Where does Data Analytics fit in the enterprise (irrespective of the size of the business)?

Everywhere, is the short answer.

Data analytics fits in every department of an enterprise as different kinds of data are collected in each of them. Below is a quick view of departments and some use cases:

Departments	Areas or Use Cases	Brief
Finance	General Ledger (GL) Reconciliation	Monthly General Ledger reconciliation could be automated to eliminate human errors and free up man-hours for better tasks
	Fraud detection	Vendor, employees, balance sheet, etc
	Data aggregation	Reading and aggregating data from various sources like pdf, Excel, databases.
	Risk analysis	Analysis of risks like business capital, investments, loans, customer segmentation, etc.
	Velocity and Quality of decision	Improved velocity and Quality of data-generated and decision took basis factual analysis by automating and eliminating human errors
	Stock market insight	Analysis of stock prices by more holistically modelling taking into consideration more variables
Procurement	Invoice and Purchase Order (PO) automation	Eliminate errors and free man-hours. Pre-built reports and data queries run from inside the ERP System
	Fraud detection	Detect the fraud as it happens and take corrective measures rather than finding out at a later time
	Vendor management	Differentiating tail spends, saving costs
	Bid and Spend management	Spend and bid, cost benchmarking, Invoice compliance, Payment term analytics and Supplier risk and performance
	Inventory Management	Optimize costs, space and run production smoothly
Product Planning	Profitability management	A simple delta drill chart could explain, by removing which parts from the production line could the profitability have been boosted further
Shop Floor	Lower cost of production	Reducing or eliminating costly unscheduled downtimes using Predictive Analytics
	Quality improvements and scrap reduction	Fault pattern identification and elimination
	Productivity enhancements	Resource Availability and Productivity enhancements
	Near real-time feedback	Take corrective measures without delay, as you get notified of actual scenarios near real-time
Human Resources	Employee experience	Measuring employee engagement, time to hire, retention rate, better planning and overall workforce management decision
Human Resources	Payroll reconciliation	Automating the Payroll reconciliation process to avoid human errors and free up man-hours
Marketing	Customer behaviour	Survey insights, trends
	Promotion	Promotion insights and optimization
	Customer experience	Combination of data and ML. Targeted messaging.
	Dealer Management	Drop laggards, cut costs on retaining dealers
Warranty	Lower Warranty costs	Lower or eliminate warranty costs by doing root cause analysis, identifying design and manufacturing flaws, eliminating fraudulent claims and claim processes
CEO’s office	Management Dashboards	The overall health of the company at fingertips: production quantity, quality, inventory, risk, profitability, costs, etc.

These common pain points businesses face can be transformed into growth opportunities through data analytics as part of the larger digital transformation journey.

Also Read: Digital transformation for SMEs, Part 2: Understanding its maturity cycle

While it may be overwhelming to cover all areas, starting with one or two key areas by prioritizing will contribute towards more efficient use of resources, risk management and better return on investment.

Stay tuned for our fourth and final article in this series. With the charting and planning in place, we tackle the implementation of digital transformation into the organization’s structure and processes.

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