Start Tackling Data Access & Quality Issues (For Real)
Do you have access to a repository of high-quality and trusted datasets? If so, are they available for constant reuse? Would someone outside of a central data team or role (think someone in marketing, HR, or R&D looking to leverage data for their day-to-day work) answer these questions the same way you did?
If your answer to any of these questions was “no,” you’re not alone. However, if you can address issues like siloed or duplicated data sources, bottlenecks, and lack of workflow automation, you’ll see massive improvements in efficiency and, by extension, higher return on investment (ROI) from AI in 2023. Check out the resources below to see top tips and stories from people who have already successfully tackled this challenge.
Operationalize & Industrialize AI Processes for Business Impact
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Start Tackling Data Access & Quality Issues (For Real)
Operationalize & Industrialize AI Processes for Business Impact
Address Any Lack of Visibility & Control
Develop a Plan for Scarce & Underused Data Experts
Move from Costly & Complex Infrastructure to a Modern Data Stack Fit for AI
DATAIKU CUSTOMER INSIGHTS
Hear how Jeff McMillan, Chief Analytics and Data Officer at Morgan Stanley, tackled data quality to drive growth and efficiency across the wealth management business.
Make AI Initiatives a Success in 2023 With These Top Trends & Tips
AI in 2023: Get the Full Ebook
Embedding AI deeply into your company’s operations, much less ensuring business impact, isn’t something that will happen overnight. Organizations need a short-term strategy that’s about delivering quick, high-impact AI wins as well as a long-term strategy, which is about enabling a transformative AI culture.
Dataiku has developed a capability framework (below) that articulates the 20 bricks organizations ultimately need to build to both operationalize and scale AI for maximum ROI and impact. Hint: Not everything needs to be perfect from the beginning, but organizations do need the right foundations in place.
By 2025, 50% of large enterprises will have deployed artificial intelligence orchestration platforms to operationalize AI, up from fewer than 10% in 2020.
"
Next Section
Address Any Lack of Visibility & Control
Source: Gartner Cool Vendors in Enterprise AI Operationalization and Engineering, 11 October 2021
Develop a Plan for Scarce & Underused Data Experts
Despite the efforts to train more AI experts (across academic, governmental, and corporate entities, for example), the truth remains: there is a lack of AI experts, particularly in less tech savvy or sexy industries. As a result, enterprises can’t rely on experts alone when aiming for massive change.
The democratization of AI within the organization will take time and can be thought about in three ways. First, setting up upskilling programs for different competency levels (a process that is unfortunately overlooked at most companies); second, setting the standards to allow more and more autonomy from non-experts; and third, creating an environment that drives and supports collaboration.
88% of companies that see positive ROI from AI train and enable non-data scientists to leverage AI.
"
ESI ThoughtLab, Driving ROI Through AI, November 2020
Move from Costly & Complex Infrastructure to a Modern Data Stack Fit for AI
Some of the key buzzwords associated with the modern data stack are managed, serverless, and low-technical expertise required. Because storage and compute are independent in the modern data stack (and because cloud data warehouses can store massive amounts of data for cheap), data transformation can be done
more on-demand, which places less of a burden on IT.
Even for organizations that have a much more complex legacy setup and therefore can’t fully leverage the simplicity of the modern data stack, the goal of providing a seamless experience for all users to work with data is a valuable takeaway. Any way you slice it, the path to eliminating costly and complex infrastructure is ensuring business objectives inform architecture strategy (not the other way around).
Organizations with diverse IT-business collaborations will deliver business outcomes 25% faster than their competitors.
"
Gartner, “The Future of Applications Depends on IT-Business Collaboration,” 20 October 2020
Featuring Insights From:
EBOOK
If addressing data quality is on your roadmap for 2023, check out the full ebook for in-depth strategies and tips.
DATAIKU EXPERT INSIGHTS
Get additional insights on tackling data quality issues from Dataiku Chief AI Strategist, Ben Taylor.
