Big Data: What it Is and Why it Is Important for Your Business

A retro computer that is painted orange against an orange backdrop for this blog on big data for businesses
A retro computer that is painted orange against an orange backdrop for this blog on big data for businesses

Big data, artificial intelligence, and machine learning are transforming businesses across every industry, which is why it's surprising that only 31% of companies say they are data-driven, despite the clear opportunities that data analytics provide.

As the amount of digital and consumer data continues to proliferate, savvy marketing teams are making use of insights generated from big data tools to build more meaningful relationships with their customers, perfect future marketing campaigns, and make sense of their competitive landscape.

Firms at the forefront of the digital economy like Amazon, Google, eBay, Facebook, Uber, and Airbnb have big data analytics at their core and have seen tremendous success in leveraging new data-driven business models to disrupt industries because of their emphasis on data-based decision-making. For innovative firms such as these, big data analytics bring speed, agility, experimentation, iteration, and the ability to fail fast, learn from experience, and execute smarter — but for others, it brings nothing but anxiety.

We get it, the industry moves so fast that it can be a challenge to keep up. Making sense of data sets through analytics can be confusing at first, but once marketers know how to draw insight from the noise, we guarantee you'll never look back! 

With this in mind, this article is designed to equip marketing professionals with the basic information they need to start using big data analytics in their strategies. In the interest of our readers, we'll keep things simple, meaning you don't have to be a data scientist or a computing whizz to understand the following! 

Table of Contents

Big data definition

Before we get into how data analytics is transforming the role of a marketer, let's first define key terms within this sphere. To start with, we're a fan of the following big data definition by Gartner:

The general consensus is that there are specific attributes that define big data. The above definition covers all but one of them: Veracity.

The Four V's of Big Data

Volume, Variety, Velocity, and Veracity (this has been a more recent addition which is likely why it's missing from the Gartner definition).

  1. Volume: The amount of data that is generated
  2. Velocity: The speed at which data is being generated
  3. Variety: The different types of data
  4. Veracity: The extent to which there are inconsistencies recorded that require additional validation

This leads us nicely onto the next term that's often used in relation to this topic, data science. We think the following definition of data science by Data Robot summarises the phrase nicely: 

"Data science is a major computing discipline, more specifically, it's the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights. Data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks that ordinarily require human intelligence. These systems then generate information that analysts and business users can translate into tangible business value." 

Now that we've cleared that up, let's move onto how big data analytics and the information found from data processing is transforming marketing. 

How is big data analytics are transforming the marketing department?

With a little help from big data tools, marketing pros are putting data-driven marketing strategies together that positively impact the following three key areas:

Customer experience:

Big data analytics allows marketers to gain inside knowledge into who their consumer is: what they like, the channels they use, what influences their decision-making process, when they want to be spoken to, etc. By having answers to these questions, a marketer can improve audience targeting, personalisation, and customer engagement, which in turn has a positive effect on retention and loyalty.

A man sitting at a chair holding his credit card, smiling at a laptop.

Marketing optimization:

Big data testing, measurement, and analysis play a key role in marketing optimization too. For example, marketers can uncover information around where best to spend their budget and the types of content and messages that are resonating with their audience. Data analytics help marketers find the sweet spot more quickly so you can reduce time wasted on trial and error and instead focus on the areas that drive the most amount of marketing ROI. 

Driving agility:

Data mining is the process of using data analytics to find anomalies, patterns, and correlations in large data sets with the goal of predicting outcomes. Having access to this type of information significantly helps marketing teams become more agile and cement their competitive position through their ability to move before others in their space.

What are the important data sources for PR and marketing professionals?

Now that we understand how big data analytics are assisting the marketing and PR department with meeting their goals, let's explore the data sources (repositories of large volumes of data) marketing professionals need to analyze to achieve this. 

