Drink the Kool-Aid? Yes, But Pick When You Drink It

In the business world, drinking the kool-aid refers to an employee’s willingness to commit fully, without hesitation and without cynicism, to their organization’s and boss’s objectives–to be a fully engaged team member.

I was thinking about a friend who recently changed jobs. He is a smart guy and always has two or things running in parallel–backup plans in case the primary activity fails. I had suggested that for the moment, he needs to drink the Kool-Aid on his primary activity. He needed to stop keeping options actively in play once he made his primary choice as maintaining options sometimes has its price. The idea was to stop thinking that the current gig was temporary. Was I wrong to recommend this?

HBR recently had a short article that suggested there is a real cost to making backup plans. The fundamental question goes something like this:

“When we think about what we’ll do if we fail to achieve our goals, are we less likely to succeed?”

The answer, according to Jihae Shin the principal investigator, is mostly a “yes.” His research concluded that people who made back-up plans achieved their goals less often than others who did not have back-up plans. But his findings did not say *not* to make back-up plans. Instead, you should be more thoughtful about the timing and level of effort you put into your backup planning.

That makes sense. Different people operate differently. For example, we want a backup plan for our son, who is focusing on a music career in college. But we do not want him spending a lot of time on the backup plan *now*. We do not want our son to be distracted from his focus on music *now*, in order to prepare later for a different career later which he may never pursue. We encourage him to think of options but to the point that at the expense of his current focus.

I think my suggestion makes sense specific to my friend’s situation. I was not suggesting that he forgo multiple threads running, but that he fully commit to the one in front of him and assume that this choice would be the solution for a very long time.

The idea is to take the opportunity as far as it will go and only then get the backup plans moving along. It was really a suggestion to stop thinking that the current objective would not be achieved and to avoid the distraction of trying to line up alternate plans prematurely.

At the right time, even temporarily, go all in, get the tee-shirt, buy the mug, think that your organization is great even if it has warts, adopt its strategy–drink the Kool-Aid. Pick a time, later, to consider options.

The future of CRM application software – today’s tech can rebaseline the norm

CRM applications used by the frontline have been around for around 20-30 years. My first consulting job was designing a CRM portal for wealth management advisors distributed around the country. Technically, it was web based and was a bit of a reach at the time but it was highly innovate and essentially had all the moving parts you see in CRM applications today. Over time, I went on to design and launch many more CRM applications covering a broad range of areas some of which won awards or were highly placed. CRM apps cover a wide range of touchpoints usages and my focus here are those CRM apps used by the frontline when the engage with the customer.

The world of CRM apps has not changed much. Today’s CRM apps are slicker, more integrated an easier to be program. But overall, they still fundamentally are hard to use, hard to enforce a process with and generally try to force you to enter in structured data all for the explicit purpose of using that data an the backend side.

In other words, the way you interact or want to interact with a customer–a fluid dance of conversations and touchpoints–comes to a jarring halt when you have to type your customer “data” into a relatively fixed, confining CRM application on your screen. Even the marketing automation space has learned that it screwed up as it realized that email campaigns have become old school and the nuances of social media marketing and messaging are the new black. After all, a growing majority of people today use email less than the previous generation, significantly less.

What is the future?

The future is not narrow list of checkboxes, pick lists, small text boxes or small fields to capture one specific concept, like the first name.

Instead the future is fluid and free flowing, much like many of the newer collaboration tools just now gaining prominence in small companies and now larger companies. It’s more about “notes” and small snippets of information versus structured screens. It’s more about searching different locations for data about customers and not requiring that all information be managed in a single tool. It’s about automating the interactions so that the right information is available to personalize a touchpoint.

