Metrics and Analytics 12.14.2021

Obstacles To Deploying Big Data in a Sales and Marketing

Shownotes

The first draft of anything is going to be imperfect, but that’s the point! This episode of Sunny Side Up features Claudia Virlanuta, CEO at Edlitera, who wants to empower even the least technical of executives to leverage data science and analytics. It starts, however, with a willingness to feel foolish or even outright fail. While some 81% of executives believe big data is key to enterprise leadership, a majority of the same sample group believe themselves to be ill-equipped and most likely unable to acquire the necessary tool kit. Not true! Claudia lists the barriers to effective data adoption and management – as well as some very concrete strategies for removing those stumbling blocks. Some of Claudia’s solutions require no more than enlisting help from team members or taking a couple of basic online courses to become conversant on your own. A big part of leveraging data science is creating and supporting its integration within the workplace culture. And you don’t have to have a Ph.D. to do that. Just be willing to feel awkward or elementary – and open to learning something new. Enjoy this info-packed session with a data science and machine learning expert who knows how to future-proof teams and businesses by turning data into profits.

About the Guest

Claudia is the CEO of Edlitera, a data science and machine learning training company helping teams learn new skills faster so they can harness the power of automation, data science, and machine learning. Daily, the Edlitera team trains engineers and analysts in Python programming, data processing, data science, and machine learning topics like NLP and computer vision. A few notable Edlitera clients include ANZ Bank, Independent Health, Wind River Software and before starting Edlitera, Claudia taught Computer Science at Harvard, worked in biotech at Qiagen, marketing tech at ZoomInfo, and eCommerce at Wayfair.

Connect with Claudia Virlanuta

Key Takeaways

Data-driven companies share some basic foundational tools: The ability to collect and mine information, and report/analyze that information. Many companies continuously test and measure the variables.

81% of 1300 senior executives surveyed said that data skills are required to qualify for leadership positions, but 67% said that they were not comfortable accessing or using data and 73% thought data skills are more challenging to acquire than other business skills. Most surprisingly, 53% believe they are too old to learn data skills.

Data-driven culture starts with an attitude of adoption at the top, but the execution takes place throughout the ranks. There are five common barriers – for which Claudia offers solutions.

Even the most non-technical executives can acquire the basics to frame, manage and optimize big data. And chances are that additional tools and skills are available within your team, so don’t immediately assume data analytics projects have to be outsourced.
It’s okay to risk making a fool of yourself, or even to fail outright. Try something new anyway. Ask the questions anyway. If you’re confused, others are very likely experiencing the same thing.

Quote

“Try not to solve the same problem twice. If you’re doing something more than once, try to look into ways in which you can clear your work log and automate as many boring tasks as you can.”

Highlights From the Episode

What does it mean to be a data-driven organization?

It’s one of those buzz words thrown around a lot today, but there are a few practical elements to being a data-driven company or organization. Foundationally you need to have the right to collect information and the ability to access and mine it. The next level (or ground floor) is what you then do with the data – reporting, and analysis. Why did something happen? There are two specific questions: Now that this has happened, what can we do as an organization? What’s the best or worst outcome that can occur as a result? Many organizations are also aware of (and frequently measure) the variables.

Who is it that can or should be using this data? Whose job is it?

Data scientists/analysts are part of the baseline that most organizations have on staff, but those people are generally not in the leadership roles. They are close to the front-line customers and information, but their power to implement change is limited. Decisions have to be made further up the managerial ranks, but this is not always a comfortable fit. A recent survey found that 81% of senior leaders believe data skills to be critically important, but more than half of those asked felt they were ill-equipped and/or too old to adopt those skills.

What are the biggest obstacles to deploying big data in a sales and marketing context?

Top ranks can set an overall positive tone towards data culture, but the adoption comes from within the teams. The five biggest obstacles Claudia has identified include 1) The Lack of understanding of how to leverage analytics to improve business. Marketing, in particular, has access to a lot of data but much of it gets reduced to analytical “busy work” that is ultimately just basic reporting generated manually, aka solving the same problem twice. 2) Lack of bandwidth among those leaders charged with culling data (usually because it’s not a priority). 3) Lack of in-house skills, which can be remedied with a manageable amount of basic training. 4) A real or perceived lack of access to data, especially when it becomes siloed or restricted because of privacy concerns, diffused data ownership, or governance. 5) Not knowing where to start! If you’re a non-tech-oriented executive, it’s very difficult to know how to make processes more efficient or how better to leverage available data resources. Claudia’s advice for breaking the logjam? Start small. At minimum develop enough expertise to frame a problem. Look for ways to free up resources and try to automate them to create more space for other processes.

What’s the best way to build data management and analytic expertise?

Edlitera frequently trains C-level individuals who start with minimal knowledge and today are running fairly tech-oriented or data-driven teams. Successful students share a positive attitude towards learning and understand that there is no shortcut. Whether they take “AI for Leaders” or a hands-on Python course, executives at all levels of the enterprise see that the payoff is immediate and lasting.

What’s a good starting point for a non-technical executive who wants to learn how to leverage data analytics?

To begin with, you must take a hard look at the things that your team is doing and the types of problems you’re solving. How are the reports generated and processed? Is there a better way? Is there a new initiative that could lend itself to trying a cool new tool or technique? Is there perhaps someone on your team who knows Python? Don’t assume that data analytics projects have to be outsourced. Chances are that there’s someone within your team who is either a hobbyist or would be excited to learn new skill sets. You can start working with them right away!

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