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5 Pitfalls To Avoid When Analyzing Large Volumes Of Data


Big data is said to be the new black gold of the digital economy. According to PricewaterhouseCoopers and Iron Mountain, 43% of companies receive little benefit from their information, while 23% do not benefit from it.

Many organizations do not know how to process and analyze the data at their disposal and so do not miss out on new opportunities. The available data must be relayed as much as possible within the company. To democratize them, they must be made accessible. And what better than data visualization to make intelligible at a glance the most complex data?

Go without specific goals

However, it is necessary to know exactly what information we want to obtain by analyzing these data, at the risk of getting lost in this gigantic flow of data. For this, we must highlight objectives: Innovation? Repositioning products? Optimization of logistics costs? And many others. These objectives will guide the analysis of the data to obtain answers to your problems. That’s why establishing a clear strategy is the first step in using data visualization.

To think that a data processing platform is enough

Many companies have equipped themselves with data storage platforms such as Hadoop, thinking that this would be enough to exploit their data in an optimal way. Although these platforms are ideal for processing and storing large volumes of data, their basic functionality does not allow users to analyze these data. For this, it is necessary to use data visualization technologies compatible with Hadoop. Thus, you will be able to analyze all these data, make the aggregates and calculations you need to get your indicators out and increase your performance.

Spread unstructured data

If big data was an iceberg, unstructured data would be the submerged part. Yet, they represent the majority of the information collected. They come from many actors (employees, prospects, users, users …) and multiple media (blog, social networks, phone calls …). These data are difficult to analyze but can be critical for decision-making in your organization. Some data visualization tools make it possible to take into account these data (often collected in a data lake) to analyze them and to reproduce them in a very simple way and in real time. Your driving will be more reliable and more relevant.

Confuse data visualization and data science

These two concepts make it possible to explore large sets of data. However, they do not respond to the same functions. Data science can extract useful information from the big data. And for this, companies rely on data scientists, experts in mathematics and statistics. Data visualization allows us to present the data for a specific purpose to answer business problems and to help decision-makers. In other words, these concepts are complementary but do not meet the same needs.

Use the wrong graphical representation

More information is rich, the less it is easy to restore. Tables with too many rows and columns will not be the appropriate solution. Prioritizing information is very important to capture the audience. It requires an efficient and ergonomic visualization. Thus, to highlight results for different geographical areas, it is better to use a map rather than a chart or table. The choice of the type of return is as important as the data communicated. And above all, do not forget that “a picture is worth 1000 words” (Confucius).

Many users think that getting a Data Visualization software will solve all their problems. However, these data require a clear and precise analysis in order to obtain the answers to your problems. So, do not get started without an established strategy.

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