Big Data is growing... too much?
In 2005 O-Reilly Media coined the term Big Data to indicate a set of data that is so large it just can’t be managed and processed through traditional business intelligence tools. Since then, the expression has quickly become a buzzword and more and more companies are learning to deal with massive amounts of information to be able to take better, more informed decisions.
Just to get an idea of vastity of Big Data, consider that more than 300 hours of videos are uploaded every minute on Youtube, while about 269 billion emails were sent and received each day in the world just in 2017. Further, data gets bigger and bigger every year: 90% of the world's data has been generated over the last 2 years.
However, as Big Data grows, more problems arise: when the data is too much, it becomes overwhelming, costly and meaningless. Unsurprisingly, a 2016 survey by IDG Research found that 90 percent of respondents had experienced issues in areas such as data access, transformation, collection and storage.
What is Smart Data?
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The solution to those challenges is for companies to shift the focus from Big to Smart Data. So what is Smart Data? Simply put, if Big Data is a massive amount of digital information, Smart Data is the part of that information that is actionable and actually makes sense. It is a concept that developed along with and thanks to the development of algorithm-based technologies such as artificial intelligence and machine learning.
Two types of smart data: Fast Data
Two primary types of Smart Data exist. One refers to the data collected by smart sensors and is associated with the internet of things (IoT) movement. In this case, an intelligent data entry point grabs information as it happens and uses it for real-time decision-making.
In other words, sensors collect external data inputs and intelligently react in fractions of a second. This type of Smart Data is also referred to as Sensor or Fast Data and stands at the base of many modern innovations, such as, for example, smart factories and self-driving cars.
... and the other type of Smart Data
The other kind of Smart Data is Big Data that has been processed and turned into actionable information, thus empowering the organization who owns it. For instance, by gathering and analyzing valuable information on the customers’ interactions, attitudes, opinions, and emotions, firms can get ahead of their competitors, especially in saturated markets.
Other examples concern industries such as hospitality and healthcare, which is using Smart Data management to overcome information silos, and the financial sector, where actionable data is used to enhance customer experience, prevent fraud and manage risks.
Overall, Smart Data greatly contributes to an organization’s success in three ways: it provides a competitive advantage, makes Big Data manageable and actionable and partially solves the costly and complex issue of data storage, as real-time data analysis makes it unnecessary to store years of information.
As such, because data loses relevancy as time goes by, it is illogical for organizations to collect every piece of data they find with the intention of using it at a later stage. In contrast, data that has been appropriately sorted and structured can be usable long past the expiration date of typical data.
Further, by defining data’s time-to-live (TTL), firms can ensure that data that is no longer useful expires after a specific length of time, taking the burden off their data storage capabilities.
Shifting from Big to Smart Data solutions
Making big data smart requires companies to structure and prioritize their data in order to define a smaller dataset that actually offers value and can be acted upon. This implies for them to stop collecting vast amounts of all possible data and start contextualizing what they have already collected.
Indeed, Big Data is often described based on five elements: Volume, Velocity, Variety, Value and Veracity. Shifting to Smart Data means trading Volume for Value and Veracity. Hence, because Smart Data is defined by its usefulness, companies should clearly define what they want to capture and how they intend to use it before beginning this shifting process.
Finally, when companies chooses to invest in a smart data strategy, they should:
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- always look for and consider the most relevant data sources;
- take a coherent and consistent approach to data to ensure data quality;
- adjust their corporate culture in order to promote the understanding and adoption of Smart Data;
- constantly review the need for organizational changes and innovations that data will highlight;
- embrace automation, as data volumes never stop increasing and without automation data could not be smart.
In conclusion, Smart Data is the future of data and virtually any business can benefit from it. Small and midsize organizations with low budgets should not let Smart Data scare them, as it offers them the possibility to reap the benefits of Big Data without bearing its full cost. The faster they will be able to collect and analyze actionable data, the easier it will be for them to take smarter decisions faster and overcome the competition.
