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From Dirty Data to Actionable Insights: The Impact of Data Cleaning in BI

Business intelligence (BI) tools can provide deep insight into your business and your customers. However, the data you get from your BI tools are only as good as the data you put in. As they say, garbage in, garbage out (GIGO). Poor quality data produces faulty results and, unfortunately, it’s a common occurrence. Gartner estimates that bad data costs businesses $12.9 million annually. While that’s far better than IBM’s estimate of $3.1 trillion in 2016, we’ve still got a long way to go.

Accurate, clean data is crucial for extracting meaningful insights. With the right data cleansing process, you can transform raw, messy data into a reliable asset for a competitive advantage.

The Challenges of Dirty Data

As data is collected from increasingly diverse sources, often without standardized formats, preparing and cleansing data becomes more complex. Even with advances in technology and AI, data wrangling and preparation can still take up as much as half of data scientists’ time.

Yet, without a robust data cleansing strategy, data is compromised in several ways.

  • Inaccurate Reporting and Analysis: Errors and anomalies in data propagate through reports and models, producing incorrect metrics and trends.

  • Difficulty Combining Data Sources: Inconsistent data makes it challenging to integrate and compare datasets. Conflicting formats, definitions, labels, and values fail to provide a consistent, holistic view.

  • Duplicated Data Distorts Results: Redundant or repeated data skews analytics by overrepresenting certain records. Counts, segments, and metrics fail to reflect unique data.

  • Incomplete Data Causes Blind Spots: Gaps in data lead to blind spots in analysis. Missing fields, sparse datasets, and underrepresented groups bias insights and gain lead to poor decision-making.

  • Data Decay: Over time, data quality can decay through human error, outdated information, or broken integrations. Unless data is cleansed and normalized, the quality can deteriorate.

The Data Cleaning Process

Transforming raw data into valuable — and actionable — insights requires several key steps, including:

  • Assessing Data Health: Analyzing datasets to detect anomalies, inconsistencies, errors, and duplication across fields.

  • Fixing Structural Errors: Resolving formatting issues and parsing problems, and standardizing schemas makes data consistent.

  • De-duplication: Identifying and deleting duplicate entries.

  • Accuracy Verification: Validating accuracy by spot-checking data against trusted sources and looking for outliers and anomalies.

  • Data Enrichment: Adding context through categories, tags, linked entities, and other metadata to enhance relationships.

  • Data Monitoring: Providing ongoing monitors and audits for sustainable quality.

The Value of Clean Data

Today, data collection is expanding at a staggering rate. About 120 zettabytes of data are created, captured, copied, and consumed annually. That’s nearly double the amount from 2020. 70% of companies say the amount of consumer data they collect has increased, according to a KPMG survey. Smart devices, IoT, sensors, and other tech are contributing to this exponential growth in data. Cloud computing has made data collection simpler.

Many companies emphasize data collection with the assumption that the more data that are gathered, the more significant the outcome. While you do need a large enough sample size to draw insights, it’s the quality of the data gathered that makes it reliable.

The value of clean data includes:

Better Decision-Making

Clean data enables trusted analytics. When data has been validated, cleansed of errors, and optimized structurally, BI tools can build a solid foundation for data models and analysis. Clean data inspires high confidence levels in the insights produced. This confidence helps decision-makers move forward instead of worrying about whether the data is accurate. Thus, actionable insights can be extracted more quickly to accelerate decisions.

Benefits Across the Organization

High-quality data also provides benefits across an organization. When data is standardized and duplicated, diverse data sources can be unified. This provides a more holistic view of data and eliminates siloed data that can provide conflicting insights.

Easier Compliance

Rigorous data practices make compliance with data regulations and governance easier. Consistent documentation and reporting on data quality, error correction, and data masking for privacy can help meet compliance and governance standards.

A Competitive Advantage

Quality, clean data provides a competitive advantage. You get improved business intelligence to make more informed decisions about your operations and customers.

When you can trust the insights you’re getting, it gives you the confidence to move forward. This translates to improved customer experience, operational efficiency, and data-driven innovation. Clean data can also significantly reduce risk. Accurate, trustworthy data helps identify potential risks and evaluate initiatives.

Trustworthy data also enables personalization when it comes to marketing and sales and there’s nearly universal agreement that personalization pays off. McKinsey research shows that data-driven personalization can cut customer acquisition costs in half and grow revenues by 5% to 15%.

These gains are only realized with clean data.


Real-Time BI Solutions for Optimizing Data Insights

Wyn Enterprises is a real-time BI solution that produces exceptional insights from quality data. With no data limitations or per-user fees, Wyn Enterprises provides a transparent, affordable solution to other BI solutions. Designed for self-service BI, organizations can reduce their dependence on IT teams and data analysts to create detailed visualizations, freeing team members up to work on higher-level work. Embedded dashboards and reports flow seamlessly into your own applications while eliminating data siloes and establishing a trustworthy, single source of truth from disparate data sources.

Wyn Enterprises is built for embedded BI to quickly find actionable insight and make better business decisions. Watch this short video demo to learn more or try the Wyn Enterprise BI solution for free.

Dan Columbus

Dan is the Director of Enterprise Sales for MESCIUS focusing on BI and data-analytics products. Dan holds a BS in engineering from Penn State and has used it for a career in technical selling and management. He is always seeking the “win-win” deal and enjoys working with clients to help them achieve their goals.

When he isn't working with data clients, he spends time with his family and enjoys traveling to new places with them. Dan also enjoys art, architecture, and loves to compete in poker tournaments. You can connect with Dan via email or on LinkedIn.

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