Big Data and analytics have changed the rules of engagement. By offering unprecedented insights into consumer preferences and behavior, Big Data has added exceptional firepower to decision-making.
One well-known example is that of Netflix, which started as a DVD-by-mail service in 1998. Its website, supported by an algorithm-based program, would analyze customer video preferences based on their past rentals, and make recommendations. Over time, by adding insights collected from other users, Netflix was able to predict what its customers would want to rent next. The result – a deeply loyal and growing customer base. In January 2016, in a letter to shareholders, Netflix reported it had over 75 million subscribers, including 44 million in the US. Today, Netflix is a global provider of streaming services, and also produces hundreds of hours of original programming around the world.
Customer experiences happen both online and offline, and customer data is collected from smartphones, mobile apps, POS systems, and e-commerce sites. Businesses can now integrate enormous amounts of non-traditional data with existing customer data. The ability to collect and analyze data from more channels – together with the ability to collate more types of data – allows businesses to ask what works and what doesn’t work, and why. The result is highly-personalized customer profiles and not merely customer target groups.
However, such dream runs are not fueled simply by sophisticated algorithms. Businesses may have the best technology in place – social listening engines, automation scripts, database feeds, connected systems, and the works. But, without the right analytical model and assurance framework, these technologies won’t take the enterprise too far.
A robust assurance framework ensures that businesses start with data that can be trusted, data processing algorithms that are authentic and insights that are actionable. This in turn leads to delivering superior customer experience, thereby transforming your customers into your fans.
In this blog, I am sharing with you four must-haves for the assurance framework to deliver marketing insights from Big Data analytics:
- Start Early To minimize Data Gaps: Assurance teams must start early and partner with marketers at the outset, to identify, design, develop, and implement data cleansing techniques. Together, Assurance and Development must also put in place, data quality checks to be applied on stored data, before it is used as an input for the analytical model.
- Ensure Static Simulation of the Analytical Model: The role of an analytical model extends much beyond input data structures, data processing algorithms, and report definitions. Besides being adaptive to new data sources, the model must be scalable and flexible, facilitating changes on the fly. It must be able to extrapolate identified patterns from historical data, and predict future business scenarios, use cases, and customer behavior. Static simulation of the analytical model, with different data variables, helps ensure the fluidity of the analytical model as it is built.
- Use Automation to Accelerate: Pre-defined, configurable test cases, coupled with analytical model based automatic test data generation help accelerate the end-to-end data value chain testing.
- Agile Delivery Mechanism Agile delivery methods help foster closer collaboration between the Business, Development and Testing teams, and ensure quicker availability of the production-ready Data-Driven-Analytical Models as they evolve.
Embracing Big Data is also about embracing big changes in your approach to Quality Assurance (QA) and testing. QA professionals have the added responsibility of understanding how Big Data influences the behavior of customers, and combining knowledge of QA & Testing with the ability to distill Big Data driven insights into customer delight. Businesses that have succeeded with Big Data are those that have also realized the importance of an assurance-driven framework. Where does your business stand? Maybe its time to evaluate, and re-think your Assurance for Big Data strategy.