As mentioned in our introduction to big data, big data essentially means all data and, as we know, data as such is meaningless. In order to harness true benefits, big data needs to be turned into actionable and smart data, with a clear focus on the purpose, (combined) insights, actions and resulting outcomes.
The true value of data lies in its application to a business process: the way it is being actioned in a business and/or customer context. To achieve that, relevant data needs to be extracted and inserted into a business workflow to automate the specific business transaction: smart data.
When this process optimization enhances the real-time customer experience, it becomes even more valuable: it becomes critical for long term business success.
What is smart data anyway?
If you remember the famous Vs of Big Data (Volume, Velocity, Variety and Veracity), smart data is about Veracity, along with another often used V: Value.
With smart data we focus on valuable data and often smaller data sets that can be turned into actionable data and effective outcomes to address customer and business challenges. It’s about analysis and interpretation of data so we can make our decision-making and business functions data-driven by putting data in the context of purpose and context.
Smart data is big data turned into actionable data that is available in real-time for a variety of business outcomes, whether it’s in industrial applications, data-driven marketing or process optimization. With smart data we’re really looking at ways to remove the noise of the sheer aspect of Volume just as fast data is about the element of Velocity. In a marketing and customer experience context, for instance, smart data is mainly seen from a hyper-personalization dimension.
The increasing focus on smart data instead of big data is strongly related with the coming algorithm economy. Artificial intelligence is increasingly used in business applications and dealing with the data from big data and the Internet of Things. Most data out there is unstructured and only with artificial intelligence and analytics unstructured data can be turned into smart data and actionable data.
The essential question in the continuously growing amount of data volumes is how to make practical use of these volumes and without analytics, interpretation and algorithms it just isn’t possible. The infographic below, by Siemens and for industrial applications, shows the challenge of volume and the need to move from big data to smart data.
Usefulness and data intelligence
To make data actionable, it’s important to know why we want to capture and use them to begin with. Even if big data is essential and we have so many data sources at our disposal, smart data is not about volume and not about technology alone. It’s about usefulness, with multiple layers of intelligence built in the way we acquire, process, analyze, store, interpret and improve data to act upon them and effectively make them useful.
Think about paper sources and Intelligent Document Recognition, for instance. Or about how unstructured data are optimized, routed and turned into insights and flows, using artificial intelligence and intelligent information management.
With fast changing business dynamics, the speed at which data are acted upon has become essential in a real-time economy too. In that context, there is fast data, to align with those fast moving dynamics and an increasingly demanding customer.
Smart process solutions
Still, very few business processes are well defined and stable enough to build business rules and clearly structured workflows to automate them. Most business processes ARE complex and very dynamic.
Smart process solutions address these complex processes that typically involve a number of internal and external stake holders, the need to connect multi-channel inputs, multiple data repositories and multiple devices.
The AIIM Industry Watch series “Case Management and Smart Process Applications” explores the status of the market adoption of such smart process applications.
Early adopters of smart business applications are achieving promising results with 40% having already achieved their initial objectives. Only 7% are disappointed with the system.