BARBARIANS at the Gate tells the story of one of the largest corporate deals in US history, the leveraged buyout of RJR Nabisco. On trips to New York, F Ross Johnson spun great tales of Wilson and the faltering tobacco business for his despondent chums. He tells the story of a boxer who does not have an inkling that he is getting badly beaten up.
Extravagance leads to downfall
It provides a gripping portrait of the extreme and extravagant behaviour in corporate America during the 1980s. Yet the narrative was one of unprecedented growth of the top and bottom lines.
A narrative that led to the downfall of Nabisco president and CEO Mr Johnson.
These are excerpts from the 1989 book about the leveraged buyout (LBO) of RJR Nabisco entitled Barbarians at the Gate: The Fall of RJR Nabisco. The book is based on a series of articles written for The Wall Street Journal.
Also a 1993 made-for-TV movie. Barbarians centres on CEO Ross Johnson, who planned to buy out the rest of the Nabisco shareholders himself. A bidding war ensued as other companies made their own offers.
For those of us who need a memory refresh, Mr Johnson, CEO of RJR Nabisco, decided to take the tobacco and food conglomerate company private in 1988 after receiving advanced news of the likely market failure of the company’s smokeless cigarette, the development of which had been intended to finally boost the company’s stock price.
Sidebar: A leveraged buyout (LBO) is one company’s acquisition of another company using a signifi cant amount of borrowed money (leverage) to meet the cost of acquisition. Along with the assets of the acquiring company, the assets of the company being acquired are often used as collateral for the loans. The use of debt, which normally has a lower cost of capital than equity, is used to reduce the overall cost of financing the buyout.
Worrying level of debt
After a hostile bidding war Mr Johnson eventually lost, the unfortunate impact of an inflated buyout price to the shareholders was the creation of a worrying level of debt for the company.
You could say Mr Johnson was blindsided because he just didn’t have the information, he needed from across the conglomerate that he controlled himself, something that would have given him
evidence-based knowledge and competitive edge he so badly needed. They had the data, they had the technology, its just that nobody was they didn’t think anyone had “laid a glove” on them.
No one had told him to keep an eye on the ref.
Sidebar: A conglomerate is an organisation made up of several different, independent businesses and or functions. In a conglomerate, one entity controls a stake in smaller subsets of the main organisation that each subset (ministries, departments, divisions, subsidiaries) conduct business operations separately. A “conglomerate” is created in several ways, including mergers or acquisitions. It consists of a number of different and distinct parts or items that are grouped together.
Each subset of Mr Johnson’s Nabisco conglomerate could operate autonomously and consolidate summary information to inform the group level, the CEO.
And that was great until it came time to integrate, not simply consolidate, but integrate data from across the businesses and make strategic decisions that would impact shareholders. In
1989? Yes, even further back than that, since 1983 when datawarehousing and what is now known as analytics, machine language, and datamining of which AI is an automated, algorithmic innovation was used by the likes of The Wells Fargo Bank, K-Mart, Wal- Mart. Many companies like Mr Johnson’s Nabisco believed they “already do that”. The Nabiscos of the business world missed out on six years of opportunity to understand their businesses and customers for strategic purposes.
Beware of the data black hole — the data warehouse
The message behind Mr Johnson’s boxer story can be interpreted as “don’t bury your head in the sand”, “what you see is what you get”, “it is what it is” is not always true.
So what is a data warehouse and how best to build one? Well, if you are thinking about owning a data warehouse or building one, remember one thing, a data warehouse is a strange way of describing what actually happens and how data can be used to maximum leverage. The correct non-static use of the term is data warehousing.
Data warehousing is an ongoing process
Data warehousing is a process. Not something you buy and install. That would more than likely be a reporting system telling you about what happened at a point in time. And if not integrated then the point in time data would be telling you about one of the subset organisations one at a time. When integration is not the approach then guaranteed your data and information will be out of synch and inaccurate from a group perspective, from the CEO perspective, from the board perspective.
Briefly the data warehousing process is as follows.
Extract data from several data sources, multiple subset entities, multiple systems, and transform the data for business context, load it into a dedicated system (repository) outside of the operational systems, then query the data and run queries and reports for the benefit of business users against the data in the repository.
That is what is considered a “left-to-right process” as shown in most data warehousing schematics.
But that is a technical view.
Business-led, right-to-left process
An effective and much better approach to data warehousing is a “right-to-left” process. This is where you start with an understanding of strategic business and operational objectives and then progressively moving “leftwards” through the repository to help define the data model or schema to support business strategy.
That in turn is used to identify what data is needed to provide the right type of data, from one or several data sources. This process helps build a prioritised roadmap across the conglomerate subsets and at the group level, help avoid data and cost redundancy.
Technical approaches more often than not result in a sound data processing environment that will not satisfy strategic intent.
We need the data to inform our thinking not the other way around. Not us telling the data what it should mean.
For example when you go to your GP to treat an ailment, they do ask you questions, take your temperature, blood pressure and other measures before they diagnose you for treatment. They don’t prescribe your treatment and then make it fit what you and they think the diagnosis and ailment should be.
The problem is not what you don’t know, but what you do know that is not quite so.
Naleen Nageshwar is a data and digital strategy consultant.
A Fijian citizen based in Sydney, he runs his own consulting practice Data4Digital and is managing partner Australia, NZ, and Pacific for AlphaZetta Data Science and analytics consulting.
For questions and feedback to naleen@data4digital.com.
