We hear the word “intelligence” applied in business all the time. But what does it really mean? The academic definition can be described as the ability to perceive or infer information, and to retain it as knowledge to be applied toward specific behaviors within an environment or context. These qualities are what we look for in people; good employees have the ability to acquire and apply knowledge and skills in ways that, hopefully, make a difference to the performance of the organization.
We also look for intelligence in our machinery and computer systems. Indeed, a “smart machine” is a device embedded with machine-to-machine and/or cognitive computing technologies that are designed to work through tasks without human intervention. Today’s smart machines might seem revolutionary, like something out of science fiction, but the truth is that technologies including the Internet of Things and computer bots are already at work today and are the next step in a long history of incremental advancements in machines and computing.
We now ask that our content be intelligent as well. Organizations today are not just collecting more and more data, they are working to get more insights and advantage out of that information. The effort involves a variety of technologies and approaches, but in the end it’s about enabling organizations to make sense of information in more agile, thoughtful and profitable ways, and to help decision makers improve corporate strategies and internal operations.
It’s as Easy as A-B-C
So how can you put more intelligence into your day to day operation? It can help to look at these three types of intelligence — artificial intelligence, business intelligence and content intelligence — and how they work together.
If you are not a dyed-in-the-wool technologist it can be difficult to understand exactly what artificial intelligence is. It can be helpful to view AI in three layers. The top layer is super intelligence, which we see a lot in Hollywood movies. This is an intelligence so advanced that humanity can’t even comprehend its scope and power … at least until Arnold Schwarzenegger appears. The next layer is general intelligence, where AI will have equal intelligence to humans (think Rosie the Robot maid from “The Jetsons”). The third layer, where we are today, is best characterized as narrow intelligence. This is task-driven AI; a system or service applied to a specific task with the goal of helping amplify and accelerate the human ability to achieve that task.
Branching from these three layers of artificial intelligence are literally hundreds of different techniques, but perhaps the most recognizable is machine learning. This is where computer systems effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.
Machines learn through several techniques:
Reinforcement learning is not unlike training a dog; you reward the dog, or the system in this case, when it does something right.
Supervised learning is like when a teacher shows a student the correct answer and then the student builds understanding from that.
Unsupervised learning, by contrast, is where the AI system tries to figure it out on its own.
Transfer learning, finally, is where the success in a previous framework is applied to a new task.
Business intelligence dates back to the 1860s when the term was coined to describe how banker Sir Henry Furnese gained profit by understanding and acting upon information about the market prior to his competitors. As computer systems evolved in the 1960s through the mid-1980s, the notion of business intelligence re-emerged to become an umbrella term to describe modern business decision-making using digital fact-based support systems. Regardless of the technology used, the ability to collect and react accordingly based on information retrieved is still at the very heart of what we call business Intelligence today. Business intelligence is sometimes used interchangeably with business analytics; in other cases, business analytics is used either more narrowly to refer to advanced data analytics or more broadly to include both BI and advanced analytics.
Business intelligence can involve the use of existing internal information as well as data gathered from outside the organization or received from third parties, enabling the analysis to support both strategic and tactical decision-making processes. Programs can also incorporate different types of advanced analytics, such as data mining, predictive analytics, text mining, and big data analytics. Data typically is stored in a data warehouse or smaller databases that hold subsets of information. Before it’s used in BI applications, raw data from different source systems must be integrated, consolidated and cleansed using data integration and data quality tools to ensure that users are analyzing accurate and consistent information.
To be successful, organizations need to not just collect and protect data, they need to get more intelligence out of the information. These can be very different data worlds. Content management systems organize unstructured data, such as text documents, while business intelligence software usually analyzes structured data, stored in databases. It is the integration of these two data worlds – and the ability to create “content intelligence” – where you will find the greatest benefit to organizational performance.
As a result, the notion of content intelligence is gaining popularity. The idea is to understand everything there is to know about a piece of content, including its context, so you can use that knowledge to guide decision making, and even automate and execute some of those decisions. Content intelligence integrates several existing technologies including machine learning, natural language processing, big data and artificial intelligence. The systems and software work to transform data into actionable insights by providing decision makers with the full context of an individual piece of content.
How can you make the most of these ABC’s of intelligence? Here are some best practices to consider:
Identify gaps in process performance. Start by defining the business process that you are trying to improve. What are the gaps in the performance of that process? What measures and outcomes do you need to achieve? And how can better intelligence-based systems and approaches help to resolve those gaps in performance?
Identify data available to drive innovation. Enterprise organizations work with a lot of data, but not all of it is valuable and useful. Work to reduce information chaos by eliminating redundant, obsolete and trivial data. From there identify what information is valuable and available that can feed and “educate” your systems and techniques.
Identify cross-functional teams to design repeatable strategies. Leading organizations employ a center-of-competency approach with team members and stakeholders from across the enterprise, including business units, IT practitioners and executives. This ensures advancements made in applications can be replicated in other areas of the organization.
Being more intelligent about automation, business intelligence and content — and how they all work together — has become an essential competitive advantage for organizations today. The approach helps leverage information in more thoughtful and profitable ways, reduce operational risks and costs, and identify and fine-tune any number of key business strategies. A well-designed strategy gives executives a more comprehensive view of the organization and its related markets. This provides a strong foundation for business process improvement and redesign, on-the-fly performance management and more agile marketing.
Are you ready to move forward? Look for providers and partners with the right mix of expertise, capabilities and vision that will allow you to make the most intelligent moves for your organization.