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Data Science

Gain deeper insights from data and learn which choices are best
Data Science proces Business Intelligence

What is Data Science?

Within Business Intelligence, the role of Data Science is gaining ground. Data Science is the collection of all available data, which correlates with each other and ensures that it can then be visualised. It differs from traditional BI in that it is much broader and brings together data that may not have the same structure at all.

For example, one task that a Data Scientist performs is to combine Facebook and Twitter data with sales figures from ERP. In this way you get an answer as to whether the trend on social media has an impact on sales. The application of Data Science is very wide-ranging. For example, it is also possible to use very large datasets and combine them with other data. For example, do you have 20 years' worth of data in a database, which you cannot use because of the size and complexity of the various structures? The Data Scientist makes a story out of it.

Applications of Data Science

The applications of Big Data and Data Science are very versatile. With the increase in the number of sensors in operational processes, an amount of data is coming at you that you cannot use directly. This requires a Data Scientist. It ensures that value comes out of that data, because you can combine it with other data. By making smart combinations, visualising data and ultimately improving processes.

Making choices more objectively

Traditional BI greatly improves the certainty on which choices are made. Data Science is the next step in this process. Compared to traditional BI, the information is more complete; compared to working without BI, it is the step from gut feeling to fully substantiated choices.

Improved forecasting

Due to the amount of data, there is more to analyse. The forecasting that you can do on sales, purchasing or finance, for example, thus becomes more advanced. The more data, the more intelligence. Processes to gather knowledge from the data also become more difficult. That is why you need a Data Scientist.

Better identify risks

By working partly with external data, but also with your own data, you ensure that the open ends are exposed. By making connections, the Data Scientist ensures that you gain insights that you previously did not have. This enables you to seal off risks in advance.

360 degree customer view

By combining data from Google Analytics, sales systems, ERP and social media, you always know what's going on around your brand or company.

A better view of the business environment

Bring in external data sources and combine them. The connections that become visible provide valuable insights.

Machine Learning

The definition of Machine Learning (ML) is that the computer is capable of learning without being specifically programmed for it. Within Business Intelligence, ML is used to say something about the future and ultimately take action. Within the BI Maturity Ladder, Machine Learning only appears in the upper parts.

Another application of Machine Learning is when you actually start analysing machine data. With ML, you can say something about when a certain component fails. The action that can follow is that the part is already being produced, even before the expected failure moment.

From that example you can think of many other examples. The only condition is that there is data. That is the input for Machine Learning. Just as in any BI tool, there has to be a lot of data in order to be able to say anything about the results achieved. The more data you have, the higher the accuracy becomes.

Predictive Analytics

A little lower in the BI Maturity Ladder is Predictive Analytics. Here the same applies: the more data, the better. Predictive Analytics tells something about the future with varying degrees of certainty. This can be done by only looking at one result and recognising patterns in it. But it is better to collect different data that can influence a result and then analyse what the connection is.
Combining different data is where you benefit from Predictive Analytics. There are connections in the different sources that you don't see if you don't analyse the data.

Big Data

The Big Data theme has risen sharply in popularity in recent years. There is no unambiguous answer as to what it means now. At one company, the interpretation of Big Data is different than at another company. But Big Data is always a dataset that is large and often in motion. Think for example of data from trucks, machines or a web service.

Terms that are related to Big Data are therefore also Internet of Things (IoT). Because of the complexity and pluriformity of Big Data, there is not just one software solution that will provide your company with Big Data.

All disciplines of the Data Scientist are covered to make a Big Data project a success. The most important thing about Big Data is that you start now. At the moment it is not very concrete for many people. So get to work with it, so that you know what you are doing when it is indispensable.

Visualisation

The final step in the Data Science process is visualisation. Without visualisation, the result is simply a database with numbers. By giving that data a face, you will understand the content and you can actively do something with the data. Azure's Analytics suite also includes Power BI. It communicates seamlessly with the data from Azure ML to give an example. By thinking in terms of demand, a dashboard is created with which you, as a user, can create value.

The Data Scientist knows which visualisations he can use to give the most complete answer to the question. Because the standard pie chart is not always sufficient, Power BI offers so-called Custom Visuals. For example, a heatmap can be a graphical image of a machine, where you can see the status of components. Or a floor plan of a parking garage in which you can see where a car is parked, including the occupancy rate and average parking duration. How the data is visualised therefore strongly depends on the question that was asked in the beginning.

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