Step One in Starting Your Data Analytics Journey

Data analytics is recognized by most business executives as a critical priority, regardless of the business unit they lead. While previously at Gartner, I would advise C-Level executives how to undergo a digital transformation and inevitably, these conversations would arrive at data analytics. The most common scenario I would hear from these executives is that they bought an analytics software license but were frustrated because they were not gaining any business value from the purchase.

More often than not, the failure wasn’t with the software that was purchased, the failure was that they purchased software too soon. Before embarking on this journey and making purchases, it is important to first identify the initial problem driving the purchase and then develop a strategy and execution plan that solves for the initial problem and also continues to apply analytics to other areas or problems within the business. That will inform you what purchases you need to make, the type of skills you need, and the data you need to gather or acquire.

However, while problem identification and strategy and execution plan creation are critical, it is important to realize perfection will never be achieved and the opportunity cost of waiting to act is immense. Actively seek low risk opportunities to gain quick wins to prove the long-term ROI of becoming a data driven organization.

The Data Analytics Journey as a Marketing Executive

If you type “Marketing Data Analytics” into Google, you get over 1 billion results. That’s a lot of data about data. As a marketing executive, you don’t need to be as knowledge as your CIO about this topic but you do need an understanding so that you can effectively partner with the CIO, other key business stakeholders, and various vendors to ensure value and ROI are generated from your efforts.

Because the marketing department is tied to revenue growth, beginning the analytics journey here is a logical place to start. Below, I listed some common reasons why other marketing leaders turn to data:

  • Improve Customer Experience (CX)
  • Personalize user experiences
  • Increase the Lifetime Value of your customers (LTV)
  • Improve marketing budget allocation
  • Identify prospects with a high propensity to convert into new, paying customers
  • Identify customers likely to leave for a competitor

In the world of data analytics, there are many terms that are closely related, therefore, the goal of this article, and the subsequent articles in this series, is to provide you with the knowledge you need to cut through the noise and get started with your journey. As you look to incorporate data analytics into your marketing department, it is important to understand the below topics:

  • Data Analytics vs. Data Science
  • Artificial Intelligence vs. Machine Learning
  • Data Governance
  • Data Management
  • Data Acquisition & Data Hygiene

The subsequent articles will dive deeper into each of the topics and provide uses cases and scenarios for how this is applied to marketing across a wide range of industries.

Data Analytics vs. Data Science

The terms data analytics and data science are often used interchangeably; however, there are distinct differences between them. Data analytics is primarily directed at solving problems that are known to exist but the answer to the problem isn’t readily available. For example, the accounting department just released poor Q4 revenue results with revenue down 10% YoY. The problem has been specifically identified: revenue is down 10% YoY. Therefore, you would use data analytics to perform statistical analysis on the Q4 sales reports to determine why this occurred. Did the average customer spend less than before? Did marketing produce less quality leads than normal? Were leads converted into paying customers at a lower rate than normal?

Data science is primarily directed at finding answers to problems that were not known to exist. At its core, data science aims to predict what will likely happen in the future and why it will happen. It is less concerned with solving for an answer to a problem that has been identified, such as why revenue decrease in Q4.

Artificial Intelligence vs. Machine Learning 

Artificial Intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. To better understand this concept, it is first important to break AI into two distinct categories: Narrow AI and Artificial General Intelligence (AGI).

Narrow AI is what has proliferated through our society from email spam filters and recommendations on Netflix to conversational bots for customer service and smart assistants like Siri or Alexa. It is focused on performing a single task with a vast increase in accuracy and reduced time allocation compared to a human completing the task. However, this intelligent system is operating under exponentially more constraints than the average human intelligence and; therefore, can’t apply its intelligence to anything other than what it was programmed to do.

AGI is littered across the Sci Fi world where AI empowered robots overtake humans for world domination. AI researchers are nowhere near the creation of a universal algorithm that allows machines to learn or act in any environment. Thankfully, for now, The Terminator won’t be at our doorstep. This distinction between AGI and Narrow AI is important to understand so that you can articulate to the business stakeholders at your organization the benefits and limitations of AI as well as debunk common misconceptions.

Machine Learning (ML) is merely a way to achieve AI. ML algorithms process large amounts of data and use statistical techniques to learn how to get better at a task without explicitly being programmed to do so. Simply, we train the algorithm with examples, not instructions or millions of lines of code. We can give the machine pictures of cats and pictures of non-cats and then feed it thousands or millions of unclassified pictures and tell it to let us know which of these unclassified pictures are cats. The more it looks at pictures, the better it is at identifying cats.

Data Governance

Most simply, data governance is a framework that establishes rules and regulations for how data flows through an organization from business unit to business unit and from person to person. The goal is to establish an appropriate balance between security and access to minimize risk, while driving business value. Organizations looking to formally define their data governance regulations should form a committee that includes IT and business stakeholders to ensure the balance is met. Additionally, many organizations hire a 3rd party consultant and/or legal representation to ensure risk is minimized and any regulatory compliance standards are met.

Data Management

By 2025, IDC, a premier global market intelligence firm, predicts worldwide data creation will increase to 163 zettabytes (1 zettabyte is 1 billion terabytes) per year. Needless to say, that is a lot of data. As organizations create a vast amount of data from various sources, an effective data management strategy is imperative. The goal of this strategy is to:

  • Break down data silos with the goal of having a single location for all valuable data generated and acquired by the organization
  • Ensure data quality standards are met
  • Ensure all business units categorize and label data in the same way (clients vs. customers vs. consumer vs. shopper as a simple example)
  • Ensure ease of access to data
  • Maintain adequate security policies
  • Define what data points the organization is looking to gather itself and what data points the organization needs to procure from a 3rd party

Data Acquisition & Data Hygiene

Without data, your analytics journey comes to a screeching halt. It is important to not only have a strategy for how you acquire data on your customers yourself (through your loyalty program, website, etc.) but also how you can acquire data from 3rd parties to better understand your customers. Not only can 3rd parties provide you with additional data points on your customers, they can also provide you with data on people that are not customers as you look to expand your acquisition campaigns.

Most organizations do a great job at acquiring some contact level details for customers but only at specific moments, such as when they sign up for a loyalty program. Data hygiene is the term for keeping your data up to date because consumer data decays at an estimated rate of 25% annually and business data decays at a rate significantly higher depending on the variables represented. Bad or inaccurate data leads to poor output from your analytics projects.

Whether you are turning to a 3rd party to add additional data points on your customers, perform data hygiene requests, or acquire prospect data for your acquisition campaigns, it is important to evaluate if they meet regulatory compliance standards such as CCPA (California Consumer Privacy Act). Additionally, it is important to understand how and where they compile their data from, how often it is updated, and the amount of data they have.

What Is Next?

You now have an understanding of key concepts that create the data analytics landscape. In my next article, I will take a deeper look into the differences between data analytics and data science. I will explore use cases across a variety of industries and ways to establish quick wins as you look to understand the short, medium, and long-term ROI of incorporating this into your strategy.

 

Erik Merle is a digital transformation expert who has advised C-Level executives on their digital transformation journey, with an emphasis on data analytics and data science. He currently works with marketing executives looking to incorporate data analytics and data science into their strategy to improve the performance of their department.

Written by Erik Merle