6 steps towards healthier data

The value of healthy data is obvious. But how do you build that practice in your own business? The difference between people who live a healthy lifestyle and those who don’t isn’t whether they know how to be healthier — it’s whether or not they prioritize diet, sleep, and exercise in their daily life.

The same is true for your data: if you don’t have the infrastructure that supports your customer 360 initiatives, those initiatives become moot. To establish healthy data practices, roles and responsibilities must be clear, tracking and auditing must be extensive (with minimal friction), and regulations must be seamlessly integrated in core processes.

As you begin to prioritize data health, you will want to build these six key steps into the fundamental fabric of your data management:

• Identification of risk factors: The best way to prepare for the future is to recognize areas of potential risk, before problems arise. This could include internal risks, such as your company’s applications, processes, and employees, as well as external risks, including partners, suppliers, and even your customers.
• Prevention programs: Good data hygiene requires good data practices and disciplines. Responsible labeling and documentation of your data (such as the insight into data trustworthiness provided by the Talend Trust Score™) makes it easier to assess and control the intake of data, producing information that is easier to understand and harder to ignore.
• Proactive inoculation: Machine learning can train your systems to recognize bad data and suspect sources before they can take hold and contaminate your programs, applications, or analytics.
• Regular monitoring: The sooner a data health issue is detected, the better the chances of an effective intervention. Just like medical wearables help us track our health between annual checkups, you should institute a practice of continuous data profiling in addition to assessments of all incoming data and regular batch checkups.
• Protocols for continuous prognosis: Over time a doctor will tweak a prescription, providing more or less medication as the patient requires. We should adopt this philosophy with our data as well: the specifics of any intervention will continuously evolve and improve, but we can’t afford not to have it.
• Efficient treatments: Any medical intervention involves a risk/benefit assessment: the clear advantages to the patient must be weighed against any potential side-effects. But that doesn’t mean you only move ahead when there is zero risk. Good data professionals know how to balance tradeoffs between things like security and efficiency to the net benefit of the company and its customers.

There may not be a single defined universal end state for data health. However, we can make data health a way of life by taking conscious steps along every stage of the data lifecycle, from before it enters the pipeline until it’s used by analysts and applications. By preparing ourselves with the best technology, people, and practices, we can protect ourselves from the most significant— and the most common — threats to data health. For a deeper dive into the value of data health in your C360 initiatives, download the full case study.