Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture
NewVantage Partners has released the findings from their sixth annual survey of how business executives view and engage with data. As in recent years, the findings indicate that Big Data, AI, and machine learning are on the minds of executives every single day. Machine learning has made a particularly strong impact over the past couple of years, with many executives praising its ability to deal with “large volumes of fast moving data”.
Aside from highlighting the effectiveness of machine learning, the results pin-point two rather contradictory trends: companies are embracing analytics like never before, but a majority of them are failing to build a data-driven business culture.
Here is a quick rundown of the findings on the impact of data software:
- 97% of respondents are investing in Big Data, AI, or machine learning
- 73% say they have already seen measurable gains from their investments
- The most common objective with AI and Big Data is to make better decisions (36%), followed closely by ‘improving customer service’ and ‘cutting operational costs’
When it comes to cultivating a data-driven culture, the numbers are not as promising:
- 99% of respondents say they want to make their business culture more data driven
- Roughly 33% say they have succeeded in doing so
This begs the question: Why is it 66% of these top executives have failed at making their business culture more data-focused, despite the admitted gains to be had with big data, AI, and machine learning software integration.
Roadblocks Remain to Creating a Data-Driven Work Environment
There are four main issues at play that stifle the growth of a metric-oriented, data-driven work culture:
Issue 1. Unclear Management Role Designation. Plenty of new positions are being created within companies to manage and interpret the fountain of new data being created by operational machine learning software. Positions like chief information officer and chief analytics officer are more commonplace than ever, and while it should help innervate the practical use of data, confusion about roles, resources, and accountability abound.
Issue 2. A Divided Work Culture. The immense benefit of machine learning algorithms is that it isolates performance indicators for every employee in a company and highlights where productivity time is being lost. ‘People analytics’ has yet to fully revolutionize office culture because departments remain isolated from each other and incorporating individual performance metrics remains a polarizing issue.
Issue 3. Poor Data Collection. It could be that a company is simply not collecting valuable data about their employees work flow and production rate. Without a large set of KPI’s at your disposal, how can you illustrate the effectiveness of data-insights to employees? How can you show employees the benefits of weekly performance reports in optimizing their productivity? You cannot.
Issue 4. Employee Skepticism. Dovetailing nicely with poor data collection, a fourth and final issue at play is employee skepticism about the value of performance monitoring. Changing the attitude of your employees takes training and encouragement, but the incentive to be more productive should be all the encouragement they need.
Developing a Data-Driven Culture Takes Time
The fact is that predictive analytics is the way of the future. Companies need to find a way to incorporate automated machine learning software into their daily operations, otherwise they risk falling behind in productivity and growth.