Posted by Shivali Anand
December 17, 2021 | 5-minute read (831 words)
Every entrepreneur seeks to tap into the potential of big data. But before you can begin making data-driven decisions, you'll need to locate reliable information, and that can be challenging to come by. The quality of the data you use will influence the soundness of your business judgments, but data quality varies greatly depending on how it was acquired, stored, cleansed and processed.
To home in on data sources that are reliable, safe and of value to your business, peruse the four steps below. We've also included an overview of the different forms of big data that businesses may use.
Analyze the information source
You can typically trust data from a quality data program that provides certified data that is evaluated and updated by a reputable publisher. Check the data statistics against the data sources as well.
Evaluate the data's quality
Proceed with caution when evaluating the quality of data. Make sure you know the source of the data and how it is defined. Plan how, when and what you'll measure, as well as who will gather data according to the plan.
Verify the data's format and accessibility
When considering the format of retrieved data, check the accessibility of the data and whether it can be tampered with. Another factor to consider is whether your data has been aggregated. If you are using data that has been aggregated, it may be flawed since the information has been consolidated and summarized.
Verify the reliability of the data
There are many trustworthy sources that can provide valuable data on a range of topics. Here are some pointers for locating a dependable data source for your company.
- Confirm that the data can be retrieved from its original source.
- There should be sufficient data to understand the big picture.
- As this world is constantly changing, always use the most recently published version of the available data.
- Double-check that the source you've chosen is current, legitimate and as unbiased as possible.
- Government organizations, corporate white papers and academic publications are good sources of data.
If you want to build up business intelligence reporting and analytics in your business, you'll need to go through the same steps whenever there is new data.
Big data's different types
There are three main forms of big data that businesses can parse to better target consumers, gather feedback on products or services and develop a stronger understanding of their market and industry:
Structured data
Structured data refers to information that can be stored, retrieved and processed in a predetermined way. It is well-organized and follows a predetermined data schema, making it simple to examine. Excel spreadsheets and SQL databases are common examples of structured data.
The five categories of structured data are as follows:
Created data
– Firms create this data for market research, such as consumer surveys.
Provoked data
– This is a compilation of audience opinions collected by rating sites such as Yelp. Customers generate provoked data whenever they rate a restaurant, company, shopping experience or product.
Transactional data
– Businesses gather data on every transaction made online or in-store to save transactional information for future use.
Compiled data
– A massive database of consumer information, such as credit ratings, location, demographics and registered vehicles.
Experimental data
– This group comprises both created and transactional data. It is developed when companies test several techniques to identify the most effective with customers.
The two main suppliers of structured data are machines and people:
Machine-generated structured data:
Machine-generated data includes all data received from sensors, machines, weblogs, medical devices, GPS units and usage statistics gathered by servers, trading platforms and financial systems. The information collected in this manner is well-structured and suited for computer processing.
Human-generated structured data:
Human-sourced data may be digitized and stored in various places, including personal computers and social media platforms. These processes record and monitor data via human input, such as registering a client, manufacturing a product or taking an order.
Unstructured data
Unstructured data has no predetermined format or organization. As a result, processing it to deliver value presents multiple challenges. A typical heterogeneous data source contains a combination of images, simple text files, videos and the like. Unstructured data is categorized into two classes by source: machine-generated and user-generated data.
Unstructured data created by machines:
Satellite imagery, scientific data gathered from experiments and radar data are all examples of unstructured machine-generated data. Another example is GPS data on cellphones.
Unstructured data created by users:
Content on websites, photos we post, films we view and text messages we send contribute to the massive amalgamation of user-generated data. This covers everything that people post on the internet daily, such as tweets and retweets, likes, shares, comments, news pieces and much more.
Semi-structured data
Unlike structured data, semi-structured data is not in a standard database format, yet it has certain organizational attributes. Semi-structured data can include web server logs or streaming data from sensors, like time, device ID stamp or email address. Semi-structured data is considerably simpler to analyze versus unstructured data.
