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Evolving Analytics: Descriptive, Predictive, Prescriptive

Makers of Modern Marketing at Oracle: Elena Drozd 

Welcome back to the Makers of Modern Marketing at Oracle! A blog series dedicated to the architects and risk-takers behind marketing at Oracle to give readers a peek into how we are building the future of digital marketing from the inside-out.

This month we had the pleasure of sitting down with Elena Drozd, senior director of data science and advanced analytics, to discuss recent leaps in analytics and where those leaps will lead. Spoiler alert: Predictive analytics is the present — and future — of digital marketing. 

Drozd spends her time at the epicenter of data science and analytics. She manages a team of eighteen data scientists and regularly acts as a bridge between their techie, analytical minds and the business side of Oracle. 

Analytics for All

Oracle’s “Analytics for All” philosophy rings particularly true for Drozd, “We should be building tools so that non-analytical professionals can have accessible data to fuel data-driven results.” While Drozd herself may hold a PhD in mathematics, she believes that enabling all employees to have access to clear, comprehensive data will best serve Oracle in this data-driven future. 


“Part of our job is to make you feel comfortable with the data, help non-analytical people use and trust data more.”


As marketers, we know that there is a plethora of data available to us, and most of us are keen to tap into every avenue possible, but how can we handle that data better? Improved visualization has played a major role in enabling data scientists to equip the less tech-minded with key information from the abundance of metrics. 

What used to require detailed, custom-built solutions can now be achieved through adept use of always-on capabilities. “Part of our job is to make you feel comfortable with the data, help non-analytical people use and trust data more,” says Drozd. “And, we’re working to create tools, which will facilitate that.”

Want to be able to walk into the office, and ask for your revenue stats first thing? Or maybe get an update on how that recent campaign is doing? Your voice command assistants Alexa or Siri can do more than read out movie times and the weather. Soon, they will be able to interpret your data for you. 

“I see Oracle Voice Assistant extending its presence into the majority of our applications.” This will be a real leap for Oracle customers, in terms of further enabling their digital transformation and empowering business leaders directly with key data insights. 

Descriptive. Predictive. Prescriptive.

Three words Drozd applies to the past, present, and future of data analytics: descriptive, predictive, and prescriptive. Descriptive analytics refers to knowing where your business stands in the industry (Who is your buyer? What are their needs?) and applying that knowledge to your future business models to drive improved results. 

Predictive is what is on the tip of everyone’s tongue. What is your customer going to do next? And how do we anticipate that? Predictive analytics has allowed marketers to create unique segments and personalize communications down to the individual. As companies continue to move from more traditional tactics with their digital transformations, predictive will continue to play a huge role in how marketers and data analysts build business models. 

But what’s next? Drozd believes we stand on the precipice of what she refers to as prescriptive analytics, “the missing piece between data scientists and business leaders: the concept of what action you should take right now when predictive intelligence tells you the most likely outcome in the future.” In fact, recent implementation on machine learning is already turning this phase of analytics into a reality. 

An Argument for Transparency

Despite these developments, computers aren’t forcing data scientists onto the endangered species list. Machine learning can make predictions and helps make sense of the data, but it needs clean data to achieve the most relevant results. Data scientists build the algorithms that identify what is important for a particular use case. However, one size does not fit all when it comes to analytics, so the data scientists and the business side need to work together to construct productive business models.  

Drozd believes that “We need to have a very deep understanding of data and relationships within the data, but also how that relates to the business on a larger scale.” The ability to run analytics using machine learning and predictive models is exhilarating, but without the right knowledge for each use case, it may not be terribly useful at all. Ultimately, applying each of these attributes to your future data analytics program will allow data scientists and marketers to gain the most in-depth view of customers and the market as a whole. 

To gain even further insights, some organizations are employing analytics centers of excellence, which are designed for a specific department and are equipped with experts in that domain. These centers gain very deep solution knowledge by pairing data scientists with domain experts who “know deeper details about what types of problems could occur and can be nimble when designing and implementing that domain”, according to Drozd. Oracle has a well-established team for this exact purpose. 

Mixing Tradition with Innovation

With nearly 13 years at Oracle under her belt, Drozd can offer some insights to future marketers and data analysts who want to see what the field has to offer. Drozd, like many others at Oracle, is a lifelong learner. Her field is constantly shifting, so she suggests that every analytics professional “keep their skills current.”

“We don’t need to approach every problem like a hammer to a nail.”

However, don’t forget the traditional roots of data science either. “We don’t need to approach every problem like a hammer to a nail. Sometimes you can achieve much more with less effort and simpler tools.” Drozd mixes this combination of new and traditional methods into her skillset to provide a well-rounded strategy that appeals across lines of business. 

“I’m one of those people they call a ‘lifer’ at Oracle,” says Drozd of her relationship with the company on a whole. “It’s the deep respect and professionalism; collaborative spirit; cross-team sharing of expertise and knowledge that I love about my team and my time at Oracle.” Plus, the unparalleled technology stack is enough to make any reasonable data scientist drool.  

Find more views, tips and thoughts on data driven marketing, click here.

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