Thanks to Data Science, we are able to make better, faster and more efficient data-driven decisions, based on existing data. With the help of Machine Learning, we can recognize patterns within data from the past in order to make statements about new data, or even predict the future. For many it still seems like a "far-from-my-bed show" to apply these techniques within their organization, but it is becoming easier and more accessible to implement this in practice.
Out-of-the-box models and algorithms are available for free and easy to use for everyone. It remains only a matter of preparing the right dataset and making the right choices for the models based on the data and the purpose. To do this, we link different data sources, select the right data from them and process and filter this. In this way we make data understandable and descriptive for an algorithm to be able to learn the patterns in the data.
Data Science solutions can be divided into three categories: Predictive, Automation and Optimization. On the basis of these categories it is also very easy to clarify that Data Science does not have to be a complex matter.
With Predictive Data Science we replace intuition and experience with data and we reduce the error margin of forecasts. Predictive solutions use advanced analytics, and by looking for patterns in current and past data, we are able to predict the future
There are multiple points where an organization can integrate predictive solutions for improved day-to-day operations. For example, a manager can allocate resources to new projects based on accurate predictions about when ongoing projects will be completed. HR departments can ask to hire more staff if they expect a heavier workload soon. And in sales, accurate forecasting is critical to budget allocation, supply and demand management, performance driving and business roadmap creation.
Some easy to spot examples of Predictive Data Science are:
– Provide accurate sales forecasts;
- Predict bill payment;
- Forecast annual sales for a new branch;
By automating with the help of data science, we make existing processes better, faster and cheaper. Automating simple processes is something we already see a lot. But with the help of data science it is possible to automate even the most complex processes. A good example of this is a chatbot. As the technology behind chatbots has evolved, chatbots can now leverage Natural Language Processing (NLP) and have grown from simple tools to indispensable tools that can interact with consumers in a very human and personal way. These smart chatbots can be applied in practice in various situations such as customer service and sales.
The processing of customer reviews can now also be automated in a smart way. NLP can look at what customers say, identify specific recurring topics, look at customer sentiment and understand the consumer conversation as a whole. Using machine learning, the data from these reviews is analyzed and used to build customer profiles and identify patterns that occur to provide insight into how customers think and act.
Some easy-to-recognize examples of Automation with Data Science are:
– Use chatbots as customer service;
- Offering alternatives in web shops;
- Classify and label customer reviews;
Many processes can be optimized on the basis of data. By linking the right data sources together and selecting the right data from them, we make processes more efficient, faster and save resources. Examples of what we can do with Optimization:
– Determine the most efficient picking route for an order picker;
– Optimize delivery routes and truck loading;
– Optimize inventory management for retailers;
A data science project is guided in the right direction by means of a proven standard trajectory for data science projects for companies. This data science trajectory is a systematic approach to solving a data science problem. It provides a structured framework to formulate problems as a question, decide the solution route and then present the solution to stakeholders. The process has been proven to be successful, always takes place in continuous coordination with relevant stakeholders and has the following six phases:
The first step in the process is to clarify the business goals and bring focus to the data science project. Within a clear definition of the goal, in addition to identifying the values to be calculated, it must also be defined how these values will influence business operations in practice. To better understand the business, data scientists meet with subject matter experts and others who can provide insight into the problem at hand. They can also do preliminary research to see how others have tried to solve similar problems. Ultimately, a clear problem is defined along with a roadmap to solve it.
The next stage is understanding the data. In this phase it is determined which data is available, what the data contains and what its quality is. It is decided which tools will be used for data collection and how the initial data will be collected. Next, the properties of the initial data are described, such as the format, quantity and available fields of the datasets. After collecting and describing the data, a first data exploration is done. The first hypotheses can be formulated and the quality of the data is validated to identify erroneous or missing data.
Data from different sources is usually unusable as raw data, because often fields are missing, or there are conflicting values and outliers. These issues are resolved during this phase and the quality of the data is improved so that it can be used effectively. In addition to improving data quality, new fields are also generated in this phase by means of transformations on existing fields. The goal is to generate good descriptive or predictive fields that a model can use to recognize and learn patterns in the data. This phase is often run through several times during the project to iteratively optimize the final model.
During this phase, based on the available data, it is specified which modeling techniques are suitable for solving the problem. This also includes the assumptions made per model. Tests are designed for the selected modeling techniques to determine the quality of the model. It is also decided which data will be used for training the model and which for evaluating the trained model. Subsequently, the models are developed with a description of the models and the parameter settings used. Finally, an assessment is made of the models, with a ranking of the various models. After this assessment, the parameters used can be revised to start a new round of modeling.
During the evaluation phase, the models are evaluated based on the business objectives. The process itself is also evaluated and any corrections made. A summary of the findings and the model is presented. Finally, it is determined whether the model is ready for deployment, or whether a new iteration is needed to further optimize the model.
During this phase, the model is implemented. The model is put into production to use live data and process it to the desired output. In addition, the output of the model is monitored to monitor the quality. The project is concluded with the delivery of a report of the process and the model, accompanied by a presentation to the stakeholders.
With a combination of the right know-how, ready-made models and algorithms, countless available examples and a proven standardized process, it is now possible to successfully realize data science solutions in the foreseeable future. This makes it possible to realize low-threshold added value, without costs having to skyrocket.
Do you want to know more about Data Science? On September 7 we will give a webinar. In this webinar we show you that data science is no longer a far-from-your-bed show. From start to finish, we take you on a journey through how to set up a data science trajectory with practical examples that show that you can achieve a lot of results with a small investment. You can register via this link.