Success in the here and now is one thing, but ensuring there’s a plan to sustain it can be just as pressing. Both business issues and customer expectations are constantly evolving, and companies simply have to adapt.
To echo the sentiments of TDWI Research: Insights in today’s business world are becoming more ephemeral, and enterprises must move beyond historic representations of business performance and get proactive.
This is where predictive analytics becomes important.
In the past, complex business intelligence and data discovery were the reserve of elite firms.
Technology reinforced by modernity
The practice of using business intelligence to look ahead is hardly a new one, but it is only now – as the rise of big data continues – that predictive analytics’ time to shine is here.
In fact, TDWI Research explains that predictive analytics is evolving to the point at which it is applicable to nearly all market sectors and industries, when in the past complex business intelligence and data discovery were the reserve of the elite firms.
The models of predictive analytics
Even as more companies look to leverage existing data against their future paths, the whole practice will typically appear under set criteria. According to Oracle, the vast majority of predictive analytics falls into three categories:
- Predictive models: These find causality amongst explanatory and dependent variables while focussing on specifics. Examples include the future preferences of customers and credit worthiness.
- Descriptive models: This model collates clusters of data that have similar elements while looking at as many variables as possible. Information that falls under this category could pertain to customer segmentation based on demographics, or company profitability predictions.
- Decision models: These find the optimal solution for a specific decision based on a precise set of data. For example, network planning, the optimisation of resources and business simulations fall into this bracket.
While the above classifications are relatively broad, there are more nuances that may be specific to one business over another. Ultimately, the best predictive analytics practices will be tailored, encompassing some or all of the well-established models.
The bottom line
As research from Intel points out, big data is being generated at a faster rate than the vast majority of enterprises will have ever had to deal with before. While more companies are data rich, finding the right tools to unlock the potential of information is where the real skill lies.
Ultimately, predictive analytics is a wider cog in the business intelligence machine. However, its rise to prominence is only set to continue, so companies that are looking to leverage existing data and shape the future of their endeavours need to act as quickly as possible.