25 Jul

What’s Smart data and how it’s different from Big data?

Smart data and how it's different from Big data

Before we try and understand Smart data, it’s imperative to know what’s Big data. As the name suggests, everything about Big data is massive. When you try to quantify it based on three aspects of data – volume, variety, and velocity, Big data is enormous.

If you consider the volume of big data, which is the total amount of data being collected by organizations, it has increased exponentially over last few years and continues to grow at a much rapid pace. It’s the same with the velocity of data – the speed at which it get’s processed, which has grown immensely as the ability and capacity of computers to crunch it has also increased enormously.

The advent of digitalization and social media has played a huge role in creating numerous sources of data. As a result, even the variety – the types of data that together make up the big data, has also increased in numbers. Putting it simply, all it means that the sheer size of big data makes it a stack of sufficient facts and figures, which might not be useful after all. And this is where we move on to Smart data.

The primary aspect that differentiates Smart data from big data is its veracity, which means extracting specific data that is accurate and more valuable. Big data turns into Smart data when it’s available in real-time and can be turned into actionable outcomes. The initiatives could vary from data-driven marketing to many other business applications. The data is more valuable as it can address both the current and future challenges facing a business and its customers.

One of the reasons for terming it as ‘Smart’ is because the data is because, with it, the focus is on improved insights that evolve from the analysis. As the emphasis is on the context, the business functions and decision-making that is driven by Smart data has a better perspective invariably.

The practice of breaking down a massive data chunk into smaller portions can be helpful in finding the solutions much more quickly as it eliminates the undesired variables. Once these little pieces are segmented into comprehensible sizes, the process of extracting insights from them becomes much quicker and efficient, which in turn translates to improved data-driven decision-making.

Also, there are several other aspects where the Smart data can be distinguished from Big data, and are discussed below.

1. Relevance

Big data usually comprises a lot of metadata – the descriptive information about the data such as its attribute, type, aspect, etc. On the whole, this type of referential info is of little use for the enterprise that captures it. As relevance is the primary aspect of any data-driven process, extracting and preserving only relevant data is critical for success.

On the other hand, Smart data eradicate the unwanted info and retains only the valuable aspects, and so is more appropriate for a firm that’s keen on solving its business problems. It is derived from a thorough qualitative analysis, which is the key aspect that ensures an organization is driven by a more accurate data translating into much better decisions and can be free from compunctions.

2. Efficacy

Traditionally, Big data has been employed in business metrics to focus on primary aspects such as improvements of retention and conversion rates. In the process, companies have been trying to identify the source of better customer experience and experimenting with various types of data. Though regular evaluations can help in improving the efficacy of the data, there was always room for improvements.

Smart data that originates from advanced resources such as real time tracking and response can offer more informed results as compared to the traditional mechanisms. Also, there is a new scope of innovation through the data-driven creation of new products and services. Progressive companies that want to stay ahead of the learning curve can map these innovations with their core competencies to create a world-class offering that catapults them ahead of their competitors.

3. Context

An organization can have their own set of metrics depending on its priorities. For instance, a firm interested in using IoT to fuel its information needs could employ long term metrics. If it’s keen on extracting new insights, it will go for mid-term metrics, while short-term metrics could be useful if the business is looking to cut down on its storage costs.

Getting the Big data to match the context can be a bit harder when compared to Smart data. It’s because the latter also takes into account the pace at which industry can change, say 2-4 years, and allow the company to use the appropriate metrics.

Apart from these distinctions, smart data also scores heavily when it comes to the debate of quantity versus quality. The veracity of the data depends on the rigorousness of the analysis, along with its extractability. Appropriately sorted and structured, it has a long life, which allows companies to identify trends reach back into the archives. Going forward, it also minimizes anomalies and project patterns. Hence, the investment in Smart data, which can only get smarter, makes sense as it is better equipped to optimize solutions and processes.