"I Don't Have Time to Build Pipelines, Do Analysis, Build Decks, and Be Strategic"

This week I sat in on a demo with an analytics professional at a nonprofit technology company. About three minutes in, he said something that's been rattling around in my head ever since:

"I work as the one and only analytics engineer, analyst—not a data scientist, but people want you to be a data scientist, even though it's not necessarily a role—and have to do essentially everything from a customer team's perspective... I don't have time to build pipelines and push data to different systems, do analysis, build decks, be strategic, right?"

That run-on sentence, delivered almost apologetically, perfectly captured what we're seeing across hundreds of conversations with customer success professionals, revenue operations analysts, and lone-wolf data people at growing companies.

The Impossible Job Description

Here's what this analyst's day looks like: He's responsible for everything the customer team needs to know. Not just customer success metrics, but the entire journey—from marketing (well, except demand gen) through sales, onboarding, adoption, retention, and expansion. He's also the person who has to figure out which features drive retention, what signals indicate churn risk, and how to tie together data from three different product analytics systems because his company grew through acquisition.

Oh, and he mentioned almost casually: "I recently had to do an analysis to look at essentially what features would lend themselves to retention, right? It took forever."

Forever. One analysis. One question that leadership needed answered to make strategic decisions about product development and customer success interventions.

This is the reality for thousands of analytics professionals at mid-market companies. They're not struggling because they lack skills—this person clearly knows his way around BigQuery, DataForm, Salesforce, Gainsight, and multiple instances of Pendo. He's struggling because he's being asked to be three people: data engineer, analyst, and strategic advisor.

And here's the thing: he can only really be one at a time.

The Data Preparation Tax

About halfway through our conversation, he described a problem that I bet resonates with anyone who's worked with data at a company that's grown through acquisition:

"We have grown via acquisition. So acquired three different companies, and long story short, I have to tie... each one of their products has its own ID... I've got IDs that don't tie to anything, until I created this master mapping table."

This is the hidden tax that nobody talks about when companies acquire other businesses. Sure, you get new customers, new products, new revenue streams. But you also get three different customer ID schemas, three different ways of tracking engagement, three different definitions of "active user."

And someone—usually a person like this analyst—has to manually stitch it all together before any analysis can even begin.

He mentioned that this process "slows him down." But it's more than that. It's not just slow—it's a creativity killer. Every hour spent writing code to map IDs between systems is an hour not spent discovering why customers in one product line have different retention patterns than another, or which engagement behaviors actually predict expansion revenue.

The analysis that "took forever"? I'd bet a significant chunk of that forever was just getting the data into a shape where analysis was even possible.

The Nonprofit Paradox

One detail from our conversation has stuck with me, and it reveals something important about how we think about data analysis.

When I asked about churn analysis, he explained: "We make tech for nonprofits, and if you know anything about nonprofits, they have limited budgets and a lot of volunteers, AKA a lot of turnover. So as we know, churn, there's not always a rhyme or reason, because humans aren't rational actors... I feel like I have boiled the ocean and haven't been able to figure anything out besides kind of like these few key things that just everybody knows."

Here's what struck me: He's convinced the problem is unsolvable because "humans aren't rational actors." But the real issue isn't that the patterns don't exist—it's that human analysts, looking at one variable at a time in spreadsheets or dashboards, simply can't see patterns that emerge from combinations of 5, 10, or 15 different factors simultaneously.

You can't "boil the ocean" by testing hypotheses one at a time. The ocean is too big. You need a different approach—one that can explore multiple dimensions simultaneously and surface the combinations of factors that actually matter.

This is the gap between what business intelligence tools show you (one slice at a time) and what data science can discover (multivariate patterns). And right now, that gap requires either advanced technical skills or a data science team. Most companies in the mid-market have neither.

What "Strategic" Actually Means

The phrase that keeps coming back to me is when he listed his responsibilities: "build pipelines and push data to different systems, do analysis, build decks, be strategic."

Notice how "be strategic" comes last? That's not an accident.

Strategy is what's left over after all the operational work is done. It's the remainder. And at most companies, there is no remainder—there's just more pipeline building, more analysis requests, more deck creation.

