Qualities to Look for in a Modern Data Analytics Platform
Data Analytics? Many people find shopping for vehicles stressful. After all, making a large purchase for everyday use raises a lot of questions. Should you buy or lease? What gas mileage would you like to achieve? What features do you absolutely need and, on top of that, what features do you want? What’s your price range?
These questions are just the tip of the iceberg when it comes to car shopping. Now imagine selecting and deploying an enterprise-grade data analytics platform with the potential to affect every facet of your company’s operations and profitability.
With everything from front-end user-friendliness to initial and ongoing costs to run, there’s a lot to consider when you’re comparing the solutions available on the market today. To get the absolute most out of your data reporting software that offers analytics, start with the basics — like these four must-have qualities — and go from there.
Ad Hoc, Self-Service Search Analytics
The good news is data analytics can be anybody’s game today. No longer is a team of data specialists needed to create reports and hand them off to non-technical users. Contemporary self-service platforms allow anyone with permission to access data insights pertaining to their line of work.
In so doing, marketers can stay abreast of subscriber behavior and campaign results by channel, while engineers dig into bottlenecks in the manufacturing process. The key here is to facilitate ad hoc analyses — those going beyond static, scheduled reports —for everyone, at any time. According to TechTarget, three key benefits of self-service business intelligence (BI) include:
- Up-to-the-minute insights not yet compiled into a scheduled report
- Line-of-business decisions are made more quickly
- IT sees a reduction in reporting requests from other teams, meaning they can work on more strategic initiatives
Interactive Drill-Down Capabilities
In addition to seeking out self-service search analytics on demand from your data analytics platform, work to ensure the insights received are fully interactive — meaning employees can drill down into insights and explore all the angles, gathering as much context as possible to feel more informed decision-making.
The “what you see is what you get” approach is severely limiting users’ ability to fully explore everything the data is telling them.
Without these click-to-drill-down capabilities, it will be challenging for users to fully explore the data insights at hand, ask follow-up questions and dig as deeply as required to make thorough decisions.
Artificial Intelligence-Driven Engine
Search-driven analytics exist to answer users’ questions as they occur. Someone enters a query; the system returns an answer. Meanwhile, an AI-driven engine automatically discovers insights and pushes them to users, helping them discover answers to questions they haven’t thought to ask.
Machine learning helps them understand over time which insights are most useful and relevant — based on human users’ feedback — then refine the insights they mine from data.
Well, here’s how CIO describes it: “We are talking about systems that can learn from data, learn from their experiences, and get better at what they do over time.” As you can imagine, this tech represents a huge opportunity for businesses to improve the quality and quantity of insights they’re receiving, while freeing human analysts of huge manual tasks.
Built for Cloud Scale
Modern BI, unlike legacy predecessor systems, is cloud-native and built for cloud-scale. What does this mean for the enterprise? Heightened agility and integration with existing business applications, to start.
This way, enterprises are able to harness the power of search- and AI-driven data analytics within existing databases rather than having to go through the extra steps of caching or moving data. It’s advantageous to utilize a modern data analytics platform with search- and AI-driven analytics, the ability to drill down into interactive insights, and solid governance to oversee it all.