DATABERG
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 make better and more informed decisions.
To get an idea of the vastness 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, the amount of data grows widely every year: 90% of the world's data has been generated over the last two years.
However, as Big Data grows, more problems arise: as the amount of data grows, 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 these challenges is for companies to shift the focus from Big Data 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 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. First 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 gathering process grabs information as it happens and uses it for real-time decision-making.
In other words, sensors collect external data inputs and intelligently react according to it, in fractions of a second. This type of Smart Data is also referred to as Sensor Data or Fast Data, and stands at the base of many modern innovations, 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 owning 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 are using Smart Data management to overcome information silos, as well as the financial sector, where actionable data is used to enhance customer experience, prevent fraud and manage risks.
Overall, Smart Data 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’ worth of irrelevant information.
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, reducing the burden on their data storage capabilities.
How to collect Smart Data
If your company is less mature in the field of Big data, it could be that you are collecting every available piece of data, loading it into a data lake, which could turn into a data swamp. Companies doing this have the mindset that they will use the data when they decide what to do with it. With this mindset, the data may lack quality, overwhelm with its quantity, or ends up being stored in a wrong format.
It would be better to collect only data that is truly relevant to the business. This means shifting from gathering Big Data to collecting Smart Data. Collecting Smart Data, rather than "all" data, can be an efficient strategy for organizations of all sizes. Focusing on collecting Smart Data allows the business to use cost-effective solutions for processing it. Gathering only the essential data can simplify the use of Self-Service BI tools, keeping personnel from getting lost in masses of irrelevant information.
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 offers value and can be acted upon. This implies for them to stop collecting vast amounts of all available data and start contextualizing what they have already gathered.
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 its usefulness defines Smart Data, companies should clearly define what they want to capture and how they intend to use it, before beginning this shifting process.
Finally, when companies choose to invest in a Smart Data strategy, they should:
- 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;
- continually review the need for organizational changes and innovations that the available data will highlight;
- embrace automation, as data volumes never stop increasing - without automation, data cannot 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 make smarter decisions faster and overcome the competition.
