goamplifi.com Open in urlscan Pro
104.21.74.168  Public Scan

Form analysis
7 forms found in the DOM

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---

Text Content

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 * [#IABV2SETTINGS#]
 * About


THIS WEBSITE USES COOKIES

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and to analyse our traffic. We also share information about your use of our site
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 * Necessary 17
   
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   page navigation and access to secure areas of the website. The website cannot
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      2
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      CookieConsentStores the user's cookie consent state for the current domain
      Maximum Storage Duration: 1 yearType: HTTP Cookie
      1.gifUsed to count the number of sessions to the website, necessary for
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    * Google
      4
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      Some of the data collected by this provider is for the purposes of
      personalization and measuring advertising effectiveness.
      
      test_cookiePending
      Maximum Storage Duration: 1 dayType: HTTP Cookie
      ar_debugChecks whether a technical debugger-cookie is present.
      Maximum Storage Duration: 3 monthsType: HTTP Cookie
      rc::aThis cookie is used to distinguish between humans and bots. This is
      beneficial for the website, in order to make valid reports on the use of
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      CRAFT_CSRF_TOKENEnsures visitor browsing-security by preventing cross-site
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      __cf_bm [x2]This cookie is used to distinguish between humans and bots.
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      and what pages have been read.
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      __insp_lmlRegisters statistical data on users' behaviour on the website.
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      __insp_session_countSets a ID for the visitor. This serves to count the
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      Maximum Storage Duration: PersistentType: HTML Local Storage
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      the number of visits, average time spent on the website and what pages
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      pdataPending
      Maximum Storage Duration: SessionType: Pixel Tracker
    * Vimeo
      1
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      vuidCollects data on the user's visits to the website, such as which pages
      have been read.
      Maximum Storage Duration: 2 yearsType: HTTP Cookie
    * ws.zoominfo.com
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      as the number of visits, average time spent on the website and what pages
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      Maximum Storage Duration: SessionType: HTML Local Storage
      ziwsSessionIdCollects statistics on the user's visits to the website, such
      as the number of visits, average time spent on the website and what pages
      have been read.
      Maximum Storage Duration: SessionType: HTML Local Storage

 * Marketing 38
   
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    * Google
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      Some of the data collected by this provider is for the purposes of
      personalization and measuring advertising effectiveness.
      
      IDEPending
      Maximum Storage Duration: 400 daysType: HTTP Cookie
      _gaUsed to send data to Google Analytics about the visitor's device and
      behavior. Tracks the visitor across devices and marketing channels.
      Maximum Storage Duration: 2 yearsType: HTTP Cookie
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      behavior. Tracks the visitor across devices and marketing channels.
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      _gidUsed to send data to Google Analytics about the visitor's device and
      behavior. Tracks the visitor across devices and marketing channels.
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      Maximum Storage Duration: 6 monthsType: HTTP Cookie
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      Maximum Storage Duration: SessionType: Pixel Tracker
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      __insp_norec_sessUsed to make live streaming of video content more
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      #-#Used to track user’s interaction with embedded content.
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      content.
      Maximum Storage Duration: SessionType: HTTP Cookie
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      Maximum Storage Duration: PersistentType: IndexedDB
      nextIdUsed to track user’s interaction with embedded content.
      Maximum Storage Duration: SessionType: HTTP Cookie
      remote_sidNecessary for the implementation and functionality of YouTube
      video-content on the website.
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      requestsUsed to track user’s interaction with embedded content.
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      ytidb::LAST_RESULT_ENTRY_KEYUsed to track user’s interaction with embedded
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      Maximum Storage Duration: PersistentType: HTML Local Storage
      YtIdbMeta#databasesUsed to track user’s interaction with embedded content.
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      yt-remote-cast-availableStores the user's video player preferences using
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      yt-remote-cast-installedStores the user's video player preferences using
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      Maximum Storage Duration: SessionType: HTML Local Storage
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      yt-remote-device-idStores the user's video player preferences using
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      Maximum Storage Duration: SessionType: HTML Local Storage

 * Unclassified 2
   Unclassified cookies are cookies that we are in the process of classifying,
   together with the providers of individual cookies.
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Cross-domain consent[#BULK_CONSENT_DOMAINS_COUNT#] [#BULK_CONSENT_TITLE#]
List of domains your consent applies to: [#BULK_CONSENT_DOMAINS#]
Cookie declaration last updated on 10/22/24 by Cookiebot



