Original URL: https://www.theregister.co.uk/2010/05/04/enterprise_analytics/

Why Enterprise Analytics - and why now?

It's big and it's heading your way

By Dan Olds, OrionX

Posted in HPC, 4th May 2010 13:06 GMT

I’ve been bloviating about predictive enterprise analytics for more than a year now, discussing it with clients, vendors, my mom, the kid who mows my lawn, and anyone else who will listen. I think it’s going to be the ‘next big thing’ in business, and thus enterprise technology.

The last time I saw a tidal wave this huge on the horizon was when I began working on virtualization (then called ‘server consolidation’) back in the mid-90s. In my mind, virtualization had to happen, because having expensive assets sit around mostly unused was something that the market was going to solve sooner or later. The rise of predictive analytics will likewise be driven by economic forces and trends.

The traditional barriers to competition, like geographic location, transportation and tariffs, have been eroded to a great extent by the move to globalization. In economic terms, the basic factors of production, land, labor, and capital can now be deployed pretty much anywhere in the world where there is a competitive advantage.

Companies can build factories or staff services wherever they are needed, or wherever local conditions are advantageous. Any of us can (with some cash, of course) take a buying trip to, say, China and buy the same items that major retailers stock here. We could then sell them over the Web and have FedEX or UPS handle our logistics.

As it’s so easy to get into business these days, it’s ramped up competition. Companies can pursue profitable niches in adjacent or even totally different markets. Making things more difficult is that fact that instantaneous worldwide communication via the web has put more leverage into the hands of potential buyers than ever before.

Think about your own buying behavior. Me, I use the web to research purchases and to price shop. I will then take a look at local options (in case I might need to return it) and make the buying decision based on the importance of the item and price differential (if any). It’s quick and easy to find multiple products and to run down their specifications.

I can also find out how others have fared with the product I want and drop it from my short list if I were to hear, for example, that it caused hair loss or unsightly rashes. (I once had a keyboard that I swear caused both. Plus it made my fingers hurt, and it spelled badly.)

Globalization and the internet have leveled almost all of the playing fields, making competition much more direct and brutal. No firm has the market power to achieve sustainable advantage over its competitors or customers. It’s easy for new guys to come in and eat your lunch with a better mousetrap than yours, at a lower price – one that they came up with after reverse-engineering and improving your “Mouse Assassin 5100” model.

There isn’t much secret sauce in this world; it’s hard to legally protect innovations, and motivated engineers can come up with workarounds and different techniques to satisfy the customer with an equivalent or better product – or they just crank up the marketing to get customers to believe that they are getting a better deal.

Our assertion that business is getting more brutal is backed up by data. A recent set of studies by the management consulting gurus at Deloitte offers an in-depth look at the causes and effects of globalization, the Internet, and the rapidly accelerating pace of change. They call this “The Big Shift” and reports are available here (pdf). While I don’t necessarily agree with all of their conclusions and prescriptions, they have done a great job of analyzing and quantifying the impact on business. Several data points are particularly telling:

US Corporate Return on Assets (ROA) is, on average, 75 per cent lower now than it was in the mid-1960s. According to Deloitte’s research, real (inflation-adjusted) ROA in 1965 was 4.7 per cent; it’s been steadily declining over the last 43 years to 0.5 per cent in 2008. ROA is a reasonably good measure of corporate profitability because it takes into account the investment required to generate returns – the buildings, tools, equipment, etc. It’s also not distorted by the debt and financial engineering that has become so prevalent in many industries.

The intensity of competition in the US has more than doubled (as measured by the Herfindahl-Hirschman Index). This calculation looks at the concentration of competitors in an industry and their market share. An industry or segment where a few players own the large majority of the market isn’t nearly as intense as an industry where a greater number of firms split the market between them. According to Deloitte’s calculations for the 50 largest US firms, the HHI has dropped from 1.4 to .06 since 1965 – signifying a much more competitive environment for at least those companies, and most likely for the economy as a whole.

Competitive positions in every industry change over time. But in the last few decades, the rate at which large, successful companies have lost their competitive position has more than doubled. They somehow lost a step to established competitors – or new entrants came into their market and ate their lunch.

In my mind, all of the above data really points to a major shift of power from suppliers to buyers. If this is the case, then what can a business do to compete? Can they do anything to protect their markets and margins? Or is it inevitable that most margins will be driven down to the point where only the lowest-cost players survive? Those aren’t easy questions to answer, particularly by an amateur economist.

There are markets where undifferentiated, dog-eat-dog competition has existed for decades. The best real-world example of this type of environment is the financial services industry. Think about portfolio managers: they’re competing against hundreds of others, making decisions based on the same publicly available information, and trying to eke out a few more basis points than the other guy.

To make their trading decisions and guide their short- and long-term strategy, they have built huge and highly complex models that attempt to mimic reality and predict future prices based on current trends and conditions. These models are their crown jewels, and are closely guarded.

They don’t stop at analyzing particular companies but also consider economic trends, demographics, societal evolution, and just about anything else that might have an effect on what they’re trying to predict. Regression helps them characterize the relevance and predictive strength of a particular factor, while techniques like Monte Carlo are used to compute the probability of the result or condition.

This type of analysis has gotten a black eye due to the recent financial meltdown arising from the mortgage-backed securities debacle. Our research into the root causes lead us to believe that the failure wasn’t in the models, but in the values input into them and the idiots who relied upon them (see our previous article). The bottom line is that the math works, but the models and the humans interpreting them can often be flawed.

But just as the crappy bookshelves I build aren’t an indictment of power screwdrivers as tools, bad results from mistaken modeling doesn’t mean the modeling techniques don’t work – they do. And it’s the next phase in business. We’re going to see more and more companies attempting to model everything from their supply chain to long-term customer behavior in a much deeper and more predictive sense than before.

While some may say that we’ve been doing this all along, I’m not so sure. Most of the business intelligence that I’ve seen in the real world tends to use company-generated data to make short-term predictions. Many companies still don’t have their internal BI systems to a point where they can be used to run through scads of data (1 scad = a hell of a lot of data) to find non-intuitive relationships.

In many cases, management uses their decision support systems to support their hunches and speculations rather than let themselves and their strategies be guided by the data. Part of this is because they don’t feel that the data is reliable – and they’re often right about this. But they are also fearful that letting data actually drive their strategy means they are less valuable. Like most important trends, the adoption of predictive enterprise analytics and what we’re calling the “Analytics-Led Enterprise” will be hindered more by internal culture than by technical factors.

As the trend emerges it’s going to have definite implications on the IT market as a whole and HPC in particular. These business problems look awfully HPC-like when you consider the workloads and scale. The same folks who can analyze and predict how gas molecules will react in a pipe can use their tools to model how shoppers will move through a crowded store… and come up with predictions of how changes will impact traffic at the cash register. It’s all data and math, right?