TECHNICAL EBOOK
Dive into the world of data labeling with this in-depth look at active learning, including use cases for data quality.
SUCCESS STORY
See how Bankers’ Bank used Dataiku to reduce time pulling data by 87% while also improving data quality.
According to Forrester, data quality issues take up to around 40% of a data analyst’s time.
"
Source
EBOOK
Go further on industrializing AI with this guidebook, which goes in-depth on practices and operating models to successfully scale.
DATAIKU EXPERT INSIGHTS
Debbie Reynolds, Vice President, Enterprise Data Solutions and Engineering at Pfizer, unpacks their journey to getting value from AI.
SUCCESS STORY
MandM Direct uses Dataiku and Google Cloud Platform (GCP) to operationalize 10x more models versus a code-only approach.
Too much control when it comes to data and AI can stunt innovation and create intensive administrative burdens; too much autonomy, and the potential for risks could increase.
An effective AI Governance program that provides the right level of both visibility
and control is critical to scaling AI — of course, this is easier said than done.
Ultimately, different contexts and audiences might tolerate different levels of transparency and control ranging from strict explainability around the management
of a model and its inputs/outputs to simple deployment sign-off and understanding
the reason for the model or data project.
DATAIKU EXPERT INSIGHTS
Dataiku RVP of AI Strategy Shaun McGirr speaks to why organizations struggle with visibility and control when it comes to AI plus shares success stories of organizations who are getting it right.
EBOOK
This ebook enables organizations on their scaling journey to navigate struggles associated with visibility and control of AI projects.
DATAIKU CUSTOMER INSIGHTS
Mike Berger, VP, Chief Data & Analytics Officer at Mount Sinai, talks about how the health care giant has been able to go from theory to practice when it comes to getting people across the business to work with data.
EBOOK
This ebook provides best practices when hiring and upskilling for AI talent, as well as delves into the common pitfalls to avoid.
SUCCESS STORY
GE Aviation upskilled thousands of people across the organization to use real-time data at scale to make better and faster decisions.
SUCCESS STORY
See how two people armed with Dataiku on the FP&A team at Standard Chartered Bank are doing the work of 70 people limited to spreadsheets.
DATAIKU CUSTOMER INSIGHTS
Daniel Kinney, VP of Global Commercial Data Science at The Janssen Pharmaceutical Companies of Johnson & Johnson, talks data scientist retention and upskilling.
DATAIKU EXPERT INSIGHTS
Dataiku VP of Platform Strategy Jed Dougherty unpacks the challenges and complexity of organizations' infrastructure and why it can be so costly (plus, importantly, how to start addressing it).
EBOOK
How can teams build and plan for architecture that is agile enough
to adapt to changing needs and technology requirements? This ebook has answers.
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Top New Year’s Resolutions for
Data, Analytics, & AI
AI in 2023: Top Tips & Trends
Get the Full PDF Ebook Featuring Insights from Snowflake and Slalom Consulting Delivered to Your Inbox
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AI in 2023: Get the Full Ebook
AI in 2023: Get the Full Ebook
AI in 2023: Get the Full Ebook
AI in 2023: Get the Full Ebook
AI in 2023: Get the Full Ebook
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Legal Stuff
Strategic Alignment and Steering
Data Product Delivery
Platform Management and Support
User Adoption
Business Transformation
Business strategy alignment
Funding and resources strategy
Program mode
AI governance
Data project design
Experimentation
Industralization
Operations
Platform and administration
Infrastructure and cloud operations
Data architecture and operations
Platform configuration and extensibility
Onboarding
User support and coaching
Training
User community management
Evangelization
Use case funnel management
Value realization
Data consumer change mangement
Find the Balance for Effective AI Governance
Too Much Autonomy
Too Much Control
Get the Full PDF Ebook Featuring Insights from Snowflake and Slalom Consulting Delivered to Your Inbox
AI in 2023: Top Tips & Trends
AI in 2023: Get the Full Ebook
How can teams build and plan for architecture that is agile enough to adapt to changing needs and technology requirements? This ebook has answers.
