Logistics: BIG DATA vs. Traditional Consulting
Big Data is a term that refers to any voluminous amount of data—structured, semi-structured, and unstructured. More generally, Big Data refers to the set of techniques used for processing and analyzing this data, with the aim of extracting usable and profitable information for companies.
However, despite the buzz it is generating, not everything is Big Data. One must resist the temptation to apply “trendy” technologies if they are not justified, and be clear about the difference between concepts such as Big Data, Machine Learning, Business Intelligence, and Business Analytics. These are different concepts, although they are often complementary.
When does it make sense to talk about Big Data?
Traditionally, data is stored in centralized architectures, whereas Big Data utilizes distributed architectures, dividing the data block into smaller portions and distributing the computing task across different interconnected computers.
In the case of large volumes of data, distributed computing allows for:
- Achieving better performance.
- Performing analysis tasks at lower costs.
- Achieving scalability.
Big Data is justified when the following conditions are met:
- Variety. Data comes from several different sources, often inconsistent with each other: ERPs, CRMs, WMSs, Excel files, GPS signals, mobile devices, etc. During processing, it is necessary to make the data “talk” to each other.
- Volume. Big Data refers to large, very large quantities—so large they cannot be managed by a single computer, on the order of petabytes of data (1 petabyte = 1015 bytes).
Information regarding customers, suppliers, orders, etc., does not fall under “Big Data.”
- Veracity. Data from multiple sources and in large quantities must be reviewed to control its integrity (the garbage in/garbage out principle applies: the quality of the output depends on the quality of the input).
Big Data processing involves the elimination of errors or uncertainties in the data, which is done during the Transformation phase of the ETL process.
- Velocity. We talk about Big Data when the immediacy of the analysis plays a decisive role. It applies to real-time data analysis, for example, for controlling facilities with sensors collecting continuous information and requiring very short response times.
Do I need BIG Data?
The answer is yes if you want to leverage different data sources (Volume + Variety) or if you want to analyze the evolution of variables in real time (Velocity) to improve your business.
Some examples:
- to take weather variables into account and cross-reference them with previous year’s sales data to adjust demand forecasting, optimize the purchasing process, and manage stock
- to leverage social media information, monitor sentiment regarding your brand, study your competition, and measure market trends
- to track variables recorded by sensors in real time (GPS position, temperature, speed, etc.) and monitor a system or a logistics network
If I am an SME, is Big Data for me?
Yes. At LISDATA, our mission is to make digital transformation accessible to all companies.
To achieve this, we implement our tools to ensure speed of implementation, data security, and, above all, a significant reduction in costs.
Furthermore, we work with agile methodologies (SCRUM), dividing projects into phases to scale the investment and evaluate the degree of compliance at each stage to ensure the success of the implementation.
Why choose the LISDATA BIG Data solution?
The key to success of a Big Data project applied to logistics (but the same would apply to production, marketing, finance…) consists of constantly guiding the IT component alongside the business component.
Why? Because data without knowledge is sterile, and knowledge without data is blind.
At LISDATA, we know this; therefore, we always provide a multidisciplinary team composed of experts in IT, Logistics, Mathematics, and Industry.
Share on Facebook
Share on Google
Share on Twitter
Share on LinkedIn
Share on Pinterest
Share on Print
Share on Email