Grow Sales Using a DataOps-Powered Customer Data Platform
Part 2 of 3
Data analytics can help drive corporate growth by providing customer analytics and ultimately actionable insights to the sales and marketing teams. Unfortunately, the fast-paced, dynamic nature of sales makes it difficult for the customer-facing teams to tolerate the slow and deliberate manner in which analytics is typically produced. In our last blog we identified seven major challenges of customer analytics:
- The Goalposts Keep Moving — Sales and marketing requirements change constantly and the requests for new analytics never cease.
- Data Lives in Silos — Data is collected in separate operational systems and typically, none of these systems talk to each other.
- Data Formats are not Optimized — Data in operational systems is usually not structured in a way that lends itself to the efficient creation of analytics.
- Data Errors — Data will eventually contain errors, which can be difficult to resolve quickly.
- Bad Data Ruins Good Reports — When data errors work their way through the data pipeline into published analytics, internal stakeholders can become dissatisfied. These errors also harm the hard-won trust in the analytics team.
- Data Pipeline Maintenance Never Ends — Every new or updated data source, schema enhancement, analytics improvement or other change triggers an update to the data pipeline. These updates may be consuming 80% of your team’s time.
- Manual Process Fatigue — Manual procedures for data integration, cleansing, transformation, quality assurance and deployment of new analytics are error-prone, time-consuming and tedious.
Overcoming the Seven Challenges
If you have managed an analytics team for any period of time, you have likely encountered these and similar challenges. However, you don’t have to accept the status quo. It is possible to implement processes and methodologies that address these challenges and enable your data-analytics team to improve their productivity by an order of magnitude while achieving a higher level of data quality. In this new approach to customer and market analytics, the data-analytics team executes at previously unimaginable speed, efficiency and quality:
Rapid-Response Analytics — The sales and marketing team will continue to demand a never-ending stream of new and changing requirements, but the data-analytics team will delight your sales and marketing colleagues with rapid responses to their requests. New analytics will inspire new questions that will, in turn, drive new requirements for analytics. The feedback loop between analytics and sales/marketing will iterate so quickly that it will infuse excitement and creativity throughout the organization. This will lead to breakthroughs that vault the company to a leadership position in its markets.
Data Under Your Control — Data from all of the various internal and external sources will be integrated into a consolidated database that is under the control of the data-analytics team. Your team will have complete access to it at all times, and they will manage it independently of IT, using their preferred tools. With data under its control, the data-analytics team can modify the format and architecture of data to meet its own operational requirements.
Impeccable Data Quality — As data flows through the data-analytics pipeline, it will pass through tests and filters that ensure that it meets quality guidelines. Data will be monitored for anomalies 24x7, preventing bad data from ever reaching sales and marketing analytics. You’ll have a dashboard providing visibility into your data pipeline with metrics that delineate problematic data sources or other issues. When an issue occurs, the system alerts the appropriate member of your team who can then fix the problem before it ever receives visibility. As the manager of the data-analytics team, you’ll spend far less time in uncomfortable meetings discussing issues and anomalies related to analytics.
Automated Efficiency — Data feeds and new analytics will be deployed using automation, freeing the data-analytics team from tedious manual processes. The analytics team will be able to focus on its highest priorities — creating new analytics for sales and marketing that create value for the company.
The processes, methodologies and tools required to realize these efficiencies combine two powerful ideas: the Customer Data Platform (CDP) and a revolutionary new approach to analytics called DataOps. Below we’ll explain how you can implement your own DataOps-powered CDP that improves both your analytics cycle time and data-pipeline quality by 10X or more.
Customer Data Platform
A Customer Data Platform (CDP) provides sales and marketing with a unified view of all customer-related data whether internal or external, in a single integrated database. Once setup, a CDP enables the analytics team to create and manage customer data themselves, without reliance upon resources from IT or other departments. This helps sales and marketing better leverage the company’s valuable data while responding to market demands quickly and proactively. The figure below shows how a CDP consolidates data from numerous databases. Each operational database becomes a data source that continuously feeds a copy of its data into a centralized CDP database.
DataOps
A CDP is a step in the right direction, but it won’t provide much improvement in team productivity if the team relies on cumbersome processes and procedures to create analytics. DataOps is a set of methodologies and tools that will help you optimize the processes by which you create analytics, manage the data-analytics pipeline and automatically deploy new analytics and data. DataOps rests on three foundational principles:
Agile Development — DataOps utilizes a methodology called Agile Development to minimize the cycle time for new data analytics. Studies show that software development projects using Agile complete significantly faster and with far fewer defects.
DevOps — In DataOps, new analytics production is automated and monitored. Automated tests verify new analytics before publishing them to sales and marketing users. This allows the analysts to focus less on the mechanics of deploying analytics and more on the creation of new insights that address sales and marketing requests. In the software development domain, the automated deployment of code is called DevOps. Prominent software industry leaders use DevOps to publish software updates many times per second while assuring quality. DataOps incorporates DevOps methods and principles to publish new analytics and data in an automated fashion.
Statistical Process Control — DataOps employs a methodology called Statistical Process Control (SPC) to assure data quality using end-to-end data pipeline automation and quality controls. SPC is a lean manufacturing method that institutes continuous testing on data flowing from sources to users, ensuring that data stays within statistical limits and remains consistent with business logic. SPC monitors data and verifies it 24x7. If an anomaly occurs, SPC notifies the data-analytics team via an automated alert. This reduces the operational burden on team members while improving data quality and reliability. Also, the quantity of data and the number of data sources can more easily scale independently of the size of the data engineering team.
When implemented in concert, Agile, DevOps and SPC take the productivity of data-analytics professionals to a whole new level. DataOps will help you get the most out of your data, human resources and integrated CDP database.
In our next blog, we will provide a high-level overview of how to implement a DataOps-Powered CDP.