Being strategic means having time to ask questions like:

• Why are we losing customers in the nonprofit sector at a higher rate than for-profit customers?
• What if we proactively reached out to customers showing early warning signs?
• Which customer segments should we focus our limited resources on?

But you can't ask those questions when you're still trying to figure out how to join three different Pendo instances to a Salesforce ID.

The Bigger Picture

This conversation represents something much larger than one overwhelmed analyst at one nonprofit tech company. It's a symptom of a fundamental mismatch in the analytics market.

On one side, we have incredibly sophisticated data infrastructure: cloud warehouses that can process petabytes, transformation tools that can handle complex pipelines, BI platforms that can create beautiful dashboards.

On the other side, we have an explosion in the number of people who need insights from data: customer success managers, marketing ops professionals, sales operations analysts, product managers.

And in the middle, we have a handful of people like this analyst, trying to translate between those two worlds while also somehow finding time to be strategic.

The traditional answer has been "hire more analysts" or "build a data team." But that's not realistic for most companies. A data engineer costs $150K+. A data scientist costs even more. And even if you could afford them, they're not solving the fundamental problem—they're just adding more people to the translation layer.

What if instead of building bigger teams to translate between technical tools and business questions, we built tools that understood business questions directly?

The Transformation Moment

There was a moment in our demo where I could almost hear the mental gears shifting. We showed him how to use spreadsheet formulas—VLOOKUPs, the same formulas he already uses—to transform data at scale. No SQL required. No pipeline building. Just the skills he already has, applied to bigger problems.

His response: "Those are all the... you have a calendar table in your [database] where you have all those things you have to... you have 10 joins on one table because you need all these one-off [date calculations]."

He immediately understood. Every analyst knows the pain of joining to a calendar table ten times just to get different date calculations. What if you could just use Excel date formulas instead?

Then we showed him machine learning models that could analyze all his churn variables simultaneously—the ones he'd "boiled the ocean" trying to understand one at a time. Models that could explain their predictions in plain English.

That's when he said: "I can probably think of several different things that I'd want to use [this for]."

Not "this might work for my one use case." Several things. Once you see that the bottleneck can be removed, you start thinking about all the questions you haven't had time to ask.

What This Means for the Market

If I'm being honest, conversations like this one are why we started building Scoop in the first place.

The business intelligence market has spent 20 years building tools for IT organizations and data teams to create things for business users. That made sense when data was scarce and analytics was rare.

But today, data is abundant and questions are everywhere. Every customer success team has churn questions. Every sales ops person has pipeline questions. Every marketing analyst has attribution questions.

The bottleneck isn't computing power or storage capacity. It's translation—the cognitive and temporal cost of moving between "I need to understand retention drivers" and "let me write a SQL query with 15 joins and 3 subqueries."

What we're seeing is a market ready for tools that work the way business analysts already think: in spreadsheets, in natural language, in questions rather than queries.

Not simpler tools—smarter tools. Tools that can do sophisticated multivariate analysis and machine learning, but express results in business terms. Tools that let you be strategic because they handle the operational work.

The Path Forward

At the end of our call, this analyst mentioned he needs to go through an IT approval process before he can even upload data. He was almost apologetic about it—"I have a whole process that I have to go through."

But he shouldn't be apologetic. That process exists for good reasons: data security, compliance, governance. The problem isn't that companies have these processes. The problem is that analytics tools have traditionally required such deep technical integration that these processes become barriers to exploration.

What if analytics tools could work within existing security frameworks, connect to data where it already lives, and enable exploration without requiring IT to build and maintain semantic models?

That's the opportunity. Not to bypass IT or eliminate governance, but to enable business analysts to ask and answer their own questions within appropriate guardrails.

An Invitation to Reflect

If you're reading this and nodding along—if you've ever felt like you don't have time to "build pipelines, do analysis, build decks, and be strategic"—I want you to know: it's not you.

The tools were designed for a different era, for different users, for different problems. You're not failing at using them correctly. They're failing to meet you where you are.

And if you're a leader with someone like this analyst on your team—someone who's incredibly capable but stretched impossibly thin—ask yourself: what strategic insights are you missing because your analyst is spending 80% of their time on data preparation?

