Everyone Loves Some Data But…

The million dollar question is - what exactly do you want to get out of the data?

Everyone has been talking about data for a good decade or so and depending on your level of data maturity, you are either still trying to find where are all of your data sources are located or you are now trying to monetize the insights gathered from your data.

Woe to you if you’re in the former bucket but no surprise many organizations, especially non digital native ones are still sadly in this bucket. Wow to you if you’re in the latter bucket, so what can you do to monetize it?

Customer data platforms, data management platforms and customer relationship management platforms suddenly became the talk of town thanks to Google’s flippant stance on third party cookies, that kept rolling back and back. Companies realized their archaic customer data collection methods and storage methods (often just in excel spreadsheets (horrors!)) are not quite cutting it.

Some are even confusing the whole customer data terminology and what it means when we talk about cookies, first party data, third party data and personal information level data. Some have all but sitting in silos or disconnected platforms that don’t talk to each other while others have none (more horrors!).

Some used to think a good data visualization and analytical tool is the holy grail to get all the answers they need by simply plugging it onto of their so-called data sources. But they soon wonder - how to plug, what to plug, where to plug and why can’t it just be plugged and played?!

Things like:

• is the data clean, updated or accurate?
• is the data in the format that is even retrievable., extractable or readable?
• do you even have the data sitting where you thought is sitting?
• is your data even categorized in the logic, classification and format that is aligned with your decision-making algorithms?
• million dollar question - what exactly do you want to get out of the data? What is the truth that you’re after?

If these were not considered before your so-called plug and play approach, then you get a ton of data yes and a ton of outputs yet but hardly any useful insights. You get more of what we call, data outputs in a format that looks like you just downloaded a gigantic excel spreadsheet or a bunch of fancy looking graphs to make you feel good about some visually appealing data formatted in a presentable manner

E.g. you might see things like:

• xx customer transactions performed over xx period
• xx customer spent over xx period

That is still not data insights, it’s just data outputs telling you how many transactions and spent over a certain period of time. What are you going to do with that without other insights around:

• who are these customers in terms of their interests and life stage needs and what is the co-relation between this and what they are spending versus not spending on?
• what did they exactly spend on and why that might be the case?
• what are their other needs and what is the possibility for that?
• what else have they spent on and why that might be the case?
• are they spending more or less on the same products/period and why that might be the case?

The difference as you can see is in terms of the why and the co-relation between the transactional data and the rationale behind it.

We first need to know what it is that we want to see and how that will help us to better understand our customers’ behavior or potential to engage more with us. It helps to have these in mind, and then work backwards to derive what we then need to have in terms of data types and sources in order to arrive at the desired insights.

It’s equivalent to knowing what is that treasure you’re seeking for so you know which location, treasure map, equipment, skills, knowledge and coordinates to get there.

So, do you know the treasure you’re after?