Evidence for this model abound:

  • CRM applications now have “chatter” or “posts” that capture a stream of unstructured notes and objects like pictures or audio clips.
  • Applications like “slack” show that collaboration and documentation is easier when it’s fluid, in context and completely searchable. Trello is the same way.
  • Many CRM applications capture only a few structured fields and most of the complexity is really around trying to capture additional customer information–which is where the application start becoming unwieldy.
  • Most CRM software tries to tie together a 360 degree view of the customer using various ad-hoc methods of integrating with other applications.  They shoehorn that “app’s” data into the CRM application to get a 360 degree view of a customer. These integration costs are often the largest costs in a CRM project.
  • CRM has started to rely on data mining and machine learning algorithms to help the advisor/rep become more productive about how to spend their time at the same time they personalize communication to the customer.
  • CRM automation is increasing as bots and other automation techniques become more prevalent…for some products and channels, customers prefer automation.

Now CRM is more than just capturing information about customers, it’s also about servicing them and using information, again in context, to order their products, resolve their issues or try to understand their behavior. Getting information from other applications into the context “flow” has proven to be very tricky.

It’s true that some data, like an order, is highly structured and needs to be in sequence properly to support the supply chain, that’s fair. But a lot of CRM data does not need the same amount of structure. When interacting with a company’s rep or a automated systems, the needs are much different. CRM apps do need to digest data of different media types and tell you what’s important. Or, at the very least, sort through the data and summarize it for you.

In other words, the future of CRM is really more like an instant messaging program like Slack or a free-form note taking application OneNote or collaborative management tool like Trello then an application framework like popular CRM platforms today. Think tweets and hashtags and AI driving data record enrichment.

It’s not about checkboxes anymore. Sales people do not really like check checkboxes. Text mining, or unstructured analysis–whatever you want to call it–is mature enough to sort through the data and fined postal addresses, email addresses, phone numbers and linkage information to connect all the dots and prepare the data for analytical use. Network analysis is mature enough to create a graph of contacts, with context, from your email and notes. This crystal ball thinking is true for both B2C and B2B although B2B has regulatory issues that suggest that it does require some additional “structure.” In fact, these techniques are in play in extremely advanced CRM scenarios such as Know Your Customer in the AML/BSA space.

A lot of what passes today for CRM software is just a jumble of straight jackets that are unneeded and run counter to how people communicate, create information and collaborate today.

Branding, advertising and social media

There were two articles this week/month on social media advertising that did not seem to overlap per se but are related.

The first is in HBR, March 2016 issue titled “Branding in the Age of Social Media.” (here) This article suggests that companies have spent billions on trying to build out their brands using social media but most of the money and effort has been a waste. The basic idea idea is that branded content and sponsorships in the past used to work because there were limited channels of distribution for the content and therefore most consumers had limited choices and had to watch what was shoved into those channels.

Today, it’s a bit different. The mulitude of channels means that consumers can filter out ads, shape their own customized content flows and create their own flow of entertainment content–much of it created by their friends. Rather suddenly, brands no longer could command the audience. The article mentions that most heavily branded companies such as Coca-Cola command less viewership than two guys sitting on a couch narrating video games (“e-sports”). Now, brand must fit into the flow of either “amplified subcultures” (groups of people with more narrow interests) or “art worlds” where new creative breakthroughs occur. Either way, you have to fit in via cultural branding where you align the brand around the culture of people in those two areas. So the brand can be there but only in the context of say, for example, the subculture of people who do not like smelly socks that come from running 10 miles a day. You have to create a story about smelly socks and positioning your laundry detergent as part of addressing the smelly socks problem (I made up the smelly socks example).

You essentially align the product/brand around a more specific theme that resonates with the target audience. Because the specific themes are more narrow, the amount of creative customization increases.

This is not a new concept. The article is really just saying that you have to create content about your brand/product that aligns with you target audience and is delivered to them through the “channels” that they watch.

I was also scanning Bloomberg Businessweek and their article “If You Don’t Know It By Now You’ll Never Make Millions on Snapchat.” (here) It described the “snapchat” phenomena, with its rapid rise, as well the challenge many similar companies have on maintaining their user volumes. The biggest issue is that they need to generate revenue and Snapchat is considered “expensive” advertising with little insights into “returns.”  One of the strategies Snapchat has taken is to focus their sales time on helping customers create stories to fit into their Discover channels and Snapchat’s model of perishable content. Still, a slightly talented musician posting just his daily musings and activities garners more views than all the biggest networks combined, daily. Ouch!

