Questions and Answers
Hi guys Ondrej Kubera David Bednar Ondrej Bothe Martin Potančok
I hope you are doing great 🙂
I had a Two part question
- What is the ambition of the book, in terms of the reader?
- Which characteristics of the reader, you wanted to target -> to improve/ benefit/should become better at, after finishing the book?
ASHISH SONI Thanks a lot for your question. For the first part - I would say the ambition is to uncover specifics and complexity of data analytics initiatives, in a structured and holistic manner. To answer the second portion - after reading the book, the reader should have better understanding of analytical work and its various types and understand the complexity and typical challenges. As a result he or she can better set up the analytics initiative for success and avoid or be ready for typical challenges. Did I answer your question? 🙂
Yes Ondrej Kubera Thank you 🙂
Hi everyone, thanks for being here! I would like to know what are the most common reasons for failure in analytics projects and are the reasons really that different depending on the type of project? Could you maybe provide an example?
Hi Tim Becker, thank you for your question.
There could be many different reasons, why the project could fail. May be more preciously, why the insight is not delivered or used. This is caused by the complexity of an analytical work itself. In the book, we are trying to describe the Framework, how to deal with the complexity - how to describe it and understand the consequences and interdependencies. We believe that this approach will limit misunderstanding and miscommunication and allow us to define and focus on the most critical aspect of the work. The fact, that there is a common agreement and understanding of the critical area is itself limiting the probability of failure.
For example, one project could fail, as we are not able to ingest/integrate data in the current IT ecosystem because of GDPR. Another could fail because we are not able to deploy an analytical model into production with proper operational support. Another could be considered a failure because the team was not able to find a model with “good enough” analytical results or design a report, that satisfies the requirements of consumers. Potentially, it could be all together as well. Did I answer your question?
Ondrej Bothe yes, thank you very much. It is clear!
Thank you for the book. I have few questions:
1) How importance do you think to implement a strategy for analytic projects selection?
2)Is this strategy should be handled by technical team during implementation or business team during initiation or it could be hybrid.
thank you for your question.
Regarding the first part: Project selection is potentially the follow-up process. First, we need to understand the type of the analytical project and the work, that need to happen to deliver successfully (from the data, IT and stakeholder perspective). Such consistency across the projects could help us to compare the initiatives one with another and decide, where to start. Also, the evaluation of business benefits needs to be considered (a lot connected with analytical maturity).
Regarding the second part: Both IT (technical team) and business need to work closely together (potentially focusing on different areas of the project component). It is important to establish the evaluation as an ongoing process as analytical initiatives are developing over time, so the importance of different aspects is changing continuously.
Also who benefits the most of this strategy:
startups, SME with limited budget or bug corporate who might have alot of projects in a pipeline.
Thanks a lot for your questions! The framework we are describing in the book can be applied to all analytical initiatives and we hope anyone can learn and apply the learning to their data analytics projects regardless the size of the company. Biggest benefits it will probably have for folks from large companies, or corporations with higher complexity of analytical, technology and data landscape respectively.
Hey Ondrej Kubera David Bednar Ondrej Bothe Martin Potančok,
Thanks for sharing the book release.
Does the book approach analytics project as integration projects (eg plugging Google Analytics and make use of it) or as internal platform business/engineering effort? (eg build an analytic stack with open source technologies). Or both aspects are discussed?
Also, it is always good to learn from failures. Were you able to get real life analytics project post-mortems into the book?
Hi Cyril de Catheu , thanks for your question! We look at data analytics projects as any initiative for which the goal is to improve decision processes by bringing insights from data (create a new insight, automatize insight, reduce the time needed to gain insight,…). Coming back to examples you mentioned - yes, in our perspective data analytics project can a be a large internal analytical platform on one hand but also small analytics component as part of software integration initiative. We dedicate portion of the book to categorization of the types of the projects and explaining different challenges associated with each of them.
Regarding the post-mortems - we can’t disclose the details of projects we worked on with our customers in the past, but we tried to share a lot of examples of potential failures throughout the text of the book. And yes, we experienced good amount of them. Honestly the another subtitle of the book could be “what can go wrong and how to avoid it” 🙂
Does it help? 🙂
> what can go wrong and how to avoid it
I like the approach and the clear definition of “analytics project”.
Helps a lot thanks Ondrej Kubera!
Have you ever dealt with a project where there is no clear owner of a company data strategy which then means ownership in analytics projects becomes a huge point of concern? e.g. whos supporting the report once its completed or a model is developed but data scientist move on and models gather dust. How would you address this and does the book assist in complex project like this?
Hi Ash Smith, thank you.
It looks like you have already read some chapters from the book and using the example from them :-) For sure it happened many times… It is difficult to help to address it - the book is trying to highlight and categorize such challenges as you stated, and bring them into the context (as there could be many others). In case you are aware of the risks at the beginning of the project, it is much easier to set up a proper expectation and so manage the delivery in time (it is interesting, that types of challenges are changing in time, as the analytical initiative is moving ahead). Did it help?
haha Ondrej Bothe I actually havn’t yet. Just quoted some big pain points i’m dealing with right now 😄 but thanks for responding.