Three methods to improve organizational data quality today
Our modern business landscape transforms constantly, yet the value of data endures. However, thriving companies don’t merely amass data -- they cultivate meaningful data.
Leaders must have trustworthy data to unlock organizational insights, but inaccurate data is still rampant in most organizations. This data costs leaders far more than missed opportunities. According to industry research, bad data costs U.S. enterprises $3.1 trillion annually. Still need convincing about the benefits of data quality? Let’s discuss the benefits of high-quality data and explain how leaders can adopt a leading data management strategy today.
Why leaders should prioritize data quality today
High-quality data is the backbone of sound-decision making and, therefore, of good business practices. Data specifically aids leaders in the following ways.
1. Improved decision-making
Making decisions without accurate or complete data is risky and often leads to poor outcomes. For example, leaders operating on bad data may alter strategy in a non-intuitive way that alienates customers. Conversely, high-quality data promotes informed choices, leading to better results and a competitive advantage. With good data, leaders are always in tune with consumer sentiment.
2. Increased efficiency
Clean, accurate data streamlines processes, reduces errors and improves overall efficiency. With reliable inputs, organizations can optimize operations, automate effectively and predict consumer or client needs. Resources reach where they have the most impact, and customers enjoy smooth, customized service.
3. Reduced costs
Quality data not only unlocks insights but also saves resources. How? By enabling organizations to more intelligently align their operational strategies with reality. Data can easily illuminate which existing processes are working and which may need to be edited or axed. Or, leaders can use data to understand traffic patterns and schedule staff more cost-effectively. Essentially, good data opens the door for cost savings across the enterprise.
4. Higher customer satisfaction
Generic experiences no longer captivate customers -- personalization is now the expectation. McKinsey research suggests that 71 percent of consumers expect retailers to provide customized interactions.
Yet mass personalization requires keen customer clarity. Quality data unravels nuanced needs, illuminating opportunities to curate distinctive value. With precise insights guiding their decisions, leaders can intimately understand their customers, predict desires and sculpt solutions specifically suited to each individual.
5. Increased revenue
Efficient operations fuel expansion, and satisfied customers drive revenue. Ultimately, high-quality data links to financial success no matter how you slice it. But how can organizations achieve data excellence?
How to improve your data management strategy
1. Implement a data quality program
A formal data quality program lays the foundation for a successful long-term analytics strategy. It establishes guidelines, workflows and accountability required to cultivate excellent data. Cross-department data standards clarify expectations around accuracy, formatting and governance. Documented processes guide employees in upholding standards through data entry, analysis and reporting. Clearly defined roles ensure oversight and ownership for sustaining quality amid business evolution.
With mature data quality infrastructure, organizations transform scattered, siloed efforts into an integrated mission centered on trustworthy information. Constructing this groundwork enables reliable data at scale.
2. Invest in data quality tools
Sophisticated data quality tools are pivotal in combating outdated or bad data. By automating standardized processes, these solutions eliminate inconsistencies that creep in through repetitive manual inputs. Algorithms surface abnormalities hidden within massive datasets more reliably than human eyes. Together, automation and analytics drive efficiency, consistency and oversight. This function is critical, especially as the amount of global business data balloons.
Leading tools will provide data dashboards that enable continuous monitoring, displaying information flows in real-time to catch deviations from quality benchmarks. Powerful analytics generate deeper insights as well, assessing various dimensions of quality. From pinpointing errors to identifying root causes and prescribing corrective steps, the advanced analytics of quality tools optimize data operations.
Preserving stellar data requires scalable precision -- a promise only innovative technologies can fulfill. Of course, employees still play a critical role. By leveraging master data management (MDM) platforms as partners, not just tools, employees can unlock scalable and successful growth for their organization’s business strategy.
3. Cleanse and deduplicate data
Regular data hygiene sustains quality in a highly dynamic business landscape. Leaders cannot simply “set and forget” their data, as it transforms rapidly -- we’re talking minute by minute. Cleansing your data defends it against inaccuracies. Meanwhile, deduplication eliminates redundancies by consolidating overlapping entries, resulting in a more streamlined data set. This function protects against misinformed decision-making and also saves organizations money by eliminating unnecessary records.
Better data, better insights and a better tomorrow
High-quality data is essential for business success. While many organizations collect large volumes of data, value cannot be extracted from these reserves unless the information is accurate and reliable. Quality data is trustworthy and enables better decision-making, improved efficiency and increased revenue.
Every business should invest in skills and systems that cultivate quality data. With a sustainable, enterprise-wide effort to data cleansing, leaders can unlock immense benefits -- from nimbler operations to happier customers and accelerated financial growth.
Brett Hansen is responsible for Go-to-Market operations at Semarchy, including marketing, business development, and alliances and partnerships. Before joining Semarchy, he was the CMO at Logi Analytics, which was acquired by Insight Software. He spent eleven years at Dell as an executive leading software product and GTM in Dell Client Group, and prior was with IBM in various marketing and channel leadership positions.