywords into the interface to get answers and make informed decisions without needing to understand underlying models of a data source or how to structure a query. For example, Ask Data even interprets intent behind ambiguous and vague inputs to guide data exploration and empowers people to refine their questions without starting over in their analysis.
Bring advanced analytics capabilities to more problem-solvers with AI
Using AI, machine learning (ML), and other statistical methods to solve business problems has largely been the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems.
While AI, ML, and natural language capabilities can be highly technical, they can also be made approachable. Simple, intuitive ML and AI-powered predictions, what-if scenario planning, and guided model building—all using clicks, not code—are introducing powerful ways for people to interact with data and perform sophisticated analyses.
Intuitive, visual analytics combined with transparent AI capabilities can augment analysis for those team members who lack technical expertise. By making smart recommendations and helping people ask on-point questions, AI-powered analytics put advanced analytics in the hands of more people. And when AI is approachable and transparent, it can build people’s confidence in analytics.
Augmented analytics can also help your business partners make valuable decisions when it matters most. This confidence, competence, ease-of-use, and transparency contribute to a Data Culture where people prioritize data over instincts and “the way it’s always been done.”
At Tableau, we are focused on getting more people to use data in their daily business workflows, regardless of their role and data skills. We see a huge opportunity to democratize data science and AI analysis, minimizing trade-offs between precision and control vs. the time to insight—and the ability to take action on these insights while they’re still relevant. We believe in empowering people to ask that next question, explore that next hypothesis, test that next idea. And now we’re taking it further to elevate people’s human judgment with practical, ethical AI.
Editor's note: This article was originally published on CIO.com.