lumtify
Türkçe

Data Analyst · Paladin

Ahmet Can Öztürk

Works across every step of analytics projects, from data engineering to analysis to visualization; gets data out of the source and into a state you can decide with.

The one who hauls data up from the bottom of the well. The least charming phase of any project (“where is this data, why is it missing, why is this date written like that”) is home turf here, and when nobody wants to look, these are the sleeves that get rolled up. Worked with retail data at Defacto and Karaca; at Lumtify, takes part in the data engineering, analysis and visualization steps of analytics projects alike.

Stubborn, and we mean that as a compliment: a number that does not add up never gets shrugged off as a rounding error, it gets traced all the way down to the source. Enjoys making data do somersaults, and rewriting a query that already works just to make it twice as fast. Alias: Paladin, the one standing in front of the team and taking the hits that come from dirty data. Heart given to yellow and navy, which is worth remembering when picking chart colours on match weeks.

Projects

Derimod

Derimod: seven projects in three layers, from data warehouse to Optail

We brought data scattered across AX ERP, Excel and CRM into a single data warehouse, built more than 20 Power BI dashboards on top, and at the top placed Optail, which makes the initial allocation, replenishment and inter-store transfer decisions. Stock analytics (lost sales, size-run breaks, idle stock) and a CRM layer followed. This page is the overview; each of the seven projects has its own page.

  • 20+ Dashboards
  • 7,000+ Optail scenarios

Defacto

Defacto DSO: setting up a data science office and four years of consulting

We started in 2021 as mentors at the hiring Datathon. Through the end of 2024 we trained the team, built the DSO's database and server infrastructure, and wrote and put into production more than thirteen projects. Today the DSO is a directorate serving Defacto's retail analytics, AI and business intelligence needs.

  • 4 years Duration
  • 13+ Projects

Defacto

Defacto Norm Staffing: calculating the staff each store should have, from data

A model that takes the store staffing decision away from actual payroll and subjective requests and grounds it in measured workload and an efficient peer in the same segment. It produces a 2025 norm, a 2026 target and an increase / reduce / keep decision for every store × role. It runs entirely on BigQuery and is fully parametric.

  • Store × role Decision unit
  • Frontier Reference

Gürmen Group

Gürmen MATS: moving inter-store transfers from Excel to a system that decides at SKU level

The Inter-Store Transfer System that moves products not selling in one Ramsey or Kip store to a store that sells them, before markdown. It selects receivers and senders by cover at option level, derives need from sales velocity, and matches at SKU level. Block, single-unit and split-and-spread transfers run on the same infrastructure. It sits on Gürmen's server as a SQL project, and results go to the ERP.

  • SKU Decision unit
  • Cover Receiver / sender

Motor Aşin

Motor Aşin: a data warehouse joining two ERPs, with dashboards and decision support

We brought data split across Logo Tiger and J-Platform into a single SQL Server data warehouse through SSIS, and built sales, stock, product, warehouse fill rate, budget, salesperson scorecard and purchasing dashboards on top. Some of the dashboards were built by Motor Aşin's own team under our mentoring. Data has flowed without interruption since October 2024. Phase three, in 2026, covers basket analysis, stock-outs and lost sales, idle stock and supplier performance.

  • 2 ERPs Sources
  • Uninterrupted Data flow

Defacto

Defacto HR Analytics: dashboards, alerts and the Norm Staffing model

In a second period with Defacto after the DSO, we consult for Human Resources' own analytics team. The scope covers turnover, demographics, academy and HRBP dashboards, time-and-attendance and bonus automation, and an alert setup connected to the company's internal assistant, with the Norm Staffing model at the centre. Ongoing since December 2025.

  • 2nd period Period
  • 4 Layers

Upily

Upily: discovery and audit of the Wonjo Kids data infrastructure

The Firebase, Adjust and RevenueCat data of Wonjo Kids, a games app for children, had been gathered in BigQuery, but the setup had grown organically and become complex. We examined more than 1,500 BigQuery objects and 330 events across seven areas: table inventory, pipelines, cross-platform matching, platform parity, taxonomy, event tracking and dashboards. Strengths, risks and a four-phase transformation roadmap were delivered in a single report.