But it is just another lesson in what we already knew.  Find the audience you want to reach, find out where their eyes are especially now that they more choices about how and where they engage, tailor your content with a message and delivery that will engage them to watch, take action or whatever. Segment, segment, segment…

That’s about it. So yes, branding (and really just general advertising) has changed. It has to be more clever/entertaining, more thoughful and more tailored to a smaller group. You cannot rely on a famous name to push your product alone and you cannot count on blanket reach to communicate.

So there is not really a lot of new news here, just a recognition that we as companies and marketers have to be more clever because the easy ways no longer work and it’s possible to get a huge ramp (given the viewing numbers) if we put that cleverness to work.

Perhaps the real news is that some people in their current jobs need to become more clever quickly or find some clever people to help them with their branding/marketing. What is wonderful at least to me, is that the volumes of eyeballs in some of these channels makes them worth paying attention to.

Got it.

Check.

Roll credits.

Platform Scale

Sanjeet Choudary has put out a book about how platforms, not pipes, are the new business model. The book is very inspiring so I recommend reading it. There are not any new ideas in it but they are packaged together very nicely. It’s very much another “explaining things” book and for the lens that it wants you to use, I think it does a good job.

The key thought behind the book is actually fairly simple:

Be a middleman. Reduce your costs as a middleman to gain share. Shift cost and risk out to everyone else, as much as possible. Allow companies to build on your platform. Reducing your middleman costs can gain you share and the best way do that is to be digital. If you only make a small slice of money at every interaction, you need alot of interactions so don’t forget the “make it big” part.

That’s really about it. There’s not alot of examples with deep insight in the book and he avoids most levels of strategic thinking entirely. The book also fails to connect what has been going on today to the massive “platforms” built in the past few decades but which are not necessarily fully digital as in the examples reused in the book. The book spends most of its pages explaining that if you can reduce transactions costs and get scale, the wold is your oyster. Of course, this is only just one model of succeeding in business and actually not always the most interesting or sustainable.

But that’s OK. Go find your “unit,” reduce that friction and make a billion. It’s a good read.

Enjoy!

Why I Like Fishing – It’s Not What You Think

Yesterday, my family went on a fishing trip.

We keep a twenty-one foot, center console fishing boat over on the Eastern Shore just off the Chester River. The Chester feeds the Chesapeake Bay. The mouth of the Chester is about 1 mile north of the Bay Bridge.

There were four of us, my wife and I and our two sons. I bought sandwiches and some chips at the nearby Safeway and we had each had our own water jugs. We brought 10 fishing poles. Four poles are heavy duty and are designed to catch larger fish deeper in the bay (around forty to fifty feet in the main channel). We had our planar boards with us to spread out the lines but we used them only once.

This was October–the stripers had just started running. The stripers (aka Rockfish) become larger by November, but we were out early to see what we could catch. Most of the time we did the following:

  • Jet out to the middle of the channel.
  • Look for bird flocks on the water.
  • Jet the boat to the seagull flock, along with several other fishing boats.
  • Fish with individual poles using a variety of lures. My youngest son is an expert fisherman so he knew which lures to use for each situation.
  • Try not to hit the other boats.
  • Catch fish.
  • Release those that were too small.
  • Catch seagulls, by accident.
  • Untangle the seagulls, unharmed.
  • When the seagulls picked up and moved, following the fish, jet to the new spot.
  • So we, along with alot of other fisherman, move from flock to flock, jetting around in the water, trying to catch legal sized fish.

That’s it! We did that for half the day.

Our “charter” started late because I was late from a Saturday meeting. We left around 1:30pm on the boat and came back right after sunset, around 6pm. As we returned to the Chester after sunset, we were not paying close attention to driving and almost hit a dock, but that’s another matter that my youngest son can explain one day to his kids when discussing boat safety.

