What is Data Productivity?
Data productivity refers to the application of an organization’s data assets to increase business productivity.
Why is Data Productivity Important?
Productivity is essential as teams are often pushed to try and do more with limited resources. Organizations now have unprecedented volumes of data and the means to store and analyze it cost-effectively. Data is a corporate resource not to be squandered because competing businesses may exploit their data better to your detriment. Wasting the opportunity to explore and learn from data resources can hurt innovation, profitability, and competitiveness.
Increasing Data Productivity
Most data assets have value. Below are more valuable data sources and their potential contribution to business productivity.
Customer data insights are invaluable. Web monitoring logs make sales and marketing functions more productive by allowing them to focus on prospects that already know the brand. Depending on which pages they visit or digital assets they download will help advise what next steps will most likely lead to a conversion.
Application developers need to know where customers’ transactions falter due to poor user interface design or bugs that stop them from completing tasks. Customer support needs the ability to playback failed transactions to troubleshoot issues. This data increases support productivity and customer satisfaction.
Visitor log files are critical for maintaining security. Denial of service attacks must be detected quickly so they can be blocked before they interfere with valid customer transactions, which can result in lost revenue.
External customer data such as social media feeds and surveys increases productivity if mined to improve service and manage reputational damage.
Sales data can be analyzed to increase productivity by performing basket analysis to find hidden associations between products which can be used for direct product placement in a retail outlet. In the case of a self-service online store, the same basket data can be used to drive recommendations that increase revenues.
System Log Data
Proper management of system logs data increases productivity by decreasing downtime of critical infrastructure services. Analyzing logs for spinning disks can expose increases in soft failures detected by the device and mitigated. As these error rates rise, the failing devices can be flagged for replacement and engineers alerted. Service uptime is often a critical metric that Service Level Agreements (SLAs) track. Maintaining high customer satisfaction and meeting SLA objectives contributes to the organization’s overall profitability, as resources can be focused on getting new customers.
Businesses can use financial data in creative ways to boost productivity. Pricing is a critical process to maximize revenues while maintaining minimal customer attrition. When a new product is launched, it must find its market price. The less productive approach is to set a price and how much salespeople must discount to win against rivals. After many sales cycles, finance data is used to perform a win-loss analysis and determine what discounts were applied to win. The actual sale price is often referred to as the street price, which customers are willing to pay. This eventually becomes the set price with discounting built-in so sales can spend less time haggling. The significant productivity gain is when setting self-service priced products based on what was learned from the previous deals.
The connected car is a productivity revolution for automotive manufacturers such as Tesla. Health information from hundreds of sensors provides data streams to the local neural network and a central system that uses machine learning to perfect autonomous driving. The car temporarily removes functions as sensors become unreliable. When the car is washed, cameras and functions that rely on these sensors recalibrate to work again without calls to roadside services or a visit to a service center.
In manufacturing, Quality Assurance sensor data is used to see how far production can be pushed before quality suffers. Tesla’s China factory now produces a new car every 40 seconds.
What Are the Processes Used to Enable Data Productivity?
Gaining data productivity is a multi-step process that includes the following:
Connecting to Data Sources
DataConnect is an example of a data integration solution that connects multiple source systems using pre-built connectors to data sources, including databases, flat files, logs, social media feeds and operational applications such as NetSuite, Salesforce, and ServiceNow.
Many data pipelines must move, clean and transform data to enable analysis.
Data much be stored on-prem or in the cloud before being analyzed.
The Actian Data Cloud contains the tools to analyze data using SQL queries, built-in summary functions, and user-defined functions to execute programs that perform calculations.
Business Intelligence Systems
Visualizations are the key to easily communicating findings in the data. Tableau, Qlik and Power BI are examples of BI tools that increase data productivity through real-time dashboards.
Actian Data Platform and Data Productivity
The Actian Data Platform forms the backbone of any Data Productivity solution initiative. Capabilities include built-in connections to hundreds of data sources, orchestration and scheduling of data pipelines, efficient cloud storage and connectivity to BI tools.