Algorithms – procedures or formulae for solving problems using data – have, through the growth of the internet, become a key part of our lives. Some would even say we are today living in an algorithmic economy; such is the incredible impact that they have.
Yet the basic fact remains: we cannot trust everything to an algorithm. Think about it: would you be comfortable if a formula decided your weekend plans for you? Could it really gauge your mood on the day and adjust the plans accordingly? Realistically, for now at least, we must still depend on our brains to make decisions.
However, if used correctly, algorithms can assist us greatly by informing fact-based and consistent decision-making.
Firstly, they are fast. This speed allows them to process very large volumes of data extremely quickly. Decisions, therefore, can be made within seconds or even milliseconds. It would be nice if our brains did work that fast, but realistically our human analytic processes simply cannot compete on speed.
Secondly, algorithms evaluate only data, i.e. the facts that have already been collected and stored in systems. Thus, any algorithmic decision is based solely on the facts alone. Subjectivity and gut feeling make human decision-making non-fact based and error-prone, which is not always the best place to start.
Thirdly, algorithms are a truly consistent form of decision-making. Presented with the same facts, algorithms will make the same decisions time and time again. Humans, on the other hand, are likely to make different decisions.
If an organisation wishes to achieve efficiencies and scale across large sales operations, standardizing the decision-making process could be the answer.
Sometimes, it is argued that we should turn every employee into a data analyst. By that, we mean empowering them to perform data analysis themselves, with the help of self-service tools.
With the algorithmic economy, however, the algorithms themselves can evaluate the facts and the present them to the employee objectively, thus mitigating the need for a plethora of human analysts.
The employee can then use this analysis to make the decisions quickly, by taking the facts and connecting them to the context, environment and goals. This sort of 'meta analysis' is a better use of the employee’s time; it speeds up the process while still allowing the employee the final say on the decision.
This approach is well captured in a recent Bloomberg BusinessWeek interview with Lyor Cohen. Lyor is arguably the most successful record company executive in the USA and in 2013 he spoke to a group of students about his approach to finding new talent.
Essentially, his job involved a great deal of monitoring radio stations, listening for the next big thing to sign to his label. When asked about the future, he explained that he is keen to use the internet to do this more quickly and efficiently.
However, he acknowledged that follow traffic is not enough on its own. Turning to the students, he stated: 'All you smart people, you could come up with an algorithm, but somebody still has to show up and say, ‘Yeah, I feel that.’'
The story shows that emotion and human feelings – 'feeling that' – is a key part of the decision-making process when music is concerned. In fact, it is important in other aspects of business too where decisions are required. We need the best facts possible delivered as quickly as possible to help us make good decisions.
The time of employees is therefore best spent by doing 'meta analysis', not by trying to compete with the processing and analytical skill of algorithms. It is a good lesson to learn when we think about the analytics of the future and how it will be effectively delivered to truly transform businesses.
Sourced from Dr Rado Kotorov, VP product marketing and chief innovation officer, Information Builders