Chief Executive Officer at Magnitude Software, directing the company’s strategy, business activities and operations.
You’d be hard-pressed to find a boardroom or C-suite that isn’t transfixed by the promise of data-driven decision-making. With the collective sum of the world’s data already in zettabytes and expectations for exponential growth, organizations are hungry for actionable insights that will help reduce operating costs, increase sales and get products to market faster.
There is certainly no shortage of data to drive operational insights. IDC projects the five-year compound annual growth rate (CAGR) for all data captured and consumed globally to hit 26% through 2024. Moreover, the firm says the amount of data created over the next three years will outpace what was created over the last three decades. From 2010 to 2020, worldwide data ballooned from 1.2 trillion gigabytes to 59 trillion gigabytes — an astounding 5,000% leap.
By 2025, the number of IoT connections will be 36.8 billion, more than double the 17.7 billion in 2020, according to Juniper Research. Experts at World Economic Forum are projecting an estimated $3.7 trillion in value creation in 2025, for the manufacturing sector alone, by optimizing these burgeoning data stores.
If companies are so data-rich, why are most still operating data blind? The reason is true insights don’t come from isolated or intermittent data points — the current state of most enterprise data operations. Querying an ERP system through a business intelligence tool is one thing, but the results don’t portray the full picture without the proper business context and framing.
Consider the simple example of quarterly financials. Finance professionals everywhere are rolling their eyes. Simple and quarterly financials don’t really go together, but they should. As you’re closing the books for the quarter, there are certain major metrics you’re absolutely looking at like sales, expenses, etc. But if you don’t have the context of some of the smaller issues that you thought might not be a big deal, all of a sudden you discover that the Euro FX rate fell off by a few percentage points, which materially affected your numbers for the quarter. This context eliminates concern, but far too often, that context is hard to come by.
Businesses of every size need these types of contextualized answers to questions about order status, production capacity, inventory backlogs and logistics updates. They need answers to such questions on a near-real-time basis, not after-the-fact reporting pulled together by once-removed business analysts or data scientists. What’s needed is continuous intelligence to help organizations react quickly to changing market dynamics and to keep pace with, or better yet, lap the competition.
Driving Continuous Intelligence
As it turns out, getting answers to the most fundamental questions about business operations and performance can be elusive for most companies. Data is likely buried in highly complex systems that aren’t easily accessible. To complicate matters, data inevitably resides in more than one of those systems, each employing different naming conventions and data models. That makes it a challenge to link data in a logical sense, let alone display it in a manner that resonates with the CFO, procurement specialist or any other business user.
That’s not the case with a continuous intelligence model, which turns data into insights and insights into action. With such an approach, companies can easily identify and correct product shortages and delays, address supply chain bottlenecks, remove wasted inventory, eliminate unnecessary purchases and production and mitigate risks related to compliance.
To understand how this shift brings intelligence to daily decision-making, consider a process as basic as procure-to-pay. Traditionally, ERP systems inform users of what’s been paid and what hasn’t — useful information, of course. But what about going a step further to create linkages between purchase orders and payments tied to specific business rules? With that one change organizations are immediately alerted to payments that are out of compliance with policies, helping to optimize cash flow and cash on hand.
Reacting To Market Dynamics
That same exercise can be applied to sourcing, distribution and production decisions, giving everyday business users the insights they need to react to changing events at just the right moment. In one real-world example, a major pharmaceutical company facing volatile supply chain conditions and border closures brought on by the pandemic was able to make agile sourcing and distribution changes that ensured products with specific expiration dates were brought to market without any waste. The data to shape the string of decisions that went into this success story didn’t originate in any one system; rather, it came from integrating and harmonizing data across disparate systems, contextualizing it and serving it up through a single control tower that delivers insights and dashboards relevant to specific business users and processes.
To achieve continuous intelligence, organizations need a context-rich data model and abstraction layer that can transform complex and arcane ERP data into actionable insights that business users can leverage quickly through simple searches and in common terms they understand. Integration and connectivity across applications, data platforms and databases is also a must. Process automation completes the picture, streamlining complex data transactions and closing the loop on insights-to-action without the burden of repetitive manual processes and potentially inaccurate data entry.
For C-level executives steering business innovation and transformation, it’s time to consider an alternative approach in the quest for true data-driven decision-making. There are three main components of a business that is committed to continuous intelligence, all of which your business should consider when identifying or designing solutions and systems to augment its processes:
1. Data Model: Integrate a data model that provides business context for your core enterprise data.
2. Data Democratization: Push data closer to business users to allow for quicker, more insightful decision-making.
3. Data Automation: Leverage capabilities to close the gap between data to insight and insight to action.
The results of making this commitment are clear: Continuous intelligence unlocks the data hidden deep within enterprise systems, transforming it into that business gold that has so many in the C-suite and boardrooms buzzing.