Data is now an essential asset that fuels business growth and competitive advantage. But manually managing endless data can become a nightmare, wasting precious time and resources. This is where data automation comes in – to streamline cumbersome data tasks and unlock deeper insights faster.
In 2023, data automation will move from a nice-to-have to a must-have for any forward-thinking company. This guide explores what data automation entails, its untapped potential, and how you can start automating your data workflows now.
Automating Data Workflows is a Strategic Priority
Before jumping into the what and how of data automation, it‘s important to understand why it matters in the first place.
According to McKinsey, organizations that champion data-driven decision making are 23 times more likely to acquire customers, 6 times as likely to retain them, and 19 times as likely to be profitable as a result.
But making data-driven decisions relies on continuously extracting value from data. This becomes increasingly challenging as data volumes and sources explode exponentially year after year.
Manually managing enormous, ever-growing data is enormously inefficient. Data engineers spend up to 80% of their time simply finding, cleaning, and organizing data – leaving little time for actual analysis and innovation.
This data drudgery also leads to:
- Costly mistakes that impact data accuracy
- Delayed insights that erode the value of data
- Unscalable processes as data volumes outpace human bandwidthData automation provides a powerful antidote by using technology to remove redundant manual interventions. Leading companies like Netflix, Walmart, and UPS rely on automated data pipelines to efficiently harness their data at scale.
Forrester predicts automation will displace 4 million data management jobs by 2022, freeing up staff for high-value tasks. Data automation is no longer optional – it‘s quickly becoming a prerequisite to stay competitive.
What is Data Automation?
Data automation refers to using technology tools to programmatically complete repetitive, manual steps involved in managing data, without human intervention. Key processes to automate include:
Extracting Data from Multiple Sources
Enterprise data lives across countless siloed sources like databases, cloud apps, websites, IoT devices, social media, and more. Manually retrieving relevant data from each source is time-consuming and prone to oversight.
Data automation allows consolidating large volumes of data from diverse sources into a central location automatically based on predefined logic. This is done using APIs, scripting, extraction tools, etc. Popular approaches include:
- ELT (Extract, Load, Transform): Extract raw data from sources and load it directly into the target warehouse, then transform
- ETL (Extract, Transform, Load): Extract data, transform it, then load into the warehouse
According to Dresner Advisory Services, 34% of organizations increased their use of ELT over the past year to gain efficiency – a trend driven by data automation.
Cleansing, Enriching, and Transforming Data
Raw data from disparate sources is often incomplete, inaccurate, and inconsistent. Manually detecting and fixing these issues is time-intensive.
Automated data transformation streamlines:
- Cleansing: Fixing incomplete, incorrect, or irrelevant data using rules, clusters, validation, etc.
- Enrichment: Augmenting data by merging with supplemental sources. For example, adding customer location data to transaction records.
- Normalization: Converting data to consistent formats and standards. Such as parsing dates into common year/month/date structure.
- Aggregation: Rolling up data to higher levels by applying formulas, summaries, etc. Such as totaling daily sales to monthly.
This results in analysis-ready, trustworthy data. According to Dataiku, up to 80% of data scientists‘ time can be spent just on finding and preparing data – automation can massively boost their productivity.
Orchestrating and Monitoring Data Pipelines
Data tasks often involve tangled dependencies that require careful choreography. Manually managing workflows across systems and teams can result in costly failures.
Data automation enables:
- Scheduling: Configuring when jobs should run including frequencies and sequences.
- Workflows: Coordinating the end-to-end flow of data tasks and hand-offs.
- Monitoring: Tracking pipeline metrics like data volumes, job failures, latency, etc.
- Alerting: Sending notifications on issues like downtime, stalled tasks, or anomalies.
With automation, data workflows can be scheduled, executed, and monitored at scale without manual oversight. McKinsey estimates 30-50% of data analysts‘ time is spent coordinating workflows – automation helps maximize their impact.
Analyzing and Visualizing Data
Turning raw data into actionable insights requires filtering, aggregation, statistical modeling, visualization, and more. Manually executing such techniques on large datasets is arduous.
Data automation can encode best practice analytics workflows to unlock insights more rapidly. Use cases include:
- Automated reporting and dashboarding
- Anomaly detection and predictive modeling using machine learning
- Text mining on semi-structured data like emails, surveys, call transcripts
- Image recognition within photos, videos, and documents using computer vision
Automating repetitive analysis steps enables faster, deeper intelligence from data. Tableau estimates up to 30% of analysis time is spent on manual data preparation – automation can help analysts focus on higher-value exploration.
In summary, data automation aims to remove the heavy-lifting involved in managing and making sense of data. This empowers people to then apply their unique skills where they add the most value.
Why Invest in Data Automation?
Accelerating data-driven decisions is a top priority for digitally savvy organizations. Data automation makes this possible by delivering manifold benefits:
1. Faster Time-to-Insight
Automating manual bottlenecks helps analysts uncover insights 5x faster on average, as per McKinsey. With seamless data flows, decision makers get the information they need, when they need it.
2. Improved Data Quality
Automating processes like error detection, validation, and cleansing results in more accurate, consistent data. Better data means better decisions.
3. Higher Productivity
Automation eliminates tedious repetitive tasks, allowing data teams to focus on high-value analysis that drives growth. This boosts data worker productivity by over 50% based on studies.
4. Enhanced Scalability
With manual processes, it‘s impossible to keep up with the deluge of big data. Automation provides flexibility to manage the data flood and changing analytics needs.
5. Lower Costs
Automation reduces human effort required for data tasks. Alteryx calculated 60-80% cost savings for analytics processes after automation.
6. Consistent Results
Automated workflows apply standardized processes to data, minimizing errors and discrepancies from manual handling.
