Where organizations automate decisions

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This article is contributed by Hassan Lâasri, consultant in data strategy, data governance and data activation.

Decisions are at the heart of any organization, whether it’s a Fortune 100 company, a start-up, or a government agency. In this article, we present the different tools that companies use to support or automate their decisions. To explore these tools, let’s start by asking where decisions are made. We will see that organizations are constantly making decisions. Some are high stakes and therefore not only visible to the organization, but also beyond, by customers, partners and competitors. Others are so routine that they become too trusted and go unnoticed by the people who pick them up every day.

Where decisions are made and when they need to be automated

In any organization there are three overlapping activities. Operations to run the business, projects to prepare for the future and decisions to optimize both operations and projects. In other words, decisions are made at all levels of the organization, from the CEO who manages the entire organization to the engineer who operates a machine. To make exploration easy, we divide the organization’s decisions into strategic, tactical and operational decisions.

Strategic decisions

By strategic decisions, we mean the set of action plans or policies aimed at achieving major or global goals. Strategic decisions are transformational in nature. Examples are a merger or acquisition, an IPO or an investment in a completely new product, such as a vaccine.

Until now and for the foreseeable future, strategic decisions cannot be fully automated. Every situation is unique and requires creative thinking that goes beyond AI. But this does not mean that the tools are missing in strategic decisions.

For decades, industries and businesses have relied on Excel spreadsheets and business intelligence (BI) tools. And with the advent of big data and machine learning, strategic decisions are now increasingly integrating data about the national economy, customers, competitors and distributors into sales forecasting and business planning.

Indeed, there are cases where organizations use sophisticated methods to support strategic decisions. In the energy, oil, gas and construction sectors, the major majors have used real options for decades to decide whether to invest in a new oil or gas field or build a bridge, much like those big investment banks do. when buying options on complex financial products on the stock exchange.

Tactical decisions

For the purposes of this document, tactical decisions means the series of actions planned and taken to achieve a particular objective. They are like strategic decisions, but have a limited scope and horizon.

Until recently, it was the norm in organizations to use the same technologies as for strategic decisions, i.e. analytical, optimization and statistical tools. But with the era of cloud computing and big data, organizations are increasingly turning to machine learning. Examples from the consumer goods sector include optimizing a brand’s advertising budget for TV, outdoor, radio, print and digital; forecasting sales of a new product in each region where the product will first be introduced; or the personalization of coupons to help customers save money, increase brand sales and increase merchant traffic.

Most of the problems organizations solve with machine learning are in fact decision problems. The need to predict is to decide, direct an action or perfect a system. In short, forecasting is a means, not an end.

Expert decisions

As the name suggests, expert decisions are made by those professionals who use their background knowledge, developed expertise and tailored heuristics to arrive at a decision.

Automating expert decisions was all the rage in the 80s and 90s. Knowledge-based and expert systems were what machine learning is today: the subject of many conferences, magazines and books. Developers, then known as knowledge engineers, interviewed subject matter experts to find out how they made decisions.

In a knowledge-based or expert system, decisions are coded in the form of condition decision rules. An inference engine checks all the conditions and fires the corresponding decisions. The inference engine also relies on heuristics that the subject matter expert has honed over the years, if not decades.

Contrary to popular belief, knowledge-based and expert systems solved too many problems where knowledge could be easily modelled, such as correlating alarms in a telecommunications network, configuring an electronic product, or troubleshooting equipment failure.

Today you don’t hear much about knowledge-based and expert systems, but about business rules, a modern approach to decision-making that doesn’t require expert interviews, but gives experts the tools to manage their own decisions, with no prior knowledge of a rules programming language.

operational decisions

Operational decisions are decisions that companies in the thousands and sometimes millions make in a single day. Financial service providers and insurance companies are typical examples of organizations where operational decisions are at the heart of the business. In every product they offer, there are cascading sets of terms and conditions, legal restrictions, eligibility criteria, and risk levels that must be checked before making a decision.

Operational decisions seem like expert decisions, but they are different. Operational decisions are often prescriptive in the sense that they implement industry regulations, internal policies or corporate strategies, regardless of the beliefs, expertise or preferences of those executing the decisions. Think of a loan officer at a bank who decides whether or not to lend a borrower based on his or her repayment history, or an insurance agent who calculates the premium an applicant must pay based on his or her health and medical treatments.

In contrast, expert decisions are often descriptive in the sense that they implement how managers or experts make choices between alternatives based on available information and expertise. Think of a doctor in a hospital deciding treatment after a diagnosis, or a trader who uses market data and a predictive model to buy a highly volatile asset.

To automate operational decisions, organizations quickly switched to decision management systems. The terminology of decision management systems hides a multitude of technologies. The simplest are decision tables, trees, and graphs. The most advanced combine rules and predictive models.

People always informed

Interestingly, regardless of the method, it requires a certain amount of human work. Regardless of the level of sophistication of the tool and the degree of automation it allows, at one or more moments a human must intervene. Either to define the problem, refine the parameters or confirm the solution.

Mathematical optimization requires practitioners to map and structure the problem into parameters, constants, and constraints; select and run an algorithm on this data; often the algorithm will never converge, in which case they will have to loosen some constraints and repeat the optimization process over and over until they find an acceptable solution to the original problem.

Machine learning requires them to break data down into training, validation, and testing data; select a model and refine hyperparameters; run the model and repeat until they are satisfied with the results. And once the model is in production, they need to monitor its performance because the new data may differ from historical data on which the model was built.

In knowledge-based systems, they have to extract domain knowledge from experts to encode it into rules; because the world is never easy to catch in one step, they often have to change rules or add new ones. Once the number and heterogeneity of the rules exceed a certain level of complexity, there is no choice but to organize the rules into manageable knowledge sources.

In decision management systems, they must monitor the impact of individual decisions on global business performance. A small mistake in a unique decision can have major consequences in terms of brand image, loss of revenue and even legal pursuits. Modern tools come with dashboards of real-time decision analytics to help users monitor and change decisions as the new situation demands.

Summary: When to Automate Decisions?

Since the early days of the computer, organizations have used decision support and decision automation systems to better manage their strategic, tactical, expert and operational decisions. Currently, big data and machine learning are covered too much in the press, media and social networks, but they are not the only decision-making elements within organizations. Instead of one method, organizations use a coordinated collection of data, systems, tools and techniques.

Until now, there is no uniform decision-making method that fits the puzzle of decision-making in organizations. For strategic and tactical decisions, practitioners continue to rely on optimization and statistics, but with the explosion of data, they are integrating more machine learning techniques. For expert decisions and operational decisions, they use decision trees, decision graphs, rules and machine learning.

Organizations use decision support tools for strategic and tactical decisions and decision automation tools, mainly for expert decisions and operational decisions. Regardless of the category of decision support tool or decision automation, people are always aware to configure, monitor and fine-tune the tool. Seeing a do-it-all algorithm isn’t ready to become a reality anytime soon.

Hassan Lâasri is a data strategy consultant and now leads marketing for Sparkling Logic, a decision management company.

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This post Where organizations automate decisions

was original published at “https://venturebeat.com/2022/03/18/where-organizations-are-automating-decisions/”