Why executives aren’t leveraging analytics to inform critical business decisions and how to change that.
Data analytics can be every company’s greatest asset. It can help corporations drive customer growth, increase productivity, and manage risks.
Well, at least in theory.
Just like you need to trust your co-workers, your employees, your peers and your partners, in order for analytics to yield the intended benefits, business leaders need to trust the data they have so they can use it to inform their actions. However, a recent survey conducted by KPMG International found that two-thirds of senior global executives don’t fully trust the quality and accuracy of their companies’ data and analytics.
As a result, many corporate executives aren’t fully leveraging the power of analytics while others are running dual processes (one managed by human and the other by machine) to validate machine generated insights. Needless to say, neither of these approaches is efficient or desirable.
We wanted to understand why. We used the recent TDWI Pulse Report findings to uncover the reasons why this dysfunction exists and published our findings in the eBook “2018 State of Data Intelligence.”
The main reasons why executives mistrust data
In order to encourage executives to use data in their decision-making processes, we need to first understand why they develop such a mistrust in analytics.
1. Limitation of spreadsheet applications
For years, spreadsheet programs, such as Microsoft Excel, have been the de facto standard for viewing data, performing calculations, and creating simple visualizations.
However, with the volume, variance and complexity of data exploding beyond the capability of these applications, users are experiencing a lot of frustration.
This is evident when manually extracting data, which comes from a variety of different sources such as PDFs and web pages, into spreadsheets. Spreadsheets usually involve a tremendous amount of manual work and manual work tends to create errors and affect the accuracy of analytics. In addition, when information from these sources is updated, analysts have to revise their spreadsheets manually.
It’s no surprise that many executives mistrust analytics.
2. Lack of governance and access control
Spreadsheet applications also lack the level of transparency required for proper data governance to ensure data integrity. For example, it’s cumbersome, if not impossible, to access change histories, data lineage, activity logs, and complete “digital fingerprints” so that administrators can review how the data has been accessed, edited, or shared.
The inability to control access to sensitive data and easily understand lineage and change history, compounds the lack of trust in data analytics produced through spreadsheet applications. In addition, each analyst has his/her own approach to data preparation. The lack of a standardized and automated process means that the quality of the analytics can vary significantly.
3. Challenges in data sharing and data discovery
When data is stored in spreadsheets on each individual analyst’s desktop, information and insights can’t be shared across the entire organization in a timely manner. Without a centralized ‘data marketplace,’ for easy and controlled exchange of data, many analysts end up performing duplicate tasks, which is not only wasteful, but can lead to multiple versions of the truth.
Some organizations have their spreadsheets in the cloud, which helps alleviate some of the version-control issues, but version control doesn’t equate to governance. With these systems, you still run into the challenges outlined above with manual effort, human error, lack of lineage and change history and you don’t have any visibility into who’s using data sets, or how it’s being used.
How to increase the trust in data in your organization
Poor data quality, lack of confidence, and weak collaborative frameworks are often the root causes of the mistrust in data, which can lead to a profound impact on the bottom line. Read our latest eBook: 2018 State of Data Intelligence. In order for executives to trust and fully leverage the power of data analytics, companies need to have a data processing and governance policy supported by a robust data preparation and sharing platform.
Altair Knowledge Hub is a team-driven data preparation platform built for enterprises. Beyond powerful and easy to use data prep capabilities that can improve anyone’s ability to work with data, Swarm also offers a host of unique features such as controlled collaboration and governance through a centralized data marketplace. Swarm is designed to help organizations cleanse and prepare a large amount of data efficiently so it can be used to generate insights that executives can trust.
Request a demo today to see how Knowledge Hub can change the way your organization leverages data analytics.
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