Every entrepreneur would like to harness the power of big data, but before you can make data-driven decisions, you’ll have to find information you can trust — and that may be a tall order.
The strength of your business decisions may depend on the quality of the data you use, but data quality can vary significantly depending on how it was collected, stored, cleaned and processed.
Data quality can also differ from source to source, but you can determine which sources of data are trustworthy, safe to use and applicable to your company by following four simple steps. Here we’ve also listed different types of big data that companies can mine.
1. Evaluate the data source
You can typically trust information from a data quality program that offers verified data that is regularly checked and updated by a trusted publisher. In addition, check the data statistics against the data sources.
2. Assess data quality
Adopt a cautious attitude for assessing your data quality. Make sure you know the source of data and how it is defined. Plan how, when and what to measure, and who will collect data according to the plan.
3. Check format, accessibility of data
When considering the format of retrieved data, check the accessibility of the data and whether it can be tampered with intentionally or unintentionally. Another consideration you should make is whether your data has been aggregated or not. If you’re using aggregated data, it may have flaws since you are reading consolidated information that may have been summarized.
4. Confirm the data reliability
Many reliable sources can provide you with relevant data on a variety of subjects.
Here are a few tips for finding a reliable data source for your business.
- Make sure the data can actually be taken from its original source.
- There should be enough information to get the big picture.
- The world evolves continuously, so always use the most recently published version of the available data.
- Verify that the source you choose is relevant, legitimate and as unbiased as possible.
- Good sources include data collected or produced by government agencies, industry white papers or academic publications.
Whenever there is new data, you’ll need to check through the same steps if you want to set up business intelligence (BI) reporting and analytics in your business.
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Types of big data
There are three different types of big data that companies can mine to better target consumers, get feedback on products or services and know their market and industry:
Structured data
Structured data is a term that refers to the data that can be stored, accessed and processed in the form of a fixed format. It is highly organized and adheres to a predefined data model and is therefore easy to analyze. Common examples of structured data are Excel files and SQL databases. Following are the five types of structured data:
1. Created data
– This is generated by businesses purposely for market research, such as customer surveys.
2. Provoked data
– This is a collection of audience views; rating sites like Yelp collect this type of data. Whenever any customer rates a restaurant, company, purchasing experience or product, they create provoked data.
3. Transactional data
– Businesses collect data on every transaction through online or in-store purchases to store transactional information for future reference.
4. Compiled data
– A giant database of consumers’ data like credit scores, location, demographics, purchases and registered cars is known as compiled data.
5. Experimental data
– This is a combination of a created and transactional data group. It is created when businesses experiment with different strategies to see which are most effective with consumers.
Each of the above data types has structured rows and columns that can be sorted. There are two sources of structured data: machines and humans.
Machine-generated structured data:
All the data received from sensors, machines, weblogs, medical devices, GPS units, usage statistics captured by servers, trading platforms and financial systems qualifies as machine-generated data. The data collected through this process is highly structured and suitable for computer processing.
Human-generated structured data:
Human-sourced information can be digitized and stored everywhere, from personal computers to social networks. These processes record and monitor data through human input, such as registering a customer, manufacturing a product, taking an order, etc.
Unstructured data
Unstructured data has no defined format or structure in storage. Therefore, it poses multiple challenges in terms of its processing for delivering value. A heterogeneous data source containing a combination of simple text files, images, videos, etc. is a typical example of unstructured data. You can categorize unstructured data into two types based on its source: machine-generated and user-generated data.
Machine-generated unstructured data:
All the satellite images, scientific data from various experiments, and radar data is a collection of unstructured machine-generated data. GPS info on smartphones that captures the user every moment and provides real-time output is another example.
User-generated unstructured data:
Website content, pictures we upload, videos we watch or text messages we send all contribute to the gigantic heap of user-generated data. It includes all the information that individuals are putting on the internet every day, such as tweets and retweets, likes, shares, comments, news stories and much more.
Semi-structured data
Information that is not in the traditional database format as structured data, but contains some organizational properties, is described as semi-structured data. It can include web server logs or streaming data from sensors, such as time, location, device ID stamp or email address. It is considerably easier to analyze semi-structured data compared to unstructured data.
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