The Problem of Plenty: How the Right QMS Can Manage the Proliferation of Data Capture

It’s called “the problem of plenty.” i

A consequence of the proliferation of data capture, the problem of plenty is simple: so much data is being captured on a day-to-day basis from so many different sources that most data systems are unable to do much more than dump it into a data warehouse and let it sit there, decaying in accuracy and value.

Existing processes and structures were designed to accommodate much smaller amounts of data. While different data sources still existed, their reduced number made managing disparate structures less of a cumbersome process; unstructured data could be easily captured and analyzed. But the advent of powerful CRM, ERP, and other BI platforms has raised a fundamental wall: there are too many kinds and sources of data to keep up with, and many modern enterprises are simply unable to meaningfully interpret the mountains of data piling up within their storage structures. ii

The problem of plenty is at heart a reductive problem; in failing to recognize and account for disparate types and uses of enterprise data, it all ends up treated identically. This oversimplification of the technological complexities – “Data is data! Let’s just store it all in one place!” – has led to rendering that data useless. It can neither be retrieved nor utilized in a timely fashion, so companies are almost always operating with obsolete data and making decisions based on ground-level conditions which no longer exist, and as such, are unable to adequately plan for the future.

The dilemma is this: a data storage and retrieval system that doesn’t fully support or enable an organization’s business goals is little more than a liability. Flat-architecture data warehousing is fundamentally unable to tackle the scope of data generated every day by dozens of incompatible business intelligence tools. It can total hundreds of terabytes.

Instead of serving as a valve that makes information actionable, it serves only as a chokepoint, suffocating future efforts. Inadequately structured and organized data can cripple a company out of the gate, preventing the adoption of new technologies and processes; for example, the advance toward predictive capabilities is aptly characterized as a “gold rush,” in which those who can’t participate are simply left behind. Current market conditions are in an ongoing evolutionary state powered by the increasing sophistication – and proliferation – of robust analytical platforms attempting to capitalize on this market desire.

In these circumstances, it’s clear that simple data warehousing is a highly reductive approach to data storage and retrieval predicated on the simple assumption that all data is created equal. Retiring this idea is a competitive necessity.

Hope is emerging in the rise of powerful data synchronization tools. Complex, intelligent programs, these tools work to flatten, reorganize, prioritize, and streamline data to make it more accessible, solving the problem of plenty before it happens. By analyzing internal relationships between data sources, its output, company organizational structures, and its interior formatting, data synchronization tools achieve the Golden Mean: accurate, comprehensive single-view data sources that any QMS can use.

The goal is to solve the critical imbalance that the data you have often isn’t data you can use, which is why these kinds of tools focus on turning mounds of data into assets that actually offer real business benefit, and are able to be quickly and easily utilized for critical operations reporting. That means that instead of turning around a report six weeks after you request the data, you can do it the same day.

The key, then, is to keep focused on data accessibility, utilizing automated operations to analyze, understand, and flatten data from multiple sources, creating a consistent single source of truth regardless of the data source. Conducted without data loss, data synchronization tools map data fields against broad spectrum categories for ease of search; this process can reduce hundreds of (often redundant) data columns into a single unified, harmonized format.

The benefit of this process should be immediately clear: data that is accessible is data that can be adequately utilized. By centering data accessibility, we create conditions for smarter, nimbler, and faster decision-making by making sure that people have the data they need right when they need it, without going through six weeks of trial and error first. iii

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