Chief Executive Officer at Magnitude Software, directing the company’s strategy, business activities and operations.
Companies have become fairly adept at leveraging data and business intelligence (BI) tools to answer specific but predictable questions. This ensures tracking for key metrics like sales goals or getting high-level views of order statuses across the enterprise. However, when it comes to today’s unpredictable and fast-moving business environment, BI reporting is no longer fast or agile enough. Achieving sustainable market advantage goes far beyond standard metrics.
As the pace of business accelerates and competitive landscapes tighten across industries, today’s leaders need to reach beyond the usual postmortem analysis to become data-driven throughout every aspect of the business. The shift is critical to fostering the agile decision-making essential for meeting strategic business goals, whether that means growing sales, boosting product innovation, reducing operating costs or becoming a disrupter in key industry segments.
The Data-Driven Reality
Businesses are certainly talking up the idea of data-driven decision-making, but the reality is that few have successfully made the leap to evolve their data and analytics initiatives in a new direction. According to a Gartner, Inc. survey, 87.5% of respondents characterized their data and analytics maturity as low, with a sizeable number of organizations still reliant on spreadsheets for analysis.
Further, NewVantage Partners’ “Big Data and AI Executive Survey 2021” confirmed that companies are struggling with what it means to be data-driven, as well as what it takes to create such a culture. According to the survey, only 48.5% of companies are driving innovation with data, and only 41.2% are competing on analytics. From an organizational transformation perspective, the picture is worse: Less than a quarter have forged a data culture (24.4%) or created a data-driven organization (24%).
Time For Change
While traditional approaches to BI have served companies well in the past, it’s time to extend beyond one-off and siloed analysis processes and embrace the concept of continuous intelligence. Such a shift is important because while the strength in traditional BI lies in a deep posthoc analysis of why something burned down — the reason behind declining monthly sales or a key supplier shortage, for example — it’s less adroit at being abstract and sniffing out why there is smoke in real time.
In contrast, a continuous intelligence approach is designed to be flexible and fluid, with the ability to look across myriad data sources, curate critical data points based on established indicators and rules and make connections that alert business stakeholders to issues demanding further examination. The upside is invaluable: the ability to systematically monitor the business to initiate a proactive response to key events, driving self-service business insights and maximizing overall impact.
Continuous Intelligence In Action
Let’s put the concept of continuous intelligence to a real-world test. While BI alone is great at reporting on how much raw material the company is procuring through various supplier channels and providing an overview of expected delivery dates, that kind of intelligence falls short of answering a more insightful question such as whether buying activity aligns with sales commitments and orders. The difference between the two analysis approaches is that one contextualizes data drawn from different sources to determine whether the organization is buying in the right quantities with the right timing and with the objective of optimizing supply chain costs, while the other is simply reporting on the basics.
To take advantage of continuous intelligence, organizations need to take a holistic approach to data analytics. They need to identify and integrate critical data sources, implement a context-rich business data model that works across the enterprise and create an environment that lets everyone — from everyday business users to data scientists and BI experts — ask business questions amid conditions that are in a constant state of flux. Infusing a governance and alerting capability that functions across all processes can enable business users in procurement or finance, for example, to determine if the company is paying vendors too quickly or if sales bookings are off compared to historical trends or forecasts. With those kinds of ongoing insights, they can quickly pivot and make their actions count.
Is it time to move beyond traditional BI?
Key indications that an organization should consider moving beyond traditional business intelligence include:
• People in the organization that make decisions, not having access to the same data and, more importantly, not having a universal view of the business and critical KPIs.
• Business leaders being unable to innovate and ask new questions without having to go back to an analytics team to reinvent the wheel.
• Insights being siloed rather than reflecting a true 360-degree view of what’s going on in the organization, factoring in all available and relevant data sources.
If this resonates, your organization is ripe for leveraging continuous intelligence. The question becomes how to make that happen. The first step is to get executive alignment and sponsorship for the shift toward operating as a data-driven business. That should be fairly easy, as it’s a great driver for long-term viability and competitive advantage. At the same time, you’ll need to enlist buy-in across business unit leaders, who must partner with IT from the onset to not only implement solutions but champion the change necessary to create a culture predicated on data-driven decision making. Old-school ways of work, where IT pushes out solutions without input and support from the business, won’t cut it when navigating change management challenges at this scale.
The continuous intelligence journey is ongoing, but with the right set of tools and commitment to cultural change, organizations can rise to the challenge and reap the rewards of data-driven business advantage.