It was wonderful weather, not too cold. Skies were overcast which kept it cooler–good for fishing of course. We had forgotten to fill the oil reservoir so the oil engine light kept coming on. We had plenty of oil, the reservoir was just low that’s all.

After the trip, we came back and had some delicious crab cakes at the house with my wife’s mom. The crabcakes were from the Bay Shore Steam Pot in Centerville. I think they are the best crab cakes on the Eastern Shore and the shop is very close to where we keep our boats.

It was our older son’s eighteenth birthday. He had wanted to go fishing. The night before, we went to a jazz concert with the Anderson twins (sax, clarinet and flute) and Alex Wintz (guitar), known as the Peter and Will Anderson Trio, in Baltimore at the fabulously cool An die Musik. Fabulous concert. All of the chairs were oversized and full of padding; relics from a regal hotel no doubt. Front row seats. The jazz seemed to infuse the next day’s boating trip.

It seemed to me that fishing was about getting things done and working together, like jazz, versus pop music or old style rock and roll both of which have a different type of energy.

Overall, we caught around forty fish but only a few were keepers. Stripers need to be twenty inches to keep, and our largest was seventeen. No matter.

While you can still catch a fish on a simple fishing pole off the dock, the larger fish need to be found. You need the right gear but it’s not excessive. You need to know some techniques to catch alot of fish to find the few keepers. You need to work as a team since steering, fishing and keeping your eyes open for the bird flock is hard for just a single person to do. My wife and I did less fishing than the kids but we helped as much as we could having been relegated to deck hands. My wife took alot of pictures and I sneaked in a few. We were fortunate to grab some pictures below the Bay Bridge with the bridge framing our fishing activities.

As we headed back to the dock for the night, I thought this was the nicest family weekend in a long time. We all worked well together on a small boat and got things done. Everything seemed to come together and it felt good. My youngest son captained the boat and it was my older son’s birthday. In a crazy, fast world, we spent a little slice of time trying to catch a few fish, together. Perhaps the fish were really not the point.

That’s why I like fishing.

 

 

yes, yet another bigdata summary post…now it’s a party

Since I am “recovering” data scientist, I thought that once in awhile, it would be good deviate from my more management consulting articles and  eyeball the bigdata landscape to see if something interesting has happened.

What!?! It seems like you cannot read an article without encountering yet another treatise on bigdata or at the very least, descriptions of the “internet of things.”

That’s true, but if you look under the hood, the most important benefits of the bigdata revolution have really been on two fronts. First, recent bigdata technologies have decreased the cost of analytics and this makes analytics more easily available to smaller companies. Second, the bigadata bandwagon has increased awareness that analytics are needed to run the business.  Large companies could long afford the investments in analytics which made corporate size an important competitive attribute. The benefits from analytics should not lead to a blanket and unthoughtful endorsement of analytics. Not every business process, product or  channel needs overwhelming analytics. You want, however, analytics to be part of the standard toolkit for managing the value chain process and decision making.

The ability to process large amounts of data, beyond what mainframes could do, has been with us for years-twenty to thirty years The algorithms developed decades ago are similar to the algorithms and processing schemes pushed in the bigdata world today. Teradata helped created the MPP database and SQL world. AbInitio (still available) and Torrent (with their Orchestrate product sold to IBM eventually) defined the pipeline parallelism and data parallelism data processing toolchain world. Many of the engineers at these two ETL companies came from Thinking Machines. The MPI API defined parallel processing for the scientific world (and before that PVM and before that…).

All of these technologies were available decades ago. Mapreduce is really an old lisp concept of map and fold which was available in parallel from Thinking Machines even earlier. Today’s tools build on the paradigms that these companies created in the first pass of commercialization. As you would expect, these companies built on what had occurred before them. For example, parallel filesystems have been around for a long time and were present on day one in those processing tools mentioned above.

