Modern self-service analytics platforms empower the ordinary business user by bringing advanced analytics tools to their desktop. The business user today does not require the help of a technical team member to discover trends and patterns, to make accurate predictions about the future, or to drive a data-driven culture in the organization. Kartik Patel points out that advanced analytics lends the capability for “analyzing change as it is taking place.” This capability provides businesses a powerful opportunity to “respond, forecast, and plan” in real time.
Within the AI category, “explainable AI” seems to be a hot topic these days as well. Augmented analytics, continuous intelligence, and explainable AI will completely disrupt the business analytics landscape in the next five years. By 2020, all BI and business analytics vendors will have to integrate augmented analytics with their platforms to remain competitive
According to Rita Sallam, Research Vice President at Gartner:
“Data and analytics leaders must examine the potential business impact of these trends and adjust business models and operations accordingly, or risk losing competitive advantage to those who do.”
In the augmented and advanced analytics scenario, most of the critical tasks like Data Quality Management, Data Integration, Master Data Management, and Metadata Management are automated to a large extent. Donald Feinberg, Vice President and Distinguished Analyst at Gartner, thinks that the “digital disruption” has created as much challenge as opportunities. A Data Science Central post discusses Gartner’s recent publication, Best Data Science and Machine-Learning Platforms Software of 2018 as Reviewed by Customers.
Advanced Analytics Use Cases: The Tour Begins
Here are some general but recent market applications of advanced analytics, which includes Big Data analytics:
- Big Data in the cloud with ad-hoc, data analysis enables users to look at selective unstructured data on a separate layer. Cloud service providers use Hadoop to deliver ad-hoc data analysis.
- A Datameer ebook contains the five Big Data analytics use cases.
- CEOs are now taking an active interest in advanced analytics. Code-sharing libraries with automated analytics platforms have made Data Science mainstream, as even senior business executives have ready analytics tools at their fingertips. The C-suite executives no longer have to run to technical teams to apply advanced algorithms to specific business problems to arrive at superior solutions.
- The presentation titled Big Data and Advanced Analytics: 16 Use Cases showcases many industry applications of advanced analytics, such as risk analysis, flexible pricing models, targeted discounts, fraud prevention, and advanced customer management.
Advanced Analytics with IoT Data: Use Cases
Some popular applications of IoT data analytics include:
- Product usage analysis
- Common analytics for consumers and business users
- Sensors and cameras working in conjunction for analyzing co-occurring or connected events
- Video analytics for surveillance and safety steps
- Social analytics
The critically important aspect of IoT data analytics is that though business users may believe that modern hardware innovations like wireless, sensors, and mobile are driving business value, in reality it is the high quality of sensor data and advanced analytics technologies like Big Data that are contributing to value-added analytics.
Advanced Analytics Use Cases by Industry
In recent times, business leaders and managers have been paying a lot of attention to building an advanced analytics vision and strategy, which involves serious considerations about the applicability of such analytics exercises. The advanced analytics vision and strategy is often closely linked to the overall business strategy, so that the primary goal of achieving operational efficiency is achieved.
Modern AI platforms offer many automated or semi-automated tools that may be easily used by finance, insurance, or healthcare business professionals to “transform data into information” for smarter decision-making and enhanced profitability. As Data Governance is also very important in these three industry sectors, machine-learning (ML) enabled AI platforms offer even greater opportunities for more accurate and more efficient decision-making. These advanced analytics technologies are helping businesses to stand apart from their competitors.
Other applications of advanced predictive analytics are discussed in Predictive Analytics Use Cases, which suggests that the true power of advanced analytics rests as much with trained experts as with advanced tools.
- Users can apply predictive analysis in conjunction with prescriptive analysis on high volumes of market and consumer data to arrive at actionable intelligence
- Predictive analytics tools can help users predict sales outcomes for the immediate future
- Past sales performance data, when used in predictive analytics, can help retailers to forecast growth due to particular factors such as change in market trends or consumer behavior
- Big Data analytics helps retailers study product-distribution channel data to reduce costs
Insurance and Financial Services
- Advanced predictive analytics is used for churn management
- Big Data, along with predictive analytics, can help in forecasting demand
- Big Data analytics plays a vital role in fraud prevention
- Risk analysis with Big Data helps to determine credit-worthiness of clients
- Data mining, NLP, and text analysis are used together to deliver better product and customer insights
Advanced analytics platforms like Health Catalyst helps in predicting risk of diabetic ketoacidosis (DKA), an acute case of diabetes, which allows early intervention.
Understanding consumer behavior is the key to marketing success. In the digital era, marketing data is gathered from a wide variety of input channels and customer touchpoints. Along with structured chat transcripts or emails, many unstructured sources of data, such as sensors, click streams, and social media generate piles of consumer data.
Advanced analytics is not merely a group of data scientists and a set of high-end analytics tools. Data Science is a complete mindset, and especially now with self-service BI and analytics, vendors are trying hard to democratize the solution platforms, so that every business user — from the CEO to the customer-service agent — can reap the benefits of data-driven insights without the presence of a Data Science team.
The final purpose of advanced analytics, as the market solutions indicate, is to make business users think like data scientists. Hence, the advanced analytics “vision and strategy” must first be aligned with the overall business strategy to enable users to reap the maximum reward from their analytics efforts.
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