10 Sanity-checks for Efficient Data Management

Jun 15, 20160 likes819 views

Marketers know that data is important yet it isn’t always the most appealing aspect of marketing for creative individuals. Everyone knows how important it is, but nobody wants to deal with it. Like it or not, data is crucial, the lifeblood of campaign success or failure. Without it, marketing automation will not deliver awesomeness. Here are ten things you need to remember in order to manage your data efficiently and keep your sanity while doing so.

10 SANITY-CHECKS
FOR EFFICIENT DATA
MANAGEMENT
MA My Automation
My Awesome

1. KEEP IT FRESH
MA My Automation
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Keeping your data
fresh means -
making sure it is accurate, in the
right format and can be updated
automatically.
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2. ADD TO IT
MA My Automation
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Always add to
your data -
as well as collating standard
details, think about what other
information will help improve
campaign performance such
as customer purchase history
for renewal and cross/up sell
campaigns; information to help
lead quality & routing such
as company size, revenue,
number of employees etc.
All information that can be
obtained from internal resources
or via third party providers.
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My Awesome

3. REMEMBER
SIZE DOES NOT
(ALWAYS) MATTER
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Don’t mistake
a good data
base for a big
database.
If you have a large prospect
base but a big proportion is not
engaged or unmarketable, why
keep it. You could save money
with you platform provider by
reducing your database size.
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My Awesome

4. ASK YOURSELF,
IS MY DATA
WORKING FOR ME?
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My Awesome

Always ask, is my
data working for me?
Always think, what is your data
strategy? There is no point having data
for data’s sake - you always need to
be thinking how it serves you, how is
your data and your data segmentation
supporting your campaigning and
targeting efforts?
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5. ALWAYS,
ALWAYS COMPLY
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Always, always
make sure that
you are compliant.
The General Data Protection
Regulation (GDPR) was
announced in April 2016 and
will come into effect after a two-
year transition period. Failure to
comply with the laws set out in
the GDPR can result in fines of up
to 4% of worldwide turnover.
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My Awesome

6. NORMALISE
YOUR VALUES
MA My Automation
My Awesome

Consider
normalised
values.
Targeting by multiple values,
for example job title or
function, can sometimes just
be too hard. Normalising
multiple values into a single
field, such as persona type,
can improve targeting.
MA My Automation
My Awesome

7. OFFER
PREFERENCES
MA My Automation
My Awesome

Be flexible.
Be seen to provide effective
preference management
services to your content
subscribers. So often,
preference management
centres are poor and tend to
be out of date with current data
governance and compliance
laws.
MA My Automation
My Awesome

8. FOCUS ON
COMPLETENESS
MA My Automation
My Awesome

Ask yourself,
how complete
is my data?
We all need email addresses
but what other data do we
need to improve campaigning
and how complete is it?
Consider progressive
profiling to improve overall
data completeness.
MA My Automation
My Awesome

9. TO PURGE OR
NOT TO PURGE?
MA My Automation
My Awesome

Ask yourself, ‘do
I actually need
all this data
sitting unengaged in my
marketing platform?’
MA My Automation
My Awesome

Always analyse.
Think, what secrets does my
data hold? Where else in your
data set can you explore to
find small, highly responsive
segments to target? How can
integration with other data
sources unlock new segments?
MA My Automation
My Awesome

TAKEAWAYS
• Bigger databases don’t always mean better
databases.
• Look at what else is hidden in your data -
always analyse.
• Never forget to focus on completeness.
MA My Automation
My Awesome

MY AWESOME GUIDE TO:
MARKETING AUTOMATION
SUCCESS
DOWNLOAD NOW
Now that you’ve got the basics of data
management down continue your journey
to success, download
MA My Automation
My Awesome

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https://www.meetup.com/sf-bay-acm/events/306888467/ A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold. While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence? The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces. However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces. Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)

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10 Sanity-checks for Efficient Data Management

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