Now that the hype around mapreduce is declining and its limitations are finally becoming widely understood,  people recognize that mapreduce is just one of several parallel processing approaches. Free from the mapreduce-like thinking, bigdata toolchains can finally get down to business. The bigdata toolchains realize that sql query expressions are a good way to express computations. Sql query capabilities are solidly available in most bigdata environments. Technically, many of the bigdata tools provide “manual” infrastructure to build the equivalent sql commands. That is, they provide the parsing, planning and distribution of the queries to independent processing nodes.

I consider the current bigdata “spin” that started a about 1-2 years ago healthy because it  increased the value of other processing schemes such as streaming, real-time query interaction and graphs. To accommodate these processing approaches, the bigdata toolchains have changed significantly. Think SIMD, MIMD, SIPD and all the different variations.

I think the framework developers have realized that these other processing approaches require a general purpose parallel execution engine. An engine that AbInitio and others have had for decades. You need to be able to execute programs using a variety of processing algorithms where you think of the “nodes” as running different types of computations and not just a single mapreduce job. You need general purpose pipeline and data parallelism.

We see this in the following open-source’ish projects:

  • Hadoop now as a real resource and job management subsystem that is a more general parallel job scheduling tool. It is now useful for more genera parallel programming.
  • Apache Tez helps you build general jobs (for hadoop).
  • Apache Flink builds pipeline and data parallel jobs. Its also a general purpose engine e.g. streaming, …
  • Apache Spark builds pipeline and data parallel jobs. Its also a general purpose engine e.g. streaming, ..
  • Apache Cascading/Scalding builds pipeline and data parallel jobs, etc.
  • DataTorrent: streaming and more.
  • Storm: Streaming
  • Kafka: Messaging (with persistency)
  • Scrunch: Based on apache crunch, builds processing pipelines
  • …many of the above available as PaaS on AWS or Azure…

I skipped many others of course and I am completely skipping some of the early sql-ish systems such as hive and I have skipped  visualization, which I’ll hit in another article. Some of these have been around for a few years in various stages of maturity. Most of these implement pipeline parallelism and data parallelism for creating general processing graphs and some provide sql support where that processing approach makes sense.

In addition the underlying engines, what’s new? I think some very important elements: usability. The tools are a heck-of-alot easier to use now. Here’s why.

What made the early-stage (20-30 years ago) parallel processing tools easier to use was that they recognized, due to their experience in the parallel world, that usability by programmers was key. While it is actually fairly easy to get inexpensive scientific and programming talent, programming parallel systems has always been hard. It needs to be easier.

New languages are always being created to help make parallel programming easier. Long ago, HPF and C* among many were commercial variations of the same idea.  Programmers today want to stay within their toolchains because switching toolchains to run a data workflow is hard work and time consuming to develop. Many of today’s bigdata tools allow multiple languages to be used: Java, Python, R, Scala, javascript and more. The raw mapreduce system was very difficult to program and so user-facing interfaces were provided, for example, cascading. Usability is one of the reasons that SAS is so important to the industry. It is also why Microsoft’s research Dryad project was popular. Despite SAS’s quirks, its alot easier to use than many other environments and its more accessible to the users who need to create the analytics.

In the original toolsets from the vendors mentioned earlier in this article, you would program in C++ or a special purpose data management language. It worked fine for those companies who could afford the talent that could master that model. In contrast to today, you can use languages like python or scala to run the workflows and use the language itself to express the computations. The language itself is expressive enough  that you are not using the programming environment as a “library” that you make programming calls to. The language constructs are  translated into the parallel constructs transparently. The newer languages, like lisp of yore, are more functionally oriented. Functional programming languages come with a variety of capabilities that makes this possible. This was the prize that HPF and C* were trying to win. Specialized languages are still being developed that help specify parallelism and data locality without being “embedded” in other modern languages and they to can make it easier to use the new bigdata capabilities.