There are various sources that generate data, but in the context of big data marketing, the primary sources are as follows:

Media Data:

Largely fueled by the introduction and growth of social media, 'media' includes billions of audio, text, and visual-based content. Media is one of the most popular sources of big data as it provides valuable insights into customer preferences and changing trends in real-time. Since this type of data resides outside of a company's firewall, it tends to be unstructured. Traditionally, unstructured data was tricky to analyse, but thankfully data management and data analytics have come a long way in helping executives with processing and making sense of external information - for example by using a social media listening tool.

Examples of media include:

  • Social media (Posts, likes, comments, reshares, photos, and video uploads)
  • Editorial content (Social shares, keyphrases, author name, and publication title)
  • Podcasts (Title, images, description, category, and author name)
  • Search engines (Search volume, trends, and traffic)

Customer Insights

To effectively create a 360-degree view of their audience, organisations must also analyze their customer data. As mentioned earlier, there are a number of benefits to this, including satisfying unmet or new needs, as well as personalisation.

Examples of customer information include: 

  • Demographic data (Company, location, gender, and age)
  • Transactional data that's usually stored in your CRM (Stakeholder contact information, products bought, renewal date, and average spend per customer)
  • Web behaviour data (Pages visited, products added to basket, and geographic location)

Business Processes

CMOs often leverage big data analytics via APIs to monitor the performance of their teams, especially if they're large enterprises that work remotely. The goal here isn't to be an annoying micromanager, but to gauge productivity, develop goals and improve the efficiency of processes.

From a marketing perspective, this might include information such as:

  • The volume of social media complaints responded to
  • Social media response time
  • Social media resolution time
  • PR campaign project projected timelines and current status

Databases

Since the explosion of data, companies have invested heavily in specialised data storage facilities, commonly known as a data warehouse. In simple terms, a data warehouse is a collection of past data that companies want to maintain in an archive. As businesses increasingly move towards storage platforms such as Hadoop and NoSQL, we're likely to see such technologies dominate and replace pre-existing data warehouses.

Examples of databases that could be stored in a data warehouse include:

  • Company emails
  • Accounting records
  • Marketing contact databases
  • Sales contact databases
An illustration showcasing how data types are connected. Small rectangles of information are presented on a grid.

Big data tools

Deriving insights from the above data types would be impossible without the support of big data solutions, specifically big data analytic tools built on artificial intelligence and machine learning. Thankfully, technology today allows us to collect data at an astounding rate, both in terms of volume and variety.

The good news is there are plenty of big data tools out there that can support you, the drawback to this is that deciding on the solution best suited to your needs can become very time-consuming. As such, it'll be very long-winded to cover every big data marketing and PR tool recommended for your analytics stack in this article, so instead, inspired by ChiefMartec's fantastic infographic, we've highlighted our favourite ones below.

An infographic showcasing the logos of key martech vendors in 2020. The infographic is split into different sections based on what the tool is used for: Advertising and promotion, content and experience, social and relationships, commerce and sales, data, management

Advertising & Promotion Analytics Tools

  1. Google Adwords
  2. Facebook and LinkedIn
  3. Adroll

Content & Experience Tools

  1. Instapage
  2. SEMrush
  3. Ahrefs

Social & Customer Relationships Tools

  1. Meltwater
  2. Marketo
  3. Intercom

Commerce & Sales Tools

  1. HubSpot
  2. Salesforce
  3. Oracle

Data Management Tools

  1. Meltwater Display
  2. Tableau
  3. Microsoft Power BI

We thought we'd do a deeper dive into the "Data Management" segment considering the steady trend we're seeing around the increased need for marketing analytics and the usage of data visualisation tools.

Brand command centres are key business intelligence (BI) tools designed for anyone that's processing large amounts of data sets from disparate solutions or has a data visualization need. They display real-time visual dashboards and present insights from data sets in one cohesive format, making data mining (spotting trends/ anomalies) much easier.

Data management and data processing can get messy when information is coming in from all angles, so we'd recommend adding a data visualization tool to your marketing tech stack. Meltwater Display (Meltwater's big data visualization tool), Tableau, or Microsoft Power BI are good places to start your search. Marketers can also take things a step further and enrich current BI reports and dashboards by connecting their data visualisation tool to an analytics engine like Apache Spark. You can find more information about Apache Spark here.

