DataOps Explained: Viktoriia Semekha Answers Your Top 12 Questions

Viktoriia Semekha, Data Operations Manager at Sparklead, shares insights on tech-savvy solutions for B2B lead generation and her crucial role in the process.

Background and Role

Can you share a bit about your background and how you became a Data Ops Manager in the lead gen industry?

I joined the industry five years ago as a researcher without any experience. Within two years, I was promoted to Data Ops Team Manager, leading a data team of about 20 people and managing 40 projects.

I started at Sparklead as a BDR to try a different side of the business, but, to tell you the truth, hearing “no” too often demotivated me. So, I switched to the role of Data Operations Manager, focusing more on contact collection and the analytical side of lead generation.

Data Transformation and Management

How do you ensure data accuracy and integrity in lead generation campaigns?

Here’s how I make sure everything stays accurate and reliable in my lead generation campaigns.

• Using verified tools (details coming up next).
• Testing audiences by batches to check results and ensure these contacts match the Ideal Customer Profile (ICP).
• Collaborating closely with team members to gather detailed feedback on the data pipelines for further adjustments, such as determining which types of contacts to include more or less, which verticals to focus on, and who should be prioritized in the database.
• Using best practices insights, based on previous experience or reliable data analysts
• Analyzing and determining the percentage of invalid data to keep part of invalid or semi-valid data in terms of 1-5% - depending on audience size and other ICP characteristics. High data quality is one of keys for successful outreach

Key Tools and Technologies for Managing Data Collection

What are the key tools and technologies you use for managing data orchestration?

There are tons of tools out there for raw data, each with its own pros and cons. Every agency has a list to choose and picks the ones that best fit for by client's case, type of workflow, and other requested preferences.

• LinkedIn Sales Navigator: It's a classic despite its bugs and not so user-friendly updates at times. It gives you access to a huge, up-to-date contact database and their segmentation. SalesNav also allows variable data integration with marketing solutions but you can't export contacts directly, and it doesn’t work well with external LI automation.
• Generect: This tool is great for finding LinkedIn contacts using some of the main SalesNav filters, grabbing emails, validating them, and formatting (all in one place). It makes LinkedIn contacts collection much easier and more effective.
• PhantomBuster: It’s a classic, reliable scraping tool. You can export contact lists while staying within all LinkedIn limits. It helps you avoid bans and is especially useful when your ICP is very specific. You can also try data sources like Google Maps or Yellow Pages, for example.
• Clay: Very promising data orchestration tool. I don’t use all its features yet since many integrations still need some work, but its waterfall email enrichment and Claygent AI are definitely worth checking out.

Challenges and Solutions

What is the biggest challenge you face in managing data for lead generation, and how do you overcome it?

The biggest challenge for me is keeping data work from getting monotonous. Working with raw data is detailed and needs focus. When I’m dealing with large research tasks, I break them into smaller chunks and mix them with other tasks to avoid mistakes and burnout. Data processing needs patience and a willingness to keep improving.

Can you share a specific example of a data-related issue you faced and how you resolved it?

Sure. Some time ago, we were working with the iGaming industry, and we expected to export a database of over 5,000 contacts easily. However, 85% of the contacts from initial trusted sources didn’t match our ICP and were outdated. The solution was to change data sources and expand our search according to the defined keywords. As a result, we ended up with a data pipeline of over 3,000 relevant contacts for the main campaign and around 2,000 contacts for ABM campaigns.

Data-Driven Strategies

How do you use data analytics, data flows to optimize lead generation strategies?

For each contact, we include not only basic info (name, job title, contact details) but also additional details (company size, industry, specialization, region). This extra information helps us receive deeper data insights for better segmentation and communication.

I believe in maintaining and comparing historical data to ensure data-driven decision-making. While global events and trends do affect metrics, having past data makes it easier to assess the success of current campaigns and spot areas for improvement.

What metrics do you consider most important for measuring the success of lead generation efforts?

Key success metrics include the number of deals closed and the quantity of active contacts in the pipeline. These metrics can vary based on outreach campaign setup and goals.

For LinkedIn communication, important metrics are Acceptance Rate and Reply Rate.

For email, Acceptance Rate is replaced by Delivery and Open Rates. There are also Bounce and Opt-Out Rates that are both necessary for measuring data quality and keeping deliverability requirements.

Collaboration and Communication

How do you collaborate with other departments, such as marketing and sales, to ensure data alignment and consistency?

Collaboration with marketing generally happen during campaign planning, where we discuss strategy. However, there’s also frequent interaction between sales, marketing, and data teams during the campaign to nurture the existing contact base.

Working closely with the sales team is essential.

From deadlines to data model adjustments and feedback on contact quality, the person working out on the database usually offers more insights than any metrics. Regular communication between team members ensures timely adjustments, keeps everyone in the loop, and helps us focus on areas that need improvement.

Trends and Innovations

What emerging trends or technologies in data operations are you most excited about?

All marketing data engineers will agree that AI features are a significant trend. Over the past year, many tools have integrated AI capabilities. While many of these solutions are still in their early stages, AI can now help formulate ICP hypotheses, suggest relevant decision-makers, simplify data transformation, and perform deeper lookalike searches for companies.

However, AI-generated results often require specialist review, and there’s always a risk of incorrect information. Despite this, AI combined with human input can greatly optimize contact data management and help achieve goals.

Another trend in marketing data engineering is allbound strategies.

Engaging cold contacts has always been harder than working with interested leads. In 2024, companies are combining channels for better contact acquisition and higher conversion rates. Techniques that worked last year may now yield half the responses.

I'm always fascinated to know which inbound or social selling tools are effective for gathering a warm lead base for further processing.

How do you stay updated on the latest advancements in data management and lead generation?

I follow industry thought leaders, test new data products and tools (comparing them with proven ones), subscribe to various newsletters, and participate in webinars. While I don’t believe in blindly following trends, insights from others can inspire solutions to my own challenges. This is my own recipe on how to master data management.

Future Outlook

What changes do you foresee in the role of data operations in lead generation over the next few years?

We're already seeing changes. Five years ago, Data Ops roles were something you could mix up with DevOps in IT due to similar letters. In sales and marketing there were people called Researchers who almost manually compiled contact lists based on ICP criteria and transform raw data. While manual research is still relevant, its effectiveness is often questioned. The shift to automation brought researchers who could handle larger volumes of contacts, but this often compromises quality. Now, data specialists focus on improving data quality without reducing quantity.

The current trend among data researchers is leveraging AI to automate custom work, achieving both volume and quality of contacts. While many tools claim to solve problems, they often either narrow the database or add irrelevant contacts.

In the coming years, the key challenge will be finding the perfect balance where data professionals can use AI tools effectively, combining them with minimal manual efforts to maintain high-quality personalized contact databases.

Final Word

How do you plan to adapt to these changes to your data governance and keep your data operations ahead of the curve?

The exciting challenge ahead is using AI to automate custom data operation tasks while maintaining high contact data quality. I've tested many data sources and tools claiming to solve these issues—some are effective, but many either reduce database quality or add irrelevant data.

My focus in the coming years will be to find the perfect balance: integrating AI tools with manual efforts to manage contact databases effectively and avoiding manual processes and make my data collection processes less monotonous.

If you want to learn more about the latest tools and solutions for lead generation, feel free to connect with Viktoriia on LinkedIn.

The Sparklead team looks forward to chatting with you at your convenience!

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