In the 17th century, a banker named Sir Henry Furnese gathered information about world events before his competitors could and used it to make strategic business decisions—and thereby made handsome profits. This was later described as business intelligence or BI. The meaning of the term has remained more or less the same (except now BI refers to the technologies for analyzing and interpreting data, too), but the strategies and scopes have changed drastically since.
The advent of the Internet and the proliferation of personal computing is what gave the impetus for change. Early access to information, though still advantageous, is no longer decisive. It is using existing data—internal and external—to unearth insights and opportunities and capitalizing on them that give an edge. In short, BI is the winning factor. Cleansing data is the foundation that underlies this.
Costs of not cleansing data for BI
The dearth of data hardly remains a challenge in BI any longer. It is, in fact, the abundance of data that may now be considered problematic—especially given that a significant percentage of it is dirty, i.e., incomplete, inaccurate, or inconsistent. This makes them unsuitable for analysis and reporting.
Dirt in data can have several negative consequences for business intelligence and impact organizations at various levels—operational, tactical, and strategic. Inefficient decision-making processes, waste of time and resources, flawed analyses which can lead to loss of trust and may require costly revisions, and missed opportunities to name just a few. These may not only adversely affect business competitiveness but can have deleterious effects on the organization, such as employee demoralization and mutual distrust between different departments.
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Cleansing data helps avoid these potential perils by ensuring that the data used for analysis and reporting are accurate, reliable, and trustworthy.
Significance of data cleansing for BI
Business intelligence starts with data acquisition. The data gathered will not immediately be ready for analysis. They have to be cleansed and validated, and if necessary, enriched and transformed, so that they can be meaningfully and gainfully used for a business intelligence project. Cleansing helps optimize the performance and efficiency of BI systems as it reduces the data volume, complexity, and redundancy.
The cleansing and enrichment of raw data make for better business intelligence which results in increased operational efficiency, improved customer relationship management, greater regulatory compliance, enhanced data visualization, more accurate diagnostic and predictive analyses, and greater trust in data. This in turn helps unearth tactical, operational, and strategic insights and facilitates better decision-making.
A clearer picture emerges when we focus on some of the effects of data cleansing on business intelligence more minutely.
Operational optimization
Cleansing data is foundational. Cleansing ensures that the insights derived from the data are accurate and reliable, and reflects the actual state of operations. This helps identify areas where resources are underutilized or where processes are inefficient. You’d then be able to effectively allocate resources, streamline operations, and increase overall performance.
Without cleansing, the analysis will almost certainly be vitiated by dirty data. The results will thus be dubious, and potentially flawed. Optimization efforts taken with faulty insights may be positively harmful and definitely suboptimal.
Performance monitoring
Accurate metrics, reliable benchmarks, and actual patterns inform performance assessment. Data that have been cleansed make it possible to establish accurate performance metrics and provide a reliable baseline for benchmarks based on accurate historical data. This minimizes the chance of misinterpretation of performance results and misguided actions.
Diagnostic and predictive analytics
Cleansing data fosters the accurate identification and resolution of issues and potential risks. Without clean data, the diagnostic and predictive analyses are either compromised or nebulous. Cleansing reduces the risks of bias that may arise from inaccuracies and inconsistencies and ensures that the issues identified are genuine and not a result of faulty data.
Visualization and reporting
Noise and dirt are rampant in raw data. They distort visualizations and can cause misleading interpretations. This can result in flawed conclusions and decisions. Cleansing ensures this does not happen.
With clean data, the visualizations precisely represent the underlying information. Visuals of clean data are neater and thus more easily interpretable. This makes quick identification of insights possible and also makes reporting easier.
Customer insights
Clean data are key to fastidious analysis that helps unravel customer behavior, preferences, and trends. And this information is vital to understand customers and tailor products and engagement according to the vagaries of the market. This enables you to anticipate customers’ needs and act proactively, whether that be targeting a prospect or reducing customer attrition.
Cleansing data: Doing it well but without the tedium
There exist plenty of sophisticated business intelligence tools and vast amounts of data that organizations can capitalize on. There is thus a tendency to think that the colossal data size invariably translates to better intelligence and insights. Cleansing is therefore often liable to be overlooked—or done poorly. But more is not always better—accuracy matters more than abundance.
The oversight is understandable. Data cleansing is tedious and unglamorous. It, as the old but perennially valid saying goes, eats up about 80 percent of data scientists’ time, leaving them with little time for analysis. This is a waste because the task can be effectively outsourced to B2B data cleansing service providers. They provide cleansing and business data processing services at reasonable costs while saving you an enormous amount of time. Outsourcing lets you shift the emphasis from cleansing to analysis.
With clean data, analyses are reliable and interpretations more robust. This becomes the source of truth that you and everybody can trust—which is critical.
