How can you choose the best data analytics platform for your company?
By: Alina Gherman
Tags: Platform, Choices, Data Analytics, Data Analytics Tools

How can you choose the best data analytics platform for your company?

There are so many data analytics platforms that choosing the right one can be a long process, but how can you start actually doing it?

The first thing to do while choosing your data analytics platform is to choose the criteria of choice to be able to compare them.

This is a very important step since the criteria of choice will determine the final choice. Here is what you definitely need to take into account while building your own criteria of choice:

Known skills, tools, company strategy

Needed functionalities for each type of user: data analyst, data scientist, ops …

Price

Known skills, tools, company strategy

Based on the known skills at your company, the user-friendly criteria and the wanted functionalities will have a different meaning.

Source: memegenerator.net— Internet Grandma Surprise

For a data scientist that is used to code, having a tool to drag and drop things can remove his ability to deliver since he may not be able to build libraries, version code, and come back to an older version of the project or have to learn the tool.

However, for data scientists that don’t have coding abilities, having a tool to guide them might be really helpful.

The known tools at your company will influence the connection that you will search for: cloud connection, database connections…

The strategy of your company might guide you to a certain type of tool and bring new choice criteria: SaaS, cloud-based, open-source…

Also, based on the strategy company you might have one tool for the whole company or multiple analytics tools for each small need/ team. All this will impact the wanted functionalities for each type of user

Needed functionalities for each type of user: data analyst, data scientist, ops …

Having the real needed functionalities is not an easy task since usually, the users don’t really know what is possible, what they need and they are always trying to assure themselves that the product will work for them in several years as well.

Here you can find just some functionalities topics that you should definitely tackle:

Connections functionalities: flat files, database connections, cloud connectors ...

Data preparation functionalities: flag, join, order, and so on

Data science functionalities: scripts but also built-in functionalities

Data visualization functionalities and connection to data visualization tools

Data storage functionalities: real-time, batch …

Security functionalities: authentification type, audit functionalities, …

Compliance functionalities: locking data, data traçability, data retention …

Product roadmap

I think that is one of the hardest things to evaluate. A great example that shows how hard it can be is the CentOS 8 End Of Life that was abruptly been shifted from 2029 to early 2021 in December 2020. This is something that needs a lot of technological watches to be predicted and that we can easily miss.

Source: makeameme.org— Roadmap: pray I do not alter it any further

You need however to take this into account. Choosing a tool that is great today for the current need but that is obsolete tomorrow is definitely not a good choice. You need to inform yourself about the product roadmap to check if they concur with your company strategy.

Price

Each data analytics product has its own price strategy: prices for sessions, number of users, types of users, size of the machine, functionalities, you name it!

Of course, you can also have open source products that don’t have prices for licenses but that doesn’t mean they are free. You have server costs, maintenance costs, and so on.

Source memegenerator.net— Product Price

Since you cannot compare them always directly, because there are so many different parameters, the “easy way” to compare them is to check how much each tool would cost you for your given use case.

Comparing platforms based on chosen criteria

Based on those carefully chosen criteria we should be able to compare multiple platforms. However, comparing them means choosing what platforms to compare.

If you think this is an easy task just take a look at the Data & AI Landscape

Source https://mattturck.com/data2020/ — Data & AI Landscape 2020

For this task, the only thing I found reliable until now is to:

to check the Gartner Magic Quadrant

to check the Data & AI Landscape

do more technology watch on the subject

speak with experts in the fields: people and companies that already have done this choice.

All this will give you the right knowledge to be able to choose what tools you should compare. If it doesn’t, it means you haven’t done enough technology watch, so just iterate more

Involved team & possible bias

Source imgflip.com — Confirmation bias at its finest

Choosing a tool for a company is not an easy task because multiples bias can intervene and it is hard to overcome all of this:

Confirmation Bias: when we are ignoring information that challenges our preexisting beliefs

Conjunction fallacy: when we are basing more on feelings than evidence

Outcome bias: when we are ignoring some decision process and overfocus on outcomes. For example, we can focus on the fact that we will have the full stack of a product and ignoring some wanted features

One way that I found can help on overcoming this bias is by trying to have feedback for multiple people, to have a feedback loop.

The final hard decision to take

Now that we have the criteria of choice and the tools that we want to compare the comparison process can finally begin!

Source https://knowyourmeme.com/photos/1364668-mary-sue: Star Wars character comparison | Mary Sue

It would be great to be able to make the best choice ever by just following the steps. However, I think it is just impossible, we can only make the best possible choice given the known facts and known biases.

However, not making the comparison will not bring us the arguments to overcome the bias and would mean basing ourselves on luck and not hard work.