Christian Buckley, Founder & CEO of CollabTalk, argues for a better infrastructure when it comes to creating our own data management solutions – starting with the way we think. Siloed thinking, by definition, creates gaps in our ability to identify and solve the right problems based on consideration of the whole enterprise, the bigger picture. As a result, we limit the ability to create solutions that scale to meet our needs. Read on and don’t miss the case study at the end – an application to social media.
Many fast food restaurants have found that limiting the number of choices in their menus actually drives sales. Research by Columbia University and others has shown that when confronted with too many choices, customers struggle to make any decisions — and end up purchasing less. Additionally, a limited menu can mean a more streamlined and efficient kitchen, a much less complex supply chain for products and services (fewer suppliers might mean lower costs), and the ability to run operations with fewer personnel.
In database management, limiting your data set can improve performance and accuracy of results and reporting, because the limited scope allows you to refine and focus on specific data types and functions, and specialize the types of solutions you deliver. A system or tool utilizing data that has been organized and optimized for specific queries, retrieving specific results, will perform better than a system that must query across multiple databases to find and present the relevant data set. The downside to this specialization, of course, is that the cost of expanding beyond these focused solution can be far greater.
I spent much of my first decade in information technology working closely with internal customers to slice and dice their data so that the specific results they needed were shown quickly. However, much of my time was inevitably spent going back into the system to add additional data (geographic, biographic, or psychographic data), and to join or index various data sources to improve query performance and to leverage relevant customer and product data.
And because the data was becoming more and more complex, the front-end tools used to access the data became more complex — we were constantly adding third-party and custom tools and reports to improve access and get more value out of this data. The almost constant reworking and shaping of data and systems came at a high cost in people, hardware, and time. Historically, the larger the data set, the slower the performance of our data queries (compute and processing time).
Because of this, what tends to happen is that end users become frustrated with the performance and request specialized, focused solutions for their own team or project, creating yet another data silo focused on short-term needs. And the process repeats itself.
Rethinking The Silo
Human nature is to solve the problems right in front of us. More difficult is to look at our problems more holistically — to step back and think about these issues long-term, and how else this data might be used. An operations team will look at solving their own needs, regardless of the potential benefits to their support or engineering teams. Likewise, support will develop solutions for support, and engineering for engineering. Architecting our systems holistically (and planning our businesses using systems-based thinking) can be difficult and time-consuming, but are necessary if we are to prepare ourselves for the unknowns.
We are siloed in our thinking about business problems, not just the data. It is understandable, since one team is not typically measured on the activities or optimization of other teams. From this perspective, not all data silo issues are bad. A smaller sub-set of data, or a spot-solution, allow us to act more quickly, developing responses that solve the problems at hand. But we can’t lose sight of the broader goals while chasing after short-term solutions.
A Way Forward
We are at a major inflection point in the history of information technology, with pressures around decreasing costs and headcount, pushing data and functions to the cloud, to enable more social capabilities, and to build out mobility solutions.
The danger is to fall into the data silo gap, and build out solutions that do not truly scale the enterprise, that do not look beyond solving the smaller problems before us without considering the long-term requirements of the business. Much of the focus of company leadership is on solving the short-term problems: lowering capital expenditures, reducing operational costs, increasing revenue, and so forth, through cloud, social, and mobile strategies. But many of these decisions are made without fully understanding the long-term costs of these decisions.
CASE STUDY …Are we creating cultural silos? Follow the link as Christian offers a further thought on Social Technology & Short-term Thinking to demonstrate how we may, in fact be creating new cultural silos and missing out on the opportunity to align social data to other systems, social data that can add significant context to our content.