‘Trusted Data’ is the biggest raw material for optimizing AI/ML and Predictive Analytics.
We're at an exciting time of innovation and transformation with the realization of the massive amount of untapped value we have in our data.
At the forefront of this revolution is the advent of Artificial Intelligence (AI) and Machine Learning (ML), and we're seeing new opportunities presented daily.
From speech, image, motion recognition and diagnostics in healthcare to fraud identification and reduction in financial sectors, customer services, and marketing in retail to operational efficiency in manufacturing to name a few.
What is Artificial Intelligence and Machine Learning all about?
AI & ML is about making machines learn, reason, think, perceive, speak and communicate using available data produced in the past to infer and predict future behavior.
This 'quest for intelligence' is at the root of AI innovation.
AI/ML generates predictive models using input from a large amount of data at its disposal, running it through complex statistical computation and probability to gain insight and predict into the future.
Companies are embracing AI as the catalyst to position themselves at the forefront of innovation; creating a competitive edge for better insight and superior creativity.
There's no doubt our appetite for cutting-edge innovation has risen in line with the massive volumes of data we have available in our business ecosystem - data truly is 'the new oil'.
An example of AI/ML innovation is the Amazon 'same-day delivery' use case.
Amazon is able to boost customer lifetime value and loyalty using customer browsing history, helping to predict the likelihood of a customer buying an item before the customer actually buys it.
By analyzing the number of times the customer reviews an item without actually dropping it in their shopping cart, the algorithm is then able to identify items with the highest probability of purchase by a customer.
Amazon ships these items to the nearest distribution center closest to the customer address before the customer even places an item in their shopping cart, making this item available from that point on for same-day delivery to the customer.
This 'just-in-time' delivery provides a better incentive for the customer to drop this item in their shopping cart the next time they browse the item and realize it's available for same-day delivery.
The secret to this success story is Amazon's data.
Amazon is at the frontline of investing in the quality of their data to harness its full potential.
This is one of the potential powers of AI/ML in sales and marketing.
But this is only scratching the surface. Innovations are ramping up across every sector. For example, we also have groundbreaking use cases in medical diagnostics and preventative healthcare.
So, what is the reality of AI/ML in your organization today? Are you sinking or swimming with competing initiatives or are you optimizing the ROI of your data with AI/ML?
According to Gartner, most organizations are not yet ready to fully exploit their data.
91% of organizations haven't reached any kind of organizational maturity with their data. Ungoverned data creates a fog of noise with limited capacity for insight.
The reality for AI/ML success lies in the quality of the source data, or in simple terms, garbage in equals garbage out.
Having 'good data' is the number one prerequisite to unlock the potentials of AI/ML. Organizations need to take control of upstream data quality and be proactive with reducing the noise in the volume of data currently used as part of their input models.
In addition to this, AI must be human-centric if it's to reach its potential. We need accountability around the input data, the algorithm, and the output model. People must be at the heart of its execution and adoption for us to really position AI for optimal insight.
Two of the biggest challenges in AI/ML adoption are:
We're barely scratching the surface of AI/ML potential simply because our data foundations are poor
We're unable to provide confidence in our findings because the input data cannot be trusted
These two problems are interrelated and can be solved if we start investing in the quality of our data and start building trust in our input dataset.
To achieve this, we need a cultural transformation of strong governance around the quality of our input data.
Why is Data Governance & Stewardship So Important to the effective execution of AI/ML?
AI can only learn from what you feed it because every output is a reflection of its input. If we want AI to be successful, we must feed it with quality data.
To fully harness the creative insight from AI, we must be intentional with investing in the quality of our data foundations. We need strong governance and the stewardship of accountability.
The problem is that most organizations' are attempting to resolve data with a 'data cleansing factory' approach but this 'data scrubbing mentality' is a mistake.
A better approach is to strengthen the controls along the data value chain to prevent the creation of errors at source. This creates greater quality, trust and optimization all along the information chain.
Using AI to drive our business and gain optimal insight is going to require more than loading massive amounts of data into sophisticated models. It's going to need sound governance and accountability of stewardship to position trusted, valuable input data set for our different models.
The precision of AI Models heavily relies on the reliability of its input data set. This cannot be undermined and must be at the forefront of every organization's strategic plan as they journey through AI/ML adoption. We have to engage the right data from the onset.
We need to draw a gameplan to invest more in Data Governance and Stewardship around the data upstream of its journey instead of heavy reliant on filtering value through the mass amount of poor data quality in our current data for insight.
I believe this is the best way for us to forge ahead and accelerate the potential of AI.
Engaging stewardship around the quality of our data ought to be our foundational goal to drive the desired outcome from our models. The benefit of this will also help resolve some of the issues of 'interpretability' currently faced by many AI models. This will no longer be an issue as the trust level around our input data increases.
As part of building governance around foundational data for AI/ML, we also need to democratize stewardship around Data and AI initiatives.
We need to engage data citizens to be part of the oversight around model use cases - formation, build and validation of our models.
We have to carry our business and data stakeholders along in our AI journey as their input is pivotal to the success and effectiveness of our models.
This will make a better case for interpretability and acceptance as our models will no longer be a 'Black Box' to consumers and stakeholders who currently see AI/ ML as a technical discipline reserved for a technical audience.
They will begin to see other opportunities for engaging AI to accelerate their own business case.
So, how do we harness the full potential of AI/ML?
We need to harmonize our rich data set with our AI initiatives.
We need to recognize the discipline of AI/ML as an enabler to do the heavy lifting for us to accelerate growth and harness creative insight from our rich data set. Not to replace human creativity.
We need to position people at the heart of our AI/ML initiatives to realize its effectiveness.
We need to recognize 'trusted data' as the main raw material for realizing the full potential of our AI/ML initiatives.
We need to be aware of our data quality and data lineage. We need to create a trusted data environment.
We need to understand the potential opportunities that lie within our data.
We need to educate our data citizen on the importance of good quality data for optimal ROI
We need to build proactive governance and a community of stewardship around our data value chain. I talk about how to build 'Formalized Cumulative Responsibilities' around your data value chain in the following article: https://lnkd.in/ewNnVTG
We need to equip & empower our data citizens with needed training & tools to create richer, trusted data.
We need to continuously measure our model output against our input data quality.
We need formalized attestation and certification of our input & output with engaged stewardship along the value chain – From Input Data to Output Model Validations.
In conclusion, AI is definitely poised to change and improve different aspects of our lives going forward.
But for us to fully realize its greatest potential, we must position it for success with our Data and our People through cumulative responsibility of stewardship around our biggest asset - data.
In short:
Governed Data yields Better/Trusted Data = Better Models = Better AI Predictions.
Do you want to optimize your AI initiative with trusted, governed, quality data to unleash the full potentials of AI but don't know where to start?
Are you having challenges with the explainability and interpretability of your AI Models?
Discuss your challenges and explore simple strategies for moving forward on a free Discovery Call: