De Check

1 / 21

Are you Byte or Business?

Byte ML / Data scientist / Engineer

Business Marketer, Product Owner, Innovation

Hybrid That unique person that knows both sides

2 / 21

How many colleagues do you have?

26-100

11-25

0-10

100 plus

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3 / 21

In which industry do you work?

If your sector is not listed in the answer options, please choose the answer that suits your organisation best.

Transport & logistics

Agency or consultancy

FMCG

Other

Non profit

Financial

Utility

Publisher/Media

Telco

Retail

Leisure

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4 / 21

Which of the statements below fits your organisation best in terms of AI maturity?

We did start, but we haven't developed concrete applications

We are quite far and have several critical AI processes live

We barely started

We did some pilot projects and have some AI applications live

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The AI Maturity Test

You’re about to start the test. To make sure that all participants complete the test from the same starting point, please note the following.

Take your own organisation as starting point. If you work for an agency, please answer the questions for your organisation. Not that of a client.
We will ask questions about three themes: organisation, technology and impact.
If your situation is not listed in the answer options, please choose the answer that suits your organisation best. To complete the test, you must answer all questions.

After completing the test, you can see how your organisation scores on each theme and compared to other participants. The score is divided in four maturity levels. The DDMA AI Committee will give you advise per theme on how to reach the next level.

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5 / 21

How many and what type of people work on AI in your organisation?

Some people are working on it (including external employees)

The value of AI is seen from workfloor all the way to board level. The business is fully supported and/or driven by AI applications. Many internal colleagues are working on it or understand it, data scientists are dispersed across the organization.

AI is widely seen as a valuable asset. There are quite some people working on it, or understanding it, both internal and external employees

No one is actually 'doing' AI. Some people are considering the benefits AI can bring.

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6 / 21

Who initiates AI projects in your organisation?

From multidisciplinary teams (data and business together)

It's initiated by individuals (e.g. data scientist or business visionaries)

It's not initiated yet

Through all layers of the organisation, including data, business and higher management

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7 / 21

To what degree do business and data scientists/engineers work together on AI in your organisation?

Always

Regularly

Occasionally

They do not

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8 / 21

How are the data science teams organised around AI projects?

We have some dedicated data science team or teams working on AI

Data science is not organised yet.

All teams working on AI related projects have data science as part of their multi-disciplinairy set-up.

We have individuals working on AI

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9 / 21

How are AI initiatives funded?

Via innovation budget

With regular budget for business projects and on top innovation budget to develop new capabilities which is expected to bring future value.

With regular marketing/ IT budgets

There is no budget for AI projects. Some budget might be set aside for researching the potential of AI.

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10 / 21

To what extent is the legal department involved in AI projects?

Legal checks are mainly done by the 'AI team' itself, no legal department is involved.

We run predefined checks at the beginning of a new project, if further legal consulting is needed, we do this on ad-hoc basis

They are not

With every project the legal team is involved from the start

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11 / 21

To what extent does your organisation consider ethical aspects of AI projects?

It is used to determine the boundaries of the experiments

We are aware of ethical issues and things like bias. In our validations steps we make sure we check for these ethical issues

This is incorporated from the start and in each step of the project. We have an ethical board that reviews the AI activities

We do not

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12 / 21

To what extent do you explain your AI models?

We do understand the innerworkings of the algorithms, but do not spend too much time trying to explain models. We mainly focus on getting results.

We do not

Internally and externally we explain on customer level specifically why a model gives a certain result. For those algorithms for which we can not give a 100% correct explanation, we estimate as good as possible.

Internally we can explain on customer level specifically why a model gives a certain result. Or we are able to identify those algorithms which are unable to explain and have taken an educated decision to use this algorithm

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13 / 21

What types of data sources are being used for AI purposes?

We analyze structured data from data dumps and/or coming from databases

We analyze all sorts of data from regular structured data to large complex unstructured data sets such as video's or a combination of sources

We don't run analyses on our data yet

We analyze structured and some unstructured data, such as text, blog posts, product/user reviews, article content, images

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14 / 21

What type of data storage solutions do you use to train AI models?

Data is sent around in CSV and Excel files.

Our models are trained with manually extracted data from traditional database solutions (like SQL or PostgreSQL) or with data sent around in CSV and Excel files. Updating the AI models is done in a similar manual way.

Our AI models are updated automatically (regularly as well as in real-time) on top of cutting edge storage solutions (like s3/hadoop/distributed database solutions).

Our AI models are trained with data stored in cutting edge storage solutions (like s3/hadoop/distributed database solutions). Next to this our models are trained with manually extracted data from traditional database solutions (like SQL or PostgreSQL). Up

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15 / 21

What kind of algorithms are being used in your company?

We use both batch and streaming and apply both regular ML as well as complex Deep Learning techniques.

We use batch and streaming (online) machine learning models, i.e., models that are updated in real-time as new data comes in and run some PoCs on Deep Learning

We use machine learning models such as SVM, Collaborative Filtering, Random Forest, etc which are trained on a regular basis (daily, weekly, monthly etc)

We don't run analyses in our data yet or use traditional statistical models such as (linear) regression, ANOVA, etc to analyze our data

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16 / 21

How is the AI infrastructure set up in your company?

Data Scientists have powerful laptops and/or a departmental server that our data scientists/analists use for AI purposes

There is no AI infrastructure available

Data is centrally stored and easily accessible for the analytical cluster of on-premise/cloud servers on which our data scientists/analists run their AI models

There is a scalable cloud/on-prem platform on which our data scientists/analists run their AI models

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17 / 21

In what time frame do you expect that a significant number of business processes are using AI?

1-2 years

More than 3 years

That's already the case

2-3 years

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18 / 21

How long does it usually take between the start of an AI project, and the first live tests?

Within 1-3 months

We have not started yet

Between half year and a year

Between 3-6 months

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19 / 21

What percentage of AI projects also goes into production?

>75%

0 - 25%

25-75%

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20 / 21

For what kind of purposes is AI used in your organisation?

We use AI to develop new features and services that support our core business

We use AI to radically innovate and improve our core business, on top of this we potentially also develop new products and services to help our business

We don’t use AI for business purposes yet, or we are unsure how to start a concrete case

We use AI to optimize/automate existing time consuming or high effort supportive tasks and/or applications where we think AI can bring value we were not able to capture without AI.

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21 / 21

How would you describe the impact AI makes in business value for your organisation?

We have a couple of AI projects, that deliver concrete business value to our organisation

We have done a lot of projects already, and multiple critical AI processes that deliver significant business value. Without AI the organization would not be able to make its targets.

We have not started yet with AI to deliver business value

We've started looking at AI, and we expect to see some business value soon. We mainly run small scale experiments, for which we measure the impact.

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The result

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Previous Start over

Great you are interested in AI in your organisation. To grow to the next level, your first steps can be the following:

Get a good understanding what AI really is and identify potential usecases for your organization
Develop the skills yourself, get an external consultant and/or hire (potentially graduation students) a data scientist
Then start working on it :). Start building and make experiments/proofs of concept (keep it simple)
Include more people in the projects and educate people about what AI is and the potential of AI for the organisation. Think about more technical people, but also colleagues from the business. Perhaps it can help your organisation to hire some external expert to boost your projects
Start organising your data analists or data scientists so they can help each other and make more impact. Perhaps train data analists to become data scientists.
Find a sponsor in senior management and get some innovation funding to fire up the engines

" data-category-ddma_test_advice_with_2_points="

Great, your organisation has already started with AI and reached level 2! Your next challenges may include:

Getting more internal employees working on AI or taking the time to learn about the impact of AI
Let business and science colleagues work together more frequently on Ai projects
See if you can set-up data science teams in more business units to spread the knowledge
Find sponsors in the regular marketing/ IT businesslines and have them make budget reservations for AI
Think about predefined checks at the beginning of a new project and check ethical issues in your validation steps
Try to explain your models on a customer level; "because of X, customer Y got this result". Or when you can't explain it, take an educated decision to (not) use an algorithm.
Start considering legal and ethical aspects of AI projects, as wel as how you can explain how your models work
You have run some AI experiments by now, and should be able to grasp the idea behind AI and the potential for your organization. Develop a vision where you want to go with AI inside your organization which supports in getting funding in place.

" data-category-ddma_test_advice_with_3_points="

Based on your answers it seems you have organised AI pretty well. To take it to the next level, you can:

Make sure the value of AI is seen from workfloor to boardlevel. Also, get as many internal employees from diverse backgrounds to learn about AI or be part of projects that include AI.
Get all layers of the organisation involved. Include higher management so heavily that they will initiate AI project themselves.
Make sure business and data scientists/engineers always work together as a multi-disciplinairy team on AI project.
Arrange budgets for your project, but on top of that find budget specifically to develop new capabilities for future value creation.
Incorporate legal and ethical aspects and colleagues in every step from start to finish. Consider using a legal and ethical board to review AI activities.
Challenge your teams to explain your AI models on customer level externally or estimate is as good as possible.

" data-category-ddma_test_advice_with_4_points="

You are on top of your game! Based on your answers, it seems you are in the highest level. Now it is important to keep that level and perhaps upgrade on some of the aspects that still need improvement. If you haven't done this yet, this might also be the time to start sharing your knowledge and experience with other organisations. Together we can do greater things with data and AI!

" data-category-ddma_test_advice_with_5_points="" data-category-ddma_test_advice_with_1_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

AI course by Jim Stolze (if you're completely new to AI)
DDMA AI Afternoon Update > keep an eye on de DDMA newsletter. Sign up here. Please note that the majority of the newsletter is in Dutch, however some events are in English!
Kevin Kelly Ted Talk
Wait but why - Coursera
Knowledge Management and Big Data in Business - edX
Data Science Specialization - Coursera
AI For Everyone - Coursera

" data-category-ddma_test_advice_with_2_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

AI course by Jim Stolze (if you're completely new to AI or plan to bring non technical people to AI)
DDMA AI Afternoon Update > keep an eye on de DDMA newsletter. Sign up here. Please note that the majority of the newsletter is in Dutch, however some events are in English!
Kevin Kelly Ted Talk
Wait but why - Coursera
Visit meetups and seminars on AI (e.g. World Summit AI)
TedX Decentralized AI: The Power and the Necessity
Machine Learning for Data Science and Analytics - edX

" data-category-ddma_test_advice_with_3_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

AI course by Jim Stolze (if you're completely new to AI)
DDMA AI Afternoon Update > keep an eye on de DDMA newsletter. Sign up here. Please note that the majority of the newsletter is in Dutch, however some events are in English!
Kevin Kelly Ted Talk
Wait but why - Coursera
Visit meetups and seminars on AI (e.g. World Summit AI)
TedX Decentrelized AI: The Power and the Necessity
Participate with your team in hackathons to boost your skills
Visit the yearly cloud events (Micrsoft, AWS, GCP, etc) to stay on top of latest developments and to learn from other customers

" data-category-ddma_test_advice_with_4_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

AI Course by Jim Stolze (if you're completely new to AI)
DDMA AI Afternoon Update > keep an eye on de DDMA newsletter. Sign up here. Please note that the majority of the newsletter is in Dutch, however some events are in English!
DDMA AI Elite
Kevin Kelly Ted Talk
Wait but why - Coursera
Visit meetups and seminars on AI (e.g. World Summit AI)
TedX Decentrelized AI: The Power and the Necessity

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You are at the start of a great AI journey. To take the next step on your journey:

Start analyzing structured data from data dumps or databases. Ideally you would start organizing your data in a central database such that data scientists/analists have full access to all data
Start training your models manually (ideally on a regular basis)
Develop more computer science skills like git, structured programming, deployment, etc.
Make sure that data scientists have powerful laptops, can use a departmental server for AI purposes or if possible have some budget for cloud AI solutions and/or VMs

" data-category-ddma_test_advice_with_2_points="

Great that you built up skills to start training AI models. To take it to the next level:

Add more complex data sources like free text, images, audio content to enhance your business - Explore more complex database systems/file systems such as Hadoop, S3 and distributed database solutions and update your models regularly with those systems
Start to look into batch and streaming (online) Machine Learning models that can be updated in real-time. Also see if you can start a proof of concept on deep learning.
Look into scaling up the on-premise server to a cluster of servers such that models can be trained in parallel

" data-category-ddma_test_advice_with_3_points="

Great that you are already running AI models, to take the next step:

Start analysing large complex unstructured data sets (e.g. video or a combination of sources)
Find ways to easily deploy and automatically update your AI models on top of cutting edge solutions like s3, hadoop or distributed database solutions
Start to look into Deep Learning models to boost the performance of your AI stack
Consider to move to a cloud solution such that your AI services easily scale (also using hosted services has much less maintenance)

" data-category-ddma_test_advice_with_4_points="

You're at the top of the AI game. Stay in touch with the latest developments by following blogs on AI and following the developments the three biggest vendors for AI services: Google, Microsoft and Amazon. Also try sharing your knowledge with the broader AI community.

" data-category-ddma_test_advice_with_5_points="" data-category-ddma_test_advice_with_1_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

" data-category-ddma_test_advice_with_2_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

Develop skills in the cloud AI functionalities
Develop non tabular skills like NLP, image processing using TensorFlow
Learn about streaming data
Get better understanding about algorithms in ML (or even more hardcore the original Stanford class be prepared for some hard core math)
Starting point for online machine learning

" data-category-ddma_test_advice_with_3_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

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Additional tips for books to read, workshops to attend or blogs to follow:

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Great that you are looking at using AI in your organisation. To take the next step:

Start experimenting with AI to deliver value
Choose some smaller and feasible AI pilot projects, and try to get those live in production
Try to timebox projects, and deliver something that could deliver value within half a year
Focus on optimising or automating time consuming or high effort supportive tasks where you can bring value that was not possible without AI

" data-category-ddma_test_advice_with_2_points="

Great that you've started delivering value through AI! To take the next step:

Try splitting up AI projects in smaller steps, so you can deliver value faster.
Try looking at adding more new features or services to existing processes and using more customer data to modify those processes.
Start more AI projects on a wider range of business processes to spread knowledge and gain more business process knowledge at the AI experts.

" data-category-ddma_test_advice_with_3_points="

Great! You are doing really well making an impact with AI. To improve even further:

Use AI to radically innovate and improve your core business or develop new products or services
Try getting more of your pilots towards actual production phase, in which those projects deliver consistent business value
Try looking at project organisation and input before a project starts, so you can timebox projects even better, and deliver value even faster

" data-category-ddma_test_advice_with_4_points="

You are at the top of creating impact with AI. Focus on keeping on top of the game, by continuously looking at what kind of new products or services could be delivered based on your data-collection, continuously optimising your processes, and keep making sure your pilots turn into critical business processes by picking the right projects.

And you can take the stage and share how you are organising what you are doing, and learn from other great organisations as well.

" data-category-ddma_test_advice_with_5_points="" data-category-ddma_test_advice_with_1_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

Read blogs on making impact with AI
Structuring Machine Learning projects - Coursera
AI For Everyone - Coursera

" data-category-ddma_test_advice_with_2_points_details="

Additional tips for books to read, workshops to attend or blogs to follow:

Reads blogs on speeding up AI e.g. clusterdata.nl
Business analytics for Data-Driven decision making - edX
AI For Everyone - Coursera
O'Reilly AI
The role of an Analytics Translator

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Additional tips for books to read, workshops to attend or blogs to follow:

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Additional tips for books to read, workshops to attend or blogs to follow:

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