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  • Hi everybody! In this video, we will focus on a fascinating

  • topicthe step-by-step process IBM’s data science team applies when working on

  • a consulting project. We believe this overview can be highly beneficial for both experienced

  • professionals and data science beginners. Well explore a best-practice framework

  • applied by one of the pioneer and leading companies in the field. This way, youll

  • get an insider’s look at how a consulting project that involves data analysis and data

  • science unfolds. In addition, well examine the results achieved

  • in IBM’s data science consulting projects with major clients from different industries.

  • Why is that important? Well, each of these initiatives serves as an invaluable lesson

  • to the rest of the companies in the respective industry. If, for example, Carrefour managed

  • to leverage AI to improve its supply chain processes, the rest of the global hypermarket

  • chains would basically be obliged to follow, if they want to keep up.

  • Alright. Let’s get right in and outline the five

  • stages of a data science consulting project. Stage one - engage the firm’s CTO.

  • Stage two - meet with the company’s SMEs and brainstorm.

  • ThreeData collection and modeling through coding sprints;

  • Four - Visualization and communication of findings;

  • And finally - Follow-up projects; Each of these steps of the process is vital,

  • so let me elaborate a bit further by describing them one by one in more detail.

  • Things start with a conversation with the firm’s Chief Technology Officer.

  • He needs to be sold on the project. Hopefully, this would result in him championing and endorsing

  • the initiative across the organization. Such buy-in enables cooperation and improves the

  • project’s chances of success. At this stage, the consulting team and the CTO will define

  • the scope of work and thelowest hanging fruits’, which will give an immediate boost

  • in terms of bottom-line results. What we mean bylowest hanging fruitis an opportunity

  • that the data science team knows is available for most companies in an industry and is easiest

  • to implement. For example, they have seen on a few occasions that supermarket chains

  • can greatly reduce food waste if they implement a predictive AI model able to adjust the timing

  • of deliveries. So, an absolute best practice when working on consulting projects is to

  • address such opportunities first, because this gives instant credibility to the project

  • team and wins support across the organization. Once the project scope has been identified

  • with the firm’s CTO, the data science consulting team will proceed to brainstorm on how AI

  • can be applied in the particular use cases that have been pre-selected.

  • To envision this a bit better, the team needs to conduct a series of interviews and meetings

  • with Subject Matter Experts - the people who work in the business day in and day out and

  • who are able to contribute greatly in terms of identifying actionable and meaningful solutions.

  • Also, in most cases, SMEs are the ones who have a good idea of what data is available

  • and can be used for the purposes of the project at hand.

  • The next stage consists of coding sprints. This is the main chunk of the work, so IBM’s

  • team organizes it in three parts. One for Collecting data and feature modeling

  • Data collection sounds likegetting the data from all places’, but it may be much

  • trickier. Depending on the scope of the project, the consulting company may need to first consolidate

  • all data in one place, called ‘a data warehouse’. In some cases, not enough data is being collected

  • and new data sources must be set up. Feature modeling is inside this step as features may

  • be chosen from the available data. Sometimes, however, very important metrics are not being

  • measured. The consulting firm can then suggest starting to collect data on that, thus changing

  • the data collection structure of the client. Another sprint for feature selection and running

  • the model for the first time Once data has been collected and features

  • have been modeled, it is time for some data science.

  • While features were modeled and kind of selected during the first sprint, they were never tested

  • in a model. So, in the second coding sprint, features are evaluated, transformed, or new

  • features are engineered, this time for predictive modeling purposes. Once this is done, the

  • first models come to life, showing the potential to the stakeholders in the client company.

  • And a third sprint to fine-tune the model and adjust it as per client requirements

  • The moment a solid model has been thought through and executed, the fine-tuning begins.

  • There are many ways in which a model can be improved. A 1% increase in accuracy could

  • imply millions of dollars in savings for the client company. Therefore, this step should

  • not be overlooked even if it sounds like the least exciting one.

  • Okay. Moving on to the fourth stage -data visualization.

  • Data visualization plays a critical role in most data science projects. However, please

  • bear in mind that the specialists who build a model are not always the ones best equipped

  • to work on the visualization of its findings. When presenting in front of a non-technical

  • business team it is much better to show Tableau or Power BI graphs rather than a Jupyter notebook.

  • And hence, the data science consulting team needs skills related to chart and dashboard

  • creation, as well as the ability to communicate in an effective way. It is not uncommon to

  • have a person whose job is to solely style such findings, giving the final touch to the

  • presentation. And this is how we reach the fifth stage,

  • namely, Follow-up projects As with any other type of consulting, the

  • secret sauce of being a successful consultant is to be able to sell the next project. And

  • then to sell the next one after that. And so on.

  • The premise is that if the consulted company sees a measurable bottom-line improvement,

  • they will certainly want to retain the consulting team and will be willing to purchase additional

  • services - from IBM in our example. This is also why consulting firms prefer to start

  • with low hanging fruitsthis allows them to show they can create value very fast. And

  • hence they improve their chances of being hired again.

  • Alright. Now that weve figured out the typical cycle

  • of a data science consulting project, let’s take a look at some of the successful use

  • cases IBM’s elite data science consulting team helped with.

  • Starting withNedbank. In the case of Nedbank, a South African bank,

  • a model predicting ATMsneed for repair was implemented and this led to important

  • efficiencies in terms of ATM reliability and maintenance timeliness.

  • In another project, IBM’s data science team helped JP Morgan implement a model, which

  • prevented the bank’s traders from engaging with trades that are not recommended by JP

  • Morgan’s powerful predictive models. Experian is one of the leading companies in

  • the information business industry. They analyze credit payments on a global scale for a number

  • of institutions. In this case, IBM’s team helped Experian leverage unstructured data

  • and combine it with structured data (that was traditionally used in Experian’s models)

  • to build a more comprehensive view of the businesses Experian is hired to analyze.

  • One can argue that data science and AI consulting is a business in its infancy. And it appears

  • that the most important ingredient, IBM’s team has mastered, is the combination of technical

  • know-how in terms of data science modeling and business understanding.

  • Truth is, a successful data science project needs both. This is precisely why we try to

  • teach you how data science can be applied in a business context in every course of the

  • 365 Data Science program. So, if you’d like to explore this further or enroll using a

  • 20% discount, there’s a link in the description you can check out.

  • We hope you found this video helpful. If you enjoyed the topic, don’t forget to press

  • the like button and subscribe to our channel here on YouTube. In the upcoming months, we

  • will prepare tons of other useful career-oriented data science videos you don’t want to miss

  • on. Thanks for watching!

Hi everybody! In this video, we will focus on a fascinating

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B1 中級

2020年IBM如何做數據科學諮詢 (How IBM Does Data Science Consulting in 2020)

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    林宜悉 發佈於 2021 年 01 月 14 日
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