This is a recurring question in these turbulent, frightening times. The organization is becoming a redundant "connector" between you and the world. Infrastructure is all you need. But that infrastructure is far too large for one person to build up - it's better to let a data machine do it. In the past decades, production was the number one central factor. Business and services were organized around this central pillar.
In a previous blog, we discussed the building blocks of a strategy map. Now, we'll discuss the various ways in which a strategy map can be structured and read.
Business Intelligence ensures that the right information reaches the right person at the right time. At least, that's the intention. If that can be achieved, you're already a step ahead. However, the real work is yet to begin: influencing the behavior of customers, managers, and employees based on essential information and insights. We can only speak of successful Business Intelligence when that is the case.
A large pharmaceutical wholesaler opted to implement a new BI tool, switching from their old one, in order to distribute and share management information. The company is currently using Business Objects and wants to make a change. The selection of a new tool can be finished as soon as: The reference visit at another pharmaceutical wholesaler leaves a positive impression. There has been an international workshop.
A strategy plotted out in a good strategy map can be a lasting competitive advantage, but only if you can make sure that you're doing different things, or doing things differently, than the competition.
The election of the Smartest Organization of the Netherlands, the Dutch Business Intelligence Award, is in full swing. Organizations from various industries are vying for the title, including an online bank, a mortgage agency, a sustainable plantation, a national bakery chain, an international trade business, a large government institution, an educational institute, a very progressive healthcare agency, a consultancy, and various retail companies.
The organization itself has to be organized. It sounds cryptic, but we're referring to the activities and resources needed to support and improve the organization or company. We could call this the organization's overhead and management. We're talking about coordination by, for example, procedures and guidelines, Human Capital Management, process management, information management, ICT, finance - basically everything that isn't part of a company's core activities. That includes management layers and steering on strategy.
Business Intelligence has been used to help organizations work more information-driven for years, and it's showing no signs of stopping. According to Daan van Beek, CEO of Passionned Group and author of the Data Science book "Data Science for Decision Makers & Data Professionals", BI still serves as an umbrella term, and even covers the Artificial Intelligence and Big Data hype. We had a conversation about data-driven working and the careful balancing act between the 'old' BI world and the 'new' AI world.
SAS software has been operational since 1976, and they are very successful. Year-over-year, their revenue shows growth. Founder and CEO Dr. Jim Goodnight has been recognized as a Great American Business Leader by Harvard Business School, and he still writes SAS programs and develops statistical models. It’s my third time attending the SAS Analysts Relations conference. Every time I wonder at the way SAS people behave and interact with each other and their customers: a flat organization, helpful to each other, creativity and innovation first, and customer-oriented. The company is an example to many other companies and it has quite a few of the characteristics of an intelligent organization.
An old term from 1960 has been revived. It's developing very quickly, powered by all applications of major data masters such as Google, Uber, Amazon, LinkedIn, Instagram, and Facebook. The algorithms were there, but the large amounts of data were missing. Artificial Intelligence never managed to get rid of this. And forecasts were not always as reliable. Now, since more and more pictures, videos, blogs, posts, and sensor data gets available, AI acquires its actual added value.