  • 1,500+ Objects examined
  • 330+ Events

Karaca

Karaca: data analytics consulting, from the data warehouse to CustoMation

Consulting alongside the data analytics team in 2022–2023 for the store and e-commerce data of the Karaca group (Karaca, Karaca Home, Emsan, Homend, Kaşmir): the warehouse's analytics layer and cube, a product and idle-stock dashboard, store and e-commerce lost sales, a capacity system design, a transfer proposal, and CustoMation, which selects campaign audiences by RFM segment. This page is the umbrella; CustoMation has its own page.

  • 6 Projects
  • 5 Brands

Banks Association of Türkiye (TBB)

TBB: data science training for bankers

Within the Banks Association of Türkiye's training programme, a 62-hour, 21-day programme called the 'Data Analytics Journey' for bank employees who use data in their work: introduction to data science, SQL, Python, statistics and EDA, visualisation, machine learning, industry cases. Sessions ran in the evenings, every module had homework and the hands-on work was done in Colab. TBB scored every module out of 10 in its participant survey; the average came out above 9.

  • 62 hours Duration
  • 9+ / 10 Evaluation

Defacto

Defacto IofTheCustomer: an NLP system that classifies customer reviews and routes them to the right team

Every product review from defacto.com and the marketplaces passes through sentiment analysis within seconds; if negative, it is assigned one of nine categories and routed to the responsible team. Built in 2021, the system has run without interruption for three years and won a Stevie award in 2023.

  • 9 Categories
  • 700K+ Reviews processed

Defacto

Defacto Sorting: a scoring algorithm that sets product order on category pages

A dynamic scoring algorithm that decides the order in which products appear when a category page opens on defacto.com. It weighs sales, clicks, broken size runs, stock and shelf life together, with a cold-start solution for new products and scenario-based weighting.

  • 5 Signals
  • Cold start New products

Defacto

Defacto ELOSS: measuring the sales lost to stock-outs in e-commerce

A weekly calculation answering 'what would have sold had the product been there' for every SKU that runs out of stock on Defacto's own site and the marketplaces. It computes lost sales from pre-stock-out velocity with special-day and trend effects, then splits them by day using the daily sales distribution. An incremental design cut a calculation that took hours down to minutes.

  • Weekly Run
  • ~30 min Runtime

Defacto

Defacto NoSales: measuring stock that has not sold for 28 days, every week, by the same rule

A weekly system that checks, for every option × store pair with stock on Sunday, whether there was a sale in the last 28 days, and reports idle stock by store, product and region. Scheduled queries in BigQuery, a five-page Power BI dashboard, a trend since 2021.

  • 28 days Rule
  • Monday 05:30 Run

Defacto

Defacto PAT: meeting analytics that survived the move from Teams to Meet

A dashboard producing attendance, talk-time and meeting-efficiency metrics from online meeting data. Built on Microsoft Teams' relational data; when the company moved to Google Meet, Meet's nested event data was mapped to the Teams schema and the dashboard continued unchanged. Row-level security means everyone sees only their own hierarchy.

  • 2 Platforms
  • Daily Refresh

Defacto

Defacto DISCO: if I give this product that price, how many units sell in 14 days?

A system that bases the markdown decision on a forecast. For every product whose price changes, we built an XGBoost model that learns the units sold in the next 14 days from stock, broken sizes, sales velocity, price history and calendar effects. The pipeline runs every day, the error is measured against actuals 14 days later, and in a demo interface the planner can try five psychological prices side by side. The MVP was completed; by management decision it was not taken into the pricing process.

