Denave, Team, Designation
It’s the age of data explosion. About 2.5 quintillion byte of data is being created each day and this pace is not in a steady state but exponentially accelerating with the growing IoT landscape. In fact, as per reports, 90 percent of the data in the world today has been created in the last two years alone.
With big data and analytics coming into picture and making some sense of the situation, we are at least a generation who understands the importance of data and prefers to act on it instead of just sitting on the same. But we also realise the fact that we’re all majorly wired-up in a technology race.
We catch up with one trend but still never can relax because the other technology trend has emerged and relaxing in the race would mean stagnation and dismissal. Thence, the race to advance with the pace of trends, continues – and for good of course! And that’s how we stumbled from Big Data to Smart Data.
Big Data vs. Smart Data
Big Data, as we are already aware, describes massive amount of data, without any distinction of being structured or unstructured. Before feeding the data into the analytic engines, there comes a step where you need to prepare that data first for becoming suitable for analytical operations onto it.
And that refinement gives what you call as ‘Smart Data’. This cleansing, filtering, and readying up of data as per the context allows for better and quicker insights and thus, efficient decision-making support. After all, trying to make sense of an unstructured heap of data is very time consuming and inefficient versus getting structured data for analysis and that explains the switch from big data to smart data.
Tracing the Journey from Big Data to Smart Data
Traditionally with big data analytics, it was more about ‘batch processing’ with the data being fed into the engine at certain regular intervals like hourly, daily, weekly etc. and then getting insights accordingly. But imagine the same scenario with burgeoning advances such as driverless cars.
A self-driving car can’t wait for hourly insight update for its functioning but would rather need a continuous stream of the same for proper functioning. Smart data processing, often also called as streaming analytics, provides just that with events and exceptions being captured at source and assessment analysis being done in a fraction of seconds for a decision output.
Let’s look at the background process or steps which goes into the conversion of big data into smart data (or creation of streaming analytics so to say):
1st Step- Right Search
Define what you are exactly looking for and then plunge into a targeted search exercise instead of randomly valuing and saving everything that you are getting.
2nd Step- Appropriate Filtering
This step is about narrowing down the options and moving towards a manageable set of data from the still unruly pile. Add filters as per your need, for example – time frame, data channel, data origin etc. A combination and permutation of filters would also give multiple data points.
3rd Step-Time for Analytics Application
With the input being quite specific in comparison to the earlier unkempt data from where we started, the engine will give you intelligent and actionable insights. Applying analytics with different filters will give you varied set of action points.
4th Step- Expand and Replicate the Model
Now you can enlarge your work area and get more and more data into the analytics engine to get further refined insights. Larger the database, more accurate the insights would be and further limitless would the possibilities be for that data leverage. After that, it is all about sharing the insights with the right people at the right time because you have a handy insight generation engine in place.
5th Step- Create a Data Ratatouille
Finally, it’s time for some intelligent amalgamation of data originating from different sources. It is all about, more the merrier at this point. This step will complete the picture and give you a thorough and nuanced view of the subject situation.
Smart Data and the 5 ‘Vs’ of Big Data
Value (output value is driven out of the data), variety (types of data), volume (amount of data), velocity (speed of data input and output), veracity (accuracy of data) are the terms often used to describe big data. With smart data coming into the picture, the ‘volume’ aspect of the big data is reduced (as seen in step 1 and 2 where only the useful information set is filtered out eventually, reducing the noise).
Industrial Applications of Smart Data
Smart data coupled with edge computing tactics is changing the way organisations fathomed functioning earlier. Elements like improved decision making, shorter TaTs, accurate and quicker probable problem spotting before actual occurrence etc. are revamping the industry sooner than we envisaged.
A quick look at a couple of applications of smart data in various fields:
Telecom: Bandwidth allocation, Cell tower diagnostics
Transportation: Monitoring and unsafe driving detection, thus accident prevention
Retail: Hyper-personalised promotions and outreach, brand-customer sentiment analysis
Financial Services: Fraud detection as well as prevention
Manufacturing: Early issue spotting and proactive maintenance
Healthcare: Monitoring patient vitals, improved life-saving statistics and reduction in re-admittance rates, disease outbreak detection
Utilities, Oil, and Gas: Power and consumption matching, smart meter-stream analysis, proactive equipment repair
Public Sector: Network intrusion detection and prevention
Moving from Swamp to Smart
Storage of data swamps into data warehouses takes efforts and also has commercials attached to it depending upon the size you’re occupying. On the other hand, being selective and wise in data collection and moving from swamp of data to smarter bits of data is a definite move towards a more targeted business intelligence model.
With the increasing leverage of AI and machine learning algorithms in aiding smartness to the data in the journey it makes from a distorted chunk to a sensible structure, smart data is enabling organisations to thrive in the times of disruption.
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