I agree with this post so much. Getting a seat at the table is more important than choosing Tableau vs Looker vs PowerBI, or Snowflake vs BigQuery. My experience speaking with over 200 leaders over the past 18 months is that only about 20% get it. Those who get it, generally grew up on the business side (or at McKinsey), and generally report into a business function (vs CIO/IT).
From my experience business leaders want something quick and dirty that they can keep changing whenever they want without anyone blocking them.
When data teams come into play, they are not into quick and dirty because they respect the infrastructure and want to create something scalable. This translates into more time needed for the final solution. Also since they are the gatekeepers of the data they will raise some blockers (e.g this logic is risky because there is a lot of manual input).
Business translate the above as “data team is slow and doesn’t give us flexibility”. So I believe it is key to have someone with technical mindset (Head of data/product) to be there and confirm that “yes data team will need more time but will build something solid that will have built in control mechanisms to prevent/spot failures early on”.
And by the way I agree with the reporting line. Teams reporting to business functions tend to get more visibility.
I work at a social impact organisation in India and am an IC here. I strongly resonated with every word of the problem definition. Will strategise this in our plans.
You are bang on the budget at disposal with a clear ROI. The decision makers and those using these data tools are unaware of the hidden leakage of those budgets on duplication of data processing that comes with multi-tool mayhem. The costs can be brought down deeply if the business wants to truly make money with the data sitting across systems. Data teams can get a seat at the table provided they are able to show that impact and maturity with their data decisions and what they choose to see at the end of the tunnel.
This table looks extremely weird to me as a Head of Data from Eastern Europe and working for a British Fintech.
We spend 6 figures for Snowflake. But salaries are extremely different.
100% it could be very different in terms of the total spent on salaries. If it were FAANG level salaries it'd be even higher.
I agree with this post so much. Getting a seat at the table is more important than choosing Tableau vs Looker vs PowerBI, or Snowflake vs BigQuery. My experience speaking with over 200 leaders over the past 18 months is that only about 20% get it. Those who get it, generally grew up on the business side (or at McKinsey), and generally report into a business function (vs CIO/IT).
I wouldn't be surprised if that number is accurate.
From my experience business leaders want something quick and dirty that they can keep changing whenever they want without anyone blocking them.
When data teams come into play, they are not into quick and dirty because they respect the infrastructure and want to create something scalable. This translates into more time needed for the final solution. Also since they are the gatekeepers of the data they will raise some blockers (e.g this logic is risky because there is a lot of manual input).
Business translate the above as “data team is slow and doesn’t give us flexibility”. So I believe it is key to have someone with technical mindset (Head of data/product) to be there and confirm that “yes data team will need more time but will build something solid that will have built in control mechanisms to prevent/spot failures early on”.
And by the way I agree with the reporting line. Teams reporting to business functions tend to get more visibility.
interesting read!
I work at a social impact organisation in India and am an IC here. I strongly resonated with every word of the problem definition. Will strategise this in our plans.
Amazing article!
You are bang on the budget at disposal with a clear ROI. The decision makers and those using these data tools are unaware of the hidden leakage of those budgets on duplication of data processing that comes with multi-tool mayhem. The costs can be brought down deeply if the business wants to truly make money with the data sitting across systems. Data teams can get a seat at the table provided they are able to show that impact and maturity with their data decisions and what they choose to see at the end of the tunnel.