Advanced Data Analysis: What Is It and What Is It Used For?

27 January, 2020 | Blog

Arbi Bouchoucha, Ph. D.

Advanced Data Analyst

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Today, data is at the core of modern businesses. Knowing how to use and understand your data will give you a competitive edge and allow you to keep up with today’s fast-paced marketplace. Advanced data analysis (ADA) will help you achieve these goals. This branch of data science spans many disciplines, like computer science, which includes programming, mathematics, statistics and logical reasoning. It consists of collecting, cleaning and arranging raw big-data (unstructured, structured and semi-structured) so new knowledge can be extracted from it. This knowledge will be used to improve decision-making and optimize business performance by increasing efficiency and reducing production losses, among other options. Data science experts carry out these tasks. They use several tools to (1) manipulate data, (2) use or build algorithms to solve complex problems that standard systems cannot solve and (3) generate results (statistics, visualizations) that users can interpret to draw conclusions.

More concretely, what can ADA offer you?

ADA transforms your data into usable information and makes it “talk.” So, instead of relying on your instincts to make decisions, you can rely on ADA to understand your client’s needs and assess potential risks, giving you the confidence to make the right decisions and stand out from your competitors. ADA is used to interpret the past, optimize the present and predict the future. In other words, ADA is like a GPS tool that will point you in the right direction and help you take the most efficient route to reach your destination, i.e. your goal.

More concretely, there are at least three areas in which ADA can play a key role in improving your company’s performance and profitability and ensuring its sustainable growth:

  • Predictive maintenance – Better manage risks for your business lines to control, predict and mitigate them by taking the necessary actions, thus avoiding potentially enormous losses in time and money. Deloitte studies show that predictive maintenance can increase productivity by 25%, reduce unplanned breakdowns by 70% and reduce total maintenance costs by 25%.
  • Internal process optimization – Control the production line to improve industrial process performance using measurements and interactive visualization tools.
  • Innovation and automation – Identify new market directions and automate business processes to maximize performance and efficiency. For example, use machine learning and deep learning techniques in the mining sector to implement predictive models in order to identify new mineral exploration targets, such as new areas useful as drilling targets.

Give us a call!

McKinsey studies show that companies that adopt an ADA strategy over the next 10 years will be able to double their cash flow. Don’t miss out!

BBA data science experts are at your disposal. Let us know what your needs and expectations are, and we’ll help you achieve your goals using our ADA expertise. Remember, your data is valuable—use it to grow your business!

This content is for general information purposes only. All rights reserved ©BBA

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