6 Key Challenges For Successful Data Science Projects

The objective world of data can sometimes be fraught with very personal challenges – companies are therefore often confronted with several obstacles in new data science projects. Especially with young data science teams, the same hurdles threaten to stumble on projects before the proof-of-concept. But even experienced teams sometimes reach their limits. But if companies master the following six challenges, the way to a successful data science project is paved.

Challenge 1: Use The Correct Data

Data science teams encounter the first challenge at the beginning of the project. As trivial as it seems, it is just as important: choosing the correct data. Nowadays, companies have large volumes of dynamic data available that can be called up in real-time. However, if teams try to answer business questions with static data, they potentially run into a problem that is time-consuming and, in the worst case, makes the entire project unusable: The data situation has changed. This same data, which the team has prepared over months of work, based on which models were created and information refined, is now out of date. It may take months to adapt the project to the new data – only to determine that the data situation has changed again in the worst case. The beginning of a vicious circle is based on the fact that static data is not suitable for drawing meaningful conclusions.

The solution to this dilemma lies in using dynamic data and good communication within the teams involved. For data to be available in real-time and in the right quality, a comprehensive solution must be implemented that promotes both communication and the data workflow. Robust data connectivity, web-based access, and collaborative functions in a common platform help meet dynamic data requirements. To work with the data in the best possible way, it should be clarified who is offering the data internally and where the data comes from: from an API, a production database, a data warehouse? Also, information on how often the data is updated,

Challenge 2: Establish Reusable Workflows

When data science teams create models in non-transparent environments, e.g., locally on their computers, this inevitably leads to problems. On the one hand, solutions have to be reproduced constantly, which costs time and money. On the other hand, employees from different areas do not have an overview of their colleagues’ respective projects and solutions. In the worst case, this leads to teams in other projects unknowingly working on the same problem. Last but not least, such a lack of transparency also points to data governance practices in need of improvement: If workflows are not firmly defined, at a certain point, it is almost impossible to find out how data has been treated, transformed, and used. This problem is also intensified,

The solution to this challenge lies in the establishment of reproducible workflows. The movement of raw data through the various processes such as cleansing, enrichment, modeling, and, ultimately, creating a new data set must be clearly defined and replicated as required. In addition, teams should be able to test the knowledge gained during production to increase the workflows’ performance. Of course, choosing the right technology also plays a role. To guarantee maximum reproducibility of the workflows, the following four questions should be answered:

  1. Does the tool prefer the structure of a workflow instead of static evaluations?
  2. Would the workflow be simple enough to create that data analysts could easily use and understand?
  3. Is the tool well thought out enough for data scientists to use instead of their previous solution?
  4. Does the device cover all aspects of deploying a data science project?

Challenge 3: Working Together Transparently And Comprehensively

How successfully data science projects can be implemented also depends on the company’s quality of communication and collaboration. In practice, it is often the different perspectives and expectations of the teams involved that potentially make efficient cooperation more difficult. While the technical staff are familiar with the world of data and programming and aim for the most efficient functionality possible, teams in the management look at the project with different eyes: For them, the scope, costs, and benefits of the project count.

So even though everyone involved is a professional, a lack of collaboration creates a breeding ground for misunderstandings and barriers that hinder productivity. This is especially the case when other teams and respective experts are not involved in processes from the start. Important insights can be missing here, and misunderstandings cannot be resolved in good time. Even if all teams are affected, the correct type of communication is crucial. Pure communication by email – also for exchanging files – quickly becomes confusing: files can be lost more efficiently, and critical stakeholders are not involved. Even worse, however, is a potential non-compliance with data governance guidelines, as is often the case with mail traffic. The key message is clear:

So to build a truly data-driven company, both technical and business-oriented profiles need to be incorporated into projects. And not just in their respective functions, but together for the best results. This works if technical employees also understand the scope, costs, deadlines, data types, or required visualizations. Conversely, business profiles need to know where the data comes from, whether it is reproducible or not, what the data workflow looks like, and how often the data needs to be updated.

A collaborative, workflow-oriented tool available to all team members can be an essential step towards efficient collaboration. Different skills, perfectly coordinated, can contribute to the success of a data project as a whole: Newbies among data scientists can cleanse and enrich the data and prototype basic models. In contrast, experienced data scientists can modify the models for improved results, and business analysts can gain insights into the relevance of the model based on the project requirements. A leading organizational hand serves as a bridge to the business profiles involved in the project. In a collaborative real-time environment, critical data can be exchanged up-to-date at any time and in compliance with data governance.

Challenge 4: Coordinating Different Skills

Another hurdle awaits companies, especially when new data science projects are introduced or the existing data science team is expanded. This is because experienced data scientists often meet younger colleagues who have just graduated from university. However, all the advantages that this mix of experiences brings with it are also faced with a decisive hurdle: the discrepancy between the traditional knowledge traditionally used in data science and the skills that data scientists learn at universities and colleges. Younger degrees, in particular, have a great deal of expertise in modern technologies such as R, Python, or Spark. In contrast, experienced data scientists have often grown up with technologies for statistical analysis such as SAS or SPSS and have steadily expanded their skills since then.

To succeed, companies have three options to choose from – each with its advantages and disadvantages. One way is to abandon old technologies and switch to new technologies. Such a change in the core architecture affects existing employees and projects: Getting used to long-established processes for new technologies can lead to frustration or reduced efficiency. On the other hand, newly hired data scientists can get used to it quickly and become productive with little downtime. Of course, this approach also works the other way around: A second way is to keep the old technologies and processes and train new employees accordingly.

The advantage is precious in that the productivity of the existing data science team is not interrupted. What seems efficient at first will show its downsides in the long run. By working with old technologies, the learning curve of new employees stagnates sooner or later, and over time, the knowledge and skills of the data science teams become visibly out of date. The result: at some point, the company can no longer adapt to technological innovations or hire top talent. For the majority of all companies, the third way – a hybrid approach – is probably best. Old technologies are retained, while new ones are used in parallel. This scenario gives incumbents the freedom to continue working with old technologies.

Challenge 5: Implement Project Planning Correctly

Especially in the initial phase of new data science projects, teams spend a lot of time discussing the problem and thinking about a solution. The plan for the actual operationalization of the solution – i.e., the model’s effectiveness on accurate data in real-time – is often only considered marginally. A big mistake that can be traced back to a project planning that can be improved can pose a multitude of challenges for data science teams. For example, it can happen that a model has already been developed for a project, and it turns out during implementation that the production environment is not compatible with the technology stack of the data science team. This extends the project,

To avoid such problems, data science projects should be thoroughly researched from handover to deployment. It must be ensured that the developing teams have access to the production environment or can replicate it. The importance of access to real-time data was already explained initially but should also be emphasized again at this point. The same applies to an established communication channel between the teams involved and the department that requested the development of the solution.

Challenge 6: Identify The Suitable Growth Projects

Despite some growing pains, sooner or later, the day will come when corporate data sciences teams overcome their primary challenges and establish themselves. After the first projects and solutions have been successfully implemented, it must be discussed which projects will continue. The temptation often arises to venture into unknown territory and develop a comprehensive solution that covers all possible needs – be it on the customer or within the company.

A sensible undertaking, but one in which the following should be considered: A technology ecosystem always consists of many moving components and variables, all of which are involved in developing solutions. The more complex the project, The makes the development of such a comprehensive and complex solution all the more costly and time-consuming – not to mention the maintenance effort in the future. If the resources in this company are distributed incorrectly, the growth potential of data science teams can ultimately be jeopardized.

In any case, data science teams should grow with their projects. Only when smaller projects have been successfully carried out can one devote oneself to more significant challenges step by step. That means smoother but also more successful growth. When undertaking large, comprehensive initiatives, companies should be aware of the investments they will have to make in this case.

Funds should then be invested more in applications that create real competitive advantages and less in those that serve technical fundamentals. Such solutions often already exist and only cost data science teams time and companies money. Open-source providers can be a good alternative here to limit costs, Save time and solve unique business needs within a provider ecosystem. In addition, the community idea is currently growing strongly with such solutions: Mutual support and insights help to know the roadmap better in advance and concentrate on the aspects of the project that are needed.

The Key To Success: Functioning Data Science Teams

Data science teams are complex and nuanced organizations with different types of people using other tools but all working towards the same end goal: successful data science projects. When collaboration doesn’t work correctly, the end goal suffers, and data science projects may never finish, be inefficient, or ineffective. Therefore, companies should repeatedly question their status quo, talk to different teams, and promote a culture of mutual inclusion and transparent communication.

Also Read : High-Performance Computing Is The Key To Tomorrow’s Mobility

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