The Reality of Data Science in the Era of Big Data - Circa 2010

In 2010, I landed my first “data scientist” role at a telecommunications and BPO (Business Process Outsourcing) company with grand visions of building sophisticated predictive models that would revolutionize customer retention by maximizing ROI. Armed with my MATLAB, MS SQL Server, and a Cassandra cluster, I thought I was equipped for anything the data world could throw at me.

Reality had other plans.

My first three months were spent not building models, but wrangling massive, messy SQL queries to produce Excel reports. I’d start each morning staring at stored procedures spanning hundreds of lines, joining dozens of tables with cryptic names like CUST_ACCT_HIST_BKP2 and SRV_USAGE_DTL_TEMP. These behemoths would time out regularly, requiring careful optimization just to extract basic customer segmentation data.

The Cross-Functional Reality

As the project expanded, I found myself managing cross-functional analytics, strategy and profitability projects with a team of 5 across 4 departments in 3 locations. This meant:

• Coordinating SQL developers in our main office with business analysts in satellite locations
• Reconciling conflicting data definitions between marketing, operations, and finance departments
• Managing expectations across time zones with limited documentation
• Translating technical concepts for diverse stakeholders with varying technical literacy

The complexity of coordinating these efforts often overshadowed the technical challenges of the data itself. Weekly status calls became exercises in diplomacy as much as data science.

ROI Breakthroughs and Challenges

Despite the challenges, we delivered meaningful results:

• Developed resource allocation strategies for clients, including 3 Fortune 500, increasing annualized revenue by $78M
• Re-engineered dialer algorithm, which increased staff utilization by 9% with projected savings of $1.2M annualized

These wins came not from applying textbook data science approaches, but from deeply understanding the business processes behind the data. The dialer algorithm improvement, for instance, required weeks of sitting with call center staff to understand their workflows before a single line of code was written.

The resource allocation strategies that drove $78M in revenue started as massive SQL queries pulling customer segment data, which fed Excel models, which then informed MATLAB simulations. This hybrid approach—using each tool for its strengths—proved more effective than attempting to force all analysis into a single platform.

The Data Engineering Reality

What nobody told me in 2010 was that approximately 90% of “data science” work was actually data engineering—building pipelines, cleaning inconsistent records, and creating reliable reporting structures. The sophisticated predictive modeling I had envisioned occupied perhaps 10% of our actual work.

-- A typical day in 2010: Massive stored procedures for basic reporting
CREATE PROCEDURE [dbo].[GetCustomerSegmentation]
    @StartDate datetime,
    @EndDate datetime
AS
BEGIN
    WITH CustomerHistory AS (
        SELECT 
            c.CustomerID,
            c.AccountType,
            ch.TransactionDate,
            ch.ServiceType,
            ROW_NUMBER() OVER (
                PARTITION BY c.CustomerID 
                ORDER BY ch.TransactionDate DESC
            ) as rn
        FROM CUST_ACCT_HIST_BKP2 ch
        JOIN Customer c ON c.CustomerID = ch.CustomerID
        WHERE ch.TransactionDate BETWEEN @StartDate AND @EndDate
    )
    SELECT 
        AccountType,
        ServiceType,
        COUNT(DISTINCT CustomerID) as CustomerCount,
        AVG(DATEDIFF(day, TransactionDate, GETDATE())) as AvgDaysSinceLastTransaction
    FROM CustomerHistory
    WHERE rn = 1
    GROUP BY AccountType, ServiceType
    ORDER BY CustomerCount DESC;
END

MATLAB proved valuable for statistical validation and specific modeling tasks, but SQL Server was our workhorse, and our Cassandra cluster remained underutilized as we struggled to migrate legacy processes to the new architecture.

Conclusion

The 2010 telecommunications data science landscape taught me that success depended less on algorithmic sophistication and more on addressing fundamental business challenges through whatever tools best fit the problem. While managing cross-functional teams across multiple locations added complexity, it also provided the diverse perspectives needed to deliver substantial ROI improvements.

For anyone entering data science today, the lesson remains relevant: the promise of advanced analytics is real, but the path to delivering value often runs through messy data, legacy systems, and cross-departmental collaboration rather than elegant algorithms alone. The $78M revenue increase and $1.2M in operational savings came not from theoretical models, but from applying data-driven insights to concrete business processes with measurable outcomes.

Editor’s Note: This article was written in September 2015, reflecting on experiences from 2010. It has been updated in 2024 to provide historical context for modern data practitioners.