The Ultimate Cheat Sheet on project selection

Modern businesses recognise the importance of driving innovation and continuous improvement through team-based projects. This does not mean, however, that organisations have gotten smarter at selecting the correct type of projects. In this cheat sheet, I list four mistakes that are commonly made, but easy to avoid.

Selecting problems that no one really cares about

It’s tragic, but teams, departments and business units still choose projects that nobody cares much about. Often, it’s because the scope of the perceived problem appears too minute, or the potential project might not be critical to business objectives. Whatever the case might be, a neglected project suffers from corporate inattention, and the team involved in it will wilt.

Selecting a solution to implement rather than a problem to investigate

Nothing alarms me more than facilitating a team of employees who, instead of investigating the root causes of a problem, have already determined the solution. I find this fairly typical in projects led either by newly-minted IT Professionals or employees who have grown too close to the process.

In this instance, a team of managers working at a college admissions office might describe their project with an ambitious title like: Automate the fee payment process with an online application. The title is already a warning sign that danger lurks ahead. By enlisting words like automate and online application, the team has already made up its mind on the solution. No problem was investigated.

There are very little exceptions to the rule. Projects should always be described from a process standpoint. The team from the college admissions office would do their project far greater justice by describing it as an effort to Reduce the turnaround time for students paying school fees instead.

Selecting a process that is in transition

Businesses nowadays face lots of turbulent change, either in technology or in regulatory compliance. Inviting teams to improve a process or function that is going to be changed soon will only waste company resources. There is no point investigating a manual leave application process, for example, when some other team is independently migrating it to an Enterprise Resource Planning (ERP) system. The work being done by the first team would quickly become redundant.

Selecting a system to study, rather than a process

A common error committed by new teams is to be too ambitious for their own good. Instead of breaking a project up into manageable bits with short durations and clear targets, they attempt to take on an entire system or framework of business functions at once. Leave such high-order improvements to management teams or portfolio managers. Ongoing work improvement teams have a better chance of success if they focus on a smaller process to study and subsequently improve. Realistic timelines and targets make for more motivated teams.

Are there particular types of projects that you consistently avoid? Do share by commenting on this post!

Analytics can save the lives of people who ignore speed limits

Data are raw facts that describe the activities that we do, and the state of the world that we live in. The volume of data that accompanies us every single moment is truly massive. Thankfully, most data remains hidden; collected stealthily by the devices we use and through the transactions we make. A simple purchase of a carton of milk from a petrol station might trigger a series of questions that businesses will find particularly useful. For example:

  • What brand of milk did I buy?
  • Which petrol station did I buy it from?
  • What time did I make the purchase?
  • Did I also top-up my car with fuel?

These questions are answered by sophisticated machines that store and analyse our data, relentlessly drawing value and meaning from text, numbers and pictures. Without machines and their software algorithms, analytics on a large scale would be impossible. Machines help us tell important and interesting stories about our data. They create insights that businesses can learn from to create better products, better services, even better work processes.

Yet, the passage of data from its starting point to insight is not so straightforward, marching through the four stages of Data, Information, Knowledge and Insight. 1

Show 1 footnote

  1. Partly adapted from Hector Cuesta, Practical Data Analysis (PACKT Publishing)