Turning Big Data into Smart Data

Big Data has become one of the key buzzwords for businesses everywhere over the last few years. With data of all kinds being produced in record amounts every year, collating and analyzing this information will give businesses more insights than ever before into their customers and their industries, and perhaps even let them predict what might happen in the future.

Here is just one of many incredible big data stats: Every minute we send 204 million emails, share 2.5 million pieces of content on Facebook, send 277,000 Tweets, and post 216,000 photos on Instagram.

Suffice it to say, there is a huge amount of data out there. But making sense of millions (perhaps billions) of data points can be time consuming and difficult without powerful technology, particularly when this data is unstructured. That’s often the case with online textual data in the form of news articles, social media posts, forums comments, and much, much more.

In fact, such is the complexity of this process that there has been somewhat of a backlash against big data. There are now ideas about big data’s importance being overstated because it is too “big” and unruly.

In a sense, this point of view is correct. Without structure, big data is unusable. It is just a mass of unrelated information that would take years to comprehend, and even then, may not yield any insights. But if structure can be overlaid effectively and analysed, that is when big data starts to become smart data. At Talkwalker, we have a way of explaining exactly how this happens, and how this process can be a bit like looking for a partner in life.

From a computer’s perspective, all social data is just words on a page from different sources such as Twitter posts, Facebook posts, news articles, blogs, and forums comments. The first step is to look for a certain topic in this data, a1s you would on Google.

Let’s say we type in “Talkwalker” into our social data analytics platform. At this stage, without any other parameters or filters, we’d have a very long list of URLs or post titles in no particular order.

As you can imagine, with such a limited filter, the insights we can gain from such information are pretty limited, too. All we would really be able to know is how many times a certain term has been mentioned online. This isn’t irrelevant information by any means. For companies looking to expand their brand awareness, this may in fact be important information.

But to make our social data really sing we, need more. The next step would be to add a variety of potential filters. A basic one, for example, would be time frame. Do you want results from just the last week or over the last two years? Or even just the last hour?

We also want to know which media channels these online “mentions” are coming from. Are they Twitter posts, YouTube videos, news articles, or blog posts? And how many are there of each?

For global companies, knowing the country where posts originate and the language they are written in is also key. Are there more posts from Britain or the US? Is there more online content about the brand in Spanish or Portuguese?

With just the addition of a few filters, our unruly ocean of social data has been divided into more manageable rivers. These rivers can then be combined and divided at will to create multiple streams of data, allowing you to isolate the information you need.

For example, for a UK brand that has just launched a new product and started a big Twitter campaign, you might create a stream of data just looking at Twitter results from the UK over the last two weeks.

At this stage, you’ve already made a big difference to the usability of your data. With these multiple filters, you can now get a clearer idea of exactly when and where mentions about a particular topic are coming from.

There are plenty of insights to be gathered at this stage, but to really make your social data smart, you need to go one step further.

If filtering this information allows us to create manageable streams of data, analytics helps us turn it into real insights that we can use to help a business grow.

Using advanced analytics, we can now look at the most important themes appearing in this data and see, for example, which words are most associated with certain brands. We can also see which influencers on Twitter or Facebook are having the biggest impact on a brand, positively or negatively. Or we can see who has been the most influential (whether it’s men or women that are talking about a topic, for example) or analyze the sentiment towards a particular campaign or product.

It’s this process of searching, filtering, and analyzing large volumes of unkempt data that brings it to life as a source of actionable insight for companies. Knowing that your brand has been mentioned 2 million times over the last month isn’t irrelevant, but knowing that your brand has been mentioned 2 million times with a quarter of those mentions coming from “men based in the US using Twitter on the evening of May 1st due to a news article published in the New York Times” is infinitely more informative.

Every level of filtering and analysis gives you key insights into your social data, but each time you go further, your data gets smarter.

Of course, being able to use and incorporate such insights into business strategy will (for now, at least!) fall to the people in the company with expertise in each area, but smart data lets professionals quickly jump from point A to point B.
And once you’ve managed to refine the data to this level, it can be used in myriad ways.

As more and more people use social networks, blogs, and forums to discuss the issues that matter to them, the insights that can be gathered from these sources become more representative of the general public’s views, and therefore more valuable and accurate.

In a crisis, a brand can monitor all mentions of their brand across multiple media channels, and then look for the hashtags most associated to their brand to find potential sources of social media negativity and react to it.

For product campaigns, data on levels of buzz in particular countries or languages can be combined with sales data to get an accurate picture of how the public is responding to a particular campaign.

For advertising, the most used keywords around a particular topic can be examined to find the right targets for messaging.

The uses of social data refined to this level really are potentially limitless and are increasing all the time. Companies and organizations in all fields, from retailers and telecommunications to political parties and NGOs, are finding the value of social data for their sector.

The final crucial stage of the process is distributing this data to the right people, in the rights formats and at the right time. Insights from social data can be used across all departments, from C-Suite to customer service. Delivering the relevant insights quickly to each department ensures you are getting the most out of the insights you have uncovered.

A sometimes under-publicized benefit of social big data is its potential to be combined with other data sources. Using APIs, or just through exporting results, social data can be combined with other customer data, data from the growing range of wearable devices, or from other “connected” devices (a.k.a. the internet of things, or IoT). Combining these powerful data sets creates an even more accurate and nuanced view of your company’s activities both online and offline, as well providing a more detailed picture of your customers.

Talk of the merits of big data has been around for some time, with some believing it is on the demise. But in reality, this is an unlikely path. As technology advances and our experience with handling giant data sets grows, our ability to manipulate this information should only increase.

ConvinceAndConvert: http://bit.ly/1KjPX1m