Gartner research has found that organisations believe poor data quality to be responsible for an average of $15 million per year in losses.
By Neville Vincent
With the rise of the digital economy, data has become an organisation’s most valuable asset.Across industries, companies are busy developing strategies to identify, capture and optimise the use of data in business decision making. The hidden problem many companies face is that while good quality data is a true business enabler, bad data can set back research, reduce or destroy competitiveness and hinder innovation.
Bad data refers to data that is incorrect, incomplete, incomprehensible, in the wrong place, irrelevant, and out of date. Poor data wastes sales time; distracts data scientists and consumes IT time synching systems that can’t communicate to each other. All of which leads to a lack of trust in the “numbers” and a lack of decision making from executives. No industry or organisation is immune and if not rapidly remedied, it can result in serious financial and reputational loss. And as customer experience begins to define brands – bad data can have significant impact on the bottom line.
Gartner research has found that organisations believe poor data quality to be responsible for an average of $15 million per year in losses. While data savvy companies like Amazon, Google and Airbnb are using their data to map and model their customer behaviour so as to serve them better; most companies have no clear view of their data.
Nearly 60% of organisations don’t measure the annual financial cost of poor-quality data. Gartner highlights that leading information-driven organisations proactively measure the value of their information assets, as well as the cost of poor-quality data and the value of good quality data. This gives them an advantage in the marketplace.
So, what steps can today’s CIOs take in order to clean up their data?
A) Centralise: Cleaning the data stack of bad data is not a simple one-off event – think long term. Start by ignoring the different channels data uses to enter the company and concentrate on a centralised strategy for data management – and evolve to ensure detection at the source.
B) Consolidate: Large organisations have multiple databases run by different departments as well as other data sources that they are unaware of. Consolidating and identifying databases and information repositories minimises creation of bad data, aiding standardisation of the company data.
C) Standardise: The most common reason why companies end up with bad data is a lack of standardisation in the collection process. Using a standardised set of parameters, not only within the company but also with suppliers and partners help to maximise clean data coming into the firm.
D) Investigate: Understand the nature of the corruption in the data. Look for corroborating data to baseline and understand the nature of the corruption. This provides an opportunity to fix anomalies and restore the pristine quality of the data.
E) Eliminate: Duplicate data is a major cause of data inaccuracy and occurs as a result of the multiple repositories mentioned earlier. It’s then compounded by human error in the process. Use the consolidation process as an opportunity to eliminate duplicates in order to arrive at the standardised baseline.
F) Sanitise: Cloud platforms, particularly Hybrid Cloud, provide an ideal environment to clean and sanitise data – with numerous data cleaning tools available.
If used correctly, data can help fuel the enterprise, add true value and greatly benefit the business. Mismanaged and mishandled, it has the ability to create dramatic declines and unfathomable falls.
In a data driven future – knowing the cost of bad data could become a matter of survival for every enterprise.
The writer is vice president, ASEAN, India, ANZ at Nutanix