While the runtimes of these embedded parallel capabilities are still fairly immature in a variety of ways. Using embedded expressions, data scientists can use familiar toolchains, languages and other components to create their analytical workflows easier. Since the new runtimes allow more than just mapreduce, streaming, machine learning and other data mining approaches suddenly becomes much more accessible at large scale in more ways than just using other tools like R and others.

This is actually extremely important. Today’s compute infrastructure should not be built with rigid assumptions about tools, but be “floatable” to new environments where the pace of innovation is strong. New execution engines are being deployed at a fantastic rate and you want to be able to use them to obtain processing advantages. You can only do that if you are using well known tools and technologies and if you have engineered your data (through data governance) to be portable to these environments that often live in the cloud. It is through this approach that you can obtain flexibility.

I won’t provide any examples here, but lookup the web pages for storm and flink for examples. Since sql-like query engines are now available in these environments, this also contributes to the user-friendliness.

Three critical elements are now in play: cost effectiveness, usability and generalness.

Now its a party.

Do sanctions work? Not sure, but they will keep getting more complex

After Russia and Ukraine ran into some issues a few months back, the US gathered international support and imposed sanctions.

Most people think that sanctions sound like a good idea. But do they work?

Whether sanctions work is a deeply controversial topic. You can view sanctions through many different lenses. I will not be able to answer that question in this blog. It is interesting to note that the sanctions against Russia over the Ukraine situation are some of the most complex in history. I think the trend will continue. Here’s why.

Previously, sanctions would be imposed on a country that is doing things the sanctioning entity does not want to happen. Country-wide sanctions are fairly easy to understand and implement. For example, sanctions against Iran for nuclear enrichment. Sanctions in the past could be levelled at an entire country or a category of trade e.g. steel or high performance computers. But they have to be balanced. In the case of Russian and Ukraine, the EU obtains significant amounts of energy from Russia.  Sanctions against the energy sector would hurt both the EU and Russia.

Sanctions today often go against individuals. The central idea is to target individuals who have money at stake. OFAC publishes a list of sanctioned individuals and updates in regularly. If you are on the list, you are not allowed to do business with those sanctioned individuals, that is, you should not conduct financial transactions of any type with that individual (or company).

The new Russian sanctions target certain individuals, a few Russian banks (not all of them), and allows certain forms of transactions. For example, you cannot transact with a loan or debenture longer than 90 days maturity or new issues. Instead of blanket sanctions, its a combination of attributes that apply as to whether a financial transaction can be made.

Why are the Russian sanctions not a blanket “no business” set of sanctions?

By carefully targeting (think targeted marketing) the influences of national policy, the sanctions would hurt the average citizen a bit less, perhaps biting them, but no so much that the average citizen turns against the sanctioning entity. Biting into the influencers and others at the top is part of a newer model of making individuals feel the pain. This approach is being used the anti-money laundering (AML) and regulatory space in the US in order to drive change in the financial services industry e.g. hold a chief compliance officer accountable if a bad AML situation develops.

So given the philosophical change as well as the new information-based tools that allow governments to be more targeted they will keep getting more complex.

Oso Mudslides and BigData

There was much todo about google’s bigdata bad flu forecasts recently in the news. google had tried to forecast flu rates in the US based on search data. That’s a hard issue  to forecast well but doing better will have public benefits by giving public officials and others information to identify pro-active actions.

Lets also think about other places where bigdata, in a non-corporate, non-figure-out-what-customers-will-buy-next way, could also help.

Let’s think about Oso, Washington (Oso landslide area on google maps)

Given my background in geophysics (and a bit of geology), you can look at Oslo, Washington and think…yeah…that was a candidate for a mudslide. Using google earth, its easy to look at the pictures and see the line in the forest where the earth has given way over the years. It looks like the geology of the area is mostly sand and it was mentioned it was glacier related. All this makes sense.

We also know that homeowner’s insurance tries to estimate the risk of a policy before its issued and its safe to assume that the policies either did not cover mudslides or catastrophes of this nature for exactly this reason.

