Bringing Order to Chaos – the ML edition

Bringing Order to Chaos – the ML edition

Machine learning is set to be central in tapping the gold mine of unstructured data. Here’s how:

According to a 2021 report from the International Data Corporation (IDC), “the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage.”

Now, most of this data – almost 80%, in fact – is set to be unstructured, i.e. datasets not following conventional models or stored in a structured database format. It can be either human-generated, such as social media, e-mails, mobile data etc. or machine-generated, i.e. scientific data, digital surveillance data, sensor data, sensor images etc.

Companies today are continuously working to find new and innovative ways to ‘manage, analyse and maximise’ the swathes of available data ranging in everything from artificial intelligence (AI) and business analytics. But decision-makers, according to technology magazine VentureBeat “are also running into an age-old problem: how (to) maintain and improve the quality of massive, unwieldy datasets?”

The answer is, of course, machine learning

Advancements in ML technology are all set to now enable organisations to process unstructured data more efficiently and up their quality assurance game. Experts opine that the value of creating timely, accurate and consistent data for modern enterprises could eventually turn out to be as vital as something like cloud computing and digital applications. In spite of this, poor data quality and management practices still cost companies an average of almost $13 million annually.

To navigate through these issues, one may apply “statistical methods to measure data shapes, which enables (…) data teams to track variability, weed out outliers, and reel in data drift. Statistics-based controls remain valuable to judge data quality and determine how and when you should turn to datasets before making critical decisions.

(Yet) while effective, this statistical approach is typically reserved for structured datasets, which lend themselves to objective, quantitative measurements.” When there are several types of unstructured data types at play, it becomes relatively easy for inaccurate or incomplete information to find their way into models. VentureBeat writes:

“When errors go unnoticed, data issues accumulate and wreak havoc on everything from quarterly reports to forecasting projections. A simple copy and paste approach from structured data to unstructured data isn’t enough — and can actually make matters much worse for your business.”

How machine learning can help

Consider, as an example, an ML model that is trained to recommend rules for data cleansing, profiling and standardisation, thereby making efforts to classify unstructured data types more efficient and precise, especially in industries such as insurance and healthcare. ML programs, to this end, can already identify and classify text data by sentiment or topic in unstructured feeds – such as on e-mails or social media records. Here are certain aspects to keep in mind whilst using ML for data quality assurance:

  • Automation is key: Automating regular manual data operations such as data decoupling and correction (which often turns out to be rather time-consuming and tedious) can be a very prudent way of opening up time for data teams to focus on more pressing and productive efforts.

Automation, ideally, needs to be incorporated straight into the organisation’s data pipeline – prepped with standard operating procedures and governance models to facilitate a streamlined flow of predicted processes around any central automation activities.

  • Human oversight still a necessity: Although machines can be trained accurately, the intricate nature of data will always require a level of expertise and complexity that only humans can provide. Ideally, tech teams need to learn to leverage technology whilst maintaining regular individual data processes.
  • Detecting root causes: VentureBeat writes:  “when anomalies or other data errors pop up, it’s often not a singular event. Ignoring deeper problems with collecting and analysing data puts your business at risk of pervasive quality issues across your entire data pipeline. Even the best ML programs won’t be able to solve errors generated upstream — again, selective human intervention shores up your overall data processes and prevents major errors.”

Quality isn’t always assured, either. It is imperative that unstructured data is qualitatively measured by self-made tests to ensure it can add value. Only 18% of companies currently leverage unstructured data – data quality is the central aspect holding them back. Once quality controls are established however, the repository can be an exceptional tool for business growth.

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