By Brynne Henn - June 25, 2020
In this guest post, Matt Aslett, Research Vice President at 451 Research shares his research on the importance of proactive intelligence. To hear more from Matt and Sisu CEO Peter Bailis on the importance of proactive intelligence for faster diagnostics, register to attend our webinar on July 15th.
The socio-economic impact of the coronavirus pandemic is demonstrating the importance of using data and analytics to make rapid, confident, and informed business decisions. At the same time, it is shining a light on the risks of relying on predictive models based on historical data. As everything has changed, the need for proactive intelligence has been elevated.
Interestingly, the issue is not a lack of data – quite the opposite, in fact. Data from 451 Research’s Voice of the Enterprise: Data & Analytics survey indicates that for the average company, the volume of data used for analytics is expected to grow over 90% in the next two years to almost 600TB.
The combination of these two trends – rapidly rising data volumes and unprecedented change – has diminished the effectiveness of predictive models and highlighted the potential value of proactive analytics.
Despite the best intentions of companies attempting to make more data-driven decisions, our research shows that the greater volume and velocity of data can lead to inefficiency. For example, the most data-driven companies are more likely to have a higher number of data silos – while 23% of all survey respondents report that they have more than 50 data silos, that proportion rises to 39% when we look specifically at the most data-driven companies – and the companies reporting the most rapid data growth take longer to provision data workloads.
Ultimately, these challenges can lead to friction between data consumers (those tasked with making data-driven decisions) and data operators (those tasked with configuring and managing data pipelines), just as they are being asked to accelerate data-driven projects.
When there is friction and delay in accessing data, it can negatively impact organizational efficiency and productivity. There is also a danger that when faced with an abundance of different data sources and a variety of complex and time-consuming reports and dashboards, decision-makers can become overwhelmed and revert to ‘gut feel’ or manual data-selection approaches that are inherently biased. This is the antithesis of a strong data-driven culture.
These ongoing challenges are exacerbated by the current economic climate and the uncertainty related to the unprecedented nature of coronavirus quarantine measures. In particular, changes in customer, employee, and supplier behavior are rendering existing data and machine learning models obsolete.
The changes seen across industries would have been unimaginable just six months ago, and many of them are likely to have a long-lasting impact. For example, more than 44 million Americans filed for unemployment benefits since mid-March 2020, according to the U.S. Department of Labor. Most of those fortunate enough to still have jobs are now working from home, and according to 451 Research’s Voice of the Enterprise: Digital Pulse, Coronavirus Flash Survey June 2020, two-thirds of organizations (67%) expect expanded or universal work-from-home policies implemented in response to the outbreak to remain in place long-term or permanently.
451 Research’s Coronavirus Flash Survey also shows that two-thirds of organizations (66%) are experiencing, or have experienced, an increased strain on internal IT resources as a result of the outbreak, although organizations are also more likely to be spending more on IT resources as a result. One particular area of investment is the shift to digital delivery of customer experience, for which more than one-third (34%) of respondents have new or accelerated investment amid the coronavirus pandemic.
While the impact of the pandemic has reduced the value of predictive models, it is clear that businesses are not without the data they need to make informed decisions and drive transformational change. What is required, however, is a more agile approach to generating actionable insight from that data.
Unlike models based on historical data patterns, proactive intelligence workflows rely on continuous analysis of operational data to detect anomalies and automate root-cause diagnostics in order to proactively explain not just what changes are occurring, but why.
Predictive models can be impacted by model accuracy drift, and the model itself decays as time elapses and circumstances change. In contrast, proactive intelligence based on operational data remains focused on – and evolves in response to – identifiable business events and anomalies, allowing businesses to maintain their agility and make informed decisions in real time.
Proactive intelligence can also automatically provide machine-learning-driven recommendations to facilitate strategic decision-making. Additionally, businesses can leverage machine learning to measure the speed of decision-making and track the real-life impact of recommended actions, with a view to closing the feedback loop in order to further improve anomaly detection, forecasting, and recommendations based on identified business impact.
According to 451 Research’s Voice of the Enterprise, Data & Analytics, Data Platforms survey, the more successful a company is with its data and analytics initiatives, the more likely it is to be successful in executing greater companywide digital transformation initiatives, such as switching from on-premises retail to online sales and fulfilment – the acceleration of which has been forced on companies thanks to coronavirus.
Using data and analytics to gain insight into customer behavior and company performance is a fundamental component of improving customer experience and reducing costs through operational efficiencies – the two most significant drivers of digital transformation.
As such, it is not surprising that the companies that are most successful with data and analytics projects are also more advanced in relation to digital transformation initiatives. Specifically, according to 451 Research’s Voice of the Enterprise, Data & Analytics, Data Platforms survey, more than two-thirds of companies rated by their employees as ‘very successful’ with analytics projects are executing a formal digital transformation strategy, compared with less than half of overall respondents.
The ability to make rapid business decisions based on fast-changing data exemplifies the agility required to not just survive but thrive amid the current socio-economic turmoil. For that reason, 451 Research anticipates greater enterprise investment in diagnostic analytics based on continuous intelligence techniques and proactive intelligence functionality.
Indeed, we believe that the ability to monitor operational data in real time, diagnose changes in business metrics, and automate appropriate decisions to improve operational efficiency and identify new revenue opportunities will increasingly be seen as critical to data-driven decision-making.
To learn more about Proactive Intelligence for Faster Diagnostics, join our webinar on Wednesday, July 15, as I’ll be joined by Peter Bailis, the founder and CEO of Sisu, to discuss this topic in more detail.