“Common Sense” Project Management (Part 3 of a 5-part Series)

Thursday, August 5th, 2010

Adequate communications with BOTH the customer and the project team is key.

Communications is a critical component of any successful project. It governs not only how well the project team works together, but also impacts the public perception of how well the project is going. Communications is a multi-faceted function that serves a variety of different purposes. I would like to focus on just two aspects of project communications; managing the customer’s perception of the project, and internal project team communications.

Managing a customer’s expectations of the project outcome is one of the most important jobs of a project manager. How often have you heard of a company having a real good earnings report, only to have their stock go down because they didn’t “meet analyst’s expectations”? For a project, not meeting expectations often results from poorly defined project requirements and deliverables, combined with inadequate communication of project status, causing the customer to assume capabilities that the project was never designed to deliver. Therefore, it is very important to explicitly communicate exactly what the project will deliver, as well as what it will specifically NOT deliver.

Keeping the customer apprised of detailed project status involves more than just holding periodic project meetings. These can be a big time waster if not properly structured and combined with other communications methods.

One ancillary communications vehicle that I have found useful in the past is to create/maintain a project notebook for each customer stakeholder.  While each project will have different communications requirements, the following list provides some examples of the content that I have found useful in the past:

  • a detailed description of the project requirements
  • important project milestones and deliverables
  • all meeting minutes
  • significant communications with key vendors contributing to the project
  • internal project memos
  • change request logs
  • analysis/tracking of major project risks and issues/problems
  • tracking of key project dependencies – especially with outside entities

Prior to each Prior to each Prior to each Prior to each Prior to each Prior to each project meeting with stakeholders, I send out revisions/updates to the project notebook reflecting the latest project status. This allows us to focus the meetings on important project issues and not waste time on the more routine status items.

You might reasonably argue that some of this information could be considered project minutia that wouldn’t be very meaningful to a project stakeholder. However, providing this level of detail tends to build an element of trust indicating that you’re not holding anything back from the stakeholders. In other words, you are making the stakeholders part of the project team, dissolving the typical “them vs. us” mentality that often exists between the project team and the end customer. Providing this level of information to the customer goes a long way in preventing unrealistic expectations from arising.

The other area of communications that is often undervalued is internal project communications. There is a school of thought that communications with team members should be confined to just those areas that they are working on, the theory being that it helps people focus on their individual tasks at hand, rather than being inundated with information extraneous to their function. While there may be some truth to this, my experience has been that some of this “extraneous” information actually increases people’s effectiveness because they are more aware of how their efforts fit into the global scheme of things. Furthermore, potential integration issues between major project components tend to be identified earlier due to this increased awareness.

A secondary benefit is that a well-informed project team functions more like a team, has higher morale, and presents a more unified project “face” to the customer (i.e. no matter which project member the customer talks to, they get the same information).

In summary, open and honest communications with the customer and internal team members increases the chances of success.  There is an old adage that if you don’t provide adequate information to people, they’ll make it up. Most projects have enough real technical challenges to deal with, without having to address issues caused solely by poor communications that could easily have been avoided.

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Data Analytics: Should We Build Iron Man or R2D2?

Tuesday, July 27th, 2010

Earlier this year, Alex Handy wrote an intriguing article on exploring the future of data analysis, which In this article Handy compared and contrasted two approaches to understanding the ever-increasing stream of data. One approach depends upon building “exoskeletal systems”, which enhance human comprehension. Hardy draws connections to this solution and “Iron Man”. The other approach would depend chiefly on autonomous robots or automated systems. This alternative, Hardy suggests, is more like “R2D2” from Star Wars. Ultimately, Handy concludes that “[d]evelopers should build Iron Man, not R2D2.”

Here at Digital Reasoning, we have been dealing with the challenges of automated understanding of massive amounts of unstructured data for years. Knowing that Tim Estes, our CEO, might have a different view on this issue, I decided to interview him. Tim has worked within the realms of unstructured data analytics, artificial intelligence, and machine learning for the past decade.

The following is our interview:

Jason Beck – In the article, one researcher suggests that developers shouldn’t build analytics robots, but rather “exoskeletal systems”. Do you agree?

Tim Estes - I think that it’s a matter of degree. The range of judgments that a machine can make as a proxy for the human is constantly and necessarily expanding. Even R2D2 was most famous for taking orders from Luke Skywalker trying to accomplish tasks from fixing the X-wing in flight to cracking into computer networks.

Just to be a little more accurate – Iron Man wouldn’t work without an AI that is close to R2D2. Jarvis (the AI program that runs’ the Stark house and the Iron Man suit) is always chatting up Tony Stark about what’s going on with the suit and the risks that are present around him. The Iron Man analogy means we seed the full situational awareness (the sensory and data input space) to the machine with the human making key decisions on the filtered and prioritized information. I think that’s about right.