What questions aren't getting asked because the questions that are asked take forever to answer?

That's the real cost. Not just the salary of the analyst or the license fees for your tools. The cost is all the insights you never discover, all the customers you don't save, all the opportunities you don't see—because your most analytically capable people are too busy being data plumbers to be strategic advisors.

The technology exists today to change this. The question is whether we're ready to rethink how analytics should work.

FAQ:

What are customer success tools?

Customer success tools are software platforms designed to help companies proactively manage customer relationships, prevent churn, and drive product adoption. Unlike reactive support tools, customer success platforms focus on ensuring customers achieve their desired outcomes with your product or service.

Common customer success tools include:

• Gainsight - comprehensive customer success platform with health scoring and automation
• ChurnZero - real-time customer success software focused on SaaS companies
• Totango - customer success platform with pre-built success programs
• Planhat - customer platform combining success, sales, and data
• Vitally - customer success platform designed for B2B SaaS teams

These tools typically track metrics like customer health scores, product usage, renewal likelihood, expansion opportunities, and engagement patterns. However, as discussed in the article, many customer success professionals find themselves needing to go beyond what these platforms offer—combining data from multiple sources (CRM, support tickets, product analytics) to get a complete picture of customer behavior.

Is customer success and customer service the same?

No, customer success and customer service are distinct functions, though they're often confused:

Customer Service is reactive:

• Responds to customer problems and questions
• Focuses on resolving immediate issues
• Success measured by response time, resolution rate, customer satisfaction (CSAT)
• Typically handles support tickets, phone calls, chat requests
• Goal: Fix problems when they occur

Customer Success is proactive:

• Anticipates customer needs before problems arise
• Focuses on helping customers achieve their business goals
• Success measured by retention, expansion revenue, product adoption, Net Promoter Score (NPS)
• Manages onboarding, training, strategic business reviews
• Goal: Ensure customers get ongoing value

Think of it this way: Customer service helps you when your car breaks down. Customer success makes sure you know how to maintain your car so it doesn't break down in the first place—and helps you understand when it's time to upgrade to a better model.

The analyst in our article is in customer success, which is why he's focused on retention analysis, feature adoption, and predicting which customers might churn—not just resolving tickets.

What is the best customer success platform?

There's no single "best" customer success platform—the right choice depends on your company size, industry, technical requirements, and existing tech stack. However, here are the leading platforms and when they work best:

Enterprise/Complex B2B:

• Gainsight - Most comprehensive feature set, highly customizable, best for companies with dedicated CS teams
• Totango - Strong for complex customer journeys and multi-product companies

Mid-Market SaaS:

• ChurnZero - Excellent for product-led growth companies, strong automation
• Planhat - Good balance of features and usability, strong data visualization
• Vitally - Modern interface, good for teams transitioning from spreadsheets

Early-Stage/Smaller Teams:

• Catalyst - Simpler interface, faster implementation
• UserIQ - Combines customer success with in-app guidance
• ClientSuccess - Straightforward platform focused on core CS workflows

However, here's the challenge highlighted in our article: Even the best customer success platform only solves part of the problem. Most CS professionals need to combine data from their CS platform with:

• CRM data (Salesforce, HubSpot)
• Product analytics (Pendo, Amplitude, Mixpanel)
• Support tickets (Zendesk, Intercom)
• Financial data (billing systems, spreadsheets)
• Custom internal systems

This is why the analyst we spoke with, despite having Gainsight, still spends so much time on data preparation and analysis. The "best" platform isn't necessarily the one with the most features—it's the one that integrates well with your existing data ecosystem and enables your team to find insights quickly.

What is business intelligence?

Business Intelligence (BI) is the process of collecting, analyzing, and presenting business data to help organizations make informed decisions. BI transforms raw data into meaningful insights through reporting, visualization, and analysis.