All of this is good hind-sight. How do we do better?

Its pretty clear from the aerial photography that the land across the river was ripe for a slide. The think sandy line, the sparse vegetation and other visual aspects from google earth/maps shows that detail. Its a classic geological situation. I’ll also bet the lithography of the area is sand, alot of sand, and more sand possible on top of hard rock at the base.

So lets propose that bigdata should help give homeowners a risk assessment of their house which they can monitor over time and use to evaluate the potential devastation that could come from a future house purchase. Insurance costs alone should not prevent homeowners from assessing their risks. Even “alerts” from local government officials sometimes fall on deaf ears.

Here’s the setup:

  • Use google earth maps to interpret the images along rivers, lakes and ocean fronts
  • Use geological studies. Its little known that universities and the government have conducted extensive studies in most areas of the US and we could, in theory, make that information more accessible and usable
  • Use aerial photography analysis to evaluate vegetation density and surface features
  • Use land data to understand the terrain e.g. gradients and funnels
  • Align the data with fault lines, historical analysis of events and other factors.
  • Calculate risk scores for each home or identify homes in an area of heightened risk.

Do this and repeat monthly for every home in the US at risk and create a report for homeowners to read.

Now that would be bigdata in action!

This is a really hard problem to solve but if the bigdata “industry” wants to prove that its good at data fusion on a really hard problem that mixes an extremely complex and large amount of disparate data and has public benefit, this would be it.

Yanukovych, Money Laundering and a Probe: The Rise of Network Analytics

I have been working in the Anti-Money Laundering (AML) for awhile. Compared to healthcare or the more general Customer Relationship Management (CRM) space, the AML and Bank Secrecy Act (BSA) is really the “shady” side of the customer–or at least it assumes that some customer are a shady and tries to find them or prevent their actions. Some estimates suggest that the aggregate impact of BSA/AML (and Fraud) regulations is only 10-20% of the total amount of dollar flow in the world so we know that while regulators and prosecutors do catch some of the bad guys alot of dollars remain on the table.

Take the recent case of the Ukraine. It’s been reported that the Swiss are launching a money-laundering probe into ousted president Viktor Yanukovich and his son Oleksander. They think the money laundering could amount to tens of billions. All told, over 20 Ukrainians are listed as targets of the Swiss probe.

In BSA/AML, Yanukovichs (father and son) is a clearly a Politically Exposed Person (PEP). And apparently the son had a company established that was doing quite well. That information usually leads to flags that up the risk score of a customer at a bank. So an investigation and PEP indicators are all good things.

Officials estimate that $70 billion disappeared from the government almost overnight. Of course, Yanukovich WAS the president of Ukraine and he was on the run up until last week. But an investigation into money laundering on tens of billions that suddenly just happened?

Recently, I attended an ACAMs event in NYC. Both Benjamin Lawsky (regulator side) and Preet Bharara (prosecution side) spoke. One of their comments was that to have a real impact on money-laundering, you have to create disincentives so that people do not break the law in the future. You can sue companies, people and levy fines. These create disincentives and disincentives are the only scalable way to reduce money-laundering–stop it before it starts. The ACAMs event was US based, but the ideas are valid everywhere. The Swiss have always had issues with shielding bad people’s money but they are playing better than before.

But the real issue is that the conduits, the pathways, were already setup to make this happen. And most likely, there have been many dollars siphoned off with the list $70 billion being the end of the train. So the focus needs to be on active monitoring of the conduits and the pathways, with the BSA/AML components being one part of monitoring those paths. After all, the BSA/AML regulations motivate a relatively narrow view of the “network” with an organization’s boundaries.

