Scrolling through LinkedIn may induce panic attacks – Big Data is coming to murder your market research once and forever. There are two types of people who post nonsense like that. Those who are afraid of it and understand neither market research nor Big Data. And those who believe they don’t need to understand it, too, and thus disregard it. We don't know which is worse.

Yet those who use Big Data for marketing then go on to apply Artificial Intelligence (AI) and analytical algorithms to analyze the data, get a considerable advantage over their competition. So how do they do it?

Big Data is not a single technology but a combination of old and new technologies that helps companies gain tangible and actionable insight. Therefore, big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. Big data is typically broken down by three characteristics:

• Volume: How much data

• Velocity: How fast that data is processed

• Variety: The various types of data

For marketing and sales purposes, the difference between the data collected through market research and Big Data is more clearer when looked at on a bigger scale. Market research uses surveys, cold calling, interviews, focus groups to collect data on certain subjects and topics from hundreds or even thousands of people, depending on the sample size. It helps to decide and understand some markets, but in the grand scheme of things – it’s as good as guessing.

Big Data is…well, big. Every login to Facebook, Netflix, bookings on booking.com, every tweet, Amazon, and Walmart purchases generate vast amounts of data. The staggering amount of data is difficult to imagine and the kinds of information it presents can be scary. Data collected, stored, and analyzed via the newest database technologies opens the doors to the world of purchase history analytics, buyer behaviors, customer relations, tastes and the way they are formed, the context of deciding, and how it changes.

These kinds of Big Data can be structured, unstructured, and complex and may predict and analyze almost any kind of customer behavior. When your company is doing it in a cloud with the newest databases – it can be done in real-time with unprecedented precision.

All the data collection and analysis techniques become irrelevant if it overshadows the most important part of market research — the design of the metrics. Understanding what data your market research requires is a prerequisite for successful modern marketing activity. The amount of data collected, analyzed, and applied for marketing with Big Data and old marketing surveys and cold calling is incomparable. But it can be overwhelming for companies who are afraid of Big Data. That’s where teams like Tentacle Solutions comes into the picture.

Where do your custom software database and B2B app come in?

When custom software is designed and developed with a deep understanding of the kind of data your business collects, operates, and stores, it will be efficient and effective for the way you operate and no one else. Why would your business need to reserve production capacities for things that you don’t do? The same applies to your custom software and its relationship with your data. 

The amount of data collected, analyzed, and applied for marketing with Big Data and old marketing surveys and cold calling is incomparable.

With the right tools, Big Data helps businesses get to grips on the data that matters the most and turn that information into a million customer scenarios, regardless of the company size. Just three examples of how Big Data solves real issues:

Anticipating customer behavior. Analyzing how customers make decisions helps businesses make better decisions of their own. Netflix famously builds predictive models based on their viewership data. But even smaller businesses like real estate management companies use Big Data to analyze a multitude of variables and how customers percept them: weather, market conditions, seasonal trends, size, and location of the property. They use it to pinpoint the best price recommendations down to weeks and days.

Maintenance Optimization. Cross-referencing essential information about the equipment – year, make, model, sensor data, log entries, errors, and even temperatures, lets the businesses model and predict possible hardware failures and schedule maintenance accordingly. It allows cutting maintenance costs and raises the reliability of the business.

Battling fraud. Hackers and fraudsters are no longer an established caricature of themselves i.e. a bunch of kids wearing hoodies. They can be teams of professionals who are after your data. Yet, with Big Data and machine learning, it is significantly easier to predict any vulnerabilities and threats. Big Data also helps save on security costs and reputational damages.

So you've got your custom software solution that collects tons of data every day, with every client, and every transaction. Naturally, it can’t rest without purpose and needs to be stored, analyzed, and presented in the most effective and efficient manner.

Now imagine this data stored in paper form and you need hundreds of workers to find, organize, and analyse each piece of information. Horrifying. Now, imagine that to analyse and cross-reference different types of data you would need to ask all your employees to literally run to the stacks of paper to bring each page to you one at a time i.e. every employee runs and delivers the same page. What can be worse?

The above horror is the reason why your Big Data needs Hadoop.

Hadoop, an open-source software framework, uses HDFS (the Hadoop Distributed File System) and MapReduce to analyze big data on clusters of commodity hardware—that is, in a distributed computing environment.

MapReduce is a software framework that enables developers to write programs that can process massive amounts of unstructured data in parallel across a distributed group of processors. MapReduce was designed by Google as a way of efficiently executing a set of functions against a large amount of data in batch mode.

The “map” component distributes the programming problem or tasks across a large number of systems and handles the placement of the tasks in a way that balances the load and manages recovery from failures. After the distributed computation is completed, another function called “reduce” aggregates all the elements back together to provide a result.