An image of a dashboard from Meltwater Display, Meltwater's own command centre solution

Big data challenges for adoption

  1. Cultural resistance to change
  2. Legacy technology solutions
  3. Executive leadership/ organisational alignment
  4. Mindset

Nobody said the adoption of big data analytics would be easy, in fact, this is a challenge experienced by many leaders. So if you’re currently struggling, it’s reassuring to know you’re not alone. Before big data analytics can realise its full potential, a number of barriers must be overcome. At Meltwater, we frequently have conversations with executives trying to get their heads around big data analytics, and more often than not, it’s the same barriers preventing their success.

For full transparency, we've listed key challenges around adoption below so you can put plans in place before they hit.

Cultural resistance to change

The greatest business challenge for most mainstream corporations is not the big data tools themselves; it's the process of organisational cultural change. In fact, 22% of companies state this is their biggest barrier to adoption

Companies are often faced with resistance to change from employees because, let's face it, we’re creatures of habit and many of us live by the mantra “if it’s not broke, don’t fix it”. But this belief has severe consequences on digitisation and big data analytics adoption, in fact, Gartner believes resistance to change is one of the top reasons most digital projects fail.

Cultural change represents a business problem, therefore requiring a business solution and business approach. 

Successful data adoption starts by understanding internal stakeholder pain points and exploring the barriers that prevent them from wanting to use big data tools in their day-to-day role. Resistance to analytics stems from various roots, with reasons spanning;

  • Difficulties justifying a need
  • Competing revenue sources
  • Fear of job loss/change in a job role
  • Slow customer adoption causing employees to ask themselves, "Is it worth it?"

Getting to the bottom of why there’s resistance to analytics is important as only then can you truly tackle it. Bear in mind that each stakeholder has their own priorities, therefore the resistance reasoning may vary depending on who you speak with.

If you’re faced with adversity, the trick is to minimise the effort needed to move to a new way of doing things. Think about these challenges from a cross-department perspective and note down the ways adopting big data tools and analytics will help them overcome this challenge, not fuel them.

Legacy technology solutions

Each day legacy computing systems are made redundant by advances in technology. Failing to keep up with such developments can have serious implications. Interestingly, legacy tech is blamed for a lot of failures around big data analytic adoption, around 50% of them in fact, according to Nimbus Ninety.

Traditional businesses in particular are hamstrung by legacy systems and decades-old data warehouses. These corporations represent the lion’s share of investment in data solutions and services. For most of these firms, big data analytics remain waters that are largely unchartered, and an opportunity that has yet to be capitalised. While most mainstream firms have invested in data projects, these firms have lagged behind in their efforts to integrate big data-driven initiatives into their core processes and operations since legacy systems are holding them back. The problem is that replacing those systems is complex, but also, more often than not, new implementations fail to match previous systems in performance or functionality and companies also can’t afford to experience blackout periods while legacy system are paused.

By taking time to analyze current workflows and the impact technology has on them, you gain critical insights into what happens when you move particular pieces of the puzzle. We would recommend working with your tech partner and reviewing operating differences before replacing legacy systems, this will help you to expose the business logic hidden away in legacy tech. Don’t make a knee-jerk reaction and pull the plug on your legacy tech either. Instead, build new big data analytics and solutions in parallel so you can slowly switch business operations across. A strong implementation stage is key to success.

Executive leadership and organisational alignment

It’s not uncommon for analytic projects to fail due to poor communication, lack of vision, and vague organisational objectives. Keeping your communication line open is critical. Research by McKinsey shows companies are between 8 and 12 times more likely to succeed with digital transformation when good communication is apparent.

If you’re not honest about your strategy progress, outcomes, and impact so that stakeholders know where they stand, employees are likely to stand against you and not with you. It’s important for everybody who is involved to be addressed — and just as important for them to feel heard too.

There needs to be clear direction from management. Explain both the smaller picture (how analytics will impact your staff or customer's daily life) and the bigger picture (how this will help the company in relation to the competition). When communicating your vision, start from the business model or the customer experience instead of from an inward goal like digitising legacy operating processes.

