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What is Data Science?

Within Business Intelligence the role of Data Science is gaining ground. Data Science is about collecting all available data, which match each other and ensure that it can be visualized later. It differs with the traditional BI because it is much wider and brings together data that may not have the same structure at all.

The assignment that a Data Scientist performs is to combine Facebook and Twitter data with sales figures from ERP for example. This way you get an answer if the trend on social media affects sales. The application of Data Science is very wide. For example, it is also possible to use large data sets and combine them with other data. Have you entered data in a database for 20 years, which you can not work with because of the size and complexity of the different structures? The Data Scientist makes a story of it.

Data Science proces - Business Intelligence

Data is your raw material, the software is the machine that makes it a product

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 eventually take action. Inside the BI Maturity, Ladder Machine Learning is only used in the upper parts.

Another application of Machine Learning is when you actually analyze machine data. With ML you can say something about when a particular component fails. The action that follows is that the component has already been produced, even before the expected failure.

With that in mind you can think about a great deal of other examples. The only condition is that there is data. That’s the input for Machine Learning. As with any BI tool, you need a lot of data in order to say something about achieved results. The more data you have, the higher the accuracy.

Hillstar Business Intelligence levert waardevolle Power BI rapportages en Analytics

Big Data visualiseren in Power BI?

Predictive Analytics

Lower in the BI Maturity Ladder is Predictive Analytics. Here too, the more data, the better. Predictive Analytics tells about the future with a variety of assurance. This can only be done by looking at one result and recognizing patterns. But it is better to collect different data that can affect a result and then analyze what the connection is.

Combining different data is where you benefit from Predictive Analytics. In the different sources there are connections that you do not see if you do not analyze the data.

Big Data

The Big Data theme has grown strongly in recent years. There is no clear answer about what it means now. At one company, Big Data is different from that of another company. But Big Data is always a dataset that is big and often in motion. Consider for example data from trucks, machines or a web service.

Terms that relate to Big Data are therefore also often Internet of Things (IoT). Due to the complexity and pluralism of Big Data, there is also not just one software solution that will provide your company with Big Data. At this moment, the Cortana Intelligence Suite within the Azure structure is the best solution to get started.

The Data Scientist’s uses all his disciplines to make a Big Data project a success. The most important thing about Big Data is that you start with it now. At the moment, it is not very clear for people what Big Data is and what it means to you. Get started, so you know what you’re doing when it’s indispensable.

Visualisation

The final step in the Data Science process is the visualization. Without visualization, the result is just a database with numbers. By giving data a face, you understand the content and you can actively do something about the results. The Cortana’s Intelligence suite includes Power BI. It communicates seamlessly with the data from Azure ML to give an example. By starting from the question, we design a dashboard that creates value for you as a user.

The Data Scientist knows what visualizations he can apply to give the fullest possible answer to the question. Because the default pie chart is not always sufficient, Power BI has Custom Visuals. For example, a heat map can be a graphical image of a machine, showing the status of components. Or a map of a parking garage that shows you what places a car is, including the occupancy rate and average parking time. How the data is visualized thus depends strongly on the question that was initially asked.

‘The world is now awash in data and we can see consumers in a lot clearer ways’, Max Levchin

Applications of Data Science

The applications of Big Data and Data Science are very wide. With the increase in the number of sensors in operational processes, a quantity of data will come down on you, which you can not directly process into value. For this, the Data Scientist is required. He makes sure that value comes from that data because you can combine it with other data. By making smart combinations, creating visualizations that add value and ultimately improve business processes.

  • 360 degree customer image: By combining data from Google Analytics, sales systems, ERP and social media, you always know what’s around your brand or business.

  • More view on business environment: Enter external data sources and combine that data with each other. The connections that become visible provide valuable insights.

  • Identify risks better: By working partly with external data, as well as personal data, make sure the open ends are exposed. The Data Scientist makes sure that you get insights that you previously did not have. This allows you to seal all risks in advance.

  • Making more objective choices: Traditional BI provides a major improvement in the certainty on which choices are made. In that, Data Science is the next step. Compared to traditional BI, the information is more complete, compared to work without BI, it is the step of abdominal feeling towards fully-founded choices.

  • Improved forecasting: The amount of data is more analyzed. The forecasting you can do on sale, purchase or finance, for example, is also more advanced. The more data, the more intelligence. The process of acquiring knowledge from the data also becomes more difficult. Therefore, you need a Data Scientist.