Tying an agile data management strategy to business goals
“I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
These, the words of Sir Arthur Conan Doyle, author of Sherlock Holmes stories, were penned in the late 1800s, and refer to being opinionated without facts, which is certainly a situation we can relate to more than a century later.
Like sailors looking out over a vast ocean, today we’re surrounded by plethora of data -- but with most of it unusable for our purposes, it needs to be filtered and analyzed before it can provide any clear insight to our business leaders.
Most large organizations have amassed many petabytes of data, often stored in inflexible legacy systems that are decades old. Now, as more and more interactions with customers become digital, there is an enormous potential to use data to better understand customers and create superior experiences – tailored to certain segments, demographics, or even to single individuals.
As we evolve from regular business processes to digital businesses, those companies that do not have a sharp focus on data will fail to keep up with their more advanced peers.
Data is absolutely crucial for an organization to realize the promise of “bi-modal” or “dual-speed” IT – a framework in which organizations can achieve resilience and efficiency in their infrastructure-data-processes value chain, while also being nimble enough to adapt to new technology trends. It is essential for today’s winning businesses to keep a dual focus: on both the “run the business” aspect (where the goal is to strengthen the core), and the “change the business” aspect (creating flexibility to innovate).
Strengthening the core requires a professional approach to enterprise data management, data quality, data integration and the delivery of data to the right people in the organization. Considering that most of the applications at the core are systems of record – storing sensitive information, customer data, and regulatory transactional logs – high data quality is of paramount importance.
Creating flexibility to innovate requires paying attention – and responding to -- customer needs. The applications in this realm, “systems of engagement,” must be able to report on customer usage behavior and trends, to allow organizations to respond to changing user needs. Often this comes in the form of real-time visualizations showing the most important information because that inspires confident action, and informs strategic decision-making.
To create both “strength at the core” and “flexibility to innovate,” a proper data management strategy is needed. While building strategy, consider which data to focus on, and how it will be acquired, stored, classified, enriched, distributed and given meaning or context. If properly set up, this strategy not only paints an accurate picture of the past and the present, but also starts to reliably predict events in an uncertain future.
An agile data management approach that considers the role of analytics in both sides of the organization’s bi-modal operations is essential. The system must provide what we refer to as a 720-degree view of the customer and the business. Decision management hubs are setup to process massive volumes of incoming data streams. Ultimately, this powers a highly targeted approach to customer engagement, and automated, streamlined processes to ensure customers receive quality experiences any time they interact with your company.
Overcoming our version of the Sir Arthur Conan Doyle‘s paradox, of being opinionated without data points, is no simple task. It requires a concerted focus on data management, a strategy that is aligned to one’s business goals, and – this is critical -- continually managed and enhanced to ensure it remains relevant as markets accelerate the pace of digital transformation.