[#IABV2_TITLE#]

[#IABV2_BODY_INTRO#]
[#IABV2_BODY_LEGITIMATE_INTEREST_INTRO#]
[#IABV2_BODY_PREFERENCE_INTRO#]
[#IABV2_LABEL_PURPOSES#]
[#IABV2_BODY_PURPOSES_INTRO#]
[#IABV2_BODY_PURPOSES#]
[#IABV2_LABEL_FEATURES#]
[#IABV2_BODY_FEATURES_INTRO#]
[#IABV2_BODY_FEATURES#]
[#IABV2_LABEL_PARTNERS#]
[#IABV2_BODY_PARTNERS_INTRO#]
[#IABV2_BODY_PARTNERS#]


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VOICES | IS “GOOD” DATA ESSENTIAL FOR AI?

Share:

In this article Chief Innovation Officer at Amplifi, Mike Evans, dispels the
myth that all data has to be perfect in order for AI to work. It’s about what
‘good’ actually means - or it’s about having the right data not necessarily
perfect data.

--------------------------------------------------------------------------------


ACCORDING TO GARTNER™, IN 2025 UP TO 30% OF GENAI INITIATIVES WILL BE ABANDONED
AFTER PROOF OF CONCEPT DUE TO POOR DATA QUALITY AND OTHER MITIGATING FACTORS.

We often hear the phrase, "AI is only as good as the data it works with." While
that holds true, it doesn’t mean every piece of data needs to be without error.
As more organisations step into the realms of AI, it’s becoming clearer that
success depends on how well the data is understood and labelled, rather than on
its perfection.

Consider a customer records database. If some records are incomplete but tagged
as such, AI can still extract valuable insights, which can, in some cases, be
even more useful than 100% perfect data. AI can also be trained to improve data
quality by learning from good versus bad examples. As long as AI recognises
which data is substandard, it can still work with it and, over time, help refine
the overall dataset.



Register for our upcoming AI Strategy Webinar


SO, WHAT EXACTLY DO WE MEAN BY "GOOD" DATA WHEN IT COMES TO AI?

It’s not about the traditional understanding of “good data” which would have you
adhering to specific quality metrics, but about ensuring that the data you want
AI to work with is appropriate for your specific use case. For example, one key
factor is timeliness: When was the data produced, and how relevant is it to your
current needs? In some cases, older data might still hold value, while in
others, more up-to-date information will be essential.


BUILDING AN AI CAPABILITY

When embarking on an AI project, it may not be necessary to spend excessive time
perfecting all your data before you get started. Instead, you should focus on
identifying the most important AI use cases and ensuring that the data
supporting those specific areas is as accurate and abundant as needed.

It’s more efficient to hone in on a narrow, well-defined use case to start with.
This allows you to concentrate on making sure the data associated with that use
case is fit for purpose. Leverage the data capabilities and technologies within
your organisation to develop a foundational data platform which can serve that
fit-for-purpose data to your AI solutions. Once you achieve success with a
focused initiative, this can serve as a blueprint for expanding into additional
AI-driven use cases across your organisation.


WHAT DO WE MEAN BY 'FIT FOR PURPOSE’?

When we say "fit for purpose," we mean that the data used should be appropriate
for the intended outcome. For example, if you are deploying a model trained to
diagnose patients based on symptoms, the data you provide must be correct and
relevant to that purpose. If you mistakenly feed it inaccurate data, such as
associating the wrong set of symptoms with a particular diagnosis, the model
could potentially produce poor results. In this case, accuracy is crucial.
However, providing examples of misdiagnoses, clearly marked as incorrect can be
valuable, as it helps the model learn to differentiate errors from correct
diagnoses. Here, the focus is on making sure the data is suitable for the task,
rather than striving for perfection.

Another example of this is a chatbot providing expertise based on a body of
articles. The relevance of those articles may diminish over time, particularly
if they become outdated or disproved. For instance, an article might have been
discredited in 2020. While it’s fine for the model to include articles that
contain incorrect information, it becomes crucial to provide context – metadata
like ‘Article X was discredited in 2020’ – so the model can decide what
information to surface and what to avoid. The context helps the model determine
the relevance and reliability of the data, ensuring that even imperfect sources
can be used appropriately.

By focusing your efforts on areas of critical importance and ensuring the data
quality is ‘good enough’ in those spaces, you can set a strong foundation for
scaling AI capabilities effectively and efficiently.


