Business Intelligence
Profitability through Operational Efficiency
In my last post, I discussed the importance of proper pricing for profitability and success. As most people know, you increase profitability by increasing revenue and/or decreasing costs. However, cost reduction does not necessarily mean slashing headcount, wages, benefits, or other factors that often hurt morale and cascade into lower quality and customer satisfaction. There is often a better way.
The best businesses generally focus on repeatability and reliability, realizing that the more you do something, the better you get at doing it well. You develop a compelling selling story based on past successes, build a solid reference base, and identify the sweet spot from a pricing perspective. People keep buying what you are selling, and if your pricing is right, money is available at the end of the month to fund organic growth and operational efficiency efforts.
Finding ways to increase operational efficiency is the ideal way to reduce costs, but it takes time and effort. Sometimes this happens through increased experience and skill. But, often optimization occurs through standardization and automation. Develop a system that works well, apply it consistently, measure and analyze the results, and then make changes to improve the process. An added benefit is that this approach increases quality, making your offering even more attractive.
Metrics should be collected at a “work package” level or lower (e.g., task level), which means they are related tasks at the lowest level that produce a discrete deliverable. This project management concept works whether you are manufacturing something (although a Bill of Materials may be a better analogy in this segment), building something, or creating something. This allows you to accurately create and validate cost and time estimates. At this level of detail, it becomes easier to identify ways to simplify or automate the process.
When I ran my company, we used this approach to win more business with competitive fixed-price project bids that provided healthy profit margins while minimizing risk for our clients. Higher profit margins let us invest in our own growth and success by funding ongoing employee training and education, innovation efforts, and international expansion, as well as experimenting with new things (products, technology, methodology, etc.) that were fun and often taught us something valuable.
Those growth activities were only possible because we focused on doing everything as efficiently and effectively as possible, learning from everything we did – good and bad – and having a tangible way to measure and prove that we were constantly improving.
Think like a CEO, act like a COO, and measure like a CFO. Do this and make a real difference in your own business!
To Measure is to Know
Lord William Thomson Kelvin was a pretty smart guy who lived in the 1800s. He didn’t get everything right (e.g., he supposedly stated, “X-rays will prove to be a hoax.”), but his success ratio was far better than most, so he possessed useful insight. I’m a fan of his quote, “If you can not measure it, you can not improve it.”
Business Intelligence (BI) systems can be very powerful, but only when embraced as a catalyst for change. What you often find in practice is that the systems are not actively used or do not track the “right” metrics (i.e., those that highlight something important – ideally something leading – that you have the ability to adjust and impact the results), or provide the right information – only too late to make a difference.
The goal of any business is to develop a profitable business model and execute extremely well. So, you need to have something people want, deliver high-quality goods and/or services, and finally make sure you can do that profitably (it’s amazing how many businesses fail to understand this last part). Developing a systematic approach that allows for repeatable success is extremely important. Pricing at a competitive level with a healthy profit margin provides the means for sustainable growth.
Every business is systemic in nature. Outputs from one area (such as a steady flow of qualified leads from Marketing) become inputs to another (Sales). Closed deals feed project teams, development teams, support teams, etc. Great jobs by those teams will generate referrals, expansion, and other growth – and the cycle continues. This is an important concept because problems or deficiencies in one area can negatively affect others.
Next, the understanding of cause and effect is important. For example, if your website is not getting traffic, is it because of poor search engine optimization or bad messaging and/or presentation? If people visit your website but don’t stay long, do you know what they are doing? Some formatting is better for printing than reading on a screen (such as multi-column pages), so people tend to print and go. And external links that do not open in a new window can hurt the “stickiness” of a website. Cause and effect are not always as simple as they seem, but having data on as many areas as possible will help you identify which ones are important.
When I had my company, we gathered metrics on everything. We even had “efficiency factors” for every Consultant. That helped with estimating, pricing, and scheduling. We would break work down into repeatable components for estimating purposes. Over time we found that our estimates ranged between 4% under and 5% over the actual time required for nearly every work package within a project. This allowed us to profitably fix bid projects, which in turn created confidence for new customers. Our pricing was lean (we usually came in about the middle of the pack from a price perspective, but a critical difference was that we could guarantee delivery at that price). More importantly, it allowed us to maintain a healthy profit margin to hire the best people, treat them well, invest in our business, and create sustainable profitability.
There are many standard metrics for all aspects of a business. Getting started can be as simple as creating sample data based on estimates, “working the model” with that data, and seeing if this provides additional insight into business processes. Then ask, “When and where could I have made a change to positively impact the results?” Keep working until you have something that seems to work, then gather real data and validate (or fix) the model. You don’t need fancy dashboards (yet). When getting started, it is best to focus on the data, not the flash.
Within a few days, it is often possible to identify and validate the Key Performance Indicators (KPIs) that are most relevant to your business. Then, start consistently gathering data, systematically analyzing it, and then work on presenting it in a way that is easy to understand and drill-into in a timely manner. To measure the right things really is to know.
Spurious Correlations – What they are and Why they Matter
In an earlier post, I mentioned that one of the big benefits of geospatial technology is its ability to show connections between complex and often disparate data sets. As you work with Big Data, you tend to see the value of these multi-layered, often multi-dimensional perspectives on a trend or event. While that can lead to incredible results, it can also lead to spurious data correlations.
First, I am not a Data Scientist or Statistician, and there are definitely people far more expert on this topic than I am. But, if you are like the majority of companies out there experimenting with geospatial and big data, it is likely that your company doesn’t have these experts on staff. So, a little awareness, understanding, and caution can go a long way in this scenario.
