Before any fruit and vegetables are sold in supermarkets, they are first harvested from various farms and orchards. Then, they are sorted into their respective categories, cleaned to remove any bacteria and soil, and packaged uniformly during the preparation stage. Finally, depending on store orders, they are sent out to various locations.

In the data analytics world, raw data goes through a similar process before it can be analyzed and reported on — and this is where ETL comes in. 

There are three main steps in the ETL process — Extract, Transform, and Load.  Together, they work to prepare your raw data for analysis and reporting.

Check out the video to learn more about the ETL process: https://youtu.be/kmS7nMK2_3k

ETL (Extract, Transform, Load) Part 1: https://youtu.be/CyVvglLMLew

For more info on ETL, visit: https://cubeware.com/ 

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In the food industry, quality control ensures that the meat, bread, and other edible items reaching you are of the best quality. Companies use various ways to manage this, from developing fixed product formulations to standardizing manufacturing processes. By following these procedures, the quality of food is kept consistent and reliable for the consumers.

In the same way, companies also need to manage and control the quality of their data. This has become increasingly important due to the enormous amount of data that’s become available and accessible in the recent decade.

As a company’s datasets grow, it’s critical to ensure that they are consistent and accurate for analyses and insights. After all, data-driven decisions are only as good as the data itself. With datasets regarding customers, finance, operations, and sales comprising the core of most organizations, Data Quality and Data Quality Management only become more vital.

In this three-part series, we’ll be covering what Data Quality is, what Data Quality Management is, as well as why both are important.

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ETL is defined as a method or process of extracting, transforming, and loading raw data from various sources into a single and centralized location. These can be data warehouses or other centralized data storage systems.

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What do you do when you outgrow your reporting system?

Meet Farmer X. They’re a manufacturer and distributor of organic foods. However, over the last decade, Farmer X experienced a huge sales growth — this was not so great for their reporting system, which was all completed on cloud-based spreadsheet applications.

With the Cubeware Cockpit, Farmer X was able to complete analyses and generate reports — both planned and ad-hoc — with swift accuracy, detail, flexibility, and transparency. 

Not only did this free up Farmer X’s budget, resources, and time from manual reporting, but the employees of Farmer X were finally able to focus on bringing their visualizations to life.

Check out the video to learn more about Farmer X's success story: https://youtu.be/0iQHPEoRwzM

To learn more about the Cubeware Solutions Platform and its applications, click here: https://www.cubeware.com

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What do people mean when they refer to Dark Data? And how is Dark Data different from regular data? 

Almost everything we do involves data —we’re either generating it or receiving it, whether we’re aware of it or not. And this adds up to, well, a lot of data — and to no one’s surprise, a lot of it is left unused. 

In short, Dark Data refers to the data that we often collect but do not use, especially for Business Intelligence purposes. 

Often times, Dark Data is what leads to businesses experiencing the DRIP syndrome, which stands for Data Rich, Information Poor. This happens when companies gather a lot of data but don't have the tools in place to turn it into valuable information. 

Well, we’ve got a lot to cover so let’s dive into more detail as to what Dark Data is and what its advantages and disadvantages are.

Check out the video to learn more about Dark Data: https://youtu.be/pbNUZU-FaSA

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In our two previous videos, we discussed what Prescriptive Analytics is, how it works, and what its examples look like. To check them out,  

[What is Prescriptive Analytics?: Part 1] https://youtu.be/pzvT6Z_b6MA

[Examples of Prescriptive Analytics: Part 2] https://youtu.be/NOo8Nc9zG20 

Despite being a pivotal advanced analytical approach, Prescriptive Analytics isn’t commonly implemented as it requires an assortment of tools and a complex understanding of the method. Companies that do employ Prescriptive Analytics, however, are able to achieve stronger goals by understanding how different actions result in different outcomes.

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Prescriptive Analytics is a type of data analytics that recommends a course of action users should take in order to achieve a desired outcome or goal. It comes after the stages of descriptive and predictive analytics.  For more info on data analytics, visit our website at www.cubeware.com

What is Prescriptive Analytics? [PART 1]

What Are Examples of Prescriptive Analytics? [PART 2]

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Prescriptive Analytics is a type of data analytics that recommends a course of action users should take in order to achieve a desired outcome or goal. It comes after the stages of descriptive and predictive analytics.

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You share a lot of data with a lot of companies — these collected datasets need to be stored securely to prevent any cases of stolen, misused, deleted, or altered data.   

Many businesses today have migrated to the digital landscape, rendering it especially critical that they implement measures that protect their digital assets. After all, companies used to protect their physical assets, so now it's time they learn to do the same with their digital ones.  

In this podcast, we’ll cover what Data Security is, what its solutions look like, and why it’s important.

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During the 2012 Brazilian Grand Prix, Sebastian Vettel was in the running to win the World Championship for the 3rd time. However, during this decisive race, Vettel’s car was hit from the back early on.

If this were any other sport, the competition would have been over. However, Vettel’s analysts and engineers swooped in the moment the impact occurred. Teams of analysts spanning across continents work together in real-time on data analytics to churn out insights and solutions.

So, let’s dive into how Data Analytics was used to help Sebastian Vettel clinch his 3rd Championship.

For more info on data analytics, visit our website at www.cubeware.com

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