What is Data Visualization or how to interpret major amounts of data?

Data Visualization allows data analysts to gain insight from their Big Data much more quickly. It is also primarily used as an ideal communication tool between analysts and the company management.

When Big Data are compressed, a new Data Visualization challenge is generated due to the amount and diversity of the variables and data handled. Analyzing information through images is simpler, so:

Managers and Data Scientists using Data Visualization to present their data are 28% more likely than those using traditional dashboards to find data relevant for their analysis and predict trends in their business. This

Improves decision-making
Reduces time spent on analysis
Facilitates trend prediction

Data Visualization Principles

Data Visualization is the most optimal way to expose the myriad of data within the reach of companies in the digital era, that is, Big Data. Thanks to this visual display of hundreds of variables and crosses* that a database can include, data analysts have a tool that can allow them to extract and present business insights much more quickly and reliably.

Principles for properly displaying information:

Simplification: present the right amount of variables.
Comparison: facilitate understanding and prevent mistakes from memory. It is important to be able to visualize all of the data in a single image.
Know where and how to show the desired information to draw attention.
Diversification: different representations of the same data may lead to new conclusions.
Look for the reason why: Data Visualization’s goal is not to ascertain what is happening but rather to provide an easily interpretable way to display the myriad of data that a company has.

Data Visualization Techniques

Generating a graph from an excel table or even from SQL like those that have been used over the last few years is relatively simple. The variables barely exceeded 100 columns and not all were related to each other. However, as variables are added and interrelated, things get more complicated. Much more complicated.

For structured data, such as sales, revenue, and so forth, simple graphs are more than enough. But for unstructured data such as texts or images that have been captured by Big Data systems, other techniques are needed to adequately display the information.

Relational Graphs

These are particularly useful for studying customers’ relationship with a company and the our products’ degree of penetration in each household. For instance, of a given telephone company’s customers, how many have only a cell phone line, how many have an entire package and how many have shown interest in additional services such as TV on demand or various types of coverage? How do these products relate to the consumers and what are the social and demographic data that match these groups?

Word Clouds

Word clouds are used to find the mode in a series of texts. They are particularly advisable for gaining insight from active listening to social media as they enable you to relate your brand or specific key words to comments about them, the feeling (good, neutral or bad) that marks these comments, and the volume of interaction they generate.

Designing Heat Maps

These maps can be grids or cartographic. They may be used to compare data that are very different from each other, for instance a company that wishes to measure the geographical penetration of its product or its potential clients in a given area as compared to their socio-economic status and their acquisition of similar products over time.

Grid

Mapping

Dispersion Diagrams

These diagrams hark back to early Data Visualization given that, in and of themselves, they cannot show more than three variables for each record (placement on the axes and size of the representation on the table). However, if we make these graphs interactive, they help us display two levels of depth, i.e. the data that are of most interest to us based on the three most important variables. And by clicking on the specific data, we get a sample broken down with the rest of the variables that we establish.

For instance, in the following graph, one can observe the improvement or deterioration in the punctuality of various airlines between 1985 and 2010 through a measurement of the number of minutes’ delay for each takeoff. By clicking on each airline, we can access the presentation of the data that explain the causes of their improvement or worsening.

The key with Data Visualization resides in choosing the display that we want to give our data based on the type of conclusion we want to be able to draw. In other words, the display needs to be chosen according to the hypothesis that we want to demonstrate.

For further information contact with us using the following email: laguilera@datacentric.es

From Big Data to Deep Data or how to get real value from your data

While Big Data enables statistical models to be built and predictions to be made, it offers too much information and provide no explanations about the whys and the wherefores of the data.
Deep Data explains the facts: why what happens actually happens and what the true drivers of purchases really are.

As that Pirelli tire commercial says, “power without control is good for nothing”. The same holds for Big Data. “Data gathering without control is good for nothing”.

This is the second lesson that companies that want to become Data Driven Businesses learn. Companies have gathered petabytes of data in the hopes that they would indicate to them what products to offer, to whom, when, where, how and why. But data in and of themselves doesn’t say much. You have to get them to tell.

Big Data is in charge of asking the questions and Deep Data is there to answer them.

The goal with which Big Data strategies are developed is not to explain what happened or to make predictions. What’s more, Big Data alone is not able to provide anything other than the numbers. In other words, with Big Data you can see:

The inhabitants in an area
Their social and economic level
Whether they are on Facebook or not…
What their annual consumption usually is…

But they don’t tell you that you should look for all of these metrics if you want to predict the amount of sales that your business campaign will have, nor do they tell you what is missing. They don’t tailor the data to your business objective or seek peripheral information to enable you to understand not only which specific action will work best, but why. They do not replace human reasoning.

Enter Deep Data

This is exactly why you need Deep Data. Data that provide you with a personal dimension to explain consumers’ purchasing decisions. So:

Before you had their zip code. Now you have their personal property tax information.
Before you had their annual consumption. Now you know what they spend each portion of their budgets on.
Before you had their social-economic status. Now you have access to their level of indebtedness and their bad debts.

With Big Data you knew where there were people who could buy your product and what they wanted to buy. With Deep Data you can make distinctions between who will buy your product and who won’t, what, and why.

From Big Data to Deep Data using an example: videogames

New business models in the gaming industry are a perfect example of the proper use of Deep Data.

When the first videogames came on the market, companies could only know what traditional market studies could tell them.

Then came consoles connected to the Internet and platforms like PlayStation Network and Xbox Live to enable multi-player games… and they sucked up each player’s data like vacuum cleaners.

Data on which customer was playing which games came to define upcoming launches, and that was only the first step. Then came the other services like subscriptions and streaming services that not only became a business line but also provided a further dimension of information about each user: what they consumed when they were not playing videogames and how much they paid for it.

With all of this information, for years franchises like Call of Duty have been adapting their launches to the most recent fads and hedging their bets using elements they know their customers will like. They have done so to the extent that they have redefined the very notion of videogames:

By creating titles that combine cartoons, sports and tournaments with shooters in successful mixes like OverWatch
By redefining their content and through pricing strategies with subscription models, expansions, updates and customized packages, all for pay.

For further information contact with us using the following email: laguilera@datacentric.es