By Roman Stanek
Analytics have come a long way since the days of relying on spreadsheets. Today, embedded analytics and the insights and recommendations they deliver are driving everyday decision making for many organizations. In fact, 53 percent of companies are currently investing in and adopting big data analytics.
While, ideally, this number would be closer to 100 percent, it still means that more than half of today’s companies are seeing the value of using data to improve their business minute by minute, process by process, department by department. By empowering their employees with personalized, contextualized, and real-time data, organizations will find themselves bridging the gap between insights and action, as well as the gap between disparate parts of the business like sales, marketing, finance, and data science.
While the competitive advantages of shifting to metric-driven business processes and outcomes are fairly obvious, it still remains challenging for many companies to make the cultural transition necessary to support more pervasive analytics. To ensure long-term success and cultivate a collective data-driven mindset, these cultural changes must be represented across all members of the C-suite. In particular, I expect to see three primary changes from the C-suite as analytics becomes more embedded.
Using predictive analytics to make confident recommendations
Too often the deployment of analytics is treated as a task rather than the ongoing, iterative process it is. For the use of analytics to become more pervasive throughout the organization, the C-suite will need to begin using predictive analytics themselves, a fact that’s supported by a recent McKinsey study that found that a common factor to successful deployment is leadership visibly committing to analytics.
It’s critical for the C-suite to take an active role in using predictive analytics so employees understand the kinds of data-driven decisions that can be made using analytics and that the executive team is highly engaged in the success of this new way of working. By using analytics to support decision making, and by developing a sense of confidence in the recommendations delivered by the analytics platform, the C-suite will be setting an example that the rest of the organization can follow. This top-down approach leads to more widespread use and greater success, which in turns leads to higher adoption rates.
Implementing machine learning to automate decisions
I expect to see the C-suite recognizing the value of machine learning for every layer of and every process in the organization—and that includes for the C-suite themselves. In fact, 79 percent of executives believe AI will make their own jobs easier and more efficient. This mentality makes it much more likely that machine learning will be successfully implemented to automate decisions in the rest of the organization. If the C-suite trusts machine learning, it’ll send a powerful message.
But the C-suite’s job isn’t done once machine learning has been implemented elsewhere. They’ll need to work closely with their teams to encourage end users throughout the organization to trust—and act on—the recommendations they’re given, which will likely require introducing user training. They’ll also need to work with data scientists on continuing to develop and refine models to ensure the algorithm successfully learns and automates the right tasks and delivers the most helpful recommendations.
Promoting a data-driven culture
Finally, developing the kind of data-driven culture that supports the use of pervasive analytics is no less important. Because these efforts often start from the top, McKinsey suggests that the CEO, CAO, or CDO should “set up a series of workshops for the executive team to coach its members in the key tenets of advanced analytics and to undo any lingering misconceptions.” Armed with this knowledge, executives will be better equipped to perform this same coaching for their own teams.
It’s also much harder for companies to see technology succeed when there’s no accompanying cultural shift that pulls people away from older, more comfortable ways of working and toward something new. To mitigate that risk, I expect to see the C-suite emphasizing the importance of developing training plans for employees as they begin to use analytics to make business decisions. By ensuring a consistent training and professional development experience at every layer and in every department, the C-suite will be creating the kind of culture where analytics can thrive.
And once the initial training and development has been addressed, it’s in the C-suite’s best interest to collect feedback to ensure that analytics is as beneficial as possible for end users. Making end users aware of the fact that their opinions and suggestions for improvements matter will enable the C-suite to guide future developments in the analytics platform.
Embedded analytics and machine learning are exponentially superior for getting the most out of your data and supporting today’s critical business decisions than prior technologies. However, using embedded analytics for the long term will require considerable effort on the part of the C-suite. By leading the way themselves and using predictive analytics, implementing machine learning, and promoting a data-driven culture, the C-suite will have a significant positive impact and ensure ongoing analytics success.
Roman Stanek is a passionate entrepreneur and industry thought leader with over 20 years of high-tech experience. His latest venture, GoodData, was founded in 2007 with the mission to disrupt the business intelligence space and monetize big data. Prior to GoodData, Roman was Founder and CEO of NetBeans, the leading Java development environment (acquired by Sun Microsystems in 1999) and Systinet, a leading SOA governance platform (acquired by Mercury Interactive, later Hewlett Packard, in 2006).
Latest posts by Timothy King (see all)
- SAS Teases SAS Viya 4 Cloud Analytics at Virtual User Conference - July 8, 2020
- Tellius Launches Tellius On-Demand with Guided Insights - July 8, 2020
- Sisu Unveils New A/B Testing and Text Analytics Capabilities - July 8, 2020