6 Big Differences Between Big Data and Smart Data

If you’ve read a tech blog at any time in the past few years, you’ve probably noticed that big data is a hot topic. That’s not surprising since companies can now extract insights from databases in ways that were impossible without its help.

However, there has been a more recent move toward smart data. Let’s take a look at six things that help you grasp the big data vs. smart data debate.

1. Smart Data Is High-Quality and Accurate

As company executives rush to take advantage of what big data offers, many of them don’t stop to ensure that the data they’re using is correct. In contrast to big data, which may be incorrect, smart data is free from errors that could make enterprises come to bad decisions that don’t reflect reality. Analysts from Deloitte carried out a survey to assess the accuracy of big data findings from third-party sources, such as companies that provide data for marketers.

It asked customers to verify the correctness of their data and came away with results that may surprise you.

 

Overall, 71% said the data collected about them was wrong. And, incorrect information was particularly prevalent concerning a person’s economic, vehicle-related or demographic data.

It’s common for businesses to purchase big data from elsewhere if they don’t have the in-house technologies to gather their own. However, whether a company gathers data in house or buys it from somewhere, they must take care to verify the authenticity and not just expect that the information is error-free.

Smart data, on the other hand, primarily refers to either information sourced by smart sensors or big data that has been screened and optimized for action. In the second case, this extra step of processing ensures accuracy and allows businesses to be more confident about the way information drives their decisions.

2. Big Data Is Not Always Targeted to Assist With Specific Business Needs

Many companies start using big data due to a desire to follow their competitors. They don’t have plans for how the data could align with particular weak points or priorities within the business. In that case, big data can never become smart data because these enterprises are likely to deal with the information in a haphazard, unfocused way.

TransUnion surveyed senior marketers in the United Kingdom and found that 71% admitted that the data they worked with overwhelmed them. Moreover, 69% said that the amount of data in their possession distracted them from their primary marketing responsibilities.

Furthermore, a different study performed by Gemalto shows that these kinds of overwhelmed feelings mean data goes to waste or gets mishandled. The research confirmed that the majority of marketers (65%) are not able to analyze or categorize all the data they store about consumers.

Plus, 54% reported they did not know where the company stored all its sensitive data. On a related note, 68% of IT professionals worry that this lack of knowledge about information and its location puts enterprises at risk for not following data protection regulations.

If companies try to go all-in with their data analytics investments and don’t have well-defined reasons for using the information they collect, the likelihood goes up that they’ll get swamped by the data. However, if companies set out purposeful uses for the data in advance, they can embrace the move to smart data.

3. Smart Data Relies on Integrated Technology and the Most Up-to-Date Metrics

Smart data is a relatively new development and its definitions vary. However, one of the things that can help you make sense of the big data vs. smart data differences is to remember that smart data is usually prepared and optimized at the collection point. Taking this approach allows you to use the data more efficiently.

One of the reasons marketers often determine that smart data is more valuable is because it’s current enough to allow them to give customers what they want and need without frustrating delays. Using smart data in intelligent ways often means integrating data collection tools with the platforms that companies already use.

Google conducted research and found that marketing leaders were 1.6 times more likely than those who lagged behind to prioritize technology integrations. The same research discovered that those who led the way when using technology to work with data were 1.2 times more likely to be advanced users of the technology compared to the laggards.

Additionally, the people identified as marketing leaders refreshed their interfaces to retrieve new data more often than their peers that were not yet leaders in their fields. That finding suggests that benefiting from smart data requires companies to have team members who understand the importance of keeping their skills up to date. Knowing your way around the data collection practices you use is key to accuracy.

4. Big Data Isn’t Necessarily Highly Contextualized

Another thing that set smart data apart from big data is that it’s highly contextualized, and this context comes from close to the source. For example, businesses using geospatial data often benefit from contextual insights that big data may lack.

Some of the most successful uses of geospatial data have occurred in the banking and finance sectors. Some of the entities within have studied maps of certain neighborhoods and determined which places are most financially stable or which ones have the most residents that are likely to default on their loans.

Electric companies can also use it to figure out which areas are most likely to have downed power lines, and 911 dispatchers might depend on it to plot the fastest routes to some heavily populated and often-served subdivisions.

Bringing context to data like this makes it easier to assess which factors make one area of a city or town different from the others around it. Big data may have context to some degree, too. But, the applications for big data typically don’t drill down to find out such minute aspects. For example, a big data application may view information about a city, but it may not have the context for street-level analysis.

5. Smart Data Promotes More Confident Decision-Making

When approaching any discussion about big data vs. smart data, you must remember that any decision made with data is only as good as the trustworthiness of that information. That’s one reason why companies that use smart data often depend on artificial intelligence (AI) algorithms. The AI helps weed out the mistakes or duplication that humans may miss.

KPMG polled executive-level respondents about what it termed a “digital trust gap.” Firstly, only about 35% of the executives polled said they had a high level of trust in the way their organizations used data and analytics. And, a vast majority (92%) said that they felt concerned about the potential negative impacts on corporate reputation if a company got something wrong when using data and analytics software.

But, something called data-supported decision-making seeks to reduce these problems and help executives have more trust in their data. Human judgment still comes into the picture, but only after AI algorithms summarize the data and give it to a person as another input to consider. That doesn’t mean smart data never makes errors, but using technology like this could find the issues that humans might otherwise miss.

6. Big Data Does Not Allow for the Level of Personalization That Smart Data Does

Big data excels at helping companies find out about broad segments of their audiences, such as millennials or baby boomers. However, since smart data is more contextualized and calibrated for specific needs, it equips companies to create specific audience segments and tailor their services or content to those groups.

Statistics indicate that focusing on personalization with this approach could help customers keep or gain customers. The results of research from Gartner showed that although marketers aim for one-to-one personalization, there’s a high price to pay for getting it wrong. That’s because 38% of the customers who responded said they’d stop doing business with a company if they found its personalization efforts “creepy.”

On the other hand, information collected in 2017 by Epsilon showed that 80% of respondents expect brands to deliver personalized content to them, and 90% find it appealing. That suggests people are ready for personalized content from brands, as long as the information doesn’t go too far and feel intrusive.

However, converting big data projects to ones that use smart data and personalize content well may require expanding the budget and hiring more data specialists. As you can see from the image below, a company’s budget, plus the lack of talent to either implement big data or run analytics continually are some of the biggest challenges that enterprises face.

 

Smart data helps companies take big data to the next level concerning personalization. But they can’t do that without adequate resources.

Make Your Data Smarter

Now that you know some of the main points in the big data vs. smart data discussion, it should be easier to implement smart data within your company. Then, you can utilize more relevant data for your needs.

Featured image via Rawpixels