We hear about Big Data quite a lot, but many people are still not sure of what it really is. As I believe it’s extremely powerful, I thought I would write an article about it to provide a better overview and help you understand what we can do with Big Data.
Like everything in the tech industry, it keeps evolving and changing almost every day. By changing it affects all that was born from Big Data, going from digital transformation to artificial intelligence, to IoT. But in order not to skip 10 steps, I would rather start off by quickly explaining what is Big Data.
What Is Big Data?
Like everything relatively new, there are many different definitions you can find out there. Big Data can be seen as a broad term for data sets, a buzzword, or a catch-phrase.
Some definitions refer to Big Data with the three Vs model: volume, velocity, and variety. In fact, Gartner describes Big Data as a “high volume with high velocity and/or a high variety of information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
If that interests you, here you can find other definitions/opinions about Big Data (from more than 40 leaders in different industries such as medicine, marketing, food, fashion, etc.), you might be surprised by the variation!
No matter what the definition is, the term “Big Data” is not only about the data itself; it also refers to the challenges, capabilities, and competencies. What seems to appear everywhere is that Big Data is a non-stop massive growth of information.
Of course, it all began with the boom of the digital era. More devices, more internet usage, more technologies used… all that translates into more information. More data. Every time we are connected, data is generated — using social media, shopping online, any app that connects to the internet.
You might ask yourself if there is a difference between “Big Data” and just regular “large data”?
A good example from Dwight deVera explains the difference between those two; “the thousands of statements and invoices that a financial director has about his clients on files is considered as large data. But log files from social media sites such as LinkedIn & Facebook are considered as big data. What is the difference? It’s the speed at which the data must be captured and available for analysis.”
Big Data is increasing the quality of sales leads, improving the quality of sales lead data, improving prospecting list accuracy, and improving territory planning, win rates, and decision-maker engagement strategies.
Big Data is part of marketers' and salespeople's work. It’s the result of this digital world we live in. In sales, it is used to improve the quality of sales leads and prospecting. In marketing, by combining big data with an integrated marketing analysis, we are able to reveal very interesting insights helping when it comes to increasing conversion rates, prospect engagement, customer lifetime value, etc.
According to Darryl McDonald, president of Teradata Applications, “Marketers are most effective in generating revenue when they are able to put all their data to work to deliver the most relevant offers to consumers.”
One of the first things you need to do as a marketer is to get the most information as you can about your target to be able to build their profile — who they are, what they do, and how they feel.
With Big Data, you can easily get significant insights about your customers — where they are, how they want to be contacted, and when. The formula can be called the 4 Rs: the right people, the right way to contact, the right time, and the right location. Basically, Big Data helps marketers to have a deep understanding of customers’ behaviors and what it is that will make them come back again and again to you, your product, or your service. Nowadays, Big Data analytics skills and digital marketing savvy are increasingly valuable to companies; they help drive more revenue, better margins, and increased efficiency, resulting in more profits.
How Is Big Data Transforming Industries?
Firstly, you have operational technologies that basically capture and store the data, and then, you have systems that provide analytical capabilities. The idea is to get the most information possible to make the best predictions and take the best decisions. We produce all sorts of data, from almost everything we use (on our devices). Going from pictures to videos, audios, messages, etc. And today, this data is used in projects of Artificial Intelligence and Machine Learning where image recognition and natural language processing are used to learn computers how to record patterns.
Big Data is contributing in so many different industries — the public sector, healthcare, insurance, banking, and much more, helping increase sales, increase productivity, lower waste, etc. Here are a few examples to get a clearer idea of how Big Data is impacting different industries.
Even before the digital expansion, there was always a lot of data around the healthcare industry. But having it under a digital aspect makes it much more powerful. Today, you can find more and more apps that are developed to help patients track their progress towards a healthier lifestyle. We can even get information about the heart rate of a patient and steps walked.
This database helps a lot to get a deeper understanding of what type of lifestyle are people leading and which diseases they get, track patterns, and make use of it all for medical research. Hello, prevention advancements! The idea is to digitize all the patient’s information, making it accessible to all the healthcare systems and making it easier to build a patient’s profile.
Also, Machine Learning technologies are used to get a stronger diagnostic regarding cancer and test results. This could mean we could find out earlier about diseases, avoiding errors in diagnostics and getting the right treatment.
In the insurance industry, Big Data enables us to better assess risks. Insurances are able to build more accurate profiles of their customers, and based on their whole database, we can have a better idea of how probable it is for a customer to make an accident, understanding better the customer’s old behavior to predict better.
In fact, by analyzing the data, we are having a deeper understanding of the customers and we can become more efficient in offering products and services that meet the client’s needs.
Big Data enables banks to get a better overview regarding their clients’ transactions and general behavior. This implies having Big Data insights about their spending habits. With this, we can have a stronger customer segmentation. Of course, by building a stronger profile, we can tailor products and services to the needs of the client, adding value.
Fraud detection has always been an important activity for banks. With Big Data, the process time of fraud detection is reduced and made much more efficient.Big Data comes in handy not only in fraud detection but also in risk management. Indeed, we can detect risks, which will help prevent losses due to bad loans or failed investments.