Friday, June 7, 2019
Business Intelligence and Analytics 20
1 3 Ways to Streamline and Economize the Manufacturing Supply Chain
https://www.manufacturing.net/article/2019/06/3-solutions-streamling-and-economize-manufacturing-supply-chain
A study conducted by the Material Handling Institute surveyed 1,000 supply chain executives to identify the top technologies projected to shape supply chain processes in the coming years. The answers included cloud computing and storage, inventory and network optimization, predictive analytics and IoT.
2 Data Is the Lifeblood of Sales Intelligence Programs
https://www.informationweek.com/big-data/big-data-analytics/data-is-the-lifeblood-of-sales-intelligence-programs/a/d-id/1334832
Many organizations are going through an analytics evolution. And this data-driven approach is no longer exclusive to technical teams as more business personnel become “citizen” data scientists — yes, even sales teams.
3 Industry 4.0: Why Manufacturers Need to Keep Their Eye on the Long Game
https://www.manufacturing.net/2019/06/industry-40-why-manufacturers-need-keep-their-eye-long-game
An industrial revolution is a fundamental shift in the way industry works that forever changes the way manufacturing is done. However, besieged by bright shiny objects of Industry 4.0, manufacturers should focus on long term goals instead of being swept up by the abundance of new technologies, such as cloud, big data, IoT, mobile, location, additive manufacturing, edge computing, miniaturization, augmented and virtual reality, artificial intelligence (AI), automation, and robotics.
4 8 Industrial IoT Trends of 2019 That Cannot Be Ignored
https://datafloq.com/read/8-industrial-iot-trends-2019-cannot-be-ignored/6452
From manufacturing to the retail sector, the infinite applications of the industrial internet of things are disrupting business processes, thereby improving operational efficiency and business competitiveness. The trend of employing IoT-powered systems for supply chain management, smart monitoring, remote diagnosis, production integration, inventory management, and predictive maintenance is catching up as companies take bold steps to address a myriad of business problems.
5 How We Can Solve Big Data’s Bias Problem
https://knowledge.wharton.upenn.edu/article/big-data-ai-bias/
Data analytics and artificial intelligence are transforming our lives. Be it in health care, in banking and financial services, or in times of humanitarian crises — data determine the way decisions are made. But often, the way data is collected and measured can result in biased and incomplete information, and this can significantly impact outcomes.
6 Visualising Geospatial data with Python using Folium
https://www.kdnuggets.com/2018/09/visualising-geospatial-data-python-folium.html
Data visualization is a broader term that describes any effort to help people understand the importance of data by placing it in a visual context. patterns, trends, and correlations can be easily shown visually which otherwise might go unnoticed in textual data.It is a fundamental part of the data scientist’s toolkit. Creating visualisations is pretty easy but creating good ones is much harder.
7 Marketing Metrics for Demand Generation
https://blog.marketo.com/2019/06/why-digital-marketing-kpis-keep-your-demand-generation-campaign-relevant.html
In our previous posts about demand generation, we explored some of the ways to drive an effective demand gen campaign. For our final blog in this series, let’s talk about KPIs—because the cold, hard truth is without metrics, no other aspects of your campaign matter.
8 The Importance of — and How to Calculate — Lifetime Customer Value
https://www.business2community.com/customer-experience/the-importance-of-and-how-to-calculate-lifetime-customer-value-02207695
Lifetime customer value, also called customer lifetime value (CLV), is one of the most important metrics out there. The logic is self-explanatory, so we won’t belabor it because you’re all smart people, but the bouncing ball goes like this:
