With the help of machine learning, companies open up new sources of revenue and reduce costs. However, to drive the topic forward in a targeted manner, managers should clarify a few essential questions.
In the past decade, data has grown in importance and is sometimes referred to as the new oil. After all, there does not seem to be an area that cannot benefit from data analysis: machine data can improve utilization. In the medical field, masses of computed tomography images are analyzed for better cancer detection.
The potential for new business models and the improvement of existing processes is higher than ever before. At the same time, it is now easier than ever to collect, store, analyze and share data inexpensively and quickly. Data lakes are therefore very popular with companies at the moment.
The question, however, is whether the benefits of the data are optimally used in practice. A lot of information, but little insight – anyone who is faced with this problem or cannot develop intelligent applications based on a data lake should heed the following seven rules:
Act In A Business And Customer-Oriented Manner!
Ask yourself what the biggest challenges for the company are. Start with a single business challenge and work backward towards the solution. Take a critical look at the tools:
Too many companies are trying to optimize their sales funnel with algorithms for self-driving cars or genome sequencing. It can be done better. Finally, there are models and preconfigured solutions for every business challenge that is up to the task and deliver higher value at a lower cost.
Work With Short Iteration Cycles!
The goal must be to get the ML system up and running quickly and easily. With small iterations through tests, proofs of concept, and pilots, the team can bring ML workloads faster and higher quality. Plan to produce a production-ready prototype in three weeks and a fully functional version in less than 90 days. Even if the system does not use the most modern models, the team will learn a lot more from the fast iteration than from a development cycle that is too long. ML transformations occur through the development of knowledge and experience – and through small, quick, and simple steps, not through multi-year planning phases.
Frequent redesigns are inevitable. Do not be afraid of mistakes at short intervals. It is essential to learn from them quickly.
Decide Wisely Between A Centralized Or A Decentralized Approach!
ML applications, like any other software, require maintenance, updates, and support. A centralized team can be effective at a low level, but innovation could suffer later. Imagine a large team working on several innovative projects. It is inevitable that at some point, a significant part of teamwork will consist of operational activities. This could be an excellent time to give the team a new home: they work for anyway within the department. It will help the ML team in the long term drive further innovation on behalf of its internal client.
Look Out For The Most Prominent Barriers Data Scientists And Developers Face
- “Dirty” data, for unstructured example records, have missing attributes or mixed data types in the same section.
- Lack of expertise
- Lack of management or financial support as ML projects requires focus and funding.
- Lack of straightforward questions to answer. Organizations are looking for improvements, but there are no specifications and clear goals to achieve them.
- Data that is not available or difficult to access
If you plan accordingly, you will find that most of these obstacles are easy to overcome. Lack of expertise? Start hiring talent before the whole company calls for experts instead of keeping the data waiting for talent. Is data not available? Start collecting data before the project starts. Is the data inaccessible? Don’t start a workshop without first asking for relevant data samples. Is the problem the lack of administration or financial support? Get the buy-in in advance. Identify those colleagues who are passionate about artificial intelligence and who can help you approve budgets and hires, make data available and connect with other stakeholders.
Overcome The Separation Between Data Science And DevOps!
“Our Ph.D. students develop ML models and write specifications. The developers then implement it in C ++.” If this sounds familiar, change the way you work in the team as soon as possible. The separation of science and production can significantly lengthen a company’s development and innovation cycles and thus lead to quality problems and a lack of responsibility for a project. There are many tools available today that enable data scientists to take a step towards engineering – and vice versa.
Pay Attention To The Relationship Between Data Scientists And Programmers!
In most cases, it depends on the maturity of the company. If the data is inaccessible or has not been maintained for years, the company likely needs more engineering and less science. However, if they already have an established data pipeline, data warehouse, and data lake, the company will probably get by with more science and less engineering. However, the company may have specific requirements that affect the skills required. As a rule of thumb, every data scientist should have two to three engineers during the construction phase and a one-to-one ratio if a system is already in use.
Use clear KPIs (Key Performance Indicators) Against Which The Success Of The Project Can Be Measured.
Imagine a program that should lead to better customer ratings. The intermediate goal of “improving user-friendliness” seems plausible initially, but it is still too unclear to measure success efficiently. The various stakeholders could later argue for a long time whether the goal was achieved or not. That would lead to wasted resources and inefficient development. More specific questions would be: “Can you measure usability improvement by the time spent on the platform, the number of videos viewed, or the number of new categories the user has explored?” Clear goals and KPIs help you make a better plan and achieve results.
Machine learning initiatives can produce extremely fruitful results. However, lack of focus, limited resources, and incorrectly set expectations can easily create frustrations. This can be avoided with an ML discovery workshop in which everyone involved discusses business and technical issues, ideas, challenges, and plans. The most significant challenges, corresponding solutions, feasibility, estimated effort, and missing skills and tools are recorded on a list. A project plan with times and responsibilities is then drawn up. However, even the best thought-out process will stall without proper focus. In this sense, observing the points mentioned above helps to exploit the potential of machine learning.