In our previous post, we have talked about the four phases of data management and outlined how to optimize business operations using smart data.
High technology, or "high tech" refers to mechanical, electronic and industrial science that is at the cutting edge: the most advanced technology available. We at Lexunit place great emphasis on bringing this kind of technology to lifeand making it accessible to all business environments.
Because everyone has data. And automation solutions can help everyone, regardless of sector or capital size.
We will now consider the first two of the four phases of data management, data collection and data storage, in order to understand how to deal with this technology.
First, let's look at how companies and businesses acquire business data.
1. Sensor Data Acquisition
Is it true, for example, that sensory data collection can be used more widely than we think? A good example of this is a fitness tracker bracelet: these gadgets are easily accessible accessories, providing a number of convenient functions and making measurements with sensors.
Sensors can measure physical values and convert them into quantifiable signals. The type of sensor depends on what is being measured. For example:
- temperature -> bimetal thermometer
- distance -> laser distance meter
- pressure -> pressure gauges, manometers
- image creation -> camera
2. Collecting and Analysing Data Through Video Analysis
Many companies use video cameras for surveillance, security reasons as well as other operations. Nowadays, it is not uncommon for gas station cameras to be constantly monitored by a software, which sends an alert if it detects a movement that resembles smoking, as this would be extremely dangerous at a gas station.
But posture and movement analysis can also be useful in a factory with a software identifying whether a worker is lifting something heavy, moving items, walking, or just standing. Turning this information into data can help analyse the work of employees and organize workflows in a smarter way based on the results, using less energy and gaining more profit.
Shops and boutiques are already using software that record the gender and age of visitors. This way, the customers of a real-life store can now be analysed offline, just like website visitors through web analytics.
3. Smart Solutions in the Office
Homes are not the only spaces that can be "smart", workplaces can be, too. The trends defined by the keywords of Industry 4.0 and the 'Internet of things' are certainly pointing in this direction. The measurement of home temperature and humidity or intelligent lighting control systems are no longer unusual. Nowadays, you can find built-in machine learning features even in simple home appliances such as an electric boiler: it's no longer necessary to pre-set the exact time and amount of energy with which it starts heating water for the evening bath, as it is able to draw conclusions from the patterns of our use of hot water and start using prediction. This way, after a while, it heats water exactly when you need it.
In the office space, digital communication between temperature, humidity and light management softwares (smart boards), and meeting management softwares make up most of the smart solutions currently in use.
4. Inventory Management with Artificial Intelligence
E-commerce is booming, many one-person businesses are launching webshops and the number of online shoppers is increasing. This also means that more and more people are confronted with the problems of warehousing, inventory management and delivery, therefore technology that can predict demand will become increasingly valuable. It's not rocket science: by carefully analysing the orders received so far, patterns can be identified and used to predict future demand to then time supply acquisition accordingly. This reduces storage time and allows you to make better business decisions. For example, let's say shipping with a new courier service would take two days longer but is 15% cheaper: is it worth choosing it? Artificial intelligence can provide specific answers to such specific questions.
5. Process Automation
Data managementhas been a prominent business process for decades: transforming incoming information in a variety of formats into other types of information. At a used car dealership, for example, paperwork, and the scanning and sorting of documents usually consumes significant resources. Sorting out data types is already a task that can be handled by machines, making it possible to use human resources for more valuable assignments than bureaucratic chores.
We understand that the development of automation, artificial intelligence and robotics does not pose a threat to employees. The human resources set free by machine-powered work can be used in other, more valuable ways, and competition in the market will force every company to do so. In the future, we will write about our vision for the near future, the potential socio-economic impact of AI technologies.
Note that the methods listed above are useful primarily because they are inputs and generate data. Their exact use in a particular set of tools doesn't matter, because you can use this data freely for any creative business development solution through machine learning.
These are the main types of data collection, with some insight into how the data can be used. Below is a quick overview of what there is to know about storing data.
Storing Business Data
Without getting too technical in this educational-informational article, we have to explain that there are two types of data: structured and unstructured data.
Structured data is information that is traditionally thought of as data, such as a note in the appropriate column and row of a table, which makes it simple and easy to search in a database like this.
While unstructured datacan be, for example, a pattern in an audio or image file. This type of data doesn't have a predefined data model, it isn't arranged in a predefined way. Thus, it's much harder to search. A pixel by itself does not carry any data besides its colour, but many pixels of a certain hue next to each other could indicate that the image is likely to depict a skin surface - this can already be a useful information for an algorithm.
Both types of data are valuable but need to be processed differently. An average company, of course, generates far more unstructured types of data than structured ones.
Data Storage in a Cloud
Whether data should go up into a cloud server besides (or instead of) local storage is an essential question nowadays. If we choose the cloud, we have to send our data digitally to an external partner, such as Google, Microsoft, or another complex software service provider. When preparing for data storage, we have to consider the amount and quality of data generated from inputs.It is easy to run into problems like one of our partners, whose speed of evaluating data was lower than the speed at which data was generated, so the analysis could have never been completed with the given settings... Therefore, it is necessary to determine in advance what kind of storage capacities we want to build.
Cloud services are also great because they are scalable, and you can ask for more anytime. It is a cheaper and better solution than buying and storing new storage devices. This, therefore, is a good argument for the cloud, in addition to the fact that it won't be hit by lightning, or, more precisely, it won't be hit by lightning everywhere at the same time, since, thanks to the divided and replicated storage (redundant storage), there is no specific physical location where we exclusively store particular pieces of our data.
But how can we guarantee data security?
This issue is not only about hackers stealing our data, but it also concerns the question whether our own employees are able to accidentally damage them. How does authorization management work in the company? Are there security backups? Where and how are they stored?
Do I need to anonymize my data?
The handling of security camera footage is restricted by law, and this is also the case when they're used for business purposes, of course.
These are the essential questions to consider when collecting and storing data, because if you solve these basic issues, you will be able to work out optimal solutions to maximize the value you can extract from the data: this is what our next post will be about.