The Challenge of Creating Meaningful Analysis

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A common anecdote these days is that “Data is the new Oil”. However, an old adage that still rings true is “garbage in, garbage out”. Poor data quality can adversely impact business decision-making processes and consequently result in poor organizational performance in crucial areas like operational processes, customer experience, and market analysis. In order to ensure the most meaningful analytics and therefore the most impactful decisions, you must build from a foundation of high-quality data across your organization. Data quality issues can be technical (e.g. lack of a cohesive integration and automation system) and non-technical such as ineffective data handling skills or lack of a strategy for ensuring that the data satisfies user requirements.

With ever-growing volumes, formats, and publishers of data, the complexities of data collection will only increase and quality issues will become more and more prevalent. Already, when it comes to data analysis, the Pareto principle typically manifests like “My analysts spend 80% of the time collecting and wrangling data and only 20% actually analyzing it.”

Why is preparing data for insightful decision-making and meaningful analysis so difficult? Here we list some of the common problems:

1. Collection

If we only addressed a single data challenge, it would be the effective collection of the raw data itself. Organizations leading the way in leveraging data analysis to enhance their business processes often require hundreds, perhaps even thousands of disparate data feed from several different providers. Some common data collection challenges include:

  • Irregular data publishing schedules
  • Format integrity / Unannounced format changes
  • Multiple locations, data stores, and connections
  • Authenticated access
  • Expiring credentials
  • Data revisions
  • Compliance and regulation concerns
  • Overlapping or redundant data
  • Quality control and reporting

Looking at the list above, it becomes clear how many barriers there are to successfully manage the collection of data. Organizations either require many human resources to manage this task or need to implement a sophisticated and reliable data management solution.

2. Validation and Improvement

How do you know if the data you are collecting is of high enough quality to build reliable analytics on top of? How do you effectively revise and improve the quality once issues are identified? Without absolute trust in the veracity of the data, the decisions based on any subsequent analysis will be suspect.

  • Is the data timely enough?
    • Real-time data, though sought after, is rarely achievable. What is the acceptable latency of the data? Making critical business decisions often depends on having the data in hand for analysis as soon as it is available from the publisher—and ideally, before your competitor has access to the same information.
  • Is the data complete?
    • Incomplete data sets can compromise most analytics. You need to ask how you can be sure that you are getting all of the data that you should be. Are there gaps in the data that you are not aware of? Once data is collected, incomplete data is often the most common, and serious, data quality issue.
  • Is the data accurate?
    • Accurate data means that, at any given time, the data in your system accurately reflects the data at the source. Collection systems need to parse different formats and data types and errors are commonplace. A comprehensive data management system needs to have the capability to assess and report on probable issues based on an expected, allowable range of values to ensure maximum content quality.
3. Consolidation

Consolidation is increasingly significant as you collect more and more data. Consolidation helps:

  • Improve analytics (including performance)
  • Decrease costs
  • Increase control for compliance and reporting
  • Simplify maintenance

Replacing low-quality and redundant data, stored in disperse data stores with high-quality data in governed and auditable data centres generate savings across strategic, tactical and operational processes and outcomes.

4. Automation

Not only is it difficult to implement successful practices to address each of the above issues, but in order to have actual, and ongoing business value, data collection, quality management, stewardship, and other critical processes must be automated, performant and stable. Collection schedules must be tuned to match the source publication schedule as closely as possible; alerts should be triggered when quality issues occur and if gaps in the data are identified, and the collection routines should be automatically triggered to fill any gaps.

Once the data is in place, and all of the above issues addressed, there is a myriad of tools to help you reliably explore, analyze, report on, and generally glean value from the data. However, if these activities are not properly taken care of, the analysis will understandably be sub-standard and potentially misleading.

So, the challenge of creating sophisticated, meaningful data analysis today is not hampered by a lack of tools available for the job, rather the vast amount of data available to us today and the complexities of collecting and preparing it for the desired outcome.

Luckily, there is an alternative to staffing a small army to get the job done. ZEMA is a Data and Analytics Platform that separates itself from the crowd on the strength of its automated data collection and validation capabilities. With more than 1,000 unique data providers and over 10,000 market data reports, the ZEMA data catalogue is unrivalled. Check out, or reach out to me directly at to find out more about eliminating the growing burden of data collection.

Data. We Get It.

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