Last Updated: 07 Dec 2021 | 10 min read | Category: Trendy Technologies |
The companies of today gather massive amounts of data. This data is precisely what you need if you wish to invest in one of the most powerful forms of technology available to business owners right now – machine learning or ML.
Columbia University has an excellent story to enlighten you about how bad data can affect a business. This story is about a project on healthcare that aimed to reduce expenses for patients needing treatment against pneumonia.
The project employed the machine learning technology, about which you’ll learn here, to sift through patient records and pinpoint the ones at the lowest risk of dying. ML was also supposed to determine who should take antibiotics at home and who should get admitted to a hospital to avoid dire consequences.
The team in charge of this project used historic data accumulated from clinics. The algorithm they created was also accurate. However, there was one crucial exception. Asthma is one of the most life-threatening conditions that may follow pneumonia. That’s why medical specialists always send asthmatic patients to intensive care units to reduce the possibility of death.
Therefore, the absence of instances of death from asthma in the data forced the algorithm to assume that asthma isn’t unsafe for pneumonia patients. In every case, the machine they were using suggested sending asthmatic patients back to their homes, when, in reality, those patients were at the highest risk of incurring complications.
From the information provided above, it becomes clear that the system of machine learning depends entirely on data. It’s the only thing that facilitates training the algorithm to do what you need it to do. It also explains why ML has been gaining popularity consistently over the years.
However, it doesn’t matter whether you have a few kilobytes or terabytes worth of data. Your expertise and experience in data science aren’t of any consequence, either. The only thing that matters is the ability of ML to make sense of the accumulated data. If it fails to do the same, the ML can turn into something dangerous, as is apparent from the example stated above.
No dataset is devoid of problems. It’s one of the main reasons why data preparation is as important for machine learning technology to work properly as data collection. Data preparation is basically a collection of procedures that can help your dataset get ready to be part of machine learning.
Data preparation is also about establishing the appropriate mechanisms of data collection. Every procedure associated with data preparation consumes a significant portion of the time dedicated to machine learning. There have been instances where it took several months to conduct this task before experts could build the initial algorithm.
When it comes to working with machine learning technology, it’s best to delegate the task to data scientists. Of course, you may not have such an expert on your team. If that’s the case, ML may not be of any use to you.
Then again, business companies failing to bring in talented data scientists usually rely on their IT engineers to learn the tricks of the trade and fulfill the role of data scientists. Unfortunately, such companies never gain what they expect.
Furthermore, dataset preparation isn’t just about narrowing down the abilities of data scientists. The datasets of machine learning can malfunction just because of a problem affecting the organizational structure and established workflows. It can also be a result of record-keepers not following instructions.
There’s a line that separates those with the ability to play around with machine learning and those who can’t. This line comes from all the years spent by the first group in gathering the necessary data. Many organizations have been collecting information year after year successfully.
Today, these organizations have so much data that they would need a truck to transport it to the cloud. Traditional broadband networks won’t be sufficient for them. On the other hand, new entrants don’t have much, to begin with. Fortunately, it’s possible to turn this disadvantage into an advantage.
If you’re new to machine learning, you should depend on open-source datasets to start the execution process. Companies like Google have tons of data suitable for ML, and they’re willing to provide you with it. The real deal is, of course, the data you gather internally. Those are the golden eggs you get from mining business decisions and company activities.
It’s also about collecting data properly. Companies often start with paper-based ledgers to accumulate information. They convert the same into “.csv” and “.xlsx” files later. However, preparing such pieces of data can be challenging and time-consuming. If you have a small but effective dataset created specifically for ML, then you’ll be in a more advantageous position.
Now it’s time to explore big data and its implications. The term “big data” isn’t just a buzzword, of course. Everyone uses it, and so should you. Attempting to utilize big data from the beginning is an excellent idea, but it isn’t just about the volume. It’s only about processing it correctly.
Large datasets make it difficult to use them properly. They don’t yield legitimate insights, either. Just because you have a lot of wood doesn’t mean you can convert it into tables and chairs instantly, even if you have the means to do so. It’s best to start as small as possible because you’ll be able to decrease data complexity.
If you’re aware of what you wish to predict, it’ll get easier for you to decide on the kind of data valuable for your business. When you formulate the problem, you have to explore data and try to consider the classification, regression, clustering, and ranking categories. Here are the differences separating each category.
You need your IT services and solutions company in the USA to come up with an algorithm that’ll answer “yes” or “no” to questions. Or, you need to form a multiclass classification. You’ll also require the appropriate answers labeled to ensure the algorithm learns from them.
An algorithm has to produce some kind of a numeric value. For instance, if you spend a lot of time coming up with the appropriate price for a product, regression algorithms can help you in this task. After all, the cost of the product depends on several factors.
You need the algorithm to ascertain the classification rules, as well as the number of classes. The primary difference between clustering and classification is that you won’t know anything about the groups or the principles of division. An example of clustering is when you segment your clients and create a specific approach to every segment based on their qualities.
A few algorithms for machine learning can simply rank objects based on several features. Ranking is what you need to recommend movies to the users of a video streaming service or display products a customer may purchase based on his/her previous transaction activities and searches.
