Enterprise companies often struggle to ascertain how to optimize all the data they collect. Big data — very large data sets — makes the problem even thornier. However, new data analysis tools such as predictive analytics and analytics as a service enable businesses to quickly harness all the data they collect and turn it into meaningful insights. Self-service analytics is the future of data analysis, letting employees without technical skills manipulate data without writing complex queries.
The introduction of analytics as a service means companies can avoid paying for additional hardware, storage, power and cooling. Utilizing a cloud-based analytics platform also means there is no need to hire expensive consultants and data scientists and allows employees without an IT background to manipulate data.
Big data for better decision-making
Big data analysis, especially predictive analytics, provides businesses with a deeper understanding of customers’ motivations and internal and external factors impacting their business. This knowledge helps companies to foresee what customers will buy, an advantage that can result in greater revenues and reduced costs in comparison with other marketing efforts.
In combination with predictive analytics, prescriptive synthesis enables companies to extract the optimum operational decisions to maximize profits under uncertain conditions.
Predictive analytics delivers significant impact across the customer lifecycle and better results than retrospective analytics (looking at past customer behavior) according to a survey of B2B marketers by Forrester Research. While 14 percent of surveyed marketers who used retrospective marketing techniques reported revenue growth higher than the industry average, 41 percent of marketers who used predictive techniques experienced revenue growth that surpassed the industry average. Forrester concluded that the use of predictive marketing analytics correlates with better business performance.
Increase revenue and reduce cost
While making better decisions provides a competitive advantage, organizations still have to evaluate whether their investment is saving them money and/or increasing revenues. The average enterprise company spends about $14 million annually on big data projects according to research firm IDG. Calculating the ROI of big data analytics can be difficult. Forbes Insights and consulting firm McKinsey conducted a survey of enterprise businesses for an analytics provider and found that 27 percent of respondents had revenue boosts of three percent or more, while 38 percent had revenue gains between one and three percent attributable to big data analytics.
On the cost side, 21 percent said that they saw a reduction of three percent or more, while 39 percent had cost reductions of one to three percent, In a smaller survey, McKinsey found that big data projects cost 0.6 percent of corporate revenues and returned 1.4 times that level of investment, increasing to 2.0 times over five years.
Big data analytics involves a lot of moving parts and variable costs, but with the availability of pay-as-you-go Analytics as a Service it is now much simpler to build and deploy customized analytics platforms with modern interfaces like self-service Visualization and Natural Language features such as chatbots and automated BI reports.
Once the complexity is reduced to a flat monthly fee or scalable usage-based fee, it becomes much easier to identify ROI quickly to understand if an investment is producing the intended ROI. No contracts mean businesses can stay flexible and pivot as needed to support their growth or take advantage of new innovations.