Core components of BI:

1. Data Collection: Pulling data from multiple sources (databases, applications, spreadsheets)
2. Data Preparation: Cleaning, transforming, and organizing data for analysis
3. Analysis: Identifying patterns, trends, and relationships in data
4. Visualization: Creating charts, dashboards, and reports
5. Distribution: Sharing insights with stakeholders

Common BI use cases:

• Sales pipeline analysis and forecasting
• Marketing campaign performance and ROI
• Customer behavior and segmentation
• Financial reporting and budgeting
• Operational efficiency and KPI tracking
• Inventory and supply chain optimization

Traditional BI tools include:

• Tableau, Power BI, Qlik (visualization and dashboards)
• Looker, Sisense, Domo (cloud-based analytics platforms)
• Excel, Google Sheets (still the most widely used "BI tool")

The challenge, as our article explores, is that traditional BI tools were designed for IT departments and data analysts to build things for business users. This creates a bottleneck: business users who need insights must either wait for analysts to build reports, or try to learn complex technical tools themselves.

Modern BI is evolving toward self-service analytics—tools that business users can operate independently without SQL or technical expertise.

How good is Power BI business intelligence?

Power BI is Microsoft's business intelligence platform, and it's one of the most widely adopted BI tools in the enterprise market. Here's an honest assessment:

Power BI's Strengths:

• Cost-effective: $10/user/month for Pro, significantly cheaper than Tableau or Qlik
• Microsoft integration: Works seamlessly with Excel, Azure, Office 365, Teams
• Strong visualizations: Wide variety of chart types and custom visual marketplace
• Frequent updates: Microsoft releases new features monthly
• Large community: Extensive tutorials, templates, and support resources
• Scalability: Can handle everything from small datasets to enterprise data warehouses

Power BI's Limitations:

• Steep learning curve: Requires understanding of data modeling, DAX formulas, Power Query
• Technical skills required: Not truly self-service for non-technical users
• Performance issues: Can be slow with complex models or large datasets
• Limited mobile experience: Mobile app is functional but limited compared to desktop
• Microsoft ecosystem lock-in: Works best if you're all-in on Microsoft

Who Power BI works well for:

• Companies already using Microsoft ecosystem
• Organizations with dedicated BI teams to build and maintain reports
• Technical users comfortable with formulas and data modeling
• Businesses needing cost-effective enterprise BI

Who Power BI struggles for:

• Non-technical business users needing ad-hoc analysis (the person in our article)
• Teams without dedicated BI resources
• Organizations needing quick insights without building formal reports
• Users who need to combine data from multiple sources quickly

The analyst in our story mentioned his company doesn't have a BI team. Power BI would likely create more problems than it solves—he'd be expected to become a Power BI expert on top of everything else he's already doing.

The bottom line: Power BI is a powerful tool, but "powerful" often means "complex." It's excellent for building formal dashboards and reports, but it's not the right tool for the 70% of analytics work that's exploratory, ad-hoc, and question-driven.

What business intelligence means (in practical terms)

While the technical definition of business intelligence focuses on tools and processes, what BI means in practical terms varies dramatically depending on who you ask:

For executives, BI means:

• Understanding company performance at a glance
• Making data-driven strategic decisions
• Identifying opportunities and risks early
• Monitoring progress toward goals

For analysts (like the person in our article), BI means:

• Endless requests for "quick" reports that take days
• Maintaining dashboards nobody uses
• Explaining why the data says something different than people expected
• Building pipelines and transforming data
• Wrestling with SQL, formulas, and broken integrations

For business users (sales, marketing, customer success), BI means:

• Waiting for IT to build reports
• Exporting data to Excel to do "real" analysis
• Asking questions that go unanswered
• Making decisions based on gut feel because data takes too long

The disconnect: BI was supposed to democratize data and enable data-driven decision making. Instead, it often creates a new bottleneck where business users depend on technical teams to get insights.

What BI should mean:

• Answers to business questions, not just dashboards
• Self-service exploration without technical barriers
• Discovery of insights you didn't know to look for
• Time for strategic thinking instead of data wrangling

The future of BI is about removing the translation layer between questions and answers—enabling business users to work directly with data using skills they already have (natural language, spreadsheets) rather than skills they need to learn (SQL, data modeling, programming).

