What is Data Science: techniques, tools, examples & 7 tips for a remarkable start

What is Data Science?

Here we provide a data science definition that anyone can understand:

Data Science is the continuous process of selecting potentially relevant data sources, filtering them, cleaning them, deeply understanding them, analyzing them thoroughly, visualizing them beautifully and extracting business value from them.

This process is well represented by the following figure in which you work up (big) data into information, insights and knowledge. You do this with or without the help of statistics and algorithms. You then translate this knowledge into the best actions (for that moment) and process improvements.

Figure 1: The purpose of data science reflected in the pyramid: from raw data to the best actions and process improvement.

In other words, you’re going to use data science purposefully to solve problems and issues in your organization. Data science is not a plaything of the data scientist but a practical science that is going to help you make better decisions. Especially operational decisions where judgment processes play a big role and many of them are made every day.

What is data science about?

This field is concerned with the processing and analysis of large amounts of data, and/or unstructured data such as videos, emails, sound clips, tweets, sensor data, etc. Data Science focuses in particular on the development and application of machine learning models. These models look for patterns and correlations in data and make them visible immediately. To make it easier to understand, we will give an example of data science for beginners: think of computers that may or may not independently bring out patterns in data and learn from them.

Assess the real Data Science meaning

You’re going to understand better the real meaning of data science if you consider where all the assessment processes are taking place in your organization. These processes assess, for example, how much an object is worth, whether it is justifiable to give someone a loan, and what the risk of fraud is in an application or claim. They also calculate, for example, what the fastest route is for a delivery driver or a salesman who wants to visit a series of customers. The application possibilities of data science & big data analytics are enormous. The trick is to collectively discover those applications in your organization. Data science, according to Hal Varian, a respected economist at Google and professor emeritus at Berkeley, will be an incredibly important, crucial competency for organizations in the coming decades.

A global data generation process

Whether it’s purchases or sensor data, searches, smart meters or audio recordings of phone calls. Customers and machines are becoming an increasingly important part of a global data generation process. We can still barely comprehend its scale and impact. But by mixing these different internal and external data sources you can eventually arrive at completely new and unexpected insights. With these you can then create new, valuable data products or data services. That, in a nutshell, is the challenge you face with Data Science management.

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The 25 key benefits of Data Science artificial intelligence

Through our years of experience with AI, Data Science & machine learning, we know better than anyone where and how to reap the benefits of Data Science. You don’t just look for quick wins (developing a standalone data science application), but also look for the long-term benefits when you start using it structurally and organization-wide. Here we list all 25 benefits of applying data science techniques:

Figure 2: The top 10 advantages of data science are summarized in a more visible way.

1. With data science analytics you avoid a jungle of spreadsheets
2. With data science tools you will achieve more sales and better margins
3. It radically accelerates assessment processes in your organization
4. With Data Science machine learning you can personalize or differentiate more efficiently
5. Data Science lets you easily combine and analyze all kinds of (big) data
6. It unburdens the IT department and operational systems
7. You develop one version of the truth, although it is not cast in concrete
8. Employees, teams and managers perform better through data science
9. With Data Science AI you prevent information overload
10. It acts as a driver for the creation and management of new knowledge
11. Data Science allows leaders to be more visionary and coachable
12. The delicate balance between brain and intuition can be improved
13. With Data Science Analytics you stimulate creative search behavior that opens new doors
14. Through continuous exposure to reliable data you know your business model better
15. With BI data science you create more involvement and loyalty among your employees
16. You will have more transparency with data science tooling and can also prevent fraud
17. Data science helps you improve the management of business risks
18. Your company becomes more flexible
19. It stimulates innovation through insights that indicate that your strategy is working
20. With data science you get a better grip on dynamics, market forces and turbulence
21. With Data Science predictive models you can anticipate and predict more accurately
22. You can start improving your data quality with data science
23. Data Science analytics effortlessly combines and analyzes unstructured data
24. It can create a more sustainable world by reducing waste
25. People can thrive better in a streamlined, healthy organization

With the above data science benefits at hand, you can now start making the business case for data science big data and describe it. If you want support in setting up or further professionalizing data science, contact one of our data science consultants here.

The choice of Data Science tools is huge

The market for Data Science tooling is growing and changing almost every day and we monitor it continuously with the BI & Analytics Guide 2024. Our data science study shows that, in addition to the well-known, larger players such as Microsoft (with data science Power BI), SAS (with Visual Analytics), IBM (with Watson Analytics), SAP and Tibco, open source has taken off within the field. Many interesting developments are taking place in this area. A lot of time is being put into the further development of programming languages like R, Python and data science tools and platforms like Hadoop, Dataiku and RapidMiner.