EBOOK
Dataiku VP of Platform Strategy Jed Dougherty unpacks the challenges and complexity of organizations' infrastructure and why it can be so costly (plus, importantly, how to start addressing it).
DATAIKU EXPERT INSIGHTS
Gartner, “The Future of Applications Depends on IT-Business Collaboration,” 20 October 2020
"
Organizations with diverse IT-business collaborations will deliver business outcomes 25% faster than their competitors.
Some of the key buzzwords associated with
the modern data stack are managed, serverless, and low-technical expertise required. Because storage and compute are independent in the modern data stack
(and because cloud data warehouses can store massive amounts of data for cheap), data transformation can be done more on-demand, which places less of a burden on IT.
Even for organizations that have a much more complex legacy setup and therefore can’t fully leverage the simplicity of the modern data stack, the goal of providing
a seamless experience for all users to work with data is a valuable takeaway. Any way you slice it, the path to eliminating costly and complex infrastructure is ensuring business objectives inform architecture strategy (not the other way around).
Move from Costly & Complex Infrastructure to a Modern Data Stack Fit for AI
5.
AI in 2023: Get the Full Ebook
GE Aviation upskilled thousands of people across the organization to use real-time data at scale to make better and faster decisions.
SUCCESS STORY
See how two people armed with Dataiku on the FP&A team at Standard Chartered Bank are doing the work of 70 people limited to spreadsheets.
SUCCESS STORY
Daniel Kinney, VP of Global Commercial Data Science at The Janssen Pharmaceutical Companies of Johnson & Johnson, talks data scientist retention and upskilling.
DATAIKU CUSTOMER INSIGHTS
This ebook provides best practices when hiring and upskilling for AI talent, as well as delves into the common pitfalls to avoid.
EBOOK
Mike Berger, VP, Chief Data & Analytics Officer at Mount Sinai, talks about how the health care giant has been able to go from theory to practice when it comes to getting people across the business to work with data.
DATAIKU CUSTOMER INSIGHTS
ESI ThoughtLab, Driving ROI Through AI, November 2020
"
88% of companies that see positive ROI from AI train and enable non-data scientists to leverage AI.
Despite the efforts to train more
AI experts (across academic, governmental, and corporate entities, for example), the truth remains: there is a lack of AI experts, particularly in less tech savvy or sexy industries. As a result, enterprises can’t rely on experts alone when aiming for massive change.
The democratization of AI within
the organization will take time and can be thought about in three ways. First, setting up upskilling programs for different competency levels (a process that is unfortunately overlooked at most companies); second, setting the standards to allow more and more autonomy from non-experts; and third, creating an environment that drives
and supports collaboration.
Develop a Plan for Scarce & Underused Data Experts
4.
AI in 2023: Get the Full Ebook
This ebook enables organizations on their scaling journey to navigate struggles associated with visibility and control of AI projects.
EBOOK
Dataiku RVP of AI Strategy Shaun McGirr speaks to why organizations struggle with visibility and control when it comes to AI plus shares success stories of organizations who are getting it right.
DATAIKU EXPERT INSIGHTS
Find the Balance for Effective AI Governance
Too Much Control
Too Much Autonomy
Too much control when it comes to data and AI can stunt innovation and create intensive administrative burdens; too much autonomy, and the potential for risks could increase.
An effective AI Governance program that provides the right level of both visibility
and control is critical to scaling AI — of course, this is easier said than done. Ultimately, different contexts and audiences might tolerate different levels of transparency and control ranging from strict explainability around the management of a model and its inputs/outputs to simple deployment sign-off and understanding the reason for the model or data project.
Address Any Lack of Visibility & Control
3.
AI in 2023: Get the Full Ebook
Go further on industrializing AI with this guidebook, which goes in-depth on practices and operating models to successfully scale.
EBOOK
Debbie Reynolds, Vice President, Enterprise Data Solutions and Engineering at Pfizer, unpacks their journey to getting value from AI.