Make sure your people understand what you’re doing, why, and where you currently are in terms of progress. Don’t be afraid to give employees a voice, after all, they’re the boots on the ground. They’re the ones who are most likely working with the processes you’re trying to change. Giving them a voice can help break down rigid company structural hierarchies and open up innovative thinking.

A group of people gathred at a working table laughing while a man stands infront of a whiteboard.

Mindset

The big data mindset is driven by data mining experimentation, discovery, agility, and a “data first” approach that's characterised by analytical sandboxes, centres of excellence, and data labs. This mindset often runs counter to or can complement, traditional hypothesis-driven approaches to data management. Whilst this mindset is in some businesses' DNA, others have to work hard to shift their old school way of thinking.

To overcome this challenge, we'd recommend executives start by identifying and asking critical business questions that will drive business value, including:

  • How can we “monetise” computing, data mining and new sources of data to new create new products and services?
  • Can we leverage digital technologies — mobile, social media, machine learning, and the Internet of Things (IOT) — to better connect with our consumers?
  • Can we use data to transform our internal and external business strategy and processes?
  • Can we find creative new uses for the data we have — new opportunities for insight, new markets, or ways of delivering our services?
  • Can we use the data that we have to be better members of our community, and leverage data for social responsibility? 

The Achilles heel of big data

While a lot of good can come from big data, managing all of that information does come with its own set of challenges. More data means more privacy and security implications with issues around ethics and transparency increasingly being discussed.

Not all data are created equal. According to IBM Watson's CTO, Rob High, it’s important that individuals and businesses understand which of their data is being analyzed, and by whom. Alternately, for businesses that trust artificial intelligence for decision-making, it’s extremely important that they understand the underlying data and assumptions fueling AI outputs so they can make a judgment call on what the algorithms are telling them, rather than taking AI at face value.

“One of the things we have to realize about AI — it’s relatively new to all of us. There’s a lot about it that we don’t all fully understand. As with any new technology, it’s really important that we be thinking now about how we do that ethically and responsibly. For us, that comes down to three basic principles. Trust, respect, and privacy,” High said while at the 2018 Mobile World Congress.

For High, this means questioning assumptions and approaching AI implementation with transparency and privacy rights at the core.

“Transparency comes down to: can we identify what sources of information are being used? Have we established the right properties, the right principles in place when we train these systems to use data that is representative of who we are, and the information that we’re using?”

Founder and Executive Chairman of Meltwater, Jorn Lyseggen, also discussed ethics, transparency, and regulation in AI with global industry experts during the launch of his Outside Insight book. “AI is so mystified,” Lyseggen said. “Only people who work with AI know what it means. My surprise was that artificial intelligence has zero intelligence. My biggest concern is that people believe too much in it. It’s very difficult to completely remove bias. AI is fundamentally biased in how it was created, trained, programmed.”

As such, he believes that when it comes to AI and big data, there will be some unintended consequences, creating the need for policy and regulation. “I do think there is a role for regulation to come in because I don’t think companies can be expected to regulate themselves.”

Lyseggen also emphasized the importance of the human element when evaluating AI output, as well as the need for a deep understanding of the assumptions that inform the algorithms in order to establish trust.

You can’t blindly follow your AI; you have to challenge it. You can look at it as a GPS – it helps you understand where you are and where you want to go. But it will be the judgment of the executives that decide ‘Do I want to climb that mountain or do I want to walk around it?’ That is the role of the human in decision-making and the future role of executives.

One of the most important things for AI and big data adoption to be successful, he argued, is that executives and decision-makers using this technology have the data science literacy or sophistication to challenge the model and to fully understand what the underlying assumptions are.

Big data management with Meltwater

So there you have it, a quick dive into one of our favourite sub-disciplines of the computing field: data science! Our top tips for big data management and how to use information gleaned from data mining will hopefully help make your internal processes more efficient, allow you to connect with customers on a more meaningful level and establish a competitive advantage.

Want to discuss integrating big data analytics into your own marketing strategy? Fill out the form below and we'll be in touch!

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