• R offers many different statistical and graphical techniques, such as linear regression and nonlinear models, classical statistical tests, time series analysis, classification, clustering, etc. It is fairly easily extensible also due to the object-oriented design of R.
• Python is an object-oriented, extensible programming language with powerful libraries for data manipulation and analysis.
• You can use both R and Python in conjunction with Hadoop and its MapReduce routines.
• RapidMiner is a platform of which only the core is open source. It provides an integrated environment for machine learning, text mining, data mining and predictive analytics.

So tools for data science BI are plentiful, but how do you ensure that you can also become successful? After all, out of ten data science & big data analytics projects, only one project eventually makes it to production, according to numerous international data science studies. It is our mission and passion to make a significant contribution to improving that success ratio. The crux is in the assessment processes.

It’s the assessment processes that make or break Data Science

In quite a few organizations, you still see that promising data science, artificial intelligence applications usually disappear from the scene quickly again (the so-called one-day wonders). There is a lot of experimentation going on, everyone is enthusiastic, but the direction is lacking and even a glimpse of a vision of the role of data science is often hard to find. The solution to this is to first get a good picture together of the decisions that are being made or should be made in your organization. By mapping these, you can link business analytics and data science to concrete decisions. The diagram below can help you with this.

Figure 3: As with data analytics, you purposefully link data science to decisions in your organization.

Start first with the operational decisions that are made daily, weekly or monthly. For example, the decision whether or not to give a startup a loan. And then go through all the steps in the diagram: reason back to the knowledge, information and data. And then pick up on the actions, performance, reflection and experience. Then think about how you could automate all the steps. In this way, all those involved can become much more aware of the added value of data science AI and the direction can be better organized. You thus leave the experimental phase and start structurally embedding data science in your processes. The total impact that data science can have increases exponentially as you allow more processes to be monitored or controlled by algorithms.

Predict maintenance with Predictive Maintenance Data Science

One of the most discussed applications is predictive maintenance with data science or Predictive Maintenance Data Science. Here again, a decision plays an important role: when to perform preventive maintenance. Whereas traditional organizations routinely perform preventive maintenance on every machine or machine part every few months or year, predictive maintenance data science aims to do it precisely at those moments when the chance of a machine failure or breakdown (a KPI example) is very present. With photos and sensors, you can trigger a data stream that you analyze with data science machine learning. Specifically, you have the patterns detected that give an indication that a component is about to break down. By only performing maintenance when it is really necessary, you not only save a lot of money, but your production capacity also increases and you don’t throw things away that are still working fine. Data Science gives you the tools to differentiate in detail, in some cases fully automated. This is how you go from that shotgun blast to a precision bombardment.

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How do you set up data science management properly?

To manage data science well, you first of all need a fresh look at the field. This is how you develop a sustainable and supported vision, so that everyone in your organization is aware of what the role and added value is. A few things are of crucial importance:

Figure 4: The Data Science Process shows what are the steps to implement data science in your company.

• Business Data Science: the business managers and decision makers are leading in developing data science applications (see the aforementioned comments on assessment processes). It should not be IT data science or an IT party. So the business is at the helm, IT supports.
• Data Science manager: this manager coordinates all strategic and operational data science within the organization. They report to the board or a member of the board. This manager is a bridge builder, knows the business inside out and makes the translation to IT. See also BI manager.
• Data Science roadmap: business and IT make a roadmap under the inspiring leadership of the Data Science manager. This contains a number of fixed elements: the strategic spearheads of the organization and how data science will contribute to these, the products and services that data science provides, the data science team with its various roles, the necessary data infrastructure (ETL data science) and the hardware and software.
• The impact on people: an often underexposed aspect of data science management is people. An overly technocratic approach to data science leaves people out in the cold while people management and change management are crucial to success. When decisions are made automatically by algorithms, you can expect resistance from decision makers who are sidelined. So think carefully about how you want to deal with this in your organization.

Data Science management takes the business and the decisions as its starting point, appoints a bridge builder as data science manager, develops a joint roadmap and has an eye for the impact of business analytics & data science on people.

Data Science Book

What Data Science techniques do you use to achieve results?

With data science predictive models & predictive analytics you are going to try to predict what might happen in the future. You are going to look for patterns in data that have predictive value. To do this, you will use the following concepts and data science techniques.

