Data is literally everywhere, whether it’s generated by first-, second-, or third-party sources. Even content is data. From smartwatches to supermarket scanners, and from online user behavior to advertising data, companies are drowning in information. How can organizations ensure they’re ethically collecting valuable data about their brands and customers, being privacy-first minded, and how can marketers organize that data to derive valuable insights?
Use Data in the Entire Lifecycle of a Product
The best products and services use data throughout the entire product lifecycle. To maximize data’s use, create a strategic plan to capture it and make it available timely to applicable teams. When creating a data-capture plan, recognize the need to use data in all product phases. There are three key pillars for data use in products, services and experiences — Create, Launch and Engage.
When organizations create meaningful products, services, brands, spaces, and experiences, strategic data design should have a front-row seat as part of the process. Products should be designed with all states of data — rest, motion, and use. When it’s time to launch a product, companies should make significant use of their data. Data-driven launches are more successful and can be the difference between a product thriving or failing. The last pillar, engage, is key to driving effective, joyful human engagement with the products. Here, data plays a key role in enabling the objectives of both the user and the organization. Customer engagement and journeys are not deterministic. They are probabilistic where intent is expressed out of band, most of the time. Design for it.
Data’s value changes based on several things, including change of state, recency, and relevance. The value and use of data impacts the brand and customer experience substantially, so companies should plan to collect all three “states” for structured and unstructured data:
- Data at rest (rows in a database, files in a filesystem, precomputed analytics)
- Data in motion (Email, Slack, IoT sensor data)
- Data in use (memory, security credentials, personalization data)
For brands to increase the value of data, increase the amount of data in motion. Maximize the data design and infrastructure for data flows and streams rather than data at rest. Relevance and recency of data should be part of the design; think of data not only from your brand perspective, but as part of the business ecosystem.
Understand the Difference Between Information and Insights
Understand and harmonize data types, their states, and context. And understand the difference between information and insights. While companies can learn a lot from the facts that information provides, it is the insights, or analysis of the data that provide deeper understanding for better decision making, uncovering reasons for behavior, and revealing valuable context. To derive the best value from data, design a nondestructive enrichment and unification process, guarded by a strong consent management system. Investing in a data management platform (DMP), can simplify data integration and management.
There are three types of analytical data:
- Decision making data: pre-computed most of the time, witch which you can see trends and make what-if analysis. This is made out of facts.
- Predictive data: this is probabilistic data plotted on a gradient. This is where all the innovation is happening.
- Descriptive data: this is data about data. It is required if you want to bring in predictive intelligence.
Descriptive data, or metadata, is often overlooked in the design process of systems. Give it the same status as any data. In fact, there are dedicated commercial and open-source products that allow for metadata ingestion, extract, transform, load (ETL), and management. Here’s a pro-tip: to maximize the investment when acquiring third-party data, ask for the metadata too.
Additionally, some data is not important and could cause more harm than good. Have a strategy to manage unused data. By design, include privacy and ethics of data right at the inception of the product. Make privacy and ethics of data part of the data strategy, and build consent-management as the guardrails for data.
Use Data to Optimize the Customer Experience
Data and design intersect and work together to create better customer experiences and outcomes. Data for customer experience has two objectives:
- Apply known and predictive data to enhance customer experience and engagement
- Understand customer experience and engagement
At the strategic design phase, engage data strategy designers to plan for these objectives.
Try to eliminate most of the friction using data and intelligence. But allowing some productive friction — the good kind of friction — is also important. Sometimes this kind of productive friction is called Next-best-offer. Apply data intelligently for this process. Use data to get out of the way or add extra steps, depending on what the data can derive from context.
Design Bias Identification in Data
In the context of privacy, ethics, and bias, use data to correct a path and reengage. Think about how to recognize data “bias” and how to eliminate it. Can “the machines” help eliminate bias? “Challenge” the data.
Today, companies can create systems that provide information about bias if designed with that purpose. Design bias trends that can measure deviations as graphs. From an AI perspective, there are deep neural network architectures like adversarial neural networks that can set adversaries to challenge the prediction of another deep neural network. There are other methods like outlier detection, novelty detection, and anomaly detection that can help identify bias.
Humans need to take an active role in guarding against bias and improving data to reduce it. Data designers need to receive training on cognitive biases and how to prepare data and design systems to reduce bias in their data designs.
Data Drives Stronger Brands
The most successful products and services have been highly influenced by data. To build stronger brands, organizations need a plan to capture data and make it part of the product lifecycle. Plan for it. Develop a strategy to capture data throughout the entire process. Recognize the need to use data in all product phases — Create, Launch, and Engage. For organizations that create meaningful products, services, brands, spaces, and experiences, strategic data design should have a front row seat in the entire process.