The majority of business and IT executives do not trust current investments in data analytics and do not believe their organizations are getting insights needed to drive business decisions.
That is the finding of a new study by TDWI entitled “Reducing Inefficiency and Increasing the Value of Analytics and Business Intelligence.” Only 11 percent of respondents said that they were very satisfied with their companies’ investments in data and analytics projects to meet strategic goals for enabling data-driven decision-making or actionable customer intelligence.
Conducted in February 2018, TDWI requested that analytics, business intelligence and data professionals provide information related to the data access, master data management, governance and stewardship practices at their organizations. The survey was sponsored by Datawatch, and collected responses from 263 business and IT executives and managers from various industries.
The survey reveals the shortcomings of many organizations in the areas of data management and governance, and the resulting business impact of poor data quality, lack of confidence and absence of collaboration.
“Data is the lifeblood for critical business activities including risk evaluation, customer engagement, business performance management, regulatory requirements and more,” noted David Stodder, senior director of TDWI Research for business intelligence. “When business users cannot access governed data, share it and collaborate on analytical outcomes, they are left feeling frustrated and ineffective. Business users need to move beyond spreadsheets and inefficient data preparation practices to a team-based intelligent analytical approach that enables smarter data stewardship.”
Key findings from the survey include:
- Spreadsheets continue to be the preferred method for analytics. Eighty-nine percent of respondents state that they input data into spreadsheets for analysis, yet 81 percent are concerned about data quality and consistency when using these files.
- The majority of those surveyed (65 percent) say that users and analysts can create analytical dashboards with self-service applications; however, only 44 percent can find or access relevant data for the reports.
- One in five (20 percent) responded that individuals can find trusted data sources without IT support, and only 18 percent can track data lineage to a source, which leads to a decay in analytical confidence.
- Inefficiency related to data preparation tasks for analytics continues to be an issue with nearly half reporting (48 percent) that users are spending at least 61 percent of their time on finding and preparing data.
- Tribal knowledge about data use and sharing sources is conducted in less formal, governed formats, including email (48 percent), word-of-mouth suggestions (45 percent) and internal and external networks (25 and 5 percent respectively). Few organizations (5 percent) are using data marketplaces for collecting this knowledge and curating trusted data sources.
- Fifty percent of respondents stated that their organizations do not have a formal data governance strategy, which directly impacts how data usage is tracked for a complete data lineage. This lineage is key to increasing trust in data; yet nearly two in five (38 percent) are only somewhat confident about the lineage of the data used in reports and analytics, and 18 percent report no confidence.
- Data stewardship and sharing insights is essential to increasing trust in data, assisting in enforcing data governance policies and expanding collaboration across the enterprise, still only 36 percent of respondents find that data stewards are helping with the selection of data sets. Additionally, a small amount (22 percent) indicate they share feedback or rate the analytical outcomes shared across the company.
“While organizations can collect data easily, it is the application of this data to a defined business strategy that is harder to implement,” the study concluded. “A complete data intelligence strategy enables business users to master data access and governance while improving team collaboration, which is essential for achieving value from data and analytic projects and enabling trust in the analytical outcomes.”
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