The Artificial Intelligence concept

With Data Science Artificial Intelligence you will develop (self-learning) computer algorithms that are able to discover existing or new connections in (big) data and make decisions themselves.

The goal is to drastically improve the effectiveness and efficiency of a process. Read more about AI here.

Machine learning is a specific technique of AI

In the field of data science machine learning computers acquire knowledge themselves without you having to explicitly program it. In fact, machine learning is learning from data by recognizing patterns in the data. Machine learning has three different categories: supervised machine learning, unsupervised machine learning and reinforcement learning. You can read more about machine learning here.

Deep learning is a specific form of machine learning

Deep learning is a specific form of data science machine learning in which algorithms learn by themselves from (large amounts of) data. In this process, an attempt is made to imitate the abilities in the brain of a human. It enables computers to solve very complex problems, precisely when using very diverse, unstructured data sets that have relationships between them.

Data mining data science is a synonym for machine learning

Data mining is where you go to find connections, patterns and correlations in structured data using machine learning, statistics and database techniques. The goal is to gain new insights that are “hidden” in the data and to acquire new knowledge.

Process mining data science: applying AI to event logs

The technique Process mining is the umbrella for a collection of data science techniques, data science methods and data science tools with which, using event logs, you will uncover, visualize, analyze, monitor and improve the actual course of business processes. Read more about process mining here.

Computer vision: recognize the flower

‘Computer vision’ literally means that the computer can see. When using supervised learning (but nowadays also unsupervised) you teach the computer to recognize an object in a picture, for example a flower. But the most common application of computer vision is face recognition. In this data science method, you use neural networks. After you train the neural network, it is able to tell you on its own whether there is a flower in a new photo or not. It gets really interesting when the neural network is trained to recognize abnormalities in plants, animals or end products, for example. An algorithm that independently performs a quality check and decides whether the end product can be sent to the customer is no longer unique.

Natural Language Processing (NLP) understands you and talks

This data science technique focuses on learning to understand language, writing and speaking. It combines techniques from AI and linguistics. NLP is often applied to digital assistants or customer service chatbots. But search engines and translation platforms also make extensive use of this technique. Nowadays, NLP-translated texts are of comparable quality to those of a human translator.

Forecasting & optimization

This category of techniques and data science methods focus on predicting trends based on historical data. Think about forecasting the prices of real estate, fuel, steel or other commodities by analyzing the patterns of a series of variables. When you learn to apply forecasting more and more effectively, you can make “the best buy” earlier than your competitors. This does not always mean that you buy more or earlier; buying less or later can also be of great benefit to an organization. This allows optimization to take place because you buy the right amount much more precisely. Finally, forecasting can of course also be applied to other processes than the purchasing process.

Beat the complexity of data science methods

The above all sounds complex and complicated but in fact all these methods and techniques boil down to using brute force computing to quickly find patterns in your data and turn them into a model. For one situation you use a data science method based on decision trees, in another situation you use linear regression or genetic algorithms. You use these methods to help the computer learn, without having to explicitly program it. Neural networks, for example, try to mimic the brain of a human being. These are also very dependent on the computing power of (large) computers. In addition, there are ready-made libraries with a wide range of techniques and methods that you can use immediately. So you don’t have to invent the wheel yourself every time. So don’t be put off by the apparent magic of data science. But when all of a person’s senses (hearing, seeing, smelling, and so on) can be better dismissed by data science techniques, it’s time to pay attention to ethics.

These 5 data science tips increase your chances of success

Finally, a handy checklist and 5 tips to increase your chances of success with Big Data Science in your organization.

1. First, develop a shared, organization-wide vision of the field, stay away from the technology initially, but experiment with it.
2. Inventory the operational assessment processes in your organization. That’s where the potential opportunities for successful data science applications lie.
3. Be aware that with data science your data quality must be of a high level, otherwise you run great risks. Incorrect data in a report can be noticed relatively quickly, but not in an algorithm that runs under the hood.
4. Put together a data science team that is not just made up of techies. Also make room for business consultants, data analytics translators and business analysts.
5. Be very aware that data science, AI, machine learning (and of course robots) can have a big impact on the current and future work of people in your organization. You can always expect resistance.

Want to read more data science tips? Then read the article ‘8 effective ways to make data science work for you‘.

About Passionned Group

Passionned Group is the specialist in Data Science issues and solutions. Our seasoned data science consultants help larger and smaller organizations transform into intelligent, data-driven organizations. Every other year we organize the Dutch BI & Data Science Award™.

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