Channel Insider content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Data quality

1 - Channel Opportunity: Remedying Data Management WoesChannel Opportunity: Remedying Data Management Woes

Solution providers can help solve data management challenges that are taxing IT organizations and have a direct impact on their revenue and costs.

2 - The Trouble With Data QualityThe Trouble With Data Quality

40% were very confident in their organizations’ data quality management (DQM) practices or the quality of data within their companies. Half indicated that the DQM practices put in place by their organizations and the quality of the data used overall were either slightly better than satisfactory, or at least good enough in general.

3 - Business Value of DataBusiness Value of Data

Just over half (51%) said data quality affects revenue, while 49% said it has an impact on costs.

4 - Impact of Data Quality on BusinessImpact of Data Quality on Business

Nearly two-thirds (65%) said that 10% to 49% of the business value can be lost due to poor data quality, while 29% said 50% or more of business value can be lost. Only 6% said that little-to-no business value is lost as a result of poor data quality. A full 85% surmise the organizations they work for think the quality of the data they have is better than it actually is.

5 - Growth in Data VolumeGrowth in Data Volume

A full 95% said they expected the number of data sources and the volumes of data in their organizations to increase in the coming year. Almost 70% expect data volumes to grow by up to 70%, while nearly 30% said they expect data volumes to increase by anywhere from 75% to nearly 300%.

6 - DQM Tools in UseDQM Tools in Use

57% said they use some form of big data tools, followed by master data management tools (54%) and data-cleansing tools (51%).

7 - Causes of Poor Data QualityCauses of Poor Data Quality

Data entry by employees (58%) was cited most often as the cause of poor quality data, followed by data migration/conversion projects (47%) and mixed entries by multiple users (44%).

8 - Poor Methods for Ensuing Data QualityPoor Methods for Ensuing Data Quality

Just under half (46%) said they find data errors by using reports and then taking subsequent corrective action as their means for DQM, while 37.5% employed a manual data-cleansing process. Another 9% said they avoid DQM completely.

9 - Limited Knowledge of Data LocationLimited Knowledge of Data Location

56% were somewhat confident, unaware of or less than confident in terms of knowing whether all the data sources required for their purposes had been aggregated prior to cleansing. Less than half (43%) were very confident in their knowledge.

10 - Future DQM PlansFuture DQM Plans

A full 65% said they are currently implementing or developing a plan. But only 24% said they implemented a plan that is actually working.

11 - DQM Tools and Services Needed MostDQM Tools and Services Needed Most

At 46%, big data tops the list of most needed DQM tools and services, followed by data cleansing (41%) and master data management (40%).

12 - Machine Learning Comes to the ForeMachine Learning Comes to the Fore

42% want to use machine learning within next 12 months, and 15% want machine learning programs in the next 24 months. Another 22% said they already had a machine-learning program. The top application for machine learning is predictive analytics (67%).

13 - Use Cases for Machine LearningUse Cases for Machine Learning

Asset management is the biggest use for machine learning, at 47%, followed by data discovery (45%) and decision-making (39%).