With the ever-increasing uncertainty and complexity in running a modern business, making decisions, calibrating to the market and taking action is an ever ongoing process. Data is the crucial element that fuels this decision-making. However, the data needs to be contextual to the specific business issues being resolved, relevant to assisting in the decision-making process or this just becomes more noise and confusion in the system.

Data literacy is a given for many business professionals to stay relevant in the market. With the capability and connected context of todays’ world, we have more than enough access to data, but does this data support people to make decisions? Thus, the market responded with the creation of a system to support decision-making, today disguised as Business Intelligence, (Modern) Data Warehouse, Data Lakes, lots of three letter acronyms and, of course, AI. Oddly enough, the term Decision Support Systems goes back to the 1950s and 1960s.

However, in the data deluge age of the 21st century, people are literally drenched in more data than we can analyse. We may wonder, do we make true and proper use of this data and do systems nowadays truly support data-driven decision-making?

Excuse me, a DSS?

A Decision Support System (DSS) is an information system that provides users with relevant information to assist them in making well-informed decisions. A DSS is built on the following principles:

  • Collection of and efficient access to the identified raw data;
  • Combined and aggregated in the right way;
  • Presented within the right context.

A transactional system, such as an ERP system, has very little to do with a DSS. A transactional system would fulfil a customers’ order whilst applying process controls and the system does this without any contemplation of the much bigger decisions. It records business transactions and gathers data, both necessary for a company’s decision-making. A DSS, however, supports the questions relating to the total orders, revenue, profit, region, and various dimensions of analysing the effectiveness of the business to serve the customer. By using a DSS, a business can constantly iterate, make decisions and support the business in moving forward.

Context and relevance

As more data literate people join the workforce, a self-service DSS becomes viable. Data without context and meaning, on the other hand, is useless for making decisions. Assume it is 21 degrees Celsius in London in January. That is highly unlikely. However, if you are unfamiliar with the concept of London winters, you will not recognize that the data is either incorrect or that there is a significant weather problem. Hence, your data needs context: apply this to more specific terms like Days’ Supply, Aged Inventory, On Time In Full, Debtor’s Days and so on. Knowing the context of the data is important. Time sensitivity and location are typically contextual markers.

Capturing what the user requires in order to make decisions is beneficial. Users are usually not concerned with the technical platform. This pleads for a solution that easily collects the user requirements and identifies how these relate to underlying systems that capture the data. From user requirements to sourcing data, and how this is managed and deployed in a single system, is part of the opportunity. When building a DSS, a company would want to build a system in the background that presents the data in a way that users understand it. Depending on the context, the information should assist the user to ask more questions, uncover trends and, ultimately, make decisions.

DSS: are we there yet?

Technology has helped people make decisions quicker and today we use tools in our everyday life that demonstrate this. Currently, more than ever, there is a value attributed to data. Data is like any other tool, it can be used to add, manipulate or destroy value. In line with the data explosion, there have been significant advances in technology to build a DSS, however there is more tooling than what humans can use. This tooling needs to incorporate methods and processes to support users in leveraging this data and support the building of a data culture in the business. These methods and processes include metrics that review usage and link how these support decisions.

Ultimately, the goal is to support decision-making rather than to generate reports or a data warehouse. Technology does not change a business, people do. With all this change and growth in our technical capabilities, we are not there yet: a DSS must be more business-oriented and focused on business change through the effective use of data for decision-making.

Read more about data and decision-making

Are you curious about how other companies make data-driven decisions and how their systems support them? Read about BAM Construction and Engineering’s integration of massive volumes of data and data sources to estimate asset maintenance, or MPS Systems’ use of sensors to help their customers get the most out of their machines.

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