The business language we all “seem” to speak and understand

Our society relies on science, in particular, the science of mathematics as this is a uniform language.

In the 1960’s the buzzword Business Intelligence was born defined as a system of sharing information across organizations. In the last decades, this has evolved into complex computer models providing insights to support decision-making up to the extend of making the decision for you.

Today everything is “data-driven” and every important business decision to be made is questioned with “Where is this based upon?”. Then the discussion breaks loose about the origin of the data and its context. If the first part of the discussion (the origin) is clarified one tends to jump directly into the rational decision-making model. Very few might argue: But what about the context of the data?

The reason why this question is rare and often avoided is that it changes the uniform language in a language of ambiguity and uncertainty due to the lack of contextual understanding.

Result? A bias for informed decision-making!

Men vs. Machine struggle

In the last decades, the primary focus of many enterprises has been on leveraging competitive advantages by gaining business insights out of numerical-data in terms of reports up to predictive analytics.

However, the non-numerical side of data (documents, pictures, diagrams, videos, audio e.g.) known under Enterprise Content Management (ECM) and the success ratio of companies that manage to gain full business advantages from it, is relatively low compared to what companies achieve with numerical data insights. In large, it is due to people preferring numbers, as they are absolute and less relative than non-numerical data.

Many ECM projects fail because many business leaders try to resolve business issues with technology, overvalue it, and forget about the surroundings like – people, processes, and governance – which are the critical success factors. It’s like an incompetent physician prescribing pills to kill the symptoms without a treatment plan to resolve the root cause.

The root cause of failing to gain business value (e.g. operational efficiency) from unstructured content is because the majority of business professionals still try to resolve a problem they can’t resolve with their human capabilities. Secondly, we are focusing on the wrong problem. Both have to do with the human tendencies of gaining control.

The underlying problem in gaining business value from unstructured content is that we are trying to orchestrate content by introducing processes and procedures to get to this so-called “single view” of information, which gives us an ultimate state of control and position to base our business decisions upon. However, achieving this single view with humans following processes and procedures is a utopia, as people are simply not good at executing such repetitive tasks for a long time, while not seeing a direct benefit of the very tasks. Secondly, as the information flows and systems are also dynamic, the processes and procedures require continuous adaptation to serve their purpose (Single View utopia).

Administrative data clerk jobs

Due to the significantly increased amount of available enterprise content and the by humans introduced processes & procedures to manage this content, many business roles have been degraded from empowered staff to administrative data clerk jobs. According to a study from McKinsey (2012) employees spend close to 20% of their time finding information to do their job, not even taking into account the effort spent on managing the content.

This leads to major business inefficiencies and secondly as important a negative impact on employee satisfaction.

The good news is, we as human begins do not have to resolve this problem by ourselves, but let computers do it for us by adopting Artificial Intelligence (AI). Instead of employing more administrative data clerks, business leaders should focus on the adaptation of AI throughout the organization, manage the business change and empower employees with AI capabilities.

Artificial Intelligence and contextual information

One of the applications where AI can support staff in better decision-making and increase operational efficiency is by automatically providing contextual information that is relevant in a certain business scenario.

For example, a maintenance engineer at an offshore wind farm stumbles across a malfunctioning gearbox of a windmill and needs to conduct further analysis onsite. During the analysis process, the engineer is supported by an intelligent content platform providing him with contextual information related to the gearbox its maintenance history, real-time data of the rotation speed and safety instructions. This holistic asset view helps the engineer conducting his analysis efficiently and safely.

Such an application of AI is Intelligence Augmentation (IA) as its aiding human decision-making rather than replacing the human cognitive functions including decision making. Although this application has a strong AI component as it mines through a range of enterprise information systems processing and interpreting millions of content items in seconds and generates this augmented content that supports the engineer and defines what is relevant for that very business scenario.

Bridging the gap between ECM and BI

Before one considers AI’s in a business context, taking over full human cognitive functions and complex business decision making, one should start meeting the pre-conditions. This brings us back to the bias of informed decision-making by lacking contextual information. If you cannot overcome this information management challenge and lack a solid ground for human-based contextual decision-making, how can you outsource the decision-making to an AI?

To improve informed decision making, and making your business AI-ready, the ECM & BI capabilities of an organization need to be aligned in an overarching Enterprise Information Management Architecture where informed decision making should take a central position. AI solutions should be considered to help create the context by structuring unstructured data residing in ECM and BI native source systems.

Results from a study (IDC 2017) conducted under Technology Influencers and decision-makers about ECM show that 60% of the respondents indicate that they are currently using or planning to implement cognitive and AI technologies to manage unstructured content in their organization.

To summarize, data is useless without context. As the volumes of data and content are increasing significantly the challenge of making informed decisions based on data by placing it in a relevant context is as compelling as ever. Organizations should make use of technology like AI to manage content and data, and Intelligence Augmentation applications to enable informed decision making.

Can we help your organization improve decision-making?

Would you like to learn how we can help your organization improving decision making and increase operational efficiency, by letting AI orchestrate your data? Contact us directly. Would you like to have a discussion with us on this subject? Please, leave your comments and questions below.