Transforming Data to Next Generation Analytics Platforms
Big data is a phrase thrown around a lot. It’s almost become a buzzword, much like thought-leader or omnichannel marketing—we all know what they mean, but their overuse gives them a “yesterday’s news” feel.
Make no mistake, though. Big data is real, it’s hard to control, and it’s necessary for your business.
The Rise of Data
Big data refers to the collective consortium of information floating out there in the web. This may include voice data, consumer behavior information, mobile data, or browsing patterns. Regardless of the type of data, though, one fact remains true: there’s too much of it out there for us to handle.
At least, too much to handle through traditional means. Traditional data warehouses are already suffering from the complex and massive data sets they’re overburdened with. Data creation is exploding faster than we can accommodate it.
Fortunately, we live in an age of great technological growth, and figuring out ways to manage the avalanche of information is a top priority for many IT providers out there.
This has given rise to advanced data analytic platforms that give businesses greater information governance and agility than traditional methods could ever provide. Though their effectiveness at data management is hard to ignore, there are a few things that businesses must keep in mind when transitioning to next generation analytic platforms.
- If you’re starting now, you’re already behind.
The transition from traditional data analysis to next generation analytics can be slow, and businesses that haven’t yet begun are starting from behind. Using analytic reporting to determine success is a process of risk taking, evaluating, and redoing. This process takes time, and even with the advanced functionality offered by next generation platforms, businesses can’t afford to wait to begin.
- You may need to upgrade your infrastructure.
If next generation analytics are about stronger reporting and greater data agility, then your infrastructure must reflect this initiative. Upgrades to your data warehousing, computing structures for high-level analysis, and even Cloud integration can all contribute to a growth-focused ecosystem.
- Training is essential.
Don’t expect to tackle an advanced analytic platform with the same approach as traditional data analysis. Operating advanced analytic software and making sense of the data will require specific employee training. Specialists and statisticians can get expensive, too—making it essential that businesses using advanced analytics understand what models they want to address and how to train staff to interpret this information accurately.
- Advanced analytics will increase flexibility.
Although reviewing advanced analytics can require specific skill sets and expertise, the value it provides is substantial. Predictive analytics is an example of advanced reporting that next generation platforms provide. By reviewing vast data sets, advanced platforms can predict trends and identify patterns in big data. This provides a substantial competitive edge to metric analysis and data measurement agility.
Advanced analytic platforms are an emerging solution to the big data dilemma. Greater governance of metric reporting and analysis helps businesses visualize their processes and provides insight into where improvements can be made. Any organization in the process of applying next generation analytics must be aware of best practices for use, and strategies for how to get the most out of its new platform.