By 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. This is what happens when data starts growing at a rapid rate. But the preeminent question is – are we able to use that data effectively?
With the bliss of IoT, enterprises can operate more efficiently, explore better revenue streams, provide better customer experiences, and lower overall operational as well as business spends. However, being able to best leverage IoT to attain such perks might seem like a hornet’s nest to many enterprises. The reason to that can be the lack of thorough data analyzation after curating large volumes of blended, structured as well as unstructured, data.
While adapting or being in the early stages of IoT implementation, enterprises must learn extensively from “small data”, which is easy to comprehend and present in an easily accessible format. On the contrary, “big data” tags along unstructured volumes of data, demanding technology aid to analyze the same. Where big data helps enterprises recognize the patterns of past best practices, ready to be augmented in the future for betterment, small data helps in going deep in minute details to stay conscious of the hurdles that must be addressed on priority. If big data helps in correlations, small data assists in causation, knowing the reason why.
Because data doesn’t analyze itself
Data does not get aggregated itself, and similarly, it doesn’t even get analyzed on its own. Big data that you have collected is going to fall into the no-account bin if out-and-out analyzation is not in place. Data must be made valuable – for small data, manual analyzation can do wonders; for big data, one must seek aid from machine learning algorithms, where the massive chunks of data can be reduced to easy-to-comprehend pieces.
Not only this but while collecting data as well, businesses must ensure that they are not missing on anything vital and are competent enough to gather data that can be further converted into information. Businesses should also be able to adapt to the latest or current data available and the foreseen changes must be incorporated by enabling an architecture ready for periodic or strategic amendments.
Data strategies one can follow
For better execution of the processes, businesses need the inclusion of information that can be acted upon immediately. To ensure the same, let’s have a look at the strategies one can follow:
- Faster accumulation of data: The faster the data is available, the quicker it will be analyzed. Furthermore, the process of making the analyzed data actionable will be speedy. This can help businesses identify all the hurdles and bottlenecks, eventually streamlining diverse business processes.
- Improved visualization of data: An enhanced way of presenting data is consequential for any business. Businesses should ensure that the data they have collected is able to highlight the key takeaways effectively. Businesses must seek the help of augmented analytics for the same. These tools use artificial intelligence and machine learning to ensure better visualization of insights by continuously updating big data sets.
- Reduced costs: Businesses can drive low on costs for preparing data to increase the processing capacity and improve their return on investments. Businesses can opt for technologies instead of staying dependent on labour. Technological inclusions can help with creating the data, analyzing it, removing anomalies, filtering redundant data, and other key tasks at lower costs than manual labour, which is prone to human errors.
The bottom line?
53 percent of the companies started big data adoption in 2017, a figure which was just 17 percent in 2015, showcasing one of the fastest adoptions. Where IoT provides massive data to organizations, analyzing it can help in revealing new opportunities to optimize their operational tasks and be more competent in the world of business. Not just this, IoT-derived data can help in enhancing productivity and profitability by converting data into information with the help of strategic evaluations. Businesses can learn from small data at initial stages of implementation and can further seek technical assistance to analyze big data to execute the resultant opportunities with confidence. Eventually, it all narrows down to collecting better or best data rather than settling with more data. Do you agree?
Anjli Jain is Managing Partner at EVC Ventures.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
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