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Winning Strategies for Successful Analytics Projects

Authors Photo Eric Yau | April 21, 2022

How Organizations Leverage Data Analytics to Turn Data into Value

For businesses to realize the full potential of their data assets, they must leverage data analytics and data prepreparation to enhance operational efficiencies, drive growth, improve the customer experience and optimize business efficiency. The most advanced companies derive analytics from their data to evaluate the competition, identify emerging business trends and ultimately, gain a competitive advantage.

Despite the massive opportunities buried in mountains of enterprise data, even the most experienced organizations fail to realize the full potential of data analytics. Across every industry, business are faced with obstacles as they attempt to convert data into insights, such as:

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Poor Data Quality

Data is arguably an organization’s most valuable asset. However, if the data’s quality is in doubt, business users across the enterprise won’t trust the data, let alone leverage it for insights. And if they do rely on poor quality data, the adverse effects of faulty insights can reverberate across the business. Bad data leads to bogus intelligence, negative customer experiences, lost revenue and missed opportunities.

To create trusted data, organizations need data quality and profiling controls to validate data’s accuracy. Data prepreparation is needed to check for completeness, consistency and conformity ensure business users are leveraging high-quality, relevant data. Balancing and reconciliation controls assure data arrives accurately across systems and applications Timeliness rules monitor file delivery and flag any late or missing files. Reasonability checks affirm that data is within expected thresholds.

By integrating a broad range of data quality checks and data prepreparation analytics s within a comprehensive data governance program, businesses can foster data quality initiatives to improve and score their data assets.

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Lack of Business Alignment and Data Governance

A primary goal of data governance is to establish trust in data and leverage it as an enterprise-wide asset, and data quality is a critical part of these efforts. As data travels through the data supply chain, it is exposed to new processes, uses and transformations, which can significantly degrade data accuracy. By establishing a comprehensive data governance strategy and incorporating data quality checks, businesses prevent downstream data quality issues from proliferating, and help build business user trust in enterprise data.

Data governance also encourages collaboration to align data understanding among IT and disparate lines of business. When different departments work together to define and document data, it establishes a common understanding of data assets and eliminates business user confusion as they leverage data for business purposes.

High quality, well-governed data lays the groundwork for meaningful analytics. The next step is empowering business users to quickly prepare and analyze enterprise data to generate actionable business intelligence.

Ineffective Data Preparation and Analytics Strategies

Many organizations still rely on IT for data access and prep of data, leading to backlogged requests, overworked resources and outdated results. Delays are exacerbated by highly technical tools that take days or weeks just to prepare data for analysis.

But increasingly, companies are implementing enterprise data governance integrated with data quality and analytics. Self-service data analytics solutions are the latest evolution in data preparation and analytics, enabling business users to take an active role in data analysis. By educating these data consumers about the source, usage and meaning of critical data, and equipping them with the ability to engage in analytics, organizations can dramatically increase the quality,volume, and immediacy of their business intelligence, helping them achieve objectives and generate ROI.

By leveraging data governance to build data trust and  data literacy, data quality, and empower more data consumers to perform data analysis, organizations can improve productivity and significantly lower overall costs. When different lines of business can selfserve their own data needs, they can leverage advanced analytics to speed up time to insights, and ultimately, increase profit.

To learn more about organizing, prepping, transforming and provisioning your data, read our whitepaper Agile Data Management: Four Critical Use Cases to see what agile data management is all about.