Big Data Analytics Chats 1: Organizational & Personal Success Factors with Cuneyt Aksakalli
By: Çağatay Kıyıcı
Tags: Big Data Analytics, Data Science, Mef Üniversitesi

Big Data Analytics Chats 1: Organizational & Personal Success Factors with Cuneyt Aksakalli Çağatay Kıyıcı Sep 7·4 min read

Cüneyt Aksakallı is my first guest for my “Case Studies in Analytics” course project in MEF Big Data Analytics Graduate Program. Cüneyt has been working on data science since 2008. He has managed the R&D and AI teams of a large-scale e-commerce operation. As a consultant, he also developed data science projects for local and international businesses especially in retail, B2B and ecommerce. Natural Language Processing is an area he has specialized in.

Cüneyt is also a seasoned entrepreneur. He has sold his first startup in retail. After long years of providing consulting services, he has recently launched a new startup offering e-commerce search engine products with SAAS business model further building on his experience on NLP and ecommerce. Building custom search engines is extremely difficult and costly for ecommerce companies. Some SAAS products in the market are trying to address this need but they require complicated manual configurations. Their e-commerce search solution is simplifying the integration and maintenance. They now have 9 customers generating over 100 millions monthly requests.

Our Discussion

We had an interesting interview for about 1 hour. In this blog post, I have consolidated Cüneyt’s insights on 2 levels:

Organizational Level:

Why do most data science projects fail?

Organizational and team level issues and how to eliminate them

Impact of improving data literacy in non-technical teams

Personal Level

Scope of data science roles in various context

Self-development in data science careers

Key capabilities in data science career

Cüneyt is interested in execution themes from our topic list. AI projects are very costly and complicated due to infrastructure costs, skilled workforce and risks involving privacy. Correct execution is the key success factor. Because, most data projects fail.

Why Most Data Science Projects Fail?

Most data science projects fail, period. Cüneyt, as a seasoned data professional with extensive project experience, started head on with this subject to our discussion. This is definitely not the sexiest subject in data science which is at the top of its hype-cycle. I believe this issue will be much more visible when the hype-cycle fades after the top.

Cüneyt elaborated on the “fail” definition. He thinks fulfilling technical requirements is the least relevant issue. Most important failure is the inability to operationalize the project and/or inability to deliver intended business results which is reported as high as 80–90% failure rate.

He has identified inability to integrate data and AI approach to the core processes of organizations as the main reason. AI and big data analytics are very strong transformative tools. Data approach should be integrated to the core processes of organizations to deliver results. This is very difficult and requires hard work. When organizations take the shortcut and position data projects as “side gig” to sit alongside core business processes, data projects lack the required attention and can not be operationalized. Cüneyt thinks orphan data projects, which meet technical requirements but deliver no business value are very common.

Orphaned Projects

One reason is the concentrated project ownership. Some data projects are initiated from top level organizations but fail to stimulate participation from functional teams. When functional teams can not participate, their experience, ideas and operational know-how will not contribute to the project requirement. Resulting project may meet top management’s requirements but will fail to deliver business value.

In some organizations, AI and data may not be understood as key drivers for digital transformations but be regarded as quick wins in marketing and PR. “me too” data projects are also, unfortunately, not exceptions in business.

Cüneyt’s approach to these problems is setting up small, diverse and empowered teams. Every functional team is represented in these project teams and bring their unique insights and domain expertise. This small team approach consistently delivered successful data analytics and AI projects.

Increasing data literacy in organizations is another powerful tool. Training on core concepts of data science boosted the effectiveness of small teams and the quality of contributions from functional team members.

But there is a caveat in collaboration: Beyond the project teams communication of the project should be handled centrally with designated team members and in a consistent way. Otherwise, communication errors may create confusion. Also, eliminating contact between developers and internal/external customers of data projects outside project teams is crucial for flawless execution. Such interactions may inject ill prepared features to projects without careful planning and trade off analysis.

Challenges of Becoming the Data Guy

There is an exploding interest for data science and AI subjects in business. Lots of hiring in data jobs and lots of candidates are trying to switch to data careers. Attention of most candidates and employers are mostly on technical capabilities. Cüneyt, prioritizes non-technical capabilities.

Communication skills to function well in project teams and collect inputs from functional team members

At least basic domain knowledge, the more the better. Good domain knowledge directly contributes to the business value of the project and quality of communication with functional teams.

Specialization (e.g. customer analytics, NLP, ecommerce, fintech, CRM, digital analytics)

Specialization with domain knowledge (e.g. customer analytics in retail, customer analytics in banking)

domain knowledge (e.g. customer analytics in retail, customer analytics in banking) Curiosity

Critical / scientific thinking capability

Specialization is good, but in most organizations data teams need to handle multiple tasks due to the small size. For example, many companies don’t have dedicated data engineering, ML Ops or visualization teams. Therefore, these tasks should be handled by data guys. These are contradicting but sad realities of most businesses and public organizations.

Among soft skills, critical / scientific thinking capability is one that connects them to technical skills. Cüneyt thinks, a solid educational background and probable doing academic studies, research, papers etc. is crucial to develop this skill. As data tools, libraries, and materials are more accessible and cheaper many data career candidates can quickly learn tools and start producing. Cüneyt believes without domain knowledge, communication skills, specialization, scientific thinking skills and lots of hard work those quick wins will not create real value to organizations.

Discussion with Cüneyt and his insights were very helpful to develop a more complete perspective of data science with personal and organizational aspects.