BIG DATA: Veracidad, valor y retos - DAnalytics

BIG DATA: Veracity, Value and Challenges

In the last entry, I told you that Velocity, Variety, and Volume characterize Big Data. Now there are two other V that tend to be used: Veracity and Value. Today I will tell you about the meaning of them and what are some of the challenges facing organizations with Big Data.

Veracity

From where did this V come? From IBM. And what does it mean? According to the RAE, Veracity means that it has the property of being truthful, that is to say, “That says, uses or always professes the truth.” The truth can be explored from several perspectives: objective / subjective, true / deceptive, credible / implausible (Lukoianova & Rubin, 2014). Why could this property be important? A lot of data is generated by human beings directly or indirectly. Sometimes we express one thing in social networks, and we end up doing the opposite. Examples of this can be found in Plesbicito por la Paz or Brexit (direct). Or there are technological advances that are created with the aim of creating misinformation (click here to read the article “False information goes further, faster and more people than the real”) (indirect). So how do we know if the data we capture is true or not? There is no magic formula to classify a data as reliable or not; my recommendation is that you do not lose your common sense and critical thinking when you see the results of the analysis of the data. And that applies whether it’s Big Data or not.

Value

The more data you have, the more value your company has. For example, in the top 10 of the most valuable companies in the world (Fortune 500 2016) were: Walmart, Apple, Amazon, Alphabet (Google), Microsoft and Facebook (Marr, 2017). Each one in a different sector of the economy, with a different business model, however, have one element in common: their ability to collect data and use it to generate competitive advantages.

How to monetize the data? The data increases the market value of the company or through the generation of capacities to create extra cost with the information that can be sold to your customers or third parties. We already saw an example of the first case. Let’s see an example of the second. Do you have a credit card? If so, be it Visa, MasterCard or American Express, then it is true what you are thinking they win everywhere. On the one hand, for the payment, they make for the service they provide. But it turns out that the service you use gives you information about your purchasing habits: where what, how much, how often, how many installments, etc. That is valuable information for many other companies. For example, retailers may be willing to buy that information because it is useful to segment customers or to identify purchasing trends in specific market niches (Marr, 2017).

5Vs and lots of challenges

What challenges do organizations face? Many. It all depends on the sector in which your organization is located, the size of it, the long-term strategic objectives that have been, that is, the challenges of an organization are specific to the characteristics of the company and its environment. What I can do is give you some questions that should be answered if you are going to start accumulating a lot of data or if you are already full of data, growing more and have no idea what to do with them.

Let’s start with Volume V. What is the information that is valuable to your business? Unless you’re Google, Facebook or Amazon, you can not afford to store all the data you generate because it’s too expensive. Then, you can answer where are you going to save the data and who is going to do that? If you do not have your storage capacity, there are many servers in the cloud where you can store your information such as those provided by Amazon, Google, Microsoft, Atlantic.net, among others. You also have to think about what is the technological infrastructure that you need to process that data and evaluate if your human resource has the technical capabilities to manipulate that information.

The V of Velocity: the processing of information in real time. Probably, in Colombia, we are a bit far from this characteristic, at least in most companies, but for those that are, my recommendation is to analyze what is essential and process that. Again, we must analyze the costs and benefits, and not lose sight of the strategic vision we want to achieve.

Finally, the V of Variety. The data comes from so many different sources and end up stored in different computers. Here you could answer: What kind of data do you collect? What kind of processing do they need? What could they serve me for? And how can I integrate them? Instead, make an inventory of the data you have access to, where they are and who has access to them. You can have infinite amounts of data, but if you do not know what you have and how they can be integrated, they are of little use to make decisions and improve the operational efficiency of your company.

After you capture and store the data, you will have other challenges: how to visualize them, how to reduce the dimensionality, how to manage the dispersion and regularize the data? As we already mentioned, the particular answers to these questions are dependent on your organization and characteristics. We will tell you the latest technological advances that exist in the market to solve challenges and take advantage of opportunities, we will give you what to think, and especially we want you to understand the concepts so that you can speak the same language with system engineers and communicate the importance of data to your suppliers and customers (internal and external).

References

  1. Lukoianova, T., & Rubin, V. L. (2014). Veracity roadmap: Is big data objective, truthful and credible?.
  2. Marr, B. (2017). Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things. Kogan Page Publishers.

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