Brainstorm for LinkedIn Posts

[pratik-IDWT]

A ticket to brainstorm posts to write for LinkedIn, which will tie back to the presentation I am working on #52

[pratik-IDWT]

1. Data Strategy / Data Team Delivery

Content Type - Nurture or Grow?
What am I trying to achieve: - Highlight how to approach your data strategy or Team?
Who am I trying to appeal to? - Anyone in orgs focused on data strategy

Market -
Message -
Delivery -

Message
How do you set up your data team and strategy for success to deliver the best outcomes for an organisation?

Having them linked to delivering business outcomes or OKRs will keep the data team aligned and focused on the organisation's goals.

From my experience, without this, there is a risk that too much time can be spent on the technical level discussions, which can detract from focusing on how the work can contribute to the organisation's growth.

Don't get me wrong, I love discussing the best way to design a pipeline framework, debate about which Python library to use and how to best model the data for a dashboard which are important. However, seeing these as tools to achieve the business outcomes helps keep the decisions moving and outputs progressing by treating data as a product.

Being outcome focused, encourages a pragmatic approach to technical decision making in the best interests of the organisation. This should avoid getting spending too long on the technical debates. The worst case? It can be changed and pivoted if isn't working.

One example I use when building a data pipeline and model for a dashboard is to keep asking "How is this information being used to inform a stakeholder's decision?" That's what most dashboards are used for and avoids building dashboards as a tick-box exercise and over-complicating the ask.

Keen to hear peoples thoughts on how their data teams are set up for success?

[pratik-IDWT]

2 - Have you considered your data strategy to enable your AI strategy?

With 2024, inevitably each company will be looking to leverage AI and see how it can be applied to their business. However to do this, you need to have a strong maturity of data to unlock and scale this.

Typically any trained or customised LLM, will need a well defined data set to utilise. However if this doesn't exist, with the governance and definitions in place, this will fail to realise benefit.

2. What's the current focus of your data team and what are your biggest challenges?

• Engineering, analytics, self serve, training?

3. Through a bit of fun data work I have built a script, that leverages the FPL API to work out the best team to choose with defined parameters. It uses the XXX package and has provided me the below team

• Reference script is on branch here - https://github.com/In-Data-We-Trust/IDWT-All-Things-SQL-And-Python-And-Code/blob/FPL-Model-initial-historical/Python_Reference/fpl_python_text.ipynb - needs to be merged still

4

[pratik-IDWT]

Some more to focus on from the inbound generation project:

• Your process and how you help clients
• The problems you solve
• Who you help
• Pre-handling objections
• And case studies (see below)