DATAIKU EXPERT INSIGHTS
MandM Direct uses Dataiku and Google Cloud Platform (GCP) to operationalize 10x more models versus a code-only approach.
SUCCESS STORY
Platform Management and Support
Platform and administration
Infrastructure and cloud operations
Data architecture and operations
Platform configuration
and extensibility
•
•
•
•
Business Transformation
Evangelization
Use case funnel management
Value realization
Data consumer change mangement
•
•
•
•
Data Product Delivery
Data project design
Experimentation
Industralization
Operations
•
•
•
•
User Adoption
Onboarding
User support and coaching
Training
User community management
•
•
•
•
Strategic Alignment and Steering
Business strategy alignment
Funding and resources strategy
Program mode
AI governance
•
•
•
•
Source: Gartner Cool Vendors in Enterprise AI Operationalization and Engineering, 11 October 2021
"
By 2025, 50% of large enterprises will have deployed artificial intelligence orchestration platforms to operationalize AI,
up from fewer than 10% in 2020.
Embedding AI deeply into your company’s operations, much less ensuring business impact, isn’t something that will happen overnight. Organizations need a short-term strategy that’s about delivering quick,
high-impact AI wins as well as a long-term strategy, which is about enabling
a transformative AI culture.
Dataiku has developed a capability framework that articulates the 20 bricks organizations ultimately need to build to both operationalize and scale AI for maximum ROI and impact. Hint: Not everything needs to be perfect
from the beginning, but organizations do
need the right foundations in place.
Operationalize & Industrialize AI Processes for Business Impact
2.
AI in 2023: Get the Full Ebook
Get additional insights on tackling data quality issues from Dataiku Chief AI Strategist Ben Taylor.
DATAIKU EXPERT INSIGHTS
Dive into the world of data labeling with this in-depth look at active learning, including use cases for data quality.
TECHNICAL EBOOK
See how Bankers’ Bank used Dataiku to reduce time pulling data by 87% while also improving data quality.
SUCCESS STORY
If addressing data quality is on your roadmap for 2023, check out the full ebook for in-depth strategies and tips.
EBOOK
Hear how Jeff McMillan, Chief Analytics and Data Officer at Morgan Stanley, tackled data quality to drive growth and efficiency across the wealth management business.
DATAIKU CUSTOMER INSIGHTS
Source
"
According to Forrester, data quality issues take up to around 40% of a data analyst’s time.
Let’s face it: Most businesses don't have
a repository of high-quality and trusted datasets. And, when they do, they may
not be easily accessible or available for constant reuse (they might even be siloed
or fragmented). That means the problem
of data quality isn’t always technological,
but organizational.
To be clear, data quality doesn’t need to be 100% perfect in order to move forward and start getting value from advanced analytics and AI initiatives. However, starting to address things like siloed or duplicated data sources, bottlenecks, and lack of workflow automation in parallel can mean massive improvements in efficiency and, by extension, higher return on investment (ROI) from AI.
Start Tackling Data Access & Quality Issues (For Real)
1.
Move from Costly & Complex Infrastructure to a Modern Data Stack Fit for AI
Develop a Plan for Scarce & Underused Data Experts
Address Any Lack of Visibility & Control
Operationalize & Industrialize AI Processes for Business Impact
Start Tackling Data Access & Quality Issues (For Real)
5.
4.
3.
2.
1.
Featuring Insights From:
Make AI Initiatives a Success in 2023 With These Top Trends & Tips
Top New Year’s Resolutions for Data, Analytics, & AI
AI in 2023: Get the Full Ebook
2.
easier said than done. Ultimately, different contexts and audiences
might tolerate different levels of transparency and control ranging from strict explainability around the management of a model and its inputs/outputs to simple deployment sign-off and understanding
the reason for the model or data project.
Too much control when it comes to data and AI can stunt innovation and create intensive administrative burdens; too much autonomy, and the potential for risks could increase.
An effective AI Governance program that provides the right level of both visibility and control is critical to scaling AI — of course, this is
3.
4.
5.
Next Section
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