R2D2 is distinct in having a measure of its own intentionality (i.e. it is autonomous in more dramatic ways than Jarvis/Iron Man suit) but they are much more close than you might think. Should humans get out of the loop in making analytic judgements? No more than we should have pilots out of the loop in flying commercial airlines at this time. But show me a pilot that can fly a 747 without computer assistance and guidance? We are already in the hybrid space. And the complexity of our technology and the explosion of the information created by machines and man assisted by machines means we will need ever increasing automation in understanding.

JB – Doesn’t the exponential growth of data and decreasing levels of available talent necessitate automated systems?

TE - Exactly. The notion that “augmented intelligence” can solve the full data problem is wishful thinking. Something has to read everything and that can no longer be a human as a matter of scale. We have to make strides to catch up intelligent systems with the complexity and scale of the data we are being inundated with.

JB – Is this an Either-Or situation? Just because someone may prefer automated systems, does this assume that there won’t be any human in the loop?

TE - I think that’s the real issue – where is the dividing line right now and where is it going to be in 5 years? Right now – machines have to read and organize everything. The race is to see who can do it accurately, at scale, and focused on the entity-level vs. the document level. In five years, the information overload will be so substantial that autonomous proxies or agents will likely be the baseline for all of these systems. In both situations, humans are in the loop. Now – they have much greater heavy lifting because nearly all of our enterprise information systems don’t really understand their data that well so the burden is on the reader. That has to change. Even when it does, we will just be enabling the humans to make better decisions in less time and less interruption of their daily lives.

JB – Does the delineation between these two approaches represent a common split in the overall text analytics community?

TE - I think so. We can either be satisfied with augmenting the status quo or we can get to the root of the issue – that software doesn’t understand natural signals that make up unstructured data. We are in a place of diminishing returns with simple classifiers and ETL (Extract, Transform, Load) architecture. The more exciting alternative, however, is to go at the semantic and scale problems with the appropriate technologies and transform the enterprise to be entity-oriented.

JB – Can you think of any example where someone tried to completely automate text mining?

TE - Not off the top of my head. I’m sure there have been. But a lot of text mining is feeding either fancy search engines (such as faceted navigation and data enriched topic clustering) or Business Intelligence frameworks.

JB – What does the future look like regarding automation?

TE - Its going to go from being reactive (search, research, and investigation) to being proactive (push, warnings, summaries). Its going to go from two major silos inside the enterprise – the human curated/ structured data and the content management/unstructured data – to being one, unified entity-oriented data store. Once this is done, programs will constantly monitor this unified data store for areas of interest to users and start to screen most everything and prioritize it. Eventually, we’ll get some real next generation automation out of this because there will be a class of actions that will be autonomously executed without requiring human intervention (such as determining the defense policy in a detected cyber-attack).

JB – What other thoughts do you have about this?

TE - I think that as we weigh the risks or errors in additional automation, we need to be wary of irrational risk aversion. The poverty of attention that most people suffer from has very real consequences even if we don’t fully understand that right now. Solutions which give small, incremental gains are unlikely to get ahead of this increasingly detrimental phenomenon. Without something reading everything and getting smarter, we are simply rolling the dice on what we don’t have time to read or consider. That’s the other side of the coin of the incremental approach.

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Security through Obscurity

Wednesday, May 26th, 2010

“Security through Obscurity” is a term often used to refer to security provided by keeping details of a system secret, or by making a system so obtuse that it is difficult to determine how it works, thus hiding its vulnerabilities. Unfortunately, I believe that there is also an application of this term to the need of identifying and tracking the important information hidden in the mountains of digital data generated each day.

While technology has provided several good paradigms for dealing with structured data (i.e. data that is structured in such a way to be easily decomposed into pre-defined fields), it has not kept pace with unstructured data, such as emails, blogs, web site content, etc. Thus, critical information is often kept “secret” through the obscurity of the sheer volume of data one must process, often manually, to reveal this information.

In response to this challenge, Digital Reasoning Systems, Inc has developed a comprehensive set of analytical tools packaged into product called Synthesys™ that essentially decomposes unstructured text into meaningful information easily understood and manipulated by a user.

This technology is based on the premise that there is order inherent in all languages that can be discovered and mathematically modeled. This has led to the development of our advanced data analytics and knowledge abstraction for unstructured data, based on a distinctive, patented mathematical approach to natural language processing.

For a better understanding of Synthesys™ and its capabilities, a down-loadable white paper (Synthesys – Technology Overview) providing a high-level overview can be found here.

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