Trying to make sense of big data solutions can seem like taking the first step of a 100-mile hike. With so many features and possibilities, as well as potential issues to be wary of, it’s easy to see why businesses are often unsure of where to begin. But with some forethought and planning, you can find the data platform that will help take your business to new heights.
As such, whether you’re comparing data warehouses, data lakes, or hybrid analytics engines such as Druid, Pinot or Clickhouse – it’s important that you make sure they have proper features that can maximize the value of your big data. That’s why we’ve compiled ten essential capabilities and features that your big data engine should possess if you want to get the most out of your data.
Automated analytics platforms allow for rapid analytical processing on large datasets without having to manually enter commands into a terminal or spreadsheet. Automated algorithms enable machine learning (ML) technologies to draw connections between different pieces of data. This feature allows for more efficient analysis that would otherwise take hours or days by hand.
Effective analytics tools should allow users to quickly access a comprehensive overview of their data, yet also permit them to dig deeper into any area they need more detail on. The insights provided should be comprehensive enough so that users can easily spot trends, detect outliers and make meaningful decisions based on their findings.
The predictive analytics capabilities offered by an analytics tool must be accurate enough to provide reliable forecasts with minimal effort from users. This will help organizations make informed decisions about future strategies and plans, as well as monitor changes in customer behavior over time such as buying patterns or browsing habits.
An effective analytics platform needs visualization tools in order to present facts and figures in an appealing manner that makes them easier for readers to understand quickly and accurately interpret trends within their data sets. For a proper understanding of complex topics like predicting future sales trends generated from disparate datasets, interactive visualizations are necessary for meaningful insight discovery. After all, not all employees are data scientists.
For machines running complex algorithms, especially those used in AI/ML projects, continuous learning capabilities are essential. Training models must be regularly updated as new inputs are gathered, which will ensure accuracy and reliability when making predictions about future events. Without this feature, models risk being outdated after just a few months due to changing market conditions or technological advancements.
When attempting to manage vast amounts of structured and unstructured data, scalability is essential for success. Most analytics solutions only support limited amounts of data before they become overwhelmed, so it’s important to look for a product that offers unlimited scaling abilities, no matter how much raw material you throw at it. This means that you won’t have to be worried about costly hardware upgrades or storage limitations when you need to crunch more numbers.
Big data can contain sensitive materials such as customer information, financial transactions, inventory levels, etc. Therefore, security should also be considered when selecting your analytics platform. The right solution should offer robust encryption protocols alongside active monitoring systems designed specifically for detecting malicious activity or attempts at unauthorized access and manipulation of datasets stored in its databases.
Cross-platform integration can benefit organizations both internally (enabling applications across various departments) as well as externally through APIs. These APIs allow third-party applications access to the enterprise’s big data resources without going through complicated integration processes between two separate systems each time something needs to be pulled out from one system into another.
As cloud technology continues to become more widespread among businesses, the requirement for analytics solutions offering either cloud storage or deployment options is now essential. By enabling users to store their data in the cloud and access it from anywhere, costly hardware systems and hosting fees are no longer necessary. What’s more, deploying these services via the cloud saves organizations a significant amount of money due to minimal maintenance requirements.
When managing large projects that include multiple teams and stakeholders, version control is essential for ensuring accurate data. This technology offers features like the ability to compare and merge versions, as well as reverting back to prior editions if needed. Not only does this help prevent duplication of efforts, but also helps to protect against data corruption due to overlapping edits and other inconsistencies.
The ten features mentioned in the article serve as a solid foundation for evaluating potential analytics solutions, but it is key to remember that when selecting your organization’s analytics platform you must factor in both current and future needs.
As technology rapidly advances, scalability and integration are essential components to look out for when selecting a product that will take full advantage of big data initiatives while still providing maximum value.