Across many industry sectors, Data and Analytics are helping organizations become smarter, increase productivity, and be better at making informed decisions and predictions. Having the right data and analytics reporting will define the difference between the losers and winners in the battle of data utilization.

Here are the top 9 data and analytics challenges you need to know and how to make your data work.

  1. Human Error: When data is handled manually, mistakes happen. People are human. Values that are copied and pasted can be easily pasted incorrectly; values can be missed; references can break; it is important to catch these errors before they impact downstream analysis.
  2. Data Volume: There is so much data out there, and the more we collect, the more we store. The more we store, the more we monitor. The more we monitor, the more work needs to be performed. Volume has storage cost, performance cost, and management cost.
  3. Format changes: Vendors change formatting all the time. Some are announced with short warning, some with long warning, and some not at all. If loaders or scrapers are being used to collect the data, your developers will need to fix all impacted scrapers as soon as possible.
  4. Revisions: Data publishers frequently go back and revise data that was published previously. How are you able to check when this happens and capture the revisions accordingly?
  5. Capture frequency: We want data on time and all the time. This is fundamentally impossible given the volume of data that is out there. We need to balance what we need versus what we want and have to understand that it is hard to match what someone intends on publishing to when it is published.
  6. Getting blocked: Running redundant data scraping processes can put an unfair load on a data vendor can cause you to get blocked from accessing the data source. Once you are on the radar, it’s hard to get off the “watch list”. Getting unblocked means reviewing and cleaning up your processes.
  7. Rogue processes: When business units rely on themselves to obtain the data they need, there’s the risk of data siloes and duplicated data processes that can have a negative impact on the integrity of data and analysis results.
  8. Overanalyzing errors: Without a standard and more systematic process in place to analyze data errors, you can easily become overwhelmed with false positives. Abnormal data records could simply be normal outliers, nulls could be valid, and
  9. Data Performance: With higher data volumes and greater data variety being handled, how can you optimize the performance of your data retrieval process? How can you ensure that your users have access to all the data they need, whenever they need it? How can you ensure that your data handling system or group isn’t overwhelmed by data requests?

These are challenges that all organizations eventually come to realize as they work through more and more data, and the most common conclusion that organizations come to is that a commercial data management software system is required—a data management system that is easy to implement, easy to use, and will allow them to not only collect all the data that they need, but also: 1) automatically monitor data for completeness, timeliness, and correctness; 2) administer the data so that users can easily access the data they require from a single, central pool of data; 3) automatically normalize the data so that it can be quickly utilized in analysis and other systems; and 4) deliver high availability and performance to meet the real-time analytical needs of businesses today.

 

ZEMA for All Your Data Management and Analytics Challenges

The award-winning ZEMA platform is an internationally recognized enterprise data management solution with a proven record in dealing with data challenges for some of the largest Fortune 500 companies. Having the largest data catalogue globally, the ZEMA platform collects, validates, administers, analyzes, automates, and provides reports on data for any industry sector