Data is the new oil? No, data is the new soil.” – David McCandless

Humans are visual creatures. A visual is processed 60,000 times faster than any form of text, and studies show that 65% of the population is composed of visual learners. Moreover, 90% of the information transferred to the brain is visual.

Marrying digestible text with striking visuals provides the best results regarding the effective presentation of information, which in turn makes it easy for audiences to understand and retain data. This very notion is the core of visualization.

In recent times, data visualization specialists have married information to high-aesthetics, taking advantage of humans’ natural affinity for beauty. When we are choosing the right data visualization type, the most important element to consider is if you’re offering people the opportunity to see insights they haven’t seen or experienced before and wouldn’t otherwise be able to decipher in written text alone.

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Everywhere you turn these days “the cloud” is being talked about. This ambiguous term seems to encompass almost everything about us. While “the cloud” is just a metaphor for the internet, cloud computing is what people are really talking about these days. It provides better data storage, data security, flexibility, increased collaboration between employees, and changes the workflow of small businesses and large enterprises to help them make better decisions while decreasing costs.

It is clear that utilizing the cloud is a trend that continues to grow. We have already predicted in our business intelligence trends article the importance and implementation of the cloud in companies like Alibaba, Amazon, Google and Microsoft.

The significance of the cloud is increasing exponentially. Gartner forecasts that the cloud services market will grow 17.3% in 2019 ($206.2 billion) and by 2022, 90% of organizations will be using cloud services.

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 “Today, big data is about business disruption. Organizations are embarking on a battle not just for success but for survival. If you want to survive, it’s time to act.” – Capgemini and EMC² in their study Big & Fast Data: The Rise of Insight-Driven Business.

In our cutthroat digital age, the importance of setting the right data questions can define the overall success of a business. It is not just important to gather all the existing information, but to consider the preparation of data and utilize it in the proper way, has become an indispensable value in developing a successful business strategy.

That being said, it seems like we’re in the midst of a data analysis crisis. Although organizations spend millions of dollars on collecting and analyzing data with various data analysis tools, it seems like most people have trouble actually using that data in actionable, profitable ways.

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To ensure a sustainable level of commercial success in our cutthroat digital age, offering value, driving consumer loyalty, and consistently surpassing your targets are of the utmost importance.

A loyal, high-value repeat customer is worth more than a cheap sale, and by implementing the right strategy, setting the right goals, and working with the right KPIs, you will achieve the results you desire.

To win on today’s commercial battlefield, incremental sales is the name of the game. By taking a more strategic approach to your business’s profitability, you will stand a far greater chance of growing and evolving your business over time.

Here we explore the meaning and value of incremental sales in the world of business, as well as the additional KPI examples and metrics you should track to ensure ongoing success.

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At present, more than 3.7 billion humans use the internet. Moreover, we humans create 2.5 quintillion bytes of data every single day – a number that is expected to grow exponentially with each passing year.

Data never sleeps and in today’s world, without utilizing the wealth of digital information available at our fingertips, a brand or business risks missing vital insights that can help it grow, scale, evolve, and remain competitive.

Concerning the collection, understanding and handling of digital data, there are two key disciplines that currently lead the way: data science and analytics. Although these two fields cross over, and share many of the same characteristics, the two are strikingly different in many ways.

That said, to spare you any confusion and offer you a clearcut insight into these two innovative fields, here we explore data science vs data analytics in a business context, starting with an explanation of the science.

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