Best data analysis tools

The "best" data analysis tool depends entirely on who's using it and what they're trying to accomplish. Here's a breakdown by user type:

For Business Analysts (Excel/Sheets experts, no coding):

• Excel/Google Sheets - Still the most versatile for small datasets, familiar to everyone
• Tableau - Best-in-class visualizations, relatively intuitive drag-and-drop interface
• Power BI - Cost-effective, good Microsoft integration, moderate learning curve
• Looker - Strong for SQL-comfortable analysts, good governance
• Scoop - AI-powered analysis with spreadsheet-based data prep (full disclosure: that's us)

For Data Analysts (SQL-proficient, some coding):

• Mode Analytics - SQL + Python/R notebooks, good for combining analysis types
• Hex - Modern notebooks with SQL, Python, visualization
• Databricks - Powerful for big data, requires technical expertise
• Metabase - Open-source, good for SQL-based analysis

For Data Scientists (advanced statistics, machine learning):

• Python (pandas, scikit-learn, statsmodels) - Most flexible, industry standard
• R (tidyverse, caret) - Best for statistical analysis
• Jupyter Notebooks - Standard environment for exploratory analysis
• DataRobot - AutoML platform for building production models
• H2O.ai - Open-source ML platform

For Non-Technical Business Users (no technical skills):

• Google Sheets - Accessible but limited
• Airtable - Database + spreadsheet hybrid
• ThoughtSpot - Natural language search for data
• Sigma - Spreadsheet interface for data warehouses
• Scoop - Natural language + ML without coding

The harsh reality: Most "best data analysis tools" lists focus on what tools can do, not what users can actually do with them. The analyst in our article could theoretically use Python for advanced analysis, but he doesn't have time to learn it—he's too busy keeping the lights on with the tools he already knows.

The truly "best" tool is the one that:

1. Matches your current skill level (or is easy to learn)
2. Connects to your data sources
3. Answers your specific questions
4. Fits your budget
5. Actually gets used (not just purchased)

Is data analysis a hard skill?

Yes, data analysis is classified as a hard skill—a technical, teachable ability that can be measured and certified. However, this classification oversimplifies the reality.

Data analysis encompasses multiple skill levels:

Basic data analysis (accessible to most):

• Creating pivot tables in Excel
• Building simple charts and graphs
• Calculating averages, sums, percentages
• Filtering and sorting data
• Using basic formulas (VLOOKUP, SUMIF)

Intermediate data analysis (requires training):

• SQL queries to extract data
• Statistical analysis (correlation, regression)
• Dashboard creation in BI tools
• Data cleaning and preparation
• A/B test analysis

Advanced data analysis (specialist skills):

• Machine learning and predictive modeling
• Programming (Python, R)
• Complex statistical methods
• Big data processing
• Algorithm development

The challenge: Most business roles require intermediate data analysis skills, but most people only have basic skills. This creates the situation described in our article—where one analyst is expected to be everything from Excel wizard to data scientist.

What makes data analysis "hard":

• Constantly evolving tools and technologies
• Requires both technical and business understanding
• Need to translate between stakeholder questions and technical solutions
• Involves uncertainty and judgment, not just formulas
• Requires domain knowledge in addition to technical skills

Will data analysis be replaced by AI?

This is simultaneously the most asked and most misunderstood question about the future of analytics. The short answer: AI won't replace data analysis, but it will fundamentally transform what data analysis means.

What AI is already replacing:

• Data preparation: Cleaning, formatting, handling missing values
• Routine reporting: Automated dashboard updates, scheduled reports
• Simple pattern detection: Anomaly detection, basic trend identification
• Visualization selection: Choosing the right chart type for your data
• Basic forecasting: Time series predictions, simple extrapolations

What AI is augmenting (not replacing):

• Hypothesis generation: AI can suggest what to investigate, but humans decide what matters
• Complex investigation: AI can run multiple analyses, but humans interpret meaning
• Strategic decision-making: AI provides insights, but humans make business judgments
• Communication: AI can generate text, but humans understand audience and context
• Ethics and governance: AI can flag issues, but humans set policies

What AI cannot replace (yet, possibly ever):

• Business context understanding: Knowing why a metric matters to your organization
• Stakeholder management: Understanding political dynamics and communication needs
• Strategic thinking: Connecting data insights to business strategy
• Creative problem-solving: Asking novel questions and designing new approaches
• Change management: Helping organizations act on insights