The Value in Big Data. Truth or myth?

Knowing how Big Data generates from each device and system connected to the Internet makes us question how much value is in such data. According to International Data Corporation (IDC) 37 % of the digital universe will contain data that might be valuable if analysed (Deloitte Insights, 2017). Whether it be feedbacks from social media, inquiries through Alexa, or information about customer locational experiences - this data should be relevant to the objectives regarding which it is collected. The ability to capture enormous volumes of big data but find and trust the value of a certain portion is a skill in today's world of research and collective analysis of big data.

How do we find value in Big Data? 

 

Source: Mung.io, 2020

The possibilities to successfully analyse data from contained systems is higher, rather than from complex and unpredictable systems which involve human interactions (Tricia Wang, 2017). It happens because of the constant changes in people’s behaviour.

According to Delloitte, 2017, there exist “the 80 percent rule”, which applies to unstructured data (traditional and non-traditional). The main difficulties from big data analytics prospective is how to create the value mostly from non-traditional unstructured data (video and audio files and still images). Many organisations across numerous industries, such as retail, healthcare, wholesale and banking, have potential to gain advantages to utilize their customers data. For instance, if retailers, who are keen to know what their customers not only say, but also feel about a new product, they can analyse their facial expressions and body language. To do so, companies have to use computer vision, video and sound recognition or advanced pattern recognition (Deloitte Insights, 2017). It allows to explore new opportunities to understand particular customer.

What are ways of gaining value from Big Data?
 

Source: Business Wire,no data

Traditional methods such as data lakes and data warehouses are time-consuming and will not bring sufficiency compared to new modern procedures.

Modern approach is about machine learning (ML) and automated programmes (AP) that help people to manage large sets of data and by identifying patterns, correlations and indicators getting value out of analysed data (Blake-Plock S., 2017). For instance, Hidden Brains (India) provides Artificial intelligence, Machine learning and Data science services and works with Amazon Alexa, Oracle, etc. to improve company-customer interaction (Kh R., 2019).

Even though, algorithms used in ML and AP need to be checked from time to time, in such ways valuable information can be gathered.

Personalized way of approaching Big Data which has been used by Amazon and Microsoft, tell us about existing algorithms that sufficiently work (Blake-Plock S., 2017). It happens only because data about their customers’ experiences (feedbacks, previous purchases, etc.) are not just stored in data lakes, on the contrary, they understand which insights is useful for each issue.

As long as new data appears, companies will be encountered with a variety of issues. Nevertheless, they will need to discover more suitable approaches.

References:

1.Blake-Plock S. (2017) Where’s The Value In Big Data? Forbes Technology Council. Available at: https://www.forbes.com/sites/forbestechcouncil/2017/04/14/wheres-the-value-in-big-data/ [Accessed: 11 February 2020].

2.Deloitte Insights (2017). Analyzing dark data for hidden opportunities Available at: https://www2.deloitte.com/us/en/insights/focus/tech-trends/2017/dark-data-analyzing-unstructured-data.html [Accessed: 13 February 2020].

3.Kh R. (2019) 30 Top Artificial Intelligence And Machine Learning Companies (2019) SmartData Collective. Available at: https://www.smartdatacollective.com/30-top-artificial-intelligence-and-machine-learning-companies/ [Accessed: 12 February 2020].

4.Wang T. (2017) The human insights missing from big data. Ted Talks. Available at: https://www.youtube.com/watch?v=pk35J2u8KqY [Accessed: 13 February 2020].