The potential for insurers to tap big data with analytics has never been greater. However, insurers must adapt to the organizational and technology requirements that come with adding new data sources to their arsenals to minimize risk and bolster profits.
With an unlimited amount of information about products and services accessible from as close as their smartphones, customers are more empowered than ever. To adapt and succeed in this increasingly competitive landscape, companies must rely on employees at the front lines of customer interactions to stay ahead of consumer demands.
Lower profit margins. Higher customer turnover. Does that sound like a familiar problem in today’s telco industry? When you’re looking to improve the profitability of your telco business, inaccurate or delayed network forecasting can lead to reduced capacity, lost revenue, and unhappy customers. It’s a difficult problem to wrangle for… Read More →
Perhaps more than any other industry, the viability of schools depends on their ability to measure and manage one key metric: student performance. To add to the pressure to perform, many in the education sector are grappling with measuring the success of their efforts on shrinking budgets, according to a… Read More →
Countries across the globe are struggling with sagging infrastructure (roads, bridges, railways, wastewater systems, etc.) that are in dire need of upgrades and replacement. But as global and regional economies attempt to regain their financial footing and governments are strained by rising deficits, many countries, provinces, and municipalities are having… Read More →
For years, forward-thinking companies have aimed for “business intelligence for the masses,” embedding the power of analytics into the process of the business users to ensure they can rapidly gain actionable insights and infused them into operations.
In a previous post, we described the second stage of the Analytics Maturity Model, “Diagnose,” which enables business leaders to visually interact and drill into their data to discover additional answers to questions that arose in the “Measure” stage. For example: Why did we have an increase or decrease in… Read More →
Many business intelligence (BI) vendors and CIOs have positioned big data analysis to focus on data reporting. They then collect and store as much data as they can hoping someone, a data scientist or a team of specialists, can analyze the data and make sense of it. However, the industry hasn’t… Read More →
Retail stores today pulsate with activity and generate streams of data about customer behaviors, customer traffic, employee performance, and inventory placement and sales. This creates incredible opportunities for retailers, their regional managers and their store managers to leverage analytics to better understand shifts in customer preferences, employee productivity, and merchandising… Read More →
In a previous post, we explained how the first stage of the Analytics Maturity Model, “Measure,” enables executives and front-line managers to obtain a quick, current status of the operational and business performance of their company.