With so many benefits, data automation is indispensable for gaining a competitive edge. According to Diffbot, 93% of data leaders are now automating processes to get an advantage.
Real-World Use Cases of Data Automation
Data automation is making an impact across many different domains and applications:
Customer Intelligence
Retailers like Starbucks and Nike synthesize data from their website, mobile apps, social media, and brick-and-mortar outlets to derive a 360-degree customer view. Automation helps them efficiently stitch together disparate customer data sources to better understand buyer behavior and preferences. This drives personalized experiences.
Predictive Maintenance
Manufacturers like ThyssenKrupp employ automation to aggregate sensor data from industrial equipment and apply ML models to detect anomalies and predict maintenance needs before breakdowns happen. This minimizes costly downtime.
Clinical Analytics
Healthcare providers like Mount Sinai automate pulling together patient vitals, lab tests, medical history, and more to present unified views. This allows doctors to provide data-driven treatment recommendations augmented by AI.
Supply Chain Optimization
Logistics firms like UPS consume trove of IoT data from vehicles and smart package tracking to derive real-time delivery insights. Automation helps manage their torrent of supply chain big data to optimize routes, inventory, and demand forecasting.
These examples showcase how data automation powers data-driven innovation in virtually every industry and function today.
Key Capabilities Needed for Data Automation
Building automated data pipelines requires assembling the right combination of technologies and platforms. Core capabilities include:
Data Integration Engine: An ETL/ELT platform that can extract data from all necessary sources, cleanse and transform it, and load it into the target warehouse or lake. Leading options are Informatica, Talend, AWS Glue, etc.
Workflow Scheduling: Tools like Airflow, Azkaban, Oozie, etc. that allow you to orchestrate data workflow steps and set schedules.
Data Quality: Components for profiling, validating, standardizing, and monitoring data to ensure accuracy and consistency.
Cloud Infrastructure: Leveraging elastic compute from cloud platforms like AWS, Azure, or GCP to enable scaling.
Containers and Microservices: Breaking workflows into modular microservices that run in containers facilitates reusability and flexibility.
Machine Learning: Incorporating ML models for predictive analytics, personalization, ranking, etc. to extract deeper insights.
Data Visualization: Turning data into visual charts, graphs, and dashboards using BI tools like Tableau, Looker, Power BI, etc.
Metadata Management: Cataloging info about data models, processes, dependencies, etc. using tools like Collibra, Alation, etc. to enable governance.
Monitoring and Alerting: Tracking key pipeline metrics and setting threshold-based alerts to catch issues.
With the right data automation building blocks in place, organizations can efficiently operationalize data-driven processes at scale.
Best Practices for Data Automation Success
Automating complex data workflows takes careful strategy and execution. Here are best practices I recommend based on proven experience:
Start Small, Demonstrate Quick Wins
When first automating, pick a contained high-value use case like automating weekly sales reports. Quickly showcasing productivity gains through pilots paves the way for larger initiatives.
Put Your Data House in Order First
Fix upstream data quality issues before feeding data into automated systems – or you risk cementing flaws into processes.
Monitor KPIs to Track Value
Quantify improvements in efficiency, accuracy, costs etc. through metrics monitoring after launching automation to showcase ROI.
Keep Tweaking and Optimizing
Continuously collect user feedback and fine-tune automated workflows through incremental improvements for maximum impact.
Plan for Hybrid DataOps Roles
Have both data engineers to build pipelines as well as data scientists transform, enrich, and analyze data collaboratively.
Democratize through Self-Service
Make it easy for casual business users to tap automated analytics through self-service BI tools instead of buried in code.
Guard Against Tech Debt
Use modern architectures and avoid legacy bottlenecks that will hinder agility. Refactor when needed.
Embed Analytics into Applications
Inject analytics directly into end user apps and operational systems so insights reach people where they work.
Apply MLOps for Reliability
Use DevOps-style processes like CI/CD, testing, and automation to deploy ML models faster and more reliably.
Following these best practices avoids common data automation pitfalls on your journey.
Look Ahead: The Future of Data Automation
Data automation has already delivered huge value. But we‘re still just scratching the surface of its transformative potential looking ahead.
Smarter Self-Service: Automating more self-service reporting, dashboarding, and predictive modeling capabilities directly for business teams.
Seamless Cloud: Multi-cloud and hybrid automation that moves data seamlessly between on-prem and cloud.
Democratization: Low/no-code tools open up automation capabilities to citizen data scientists.
Embedded BI: Analytics seamlessly infused into real-time ops vs. after-the-fact reporting.
Automated Machine Learning (AutoML): Automating rote tasks in ML model development like data prep, feature engineering, hyperparameter tuning, algorithm selection etc.
Natural Language Processing (NLP): Parsing unstructured text data like emails, chats, documents using NLP to extract insights.
Recommendation Engines: Automating recommendations of content, products, treatments etc. tailored to each user based on their data.
Enhanced Monitoring: Real-time observability into the health of data pipelines, with automated remediation.
Data Mesh: Decentralizing data management and pipeline automation across domains/teams vs. one monolith.
The data automation journey never ends. By staying at the leading edge of these innovations, you can maximize value from ever-growing data assets.
Gear Up for Your Data Automation Takeoff!
Data automation is mission-critical for organizations to sustain competitive advantage today. Manual data processes simply can‘t keep pace with explosive data growth across scattered sources.
Automating error-prone manual work allows your data teams to focus on critical analytical and innovation initiatives that truly move the needle for your business.
Now is the time to start assessing your automation opportunities, proving value through targeted initiatives, and gradually scaling. With the right strategies, you can ensure data fuels decisions intelligently without becoming an Achilles heel.
What data processes are top automation priorities for your organization today? Feel free to reach out if you would like help jumpstarting your data automation journey! I would be happy to offer strategic guidance based on proven experience.