If we want to really crack down on the large scale movement of funds, it will not be enough to have the financial institutions–which have limited views into corporations–use traditional BSA/AML and Fraud techniques. A layer of network analysis is needed at the cross-bank level that goes beyond filing a suspicious activity report (SAR) or a currency transaction report (CTR). And this network analytical layer needs to be intensely and actively monitored at all times and not just during periods of prosecution. While the Fed uses the data sent back from a company’s SAR and CTR (and other reports) and in theory acts at the larger network level, it is not clear that such limited sampling can produce a cohesive view. Today, social media companies (like Facebook) and shopping sites (like Amazon) collect an amazing amount of information at a detailed level. NSA tried to collect just phone metadata and was pounced on. So the information available in the commercial world is vast, that which the government receives is tiny.

In other words, the beginnings of an analytical network is clearly present in the current regulations, but the intensity and breadth of the activity needs to match the scale of the problem so that the disincentives dramatically increase. And while it is very difficult to make this happen across borders or even politically within the US, its pretty clear that until the “network analysis” scale either increases its “resolution” or another solution is found, large scale money laundering will continue to thrive and most enforcement efforts will continually lag.

Its a balancing act. Too much ongoing monitoring is both political anathema to some in the US and it can be very costly. Too little and the level of disincentives may not deter future crimes.

Pop!…another $70 billion just disappeared.

Should companies organize themselves like consultancies? If they do, they need to hire like them as well.

A recent HBR article (October 2013)  mentioned that P&G and other companies are rethinking how they organize themselves. The basic idea is that instead of having fixed organizations, companies should organize themselves like consultancies–everything is a project and you assemble/disassemble teams as needed to solve problems. There will be some ongoing operations that do require “flat” jobs–jobs that more repetitive but still require knowledge workers?

The article begs the question of whether organizing into projects and flexible staff (like consultancies) is a good thing for companies that are heavily knowledge worker based. Part of the proof that knowledge work is becoming more dominant is by looking at the decreasing COGS and increasing SG&A lines on financial statements. COGS indicates decreasing amounts of “blue-collar” work over time while SG&A is a good proxy for white-collar, knowledge worker type jobs.

So is it?

My view is that it is not so cut and dry. Consultancies create large labor pools at the practice area level that generally have a specific industry expertise. Generally, there are horizontal practices for people who specialize in skills that cut across industries. Typically, these practice areas are large enough that the random noise (!) of projects starting and stopping creates a consistent utilization curve over time. And a management structure, for performing reviews, connecting with people, is still needed to ensure consultants feel like they have a home.

Another important aspect quoted in the article is the creation of repeatable methodologies that consultants are trained on so that knowledge can be codified instead of horded.

Consultancies are good, but not super great, at knowledge management and sharing deliverables so that practices that have proven themselves to work can be re-used in other projects or contexts.

Let’s look at companies:

  • Companies have people, often fairly substantial groups, that are focused on a horizontal area e.g. finance, marketing, IT, customer service. Companies are often organized by product which also forces it to be organized by industry, but there are many variations to this model.
  • Companies try to organize activities into projects. Not everything can be a project e.g. ongoing operational support of different kinds. But companies do try to kick-off efforts, set deadlines, integrate teams from different groups, etc.
  • Companies share deliverables from one project to another. Unlike consultancies, the pool of deliverables is often narrower because of the corporate boundaries and sharing within an industry are often not as robust as in a consultancy. Companies that hire talent from the outside frequently can bring these elements in, however.
  • Groups share resources, although not as robustly as consultancies, across projects and groups. Companies are less robust at true sharing because inside of companies, people count is often a measure of power. At consultancies, revenue and margin usually are the primary metric, but of course, these are only achieved through resources.

Companies today are already employing many elements of what this model calls out. Most companies are not as robust as consultancies at some aspects. But are these differences the primary reason why consultancies have shown good resilience to execution in different circumstances?

There is probably another aspect. Consultancies typically seek out and retain a large amount of quality talent. Companies, to varying degrees, do not always hire highly talented individuals. Their pay, performance management approach and culture do not attract the best talent in the marketplace.

While companies could improve certain areas of their capabilities, there was an entire part of the story that was missing in the HBR article–a focus on top talent across the entire company and not just for a few key roles.