What if you could accurately predict, in fact even know, your quarterly results at the beginning of a quarter rather than at the end of a quarter?
That is the question that Eric Robinson and I explored many times over the twenty years we collaborated together.
If you could KNOW NOW and you could see any problems coming up, you could start interventions immediately and not have to wait 3-6 months to make changes.
As underlying technologies like Microsoft Office 365 and Oracle Financials migrated from client/server applications to the cloud, our ideas became easier to implement. One of the biggest barriers to analyzing large amounts of unstructured information is having to collect that information from scattered computer systems. With that information now in the cloud and robust APIs available, our ideas went from thoughts to prototypes.
I was reminded of our musings while watching a 60 Minutes segment on Outbreak Science:
“When you’re fighting a pandemic, almost nothing matters more than speed. A little-known band of doctors and hi-tech wizards say they were able to find the vital speed needed to attack the coronavirus: the computing power of artificial intelligence. They call their new weapon “outbreak science.” It could change the way we fight another contagion. Already it has led to calls for an overhaul of how the federal government does things. But first, we’ll take you inside BlueDot, a small Canadian company with an algorithm that scours the world for outbreaks of infectious disease. It’s a digital early warning system, and it was among the first to raise alarms about this lethal outbreak.
“It was New Year’s Eve when BlueDot’s computer spat out an alert: a Chinese business paper had just reported 27 cases of a mysterious flu-like disease in Wuhan, a city of 11 million. The signs were ominous. Seven people were already in hospitals.”
Their prediction was used by a Toronto hospital to buy the necessary ventilators and personal protective equipment months before US hospitals, states, and the Federal Government started scrambling for these goods.
They are a current example of Knowing Now what is going to happen months into the future.
Eric and I realized that we could do a similar prediction from our experiences with large scale eDiscovery with all the “digital exhaust” flying around a corporation. Yet, it still took a couple of chance encounters to realize how easy it would be.
The first encounter happened at an FTI Consulting senior manager’s retreat where Heidi Gardner presented her research on lessons from professional services firms. She found that the more partners collaborate with each other the more revenue they generate.
“Today’s professional services firms face a conundrum. As clients have globalized and confronted more-sophisticated technological, regulatory, economic, and environmental demands, they’ve sought help on increasingly complex problems. To keep up, most top-tier firms have created or acquired narrowly defined practice areas and encouraged
partners to specialize. As a result, their collective expertise has been distributed across more and more people, places, and practice groups. The only way to address clients’ most complex issues, then, is for specialists to work together across the boundaries of their expertise.
“When they do, my research shows, their firms earn higher margins, inspire greater client loyalty, and gain a competitive edge. But for the professionals involved, the financial benefits of collaboration accrue slowly, and other advantages are hard to quantify. That makes it difficult to decide whether the investment in learning to collaborate will pay off. Even if they value the camaraderie of collaborative work, many partners are hard-pressed to spend time and energy on cross-specialty ventures when they could be building their own practices instead.
“And no wonder. This kind of collaboration is difficult. It’s different from mere assembly (in which experts make individual contributions and someone pulls them all together) and from sequential, interdependent projects (in which a professional adds to a piece of work and then hands it over to the next person to work on). It’s much harder than simply delegating to junior staffers. It’s also not the same as cross-selling, when partner A introduces partner B to her own client so that B can provide additional services. True multidisciplinary collaboration requires people to combine their perspectives and expertise and tailor them to the client’s needs so that the outcome is more than the sum of the participating individuals’ knowledge.”
“To illustrate how collaboration enhances a professional’s ability to generate business, let’s compare two nearly identical lawyers. Both graduated from law school the same year and are in the same practice area at the same firm. They billed nearly the same number of hours in a given year, but it’s clear from the diagram that they spent those hours in very different ways. Lawyer 1 brought six other partners into his own client work, half of whom were not from his own practice area (as shown by the gray dots). Lawyer 2 involved more than 30 other partners in work that he generated, two-thirds of them from outside his practice. Lawyer 2’s multidisciplinary approach paid off: Total revenue that year from his clients was more than four times higher than revenue from Lawyer 1’s.”
As Eric and I watched the presentation, we realized that the data was pulled from a standard time and billing system that we used. We thought we could analyze that data to find our high, medium and low collaborators. We got permission to access a subset of the data to test Gardner’s theories on our professional services firm. Our hypothesis was that we could educate the medium collaborators to become high collaborators to dramatically increase the firm revenues. Heidi Gardner confirmed our hypothesis was the core of her analysis and training with her clients.
Our next chance encounter occurred when we were introduced to Cliff Dutton, who was Director of eDiscovery for AIG. He shared with us research he did with Brown University colleagues. The article (“Empowering Board Audit Committees: Electronic Discovery to Facilitate Corporate Fraud Detection“) demonstrated a principle that Eric and I believed, but had not been able to prove. Their research showed that the elements of fraud were present in a corporate information system 60 days before it was identified by regulatory authorities.
“A number of recent accounting scandals in public companies have illuminated the need for enhanced oversight capabilities by board audit committees. The current reliance of boards on corporate financial statements reviewed by independent auditors is deficient; there is a delay in audit committees receiving these reports. Moreover, these reports transmit historic rather than real-time information to directors. In this paper we investigate if analytical techniques utilized by the U.S. Department of Homeland Security to provide advance warning of potential terrorist threats can be employed to strengthen corporate governance of public companies. The narrative reads that board audit committees can utilize electronic analysis of corporate email as an advanced corporate fraud detection system. Our application of these techniques to one actual case of corporate fraud indicates that an electronic monitoring system would have signaled an alarm of aberrant behavior to the board audit committee 60 days in advance of initial inquiries by regulatory authorities.”
The third chance encounter came during a conversation with Ade Miller at Conga. He asked me what the future of artificial intelligence and machine learning (AI/ML) was in the contract lifecycle management (CLM) market. I led off the discussion by asserting something I discovered twenty years before. I believed that a corporation was the sum total of its formal AND informal contracts. The formal contracts are the ones with customers and suppliers. The informal contracts are all the “commitment” documents inside a corporation like business plans, compensation plans, product plans, product marketing plans, employment contracts and yearly performance reviews. These formal and informal documents have the dates to deliver future commitments which have revenue and cost implications. The informal commitments are also embedded in Customer Relationship Management (CRM) systems like Salesforce. All of this information along with financial data is in the “cloud.”
I shared with Ade the experience at Primus Knowledge Solutions (PKSI) that illustrated “no one person knows, but the network does.” We spent a year negotiating a large contract with Compaq to make sure that we could financially recognize all the revenue when the contract was signed. Little did we know that the sales person had committed that we would provide onsite support to the client in an email. This email was not shared with the PKSI executive team. When the customer called to exercise the sales person’s commitment, we realized that we had to restate our revenues for the previous quarter. Our stock price tanked. Oops!
No one person knows, but the corporate network does.
Armed with our knowledge of how to collect and analyze large volumes of unstructured emails and documents and structured financial information, Eric and I prototyped a system that showed we could Know Now what our future financial situation would be.
As we exist here in the middle of the corona virus crisis, you might ask “so what happens when there is a black swan event that comes out of nowhere?”
The BlueDot outbreak risk analytic software can be easily combined with Know Now to prepare for a Black Swan event. Not only are corporate data in the cloud, so is everything that BlueDot is analyzing. So even unexpected events can be incorporated into Know Now.
No one person knows, but the globally interconnected World Wide Web does.
If only we knew where to look and how to analyze.