Businesses large and small have been using Twitter as a means of engaging and communicating with consumers. Just by visiting Twitter and searching for some of the big name brands such as: Nike, Google, Samsung or HSBC reveals that they have multiple Twitter accounts, often dedicated to countries, brands and products.
Businesses want to understand Twitter data, whether their activities have any impact on their brand awareness and perception and ultimately on their bottom line. To do this businesses need to understand Twitter in the wider context of other datasets and performance KPIs. These can be other social media data sets, but social media in itself is a silo that businesses want to break out of.
The work of acquiring Twitter data from multiple accounts, combining it with other data sets and manipulating it to gain insights is a daunting task for two reasons, which we can understand using the data landscape model:
- Twitter data is light but distant. You know that data is available down to the tweet level, but work is required to acquire the data
- Once this work has been done, Twitter data often becomes close but dark against original expectations. How do you get meaning out of millions of seemingly random tweets?
Most businesses are not equipped to dig for insights in Twitter content data, let alone combine with additional datasets before they start digging. The situation, however, is changing.
In this DataShaka Case Study: Twitter Data In The Race For Context outlines the journey that many business have gone on - how they have overcome the data unification challenge using the DataShaka platform and how they have successfully manipulated data using DataShaka tools to unlock and extract meaningful insights for their business.