Before we dig into that more, let’s think about what your goal is:
- Do you want to be able to identify and understand a particular trend – reinforcing actions and/or behavior? –OR–
- Do you want to understand what triggers a specific event – initiating a specific behavior?
Both are important, but they’re different. My focus has been identifying trends so that you can leverage or exploit them for commercial gain. While that may sound a bit ominous, it is really what business is all about.
A popular saying goes, “Correlation does not imply causation.” A common example is that you may see many fire trucks for a large fire. There is a correlation, but it does not imply that fire trucks cause fires. Now, extending this analogy, let’s assume that the probability of a fire starting in a multi-tenant building in a major city is relatively high. Since it is a big city, most of those apartments or condos likely have WiFi hotspots. A spurious correlation would be to imply that WiFi hotspots cause fires.
As you can see, there is definitely the potential to misunderstand the results of correlated data. A more logical analysis would lead you to see the relationships between the type of building (multi-tenant residential housing) and technology (WiFi) or income (middle-class or higher). Taking the next step to understand the findings, rather than accepting them at face value, is very important.
Once you have what looks to be an interesting correlation, there are many fun and interesting things you can do to validate, refine, or refute your hypothesis. Even without high-caliber data experts and specialists, you can likely identify correlations and trends that can give you and your company a competitive advantage. Don’t let the potential complexity become an excuse for not getting started. As you can see, gaining insight and creating value with a little effort and simple analysis is possible.
Getting Started with Big Data
In Sales, I have the opportunity to speak with many customers and prospects about many things. Most are interested in Cloud Computing and Big Data, but they often don’t fully understand how to leverage the technology to maximize the benefits.
Here is a simple three-step process that I use:
1. For Big Data, I explain that there is no single correct definition. Because of this, I recommend that companies focus on what they need rather than what to call it. Results are more important than definitions for these purposes.
2. Relate the technology to something people are likely already familiar with (extending those concepts). For example: Cloud computing is similar to virtualization and has many of the same benefits; Big Data is similar to data warehousing. This helps make new concepts more tangible in any context.
3. Provide a high-level explanation of how “new and old” are different and why new is better using specific examples that they should relate to. For example: Cloud computing often occurs in an external data center – possibly one where you may not even know where it is- so security can be even more complex than in-house systems and applications. It is possible to have both Public and Private Clouds, and a public cloud from a major vendor may be more secure and easier to implement than a similar system using your own hardware;
Big Data is a little bit like my first house. I was newly married, we anticipated having children, and also anticipated moving into a larger house in the future. My wife and I started buying things that fit into our vision of the future and storing them in our basement. We were planning for a future that was not 100% known.
But our vision changed over time, and we did not know exactly what we needed until the end. After 7 years, our basement was very full, and it was difficult to find things. When we moved to a bigger house, we did have a lot of what we needed. But we also had many things that we no longer wanted or needed. And, there were a few things we wished that we had purchased earlier. We did our best, and most of what we did was beneficial, but those purchases were speculative, and in the end, there was some waste.
How many of you would have thought Social Media Sentiment Analysis would be important 5 years ago? How many would have thought that hashtag usage would have become so pervasive in all forms of media? How many understood the importance of location information (and even the timestamp for that location)? I guess it would be less than 50% of all companies.
This ambiguity is both a good and bad thing about big data. In the old data warehouse days, you knew what was important because this was your data about your business, systems, and customers. While IT may have seemed tough in the past, it can be much more challenging now. But the payoff can also be much larger, so it is worth the effort. You often don’t know what you don’t know – and you just need to accept that.
Now we care about unstructured data (website information, blog posts, press releases, tweets, etc.), streaming data (stock ticker data is a common example), sensor data (temperature, altitude, humidity, location, lateral and horizontal forces), temporal data, etc. Data arrives from multiple sources and likely has multiple time-frame references (e.g., constant streaming versus updates with varying granularity), often in unknown or inconsistent formats. Someday soon, data from all sources will be automatically analyzed to identify patterns and correlations and gain other relevant insights.
Robust and flexible data integration, data protection, data governance, and data privacy will all become far more important in the near future! This is just the beginning for Big Data.
Using Technology for the Greater Good
My company and my family funded a dozen or so medical research projects over several years. I had the pleasure of meeting and working with many brilliant MD/Ph.D. researchers. My goal was to fund $1 million in medical research and find a cure for Juvenile Arthritis. We didn’t reach that goal, but many good things came out of that research.
Something that amazed me was how research worked. Competition for funding is intense, so there was much less collaboration between institutions than I would have expected. At one point, we were funding similar projects at two institutions. The projects went in two very different directions, and it was clear that one would be much more successful than the other. It seemed almost wasteful, and I thought there must be a better, more efficient, and cost-effective way of managing research efforts.
So, in 2006 I had an idea. What if I could create a cloud-based (a very new concept at the time) research platform that would support global collaboration? It would need to support true analytical processing, statistical analysis, document management (something fairly new then), and desktop publishing. Publishing research findings is very important in this space, so my idea was to provide a workspace that supported end-to-end research efforts (inception to publication, including auditing and data collection) and fostered collaboration.
This platform would only work if there were a new way to allow interested parties to fund this research that was easy to use and could reach a large audience. Individuals could make contributions based on areas of interest, specific projects, specific individuals working on projects, or projects in a specific regional area. The idea was a lot like what Crowdtilt is today. This funding mechanism would support non-traditional collaboration and hopefully greatly impact the research community and their findings.
Additionally, this platform would support the collection of suggestions and ideas. Good ideas can come from anywhere – especially when you don’t know that something is not supposed to work.
During one funding review meeting at the Children’s Hospital of Philadelphia (CHOP), I made a naïve statement about using cortisone injections to treat TMJ arthritis. I was told why this would not work. A month or