Avoid tech discrimination to make data driven decisions

Having all options on table

One good afternoon some of us were discussing about different database choices for a problem at hand and which one should we evaluate next.

Coming down to 2 choices, let say DB_X and DB_Y one of us mentioned that DB_X is only 4–5 years old while DB_Y is 14 years old hence DB_Y might have better chance here. This one statement coined the concept of Technology discrimination and how it’s impacting our decision to decide on tools/technology. This blog is my understanding of what all differentiators clouds our judgement in decision making.

Basis of discrimination

1. Age: As I mentioned in previous part, older tech definitely gets preferred over recent one, One of the main reason for that is because it has evolved over a period of time to support more use cases in industry. I agree new tech might not be suited for so many use cases but can be better suited for one particular use case at table right now.
2. Creator : Company behind the innovation is defenitely one big factor for tech to be popular. For example, if messaging framework gets developed by Google will be received differently than the one which is not so popular in industry. I am not a big Google fan here, so I would say developed by Netflix vs any other company.
3. Community Support: Before we decide to use even a small library we consider a community support for it, which is a very important aspect to consider. Software engineers being social beings would like to ensure that in case of problem and crisis we are not alone, if something is wrong with our choice how many more people will suffer with it so we will have solution coming out at faster.
4. Marketing : Technology becomes pretty famous and reliable if there are conferences happening around it. Like Redis is still very famous in industry over Hazelcast because of conferences and marketing around it.
5. Prior Experiences: People tend to prefer technologies they are familiar with or have previous experience using it. This bias can lead to the selection of technologies based on personal comfort rather than objective evaluation of suitability for the project. If feel this is one of the most important factor over anything else to reduce learning curve and knowing the risks and unknowns of the choice.
6. Hype Bias: Something branded as cool to use or suddenly industry is talking about it. Again as social people we don’t want to be tagged as outdated so consider using the framework which big giants are using even if the complexity at hand does not really needs it. May be the problem can be solved by simple in house class rather than understanding/learning new framework here.
7. People bias: Favouritism and credibility pays big role in influencing the decision. See this happening a lot where suggestion coming from one particular big shot is treated with higher weightage than someone who is fairly new. People will also have biases and preferences over their ideas and choices over others.

Though I mentioned these factors considering the angle to tech choices but so many of them applies to design choices or any other choices we make everyday during software development.

If there is discrimination and biases, there will be victims as well.

Victims

• Technology: I think the tech which was not selected missed the opportunity to support one more use case and an opportunity to get some popularity in industry.
• Product: The product which could have been better if biases were kept aside, even the future product with similar use cases.
• People: People miss the opportunity to learn real reasons behind choices.

Taking neutral stand

It’s very hard to disassociate any preference to any idea, specially if it has come from you or from your people you trust a lot in that area.

It’s not a bad idea to consider biases in decision making but it’s important to be conscious about their influence on choices.

For 1.5 years I have been working with the practice of keeping all the options on table, look at them with same lens/criteria and come up with pros and cons of each of them and weightage associated with it. Doing a mental check about what all factors influencing decisions and are these the right ones. PUGH matrix is one of great tool to do this exercise.

It’s a wonderful exercise as it expands thinking when you look for more data points to come up with pros and cons and keeps the feelings/biases aside.

These approach helps me in up-skilling people I work with, because experience of a software engineer here does not matter, its just data driven decisions. People get clear understanding why certain choices are made and where else these decisions can be leveraged in future.

I am becoming better and better in taking neutral stand and helping other team members to see a value with this approach. We do consider some of these biases mentioned above as criteria as applicable but keep a mental check till what level its influencing us.

Just to end my blog my current company Thoughtworks and Thoughtworkers are famous for its tech huddles and if everyone in the huddle takes neutral stand and just evaluate ideas based on pros and cons, discussion becomes so beautiful and fulfilling and all of us learn something out of it because of urge to get agreed on specific idea is not there and intent is:

“ LET THE BEST ONE WIN FOR SITUATION AT HAND”

Mention in the comment if you see any other factors and biases which stops making data driven decisions.