Analytics

Data analytics

Every business needs customers to survive. Business need to acquire customers, nurture them, make them happy, solve their issues and money from them. To do this businesses need to know the who, what, why and the how of their customers. Data Analytics gives businesses the insights they need to make these decisions.

Data visualization

Did you know 30 to 50% of your brain is devoted to visual processing. Human beings are wired to be visual. It's also been proven that we have 323% better performance on tasks when we learn those things with images compared to without images. You'll remember 60% of something when you learn it with imagery verses 6% without it.

Customer analytics

To be ahead and act proactively, businesses need to know what their customers might do in the future. Will they buy your product? Will they switch? Are they happy with your product? Are they going to be dissatisfied? Will they buy more? Businesses need answers to these questions to identify the right customer, channels and offer.

Data mining

Data mining is not merely statistics on big data. Data mining algorithms were created in a post-computing environment to solve post-computing business/complex problems. They are qualitatively different from traditional statistics techniques. When traditional techniques are used, they're used in the service of different purposes.

Data management

Your business depends on and generates data. Product orders, government records, student transcripts, emails, project documents, even chat logs. These data can piles up quickly. Causing risk for organizations whose operations depend it. How do we make sure that we can access the data we need when we need it? Data Management

Data modeling

Data Modeling help businesses predict customer's future behavior, target prospects and identify additional products the customer might buy. When customers have problems, models can identify the right resources to solve the problems. Businesses can achieve this with lower cost and higher effectiveness than traditional means.

Some visualization principles

Some scientific principles behind how we process visual information. It really helps to understand these principles when you think about how to visualize data.

Proximity. Human beings are by default, see things that are near each other as being together. So for instance, when you look at a scatter plot, we see patterns, right? We see the dots that are all clustered near each other as having something to do with each other. This is just how we recognize patterns.

Next one is Similarity. We see things that look similar and assume that they go together. So this one seems to override proximity, right? So we'll sort of categorize things in this way. Whether similarity is in shape, or in size, or in color.

The next principle is known as Parallelism. The idea here is that we will see parallel lines and assume that they go together or that they have something going on that's similar and the things that aren't parallel, are different, right? And again, this is deep brain science. This is how we all perceive these things.