Big data, artificial intelligence, deep learning, and machine learning are transforming businesses across every industry. So you could say it's pretty surprising that only 31% of companies say they are data-driven, despite the clear opportunities that data analytics provide.
Companies at the forefront of the digital economy like Amazon, Google, Facebook, Uber, and Airbnb have big data analytics at their core. These behemoths have seen tremendous success in leveraging data-driven business models to disrupt industries because of their emphasis on data-based decision-making.
For innovative businesses such as these, big data analytics brings speed, agility, experimentation, iteration, and the ability to fail fast, learn from experience, and execute smarter.
But for others, it can bring 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 you know how to draw insight from the noise, we guarantee you'll never look back!
This article is designed to equip marketing professionals with the basic information they need to start using big data analytics in their strategies. We'll keep things simple — you don't have to be a data scientist or a computer whizz to follow along!
Table of Contents
Big Data Definition
How Big Data Analytics Are Transforming Marketing Departments
Important Data Sources for PR and Marketing Professionals
Big Data Tools
Common Big Data Adoption Challenges
The Achilles Heel of Big Data
Big Data Management with Meltwater
Big Data Definition
Before we get into how data analytics is transforming the role of a marketer, let's first define what it means. We're fans of the following big data definition by Gartner:
This amount of digital and consumer data allows savvy marketing teams to: build more meaningful relationships with their customers, improve future marketing campaigns, and better understand the competitive landscape.
The four "V's" of big data
Volume, Variety, Velocity, and Veracity. (Veracity has been a more recent addition, which is likely why it was left out of the Gartner definition).
- Volume: The amount of data that is generated
- Velocity: The speed at which data is being generated
- Variety: The different types of data
- Veracity: The extent to which there are inconsistencies recorded that require additional validation
This leads us nicely onto another term that's often used in relation to the topic of "big data": data science.
The following definition of data science by Data Robot summarizes it nicely:
"Data science is a major computing discipline...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 (through image recognition), 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."
In short: data science is the art of categorizing and distilling the complex and vast big data that you and your team gather.
How Big Data Analytics Are Transforming Marketing Departments
With a little help from big data tools, marketing pros are putting data-driven marketing strategies together that positively impact many key areas, especially the following:
Customer experience
Big data analytics allow marketers to gain substantial and meaningful knowledge into who their consumer is: what they like, the channels they use, what influences their decision-making process, etc.
By having this information, a marketer can improve audience targeting and personalization, which increases customer engagement and helps with retention, longevity, and brand loyalty.
Tip: Think about your customer data management (CDM) to gain more actionable insights from your data and learn more about customer experience in terms of the best customer experience software, CX trends, CX examples, customer experience design, and CX statistics.
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 resonate 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 shore up their competitive position.
Important Data Sources for PR and Marketing Professionals
Let's explore the data sources (repositories of large volumes of data) that marketing professionals need to analyze to achieve success with using big data for insights.
There are various sources that generate data, but in the context of big data marketing, the primary sources are as follows:
Media data
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. It includes billions of audio, text, and visual content types.
Since this type of data resides outside of a company's firewall, it tends to be unstructured. Traditionally, unstructured data has been tricky to analyze, 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 data include:
- Social media (Posts, likes, comments, reshares, photos, and video uploads)
- Editorial content (Social shares, key phrases, author name, and publication title)
- Podcasts (Title, images, description, category, and author name)
- Search engines (Search volume, trends, and traffic)
Tip: Learn more about image recognition software and tools and understand how image recognition works.
Customer insights
To effectively create a 360-degree view of their audience, organizations must analyze their customer data. As mentioned earlier, there are a number of benefits to this, including satisfying unmet or new needs, as well as personalization.
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 behavior 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
Tip: Learn how the Meltwater Suite serves enterprises
Databases
Since the explosion of data, companies have invested heavily in specialized 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.
Tip: Learn the difference between a data lake and a data warehouse
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
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.
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 would be very long-winded to cover every big data marketing tool recommended for your analytics stack in this article. So instead, we've highlighted our favorite ones below.
Advertising & promotion analytics tools
- Google Adwords
- Facebook and LinkedIn
- AdRoll
Content & experience tools
- Instapage
- SEMrush
- Ahrefs
Social & consumer insights tools
- Meltwater
- Marketo
- Intercom
Commerce & sales tools
- HubSpot
- Salesforce
- Oracle
Data management tools
- Meltwater Display
- Tableau
- Microsoft Power BI
Considering the steady trend we're seeing around the increased need for marketing analytics and the usage of data visualization tools, it's worth taking a deeper dive into data management:
Brand command centers 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 or anomalies) much easier.
Master 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, 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 visualization tool to an analytics engine like Apache Spark.
Common Big Data Adoption Challenges
Cultural resistance to change
Legacy technology solutions
Executive leadership and organizational alignment
Mindset
Nobody said the adoption of big data analytics was easy. In fact, many leaders still find the adoption of leveraging big data to be challenging.
So if you’re currently struggling, know you’re not alone.
Before big data analytics can realize its full potential, you may need to overcome some common barriers:
Cultural resistance to change
The greatest business challenge for most mainstream corporations is not the big data tools themselves; it's the process of organizational 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 for digitization and big data analytics adoption.
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 from:
- 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 minimize 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 largely uncharted waters — an opportunity yet to be capitalized on. They may be lagging behind in their efforts to integrate big data-driven initiatives into their core processes and operations, because legacy systems are holding them back.
Replacing those systems is complex, so it's an understandable issue. New implementations may 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 organizational alignment
It’s not uncommon for analytic projects to fail due to poor communication, lack of vision, and vague organizational 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, not with you. It’s important for everybody who is involved to be addressed — and for them to feel heard.
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 digitizing legacy operating processes.
Tip: A competitive intelligence database can help you prove your point.
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.
Mindset
The big data mindset is driven by data mining experimentation, discovery, agility, and a “data first” approach that's characterized by analytical sandboxes, centers of excellence, and data labs.
This mindset often runs counter to traditional hypothesis-driven approaches to data management. Whilst this mindset is in some businesses' DNA, others have to work harder to shift away from an 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 “monetize” 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 is 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 AI 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,” Rob High, Mobile World Congress 2018.
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?”
Meltwater Founder and Executive Chairman, 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 favorite 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.
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