The COVID-19 pandemic has accelerated the rise of the digital economy, and with it, the significance of the role that data plays in business organisations. Today’s business operations and processes are fast becoming data-dependent and data-driven.
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 data of good quality 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Bad data refers to data that is incorrect, incomplete, incomprehensible, in the wrong place, irrelevant, and out of date. Practically speaking, poor data wastes sales time, distracts data scientists and consumes IT time spent on syncing systems which cannot communicate to each other. All of this leads to a lack of trust in the ‘numbers’ and a lack of decision making from executives.
Many enterprises struggle with the accuracy of the data that they use for day-to-day activities. No industry or organisation is immune and, if not rapidly remedied, the problem of using bad data can result in serious financial and reputational loss.
Data builds customer insights, which can make or break brands and have a significant impact on a company’s bottom line. Unfortunately, many businesses have reported that poor data is the primary hindrance in improving customer experience and as many as 69% have already felt the impact of this. The analysis of bad data can come at a major cost for businesses, with inaccurate insights sabotaging expansion plans or purchases, some worth millions of dollars.
IBM estimates that poor data quality costs the US economy some US$3.1 trillion per year. As companies digitalise, their data and information environments become more complex, indicating that the losses are likely to increase unless the issue of bad data is tackled in a timely manner.
The lack of awareness about the need to nurture data is widespread. While data-savvy companies like Amazon, Google and Grab are using their data to map and model consumer behaviour to serve their customers better, most companies have no clear view of their data. In fact, in a recent IDC survey, more than 80% of IT leaders reported data sprawl - the overwhelming amount and variety of data produced - as one of the most critical problems they face today. Organisations that lack data management incur 66% more operational costs as well. By proactively measuring the value of their information assets, as well as the cost of poor-quality data and the value of good quality data, leading information-driven organisations gain a strategic advantage in the marketplace.
So, what steps can businesses take to clean up their data?
1. Centralise: Cleaning the data stack of bad data is not a simple one-off event; businesses must think long-term. Start by ignoring the different channels data uses to enter the company, concentrate on a centralised strategy for data management, and evolve to ensure detection at the source.
2. Consolidate: Large organisations have multiple databases run by different departments and other data sources that they are unaware of. This is a key factor that is compounded for enterprises with multiple branch locations. Consolidating and identifying databases and information repositories minimises the creation of bad data, aiding standardisation of the company data. The Malaysian Government Central Data Exchange (MyGDX) was launched in 2018 to do just this, by gathering data under an accessible, centralised and integrated system for all government agencies to leverage.
3. Standardise: Analyse your data to understand it better. The most common reason 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, will help to maximise clean data coming into the enterprise.
4. Investigate: Look for corroborating data to baselines and understand the nature of the corruption. This provides an opportunity to fix anomalies and restore the pristine quality of the data.
5. Eliminate: Duplicated data is a major cause of data inaccuracy and occurs as a result of the multiple repositories mentioned earlier. It is then compounded by human error in the process. Use the consolidation process as an opportunity to eliminate duplicates to arrive at the standardised baseline. Getting there may take time but is critical in providing quick access to customer information and improving business intelligence.
6. Sanitise: Cloud platforms, particularly hybrid cloud, provide an ideal environment to clean and sanitise data, with numerous tools.
There is a broad consensus that if used correctly, data can help fuel the enterprise, add true value and greatly benefit the business. What is also becoming clear is that there is no such awareness surrounding bad data, and the harm it can have on the company’s reputation, efficiency and profitability.
As we become more dependent on data as a society, the value of that data will increase. Data is an asset that provides limitless possibilities for enterprises, but only if it is managed properly. Otherwise, mismanaged and mishandled data can potentially create dramatic declines and unfathomable falls.
Malaysia has been investing heavily in the development of its digital economy, expecting it to be a key driver for the nation’s growth. In fact, the government’s 2020 budget includes a RM22 billion investment to strengthen connectivity and the adoption of 5G, digital and business automation.
Besides strong government support, the success of its digital economy will also depend on whether the private sector can inculcate a healthy data culture across industries. Good data, in this case, will be a critical enabler for the country to maintain this growth trajectory and achieve a strong economic rebound post-pandemic.