  • Price → units Question
  • Two-way Direction

Derimod

Derimod data warehouse: from AX, Excel and CRM to a single analytics database

The layer the other Derimod projects sit on. We brought AX ERP's transaction-level data, the teams' Excel files and the CRM together in one analytics database on SQL Server: schema and naming standard, procedure registry and log mechanism, daily flow with SSIS, uninterrupted loading with partition switch, delta detection with RowHash. Both the dashboards and Optail are fed from this warehouse.

  • 3 Source systems
  • 7 Schemas

Derimod

Derimod business intelligence: 20+ Power BI dashboards fed from one warehouse

We turned the clean data in the warehouse into dashboards every Derimod team can open and interpret directly: sales, product, customer, budget, store and warehouse capacity, logistics, e-commerce operations, Optail success reports. The dashboards follow a four-step catalogue (reporting, deep analysis, trading, CEO) and are all fed from the same warehouse; no report has a separate data source.

  • 20+ Dashboards
  • 4 Catalogue steps

Derimod

Optail Initial Allocation: first distribution of new product with a master plan, store grouping and capacity

When a new product leaves the warehouse there is no sales data yet; the decision rests on similar products' history, store groups and capacity. For Derimod we built the Master Plan screen, where planning is done before and throughout the season, along with store clusters and fill and dispatch-priority management; footwear and bags went live first, then apparel. In 2026 Flow Through, open-quantity allocation and simulation are being added.

  • 3 Categories
  • Master plan Decision input

Derimod

Optail Replenishment: automatic warehouse-to-store feed for what sells

Replenishment, live since October 2024, manages the warehouse-to-store flow by rate of sale: how many weeks of cover, minimum lot, which warehouse, which store group. In the first two months two thirds of replenishment came from Optail; in 2025 it approached 95% in footwear. We measure whether every replenishment turns into sales; Optail's sell-through rate is higher than both manual replenishment and last year.

  • ~95% Automation
  • ~1.5× Sell-through

Derimod

Optail Inter-Store Transfer: size-run consolidation, out-of-collection and season-end transfers

Optail's first module. It started with block transfer in February 2024; on top came the consolidation transfer that gathers broken size runs, the out-of-collection transfer that moves product to stores that do not carry it, closing-store clear-out, and the class-based transfer that concentrates product in selling stores at season end. Transferred goods sell through within 21 days at five times the manual rate.

  • ~25% Sell-through
  • 5 Transfer types

Derimod

Derimod stock analytics: lost sales, size-run breaks, idle stock and the success of every shipment

The layer that measures whether Optail's decisions work. We defined lost sales, size-run breaks and non-selling stock and tied them to weekly dashboards; made stock in warehouses and stores and every product movement (in transit, on dispatch order, incoming) visible; computed the sell-through of initial allocation, replenishment and transfer with a first-in–first-out link. In one season footwear lost sales fell from 14% to 10% and size-run breaks from 10.6% to 8.4%.

  • 14% → 10% Lost sales
  • 10.6% → 8.4% Size-run breaks

Derimod

Derimod CRM analytics: customer 360, a campaign success datamart and dashboard

Once the stock side had settled, the customer's turn came. We connected customer, membership, points and campaign data from Dynamics CRM, Shopify and the ERP to the same data warehouse; in place of campaign results summarised in a single line, we built a star schema at receipt-line grain. It tracks MASS and loyalty campaigns and points earned and spent by channel, segment and product. Daily segment and points updates are live and the customer 360 view is a prototype; churn and next best offer are on the roadmap.

  • Receipt line Grain
  • 2 Campaign types

Karaca

Karaca CustoMation: campaign audience selection by RFM and behavioural segments

We took the question of who a campaign goes to out of Excel filters and into a single tool. CustoMation selects customers for the Karaca group's five brands and eleven channels by RFM segment, brand-level behavioural segment, product group purchased, brand and recency window. Include and exclude rules are defined together, every selection is saved as a scenario and can be compared with past runs, and the audience goes to the campaign tool as Excel.

  • 10 + 19 Segments
  • 8 Filter dimensions