Bill Lee, a former business consultant at the now-defunct accounting firm Arthur Andersen, has seen more than his fair share of data analytics companies. Many of them, run by data scientists, propose to help companies manage their businesses with the help of better data. The results can sometimes be disastrous<\/p>\n
Lee gives the example of a university that wants to optimise its teaching staff\u2019s class schedules so that they do not have long breaks between each class. On paper, this sounds beneficial for both the institution and staff. In reality, most teaching staff would resent back-to-back sessions or find it difficult to adhere to a schedule that is too tight.<\/p>\n
\u201c[Successful implementation of data analytics] depends on the art and not the science,\u201d says Lee. \u201cWhen science produces the output, how do we use the output and implement it in such a way to help the organisation progress and not panic?\u201d<\/p>\n
At Azendian Solutions, Lee aims to do just that. The data analytics start-up, where Lee is regional managing partner, aims to marry the hard science of data analytics with the art of implementing a model that will help drive a company\u2019s success.<\/p>\n
\u201cOrganisations are made of human beings. [Data] models are perfect because of mathematics \u2014 one plus one will always be equal to two. However, human beings are not perfect. I cannot measure a day and say a person takes only 45 minutes for lunch, goes to the bathroom only three times for not more than a minute and 45 seconds, and takes five minutes and 30 seconds to change a brake pad,\u201d says Lee.<\/p>\n
\u201cIf you follow that tightly, the model will calculate that tightly for you. But three to six months down the road, you\u2019ll see your attrition rate go up because the human cannot take it. Humans cannot follow the hard regime that the model crunches out,\u201d he adds. Lee says it is better to sacrifice up to 15% productivity than to suffer a high attrition rate.<\/p>\n
Nothing new under the sun<\/strong><\/p>\n Lee founded Azendian with a group of friends three years ago, and attributes the company\u2019s success so far to the diversity of experiences and opinions in the group. \u201cEach of us has different strengths and we all rely on each other,\u201d he says.<\/p>\n Azendian is focused on solutions for educational institutions, hospitals, residential estates and companies in the aviation, maritime and transportation industries. One of its most popular products is a smart campus solution that helps universities optimise teaching staff\u2019s hours and predict when a student might need academic intervention, among other predictive solutions.<\/p>\n \u201cWe do not invent new solutions to solve new problems. The solutions we have use new techniques to solve some of these current challenges much more productively and effectively,\u201d says Lee. \u201cWe also do not believe in hiring a whole bunch of engineers to build a product and hoping someone will buy it.\u201d Instead,Azendian looks for partners that want to use data analytics to find solutions to specific challenges. \u201cWe develop a solution together with them after which the alpha or beta version of the product is ready.\u201d<\/p>\n At times, Azendian\u2019s models might show that companies have more than enough manpower for their needs. However, Lee says he typically advises clients not to retrench employees. \u201cIn any system, there is already a natural attrition rate. The advice we always give to our clients is let the natural attrition rate manage the headcount,\u201d he says.<\/p>\n There are also certain lines that Azendian does not cross. While data scientists typically prefer to have as much data as possible, the use of some data to generate models would raise ethical or moral concerns. \u201cIn this part of the world we\u2019re living in, with multi-racial and multi-religious contexts, when you build a model, you have to be sensitive. I always tell my managers and data scientists: What\u2019s legal may not be ethical, so be careful. Your ethical code should be tougher than what [the Personal Data Protection Act] requires you to do, otherwise it might come back and bite you at the end of the day,\u201d says Lee.<\/p>\n \u201cSimple example: We can actually predict staff performance. But when you build a model of employee performance, do you put in race and religion as one of the variables? I don\u2019t think it\u2019s going to be helpful to see race and religion as a predictor of performance, as there\u2019s nothing you can do about it. As a CEO, I would rather not know about it.\u201d<\/p>\n Beyond Singapore<\/strong><\/p>\n Azendian received $4 million in funding from Singapore Technologies Engineering\u2019<\/strong>s corporate venture capital unit, ST Engineering Ventures, in March. The choice to take money from the locally listed engineering firm was a deliberate one, according to Lee.<\/p>\n \u201cWhen I was looking for funding to grow the business, I actually received a better valuation from other organisations. But we decided to go with ST [Engineering] because it was a very good match. They need people with data analytics skills, and data analytics does not live in a vacuum. We need data and data is generated by systems, and they are very good at that. They are strong in building sensor and [Internet of Things] networks, engineering solutions and enterprise systems, all of which use data that I need,\u201d says Lee.<\/p>\n Azendian currently has over 25 customers utilising its data models to optimise organisations. The start-up has offices in Singapore and Kuala Lumpur, Malaysia, and has plans to expand to the rest of Southeast Asia in the next two years. It has identified Thailand, Indonesia, the Philippines and Vietnam as potential markets for expansion.<\/p>\n \u201cWe believe that the rest of Asia holds a lot of promise for us also,\u201d says Lee. \u201cOne of the key reasons we started this company is because we believe that an Asian company can be in a hi-tech area and be successful.\u201d<\/p>\n