Big Data is the new El Dorado for companies in the 21st century, and data management is becoming a key concern for many organizations involved in large-scale projects.
But how do you process and analyze several tons of data? At a glance, how can you make even the most complex data speak for itself? How can you make it intelligible?
So that you can understand all of this, here are 6 data management tips that will help you effectively harness very large volumes of data.
Adopt a well-recognized strategy
Companies receive and process immense data flows every single day — a genuine maze where you can easily get lost. Before even analyzing the data, companies must therefore know what kind of information they are looking to extract from it.
In other words, companies must implement a genuine strategy and set out various objectives. Innovation, cost optimization, product repositioning, etc. The possibilities are endless. In any case, these objectives will serve as a roadmap, making it possible to know what to look for and where to find it. You will therefore be able to conduct data analysis to find an exact solution to your problems.
| Customer Testimonial:
Airbus analyzes very large volumes of data for its BIO project |
Airbus | Analysis of a large data volume for its BIO project
Organize and classify data
To effectively manage very large volumes of data, meticulous organization is essential. First of all, companies must know where their data is stored. A distinction can be made between:
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- Inactive data, which are stored in files, on workstations, etc.
- Data in transit, which are found in e-mails or transferred files, for example.
The category to which each piece of data belongs, as well as its owner, must therefore be identified. Customer files, banking information, financial reports, health data, etc. Depending on their nature, data will need to be processed differently, particularly in terms of security and confidentiality.
It is also vital that companies understand how the data is used. What links are there between the data and the company’s various business activities? Are they used regularly or rarely? For what purpose?
The level of priority of the data must also be assessed, as well as its sensitivity (in terms of security).
Do not overlook unstructured data
As we have seen, organization is a key component of data management. However, unstructured data account for a significant proportion of the information collected by companies. In fact, the majority of the data owned by companies is most often unstructured.
It is therefore imperative to compile a list of all the data available at the organization, whether dormant or actively used. However, these data are difficult to analyze, especially because they have been derived from many actors and sources: employees, customers, social networks, small desktop servers, laptops, etc.
Despite this, these data often prove essential to decision-making processes, so they must be taken into account. Gathered in a data lake, these unstructured data can be easily analyzed and retrieved using a dedicated data visualization tool.
| Customer Testimonial: |
CACEIS Bank | Conciliate Analytics and digital transformation thanks to dataviz
Capitalizing on data visualization
Many companies are equipped with a data processing platform. However, although this kind of software is perfectly adapted to store billions of lines of data, it does not allow users to optimally harness such data. To conduct an in-depth analysis of these data, they must be fed through a data visualization tool, in order to generate key performance indicators (KPIs) and perform all the necessary aggregates and calculations.
Organizations also tend to rely on data scientists, that is, statisticians and mathematicians, to extract information from Big Data. Nevertheless, data science cannot present data intelligibly to provide exact solutions to business problems. Decision makers must therefore draw on data visualization to make strategic choices based on large volumes of data.
Choosing the right graphical representations
Organizing and managing data on a large scale involves very dense and rich information. However, the more complex the data are, the more difficult it is to visually represent them. The information must be prioritized and displayed in a way that the recipient fully understands.
This is where data visualization takes on its full meaning once again, since it allows you to easily switch from one graphical representation to another, according not only to the information communicated, but also to the audience. Curves, histograms, tables, maps, etc. Each format has its own specificities and is more or less adapted to different types of data.
| To learn more, see:
DigDash embedded analytics: a real asset for sites and software |
L’embedded analytics DigDash : un véritable atout pour les sites et logiciels
Harnessing the cloud’s potential
Nowadays, cloud computing is everywhere in businesses. It reduces capital expenditure on software and associated services, on the one hand, while its flexibility and its potential to bring about economies of scale make it particularly attractive, on the other hand.
But cloud computing can also become a valuable ally, in terms of managing large volumes of data. In fact, industry players now allow organizations to switch between their data center and the cloud, in order to better distribute their workload and data.
To ensure fully transparent data management, it is even possible to physically access company data in the cloud provider’s data center. You will know exactly where the data is stored and how it is managed, even when there are billions of lines.
If you want to go even further in terms of data security, confidentiality and accessibility, you may wish to consider HDS-certified cloud hosting.
In conclusion, while data visualization is an essential tool for managing very large volumes of data, its use is not sufficient on its own. It must be part of a very precise strategy and requires meticulous work to be performed to identify and classify the organization’s data, whether structured or unstructured. This process is essential if you wish to avoid the pitfalls of data analysis.