# data[].attributes.body
a)In which key areas should we set benchmarks for (e.g. claims/disputes, enforcement, education, service standards)? Are we already tracking them internally?

b)In such key areas, which local/foreign organization would be the best-in-class? Are they operating in similar environments with similar context? Do they have similar enforcement tools and technological capabilities?

c)In such key areas, what are the indicators and rates to set to measure ourselves against?

# data[].attributes.body
a)What types of data do we have and what are the data gaps we face for data reporting and effective sensemaking?

b)What processes, systems and capabilities should we put in place to support strengthened sensemaking?

c)How can we effectively and safely share data across MOM-TAL departments?

# data[].attributes.body
a)What are the current key gaps and painpoints of customers?

b)How can we better support customers who face difficulties navigating our processes?

c)What capabilities (e.g. digital innovations) do we need to enhance customer engagement, streamline service delivery, and create a more intuitive and responsive customer journey?

# data[].attributes.body
a)How can we develop a cohesive strategic comms strategy to nudge employers towards workplace harmony?

b)How can we leverage comms to improve compliance and promote progressive practices? What new channels or approaches should we explore/ engage more to reach stakeholders?

c)How can we better shape and turn around our defensive narratives e.g. in the Employment Standards Report, given increasing media and public scrutiny and against a backdrop of rising claims and high-profile retrenchment cases?

# data[].attributes.body
Need LRWD-TAL strategic comms team to look at what messages we are pushing out, our messaging strategy. Feedback into MOM horizontal Comms.

And then develop differentiated segmented approach on who to target (based on data), what to say, when to say, how to say (no need fancy tiktoks).

[not sure if gamified app will work for employers, who dont have time or interest to do the apps; busy construction ERs may not want to read your emails]

# data[].attributes.headline
It's ok if employers dont know everything

# data[].attributes.subject
It's ok if employers dont know everything

# data[].attributes.body
ok for employers to not know everything. But make everything available in a one-stop portal - but needs to be readily accessible

easy to use, easy to understand.

Just-in-time

# data[].attributes.body
A1) Comms effectiveness impeded due to language barrier for MWs

A2) Hassle of in person statement taking

A3) Repeating stories not in email

A4) Cannot find/understand info, unable to self-help to source for right content or where to go for assistance

# data[].attributes.body
Problems in conveying policies and guidelines to public, because we have limitations and constraints internally, such as legal jargons...etc. hard to put in layman terms to let customers understand while being legally accurate

Online services hard to navigate for the non digital savvy.

Consistency of advice to public. Public may go all over the place to get answers they want to hear. And then if diff answers, will blame mom or tadm...etc

Customers just want to speak to someone, but has no contact number, i.e. tadm. End up they call tafep hotline.

# data[].attributes.headline
workplace harmony  /tripartism is challenging with shorter employment contracts/relationships

# data[].attributes.subject
workplace harmony  /tripartism is challenging with shorter employment contracts/relationships

# data[].attributes.body
In the past, EEs stay longer in a given company, so they may be motivated to try to maintain good r'ship, make sacrifices now and be rewarded later when times are better.

Now, hard to do the same. We shouldn't expect the same

# data[].attributes.body
to show the hard stuff that employers have to go thru , why they retrench, the scope of info, etc. Genuinely try, but have difficulty

# data[].attributes.body
How to better support customers:

Provide more accessible services to MW. Provide online translation of info.

Provide all the info to workers during onboarding.

# data[].attributes.body
What capabilities we need:

Develop an AI plan, so to provide fast replies instead of having to speak to a human.

Have a single set of advisories for tadm & tafep.

Scenario based questions. Do we answer?

# data[].attributes.body
B&C1) AI driven voice bot to perform translation, no need to repeat story

B&C2) Automated video statement taking with censors (24/7 booth). Need to ensure integrity

Online portal for statement taking for simple cases

B&C3) Capturing voice calls, summarize issues and pass on to depts

Know issue, ask the right question. Proper

B&C4) AI Bot to answer questions based on robust KM

More robust search engine, content editing easy to understand.

(Can we include private FAQs and scenarios to public domain so that it is searchable??)

# data[].attributes.body
Data gaps - data currently tracked for case management purpose. Doesn’t allow us to answer/ assess drivers of salary/employment claims increase

Need an exercise to think through the key data fields to collect and track (unavoidable financial reasons vs dispute - straightforward)

Fundamentally review our current model - do we move to a business surveillance concept? Need a common set of data for 360 view of the employer/ worker to assess company health. Change in role: TADM goes beyond GP role of curing symptoms only and refer to TAFEP (TCM) and ESE (hospital). Room to resolve problems at the root.

We need to carry out an exercise to clearly identify what can be prevented, what data we need to prevent, what are structural impediments to collecting the data.

# data[].attributes.headline
Data strategy and sense-making (GROUP 4)

# data[].attributes.subject
Data strategy and sense-making (GROUP 4)

# data[].attributes.body
1) TADM CLAIMS DATA:

Things we know: industry, type of claim (salary vs non salary, overtime,), period of arrears, profile of workers affected)

Data gaps: type of data to be classified in a quantitive way, such as underlying causes of salary cases, profile of companies, period of operation, profile of workforce size.

Effective sense-making: so that we can better upstream our education, caliberate our penalty frameworks

B) identify the relevant quantitive classifications beforehand to better sensemake

C) regularly discuss with departments to identify data points that each team is looking at and create a platform of sharing e.g. like using a shared dashboard. But need to make sure there is a divisional alignment on the data points. This must be done with greater feedback and accountability of the data used so that the officers sees the overall purpose and meaning of data collection.