There’s a possibility that you can solve the problems affecting your business with this straightforward segmentation. You’ll also be able to adapt your dataset accordingly. Just avoid over-complicating the problem.
The task of creating a data-driven culture in a business company is usually the most difficult one in this initiative. You’ll learn more about it if you go through this story on the strategies of machine learning. Now, if you’re going to use ML to conduct predictive analysis, you must first be ready to wage war against data fragmentation.
To that end, you only need to take a look at the technologies designed by companies like Moon Technolabs for travelers and tourism agencies. These technologies suffer from the analytics-related issue of data fragmentation. The departments of hotel businesses in charge of the physical properties have to deal with classified client information.
These people know the amenities chosen by their guests, their credit card numbers, the way they use room service, their residential addresses, and even the food and beverages they order. Conversely, the website that allowed the guests to book rooms may consider them nothing more than strangers.
This data gets transferred to various departments and also different tracking zones within each department. Marketers will have access to a CRM, but the customers of the platform have no idea of web analytics.
It isn’t always possible to direct every stream of data towards one centralized storage depot, even if you have multiple channels of acquisition, engagement, and retention. Nevertheless, it’s manageable in most instances.
Data engineers are part of the teams working with the best IT services and solutions company in the USA – Moon Technolabs. They should be the ones to create data infrastructures. Of course, during the earliest stages, even software engineers with a bit of experience in database handling should be able to take care of this necessity.
There are two types of data collection methodologies worth mentioning.
The first method of data collection incorporates depositing the data in a warehouse. These storages are mainly for structured records or SQL records. It basically means that they fit into conventional table formats. Everything from your payroll, sales records, to CRM data belongs to this category.
Another conventional attribute of managing warehouses is about transforming the data before loading it there. To do that, you must know the data pieces you need and how they must appear to ensure you complete the processing part before storing the same. This approach is called ETL or Extract, Transform, and Load.
Unlike warehouses, data lakes are storages capable of storing both structured and unstructured data, including audio records, images, PDF files, and videos. Structured data, however, don’t need transformation before storing. You’d load the data as-is and think about how you can process it later.
This process is called ELT or Extract, Load, and Transform only when you think it’s necessary. If you wish to understand the differences between ETL and ELT, just read this.
Even the best machine learning techniques won’t be of much use to you if you don’t inspect the quality of your data. The first thing you need to know is whether the data is trustworthy at all or not. Poor-quality data can prevent the most technologically-advanced ML algorithm from doing what it should.
Human error is, undoubtedly, tangible. To get an appropriate estimate of how often mistakes happen, simply check a subset of data gathered and labeled by humans.
Sometimes, an individual may end up duplicating the same record because of an error in the server or if there’s a storage-crash event. The same happens after cyber attacks. You have to determine how these events leave their mark on your data.
There are a few ways to manage omitted records that you can learn from the specialists of machine learning techniques. Once you learn them, you can use them to estimate whether the value is critical or not.
Just think for a moment that you sell home appliances in America and you want to branch out to Europe. Do you think you can rely on the same data for stock and demand prediction?
Consider another situation where you’re attempting to get rid of the risks affecting your supply chain and filtering out unreliable suppliers. To do that, you have to use several attributes. If the labeled dataset of yours has about 1,500 entries described as reliable, and if there are 30 untrustworthy suppliers, the model won’t get as many samples as it needs to segregate the unreliable ones from the reliable ones.
Data formatting usually points to the file format of your choice. It shouldn’t be a problem for you to convert your dataset into a specific format that your ML system can understand. You have to learn machine learning techniques to do that, of course.
It’s all about records’ consistency. If you aggregate data from various sources, or if multiple individuals update your dataset manually, you have to find out whether they wrote all the variables of every attribute consistently.
Business owners find it tempting to use as much data as possible because they get the chance to incorporate big data. It isn’t a path worth following, though. Indeed, it’s perfectly natural for you to want to gather as much data as possible. However, you should reduce it instead of increasing it if you’re outfitting a dataset to complete a specific task.
As you know everything about the target attribute, your common sense will help you do the rest. You should be able to estimate the critical values, as well as the ones that’ll add more complexity and dimension to the dataset devoid of forecasting contributions. This particular approach is what ML experts describe as “attribute sampling.”
There’s another approach called “record sampling.” It implies that you have to remove records with erroneous, missing, or less representative values to augment the accuracy of prediction.
This technique will also come in handy in the future when you require a model prototype to contemplate whether a specific method of ML delivers the results you expect, as well as gauge the ROI of your initiative.
You should be able to reduce data even further by aggregating the same into broader records. To do that, you just have to divide the attribute data into several groups and draw the number for each.
Instead of going through the most in-demand products on a specific day through five years of the existence of an online store, you have to aggregate them to monthly or weekly scores.
There are several other dataset preparation methods for you to explore, understand, and incorporate. Nevertheless, the ones described above should be enough for you to get started.
Just don’t make the mistake of leaving your in-house IT experts to deal with it. You need data scientists if you want to leverage the power of ML for your business.
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