Understanding Big Data and Data Mining

Have you ever heard the term ‘big data’ and wondered what it was all about? To many, it may seem like something that’s simultaneously ubiquitous and vague. The term refers to the fact that data is all around us (for instance, in cloud storage) and this data has the potential to be used for generating new insights to apply to all kinds of business and organizational activities.

Because there’s so much information available today, a key discussion in business and tech sectors is how to gather and analyze these massive amounts of data for use in ways that can benefit both businesses, consumers, and society as a whole.

How organizations choose to go about analyzing this data determines the purpose that it’s used for. Read on to discover more about the different ways that big data is being used in the context of higher level decision-making and strategy building.

What is Big Data?

The term ‘big data’ simply refers to the massive amounts of digital information that is available today. Organizations must use special storage solutions and processes to effectively utilize it. Most revenue from big data is via hardware, and the biggest companies that provide big data services include tech companies like IBM and Oracle. It is thought that the value of these services will grow to $274 billion globally by 2022.

With technological advancements, data is becoming easier to access and cheaper to store, which is why there seems to be so much of it today. To this end, organizations must constantly be on the lookout for the most relevant ways to use data. This might mean figuring out how to use historical data with real-time information, for instance, or how to effectively combine qualitative and quantitative data to better target a marketing campaign.

What Does ‘Big Data’ Mean in the Current Landscape?

Big data today is mostly relevant to businesses and marketers who are looking for ways to use it in the context of marketing to customers. In a broader sense, however, it is increasingly being used for overall efficiency, to do market and product research, and to generally improve any process that our organization centers around.

Here’s an example that illustrates how we previously used data on a small scale, for comparison. We may have had a simple database program to manage basic information about our customer base: name, phone number, address, maybe even some basic information about their buying habits. This was all ‘stored’ directly on a single computer or server that was relevant to our organization, and we maybe would use the information as a part of generating quarterly reports to tell us more about demographics and basic purchasing habits.

Now, the sheer volume of information that is actually and potentially available to and about customers is likely to be constantly available via, for instance, mobile devices – so we are sending and receiving information pretty much on a consistent basis.

The companies that we follow on social media gather information about our behavior via clicks and ‘likes’; our phones collect geographic data about us in order to offer maps that show where we are in real-time and give us information about public transit; and we can program our home thermostat to adjust itself according to our daily activities and rituals, and even control it when we’re away from home.

As companies become more and more capable of collecting and analyzing different types of data, they can offer more personalized, accurate services, often in real-time. They can also find patterns in systems, understand real-time customer buying habits, understand risk, and even gather information about internal company issues, such as those relating to security.

Who Uses Big Data?

Big data is used across pretty much every major sector, helping retailers revolutionize supply chain processes and health care professionals track and monitor patients offsite, but in real-time. Here are a few more examples of the many ways we are utilizing big data today:

City planning: Big data can help cities become more efficient, both in terms of traffic flows and overall energy use. ‘Smart’ cities, for instance, are connecting different levels of infrastructure and transport, as well as using specialized devices to monitor things like water usage in real-time. For instance, Barcelona, Spain, uses monitors to reduce water level usage in city parks, and Los Angeles, US, uses road sensors to control traffic lights around the city to regulate traffic flows and ease congestion.

Machine learning: Big data tools are a major part of what helps machines ‘learn’ how to operate in the environment. This is essentially artificial intelligence (AI). A good example is how self-driving cars operate via a combination of pre-programmed information and the use of specialized sensors and GPS navigation. It is the combination of data that machines are able to gather, synthesize, and use that allows cars to ‘self-drive’ safely.

Marketing: Marketers across all industries need to understand their customers better in order to anticipate their needs; and in the world of micro-moments and mobile purchasing, this is becoming more and more difficult. Thus, marketers are gathering information in different ways in order to try to personalize and customize services. Think about RFID tags on ski lift tickets, which can help manage and track traffic at different touch points on the mountain; or of the way we can use our mobile devices to pre-pay for a coffee before we arrive. These are examples of how marketing is getting more customized for consumer preferences, while also helping businesses become more efficient.

How Can Digital Marketers Use Big Data Effectively?

When it comes to digital marketing, professionals have plenty of opportunities to apply big data to various activities and boost their ROI – here are a few examples.

Better targeting and customization: One of the key uses of big data collection and analyzation is to better understand the needs of the customer and create more personalized, targeted advertising campaigns. AI is becoming more and more capable of collecting and combining data from different channels, thereby providing new clues towards not only what people are purchasing, but how and why they make the purchasing decisions they do. Netflix, for instance, is already analyzing global patterns of viewing behavior and using that to create more personalized recommendations.

User experience: The best types of experiences for users today are frictionless. When we talk about user experience (UX), we are (usually) talking about anything that could get in the way of users making a purchase via your website. Analyzing data from various channels can help to understand why people aren’t following through with a given phase of the buying journey, thereby helping to improve the overall experience in future.

Key business decisions: Analyzing big data in the right way lets us find ‘gaps’ in systems and processes that may be inefficient, thereby leaving more room to manage various budgets appropriately. In addition, this level of information gathering and analyzing can help managers learn how to make decisions more accurately in real-time, while eliminating gaps and mistakes.

What is Data Mining and How is it Useful for Marketers?

The term ‘data mining’ refers to the process of combing through large data sets (metadata) to find patterns and relationships in order to apply it to a particular purpose, such as improving efficiency, targeting marketing activities, or cutting costs. The idea is that data mining is uncovering specific variables which would then be useful in a business or organizational context.

A simple example of this is cluster analysis, which is when a targeted group is identified based on common features. So, a customer database could be combed and reorganized (clustered) based on specific attributes, such as whether or not a person is a parent, or if they are a senior citizen.

Anomaly detection is another example of a data mining process. It involves identifying mistakes or problems in a system to eliminate inefficiencies.

Big Data, Data Mining and AI

It used to be that analysts had to rely on particular subsets of data to understand the world and make corresponding decisions; now, we have access to such massive sets of data that there’s less focus on gathering and more of a focus on what we’re doing with it. And because there’s so much available, more organizations are becoming highly data-centric, using data as the primary means to which they understand and drive business processes.

Advancements in machine learning and AI are making it possible for us to mine, analyze, and apply increasingly larger sets of data towards practical applications. Big data allows AI to essentially come alive – it’s the fact that AI can access it so easily now that makes it able to ‘learn’ and problem-solve at increasingly complex levels and apply this in meaningful ways.

Big Data: Future Directions

Companies today understand the relevance of data and analytics with regards to decision-making and marketing activities – but it’s not about how much data is available to a business, it’s about how businesses are using it that matters.

There’s plenty of potential for companies across multiple sectors to use big data to make system more efficient and services more accurate. But organizations must ensure that customers are aware not only of the types of data being used, but how it is being used. The controversy surrounding Facebook’s data breaches is a perfect example of how the mishandling of big data can go in the entirely wrong direction, and fast.

To this end, privacy is probably the biggest concern going forward. In order to avoid ethical problems, organizations must have a clear handle on how they are using, storing, and analyzing data, and be extremely diligent about offering transparency about these issues to the public.

Other trends we’re going to see in the realm of big data include advancements in machine learning (AI), a new push for data scientists and managers (CDOs, or Chief Data Officers), bigger investments in big data tech, and an overall increase in productivity as analytics technology becomes more advanced and accessible.