LEVERAGING IMPERFECT DATA IN GPT

General-purpose models like GPT offer a great example of how even so-called
"bad" data can be valuable, provided it is properly contextualised. In AI, data
is only as good as the metadata surrounding it. Models like GPT examine vast
datasets, encompassing everything from articles and comments to ratings and
original content, whether good or terrible. However, what truly enhances the
data is the surrounding context—what people thought of it, how much it was
consumed, and how it was interacted with.

GPT models are trained on data available on the public internet – and we all
know that not all of that information is good or accurate. However, the internet
also provides a wealth of context – trusted sources, ratings, number of visits,
citations, and links – which helps AI determine which information is most likely
to provide the right answers. This surrounding context plays a crucial role in
helping models like GPT sift through the vast amount of content and make more
informed decisions about which data to prioritise.


RAG APPROACH VS. TRAINING AI FROM SCRATCH

Many organisations are not building AI models entirely from scratch but are
instead using a Retrieval-Augmented Generation (RAG) approach or fine-tuning
existing models with their own data.

Building & training a model from the ground up requires massive, diverse
datasets and considerable computational resources. In contrast, a RAG approach
starts with a pre-trained model and augments it with a smaller, more targeted
dataset specific to the organisation's needs. This approach is far simpler than
training a model from the ground up, yet still can deliver very impressive
results, making it a practical choice for many companies.

When we see statements about AI failing due to poor data quality, it’s essential
to remember that the term "quality" is incredibly broad. The standards of data
required by a medical diagnostic solution differ vastly from those needed for an
internal-facing 'expert' chatbot. The success of any AI initiative is highly
dependent on the specific context and goals of that initiative, as is the data
quality needed to make it successful.


PROMPTS ARE JUST AS IMPORTANT AS DATA

In a RAG approach, the prompts you give to an AI model can be equally as
important as the data it processes. The questions you ask and how you frame them
can significantly influence the outcome, helping the AI model arrive at the
answers you need. It’s critical to use your data to generate effective prompts,
and your data must be in good enough shape to support this process. This is an
area where external help from an expert data consultancy like Amplifi can help
transform your data into the right prompts to drive the most relevant and
accurate responses from RAG solutions.


HOW AMPLIFI DRIVES AI SUCCESS WITH FOCUSED, FEASIBLE USE CASES

When working with clients, our goal is to ensure that AI is not just a buzzword
but a practical and impactful tool that drives real business improvements. To
achieve this, we guide our clients through a structured approach that begins
with focusing on their core business use cases – those that we believe has
significant potential to benefit from AI. Our process is broken down into three
key stages:

A) Is it a feasible use case?

The first step is to evaluate whether the identified use case is practical and
feasible to be solved using the AI technologies available today. We’ll work
closely with your organisation to ensure that the problem you want to solve can
genuinely be addressed through AI, and whether the necessary data and
infrastructure are in place. This phase is crucial because it helps avoid wasted
time and resources on projects that aren’t a good fit for AI.

B) Do you have the right building blocks?

Once we confirm the feasibility of the use case, the next step is to assess
whether you have the foundational elements needed to bring the use case to life.
This includes looking at the quality and structure of your data and metadata,
identifying any gaps, and deciding whether data enrichment or quality
improvements are needed. Additionally, we evaluate your data capabilities to
consider whether other processes or tools are needed to support the AI models
you are working on today and how to scale these for future use cases.

C) Bringing AI expertise to the table

Finally, we apply our AI expertise to help you design and implement your
solutions. From fine-tuning models to ensuring they are still aligned with
business goals; we provide guidance on best practices and continuous
improvement. Whether it’s through enriching datasets, improving data quality, or
helping design processes that maintain data integrity, we ensure that any AI
solution is effective, scalable, and sustainable for the long-term.

If you’re looking to take the next step with AI, or you want to ensure your data
is ready for your specific use-case, Amplifi are well positioned to assist you
at any stage of your journey. You can reach out to myself, or any of our data
experts here. Or, if you’d like to hear more, download our guide 6 expert tips
for driving value with AI below!


DOWNLOAD GUIDE | 6 EXPERT TIPS FOR DRIVING VALUE WITH AI

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ABOUT THE AUTHOR

Mike Evans is Chief Innovation Officer at Amplifi.

He is responsible for ensuring the evolution of Amplifi’s service offering to
continuously achieve excellence in delivering modern data ecosystems to our
clients. If you would like to speak with Mike about implementing AI within your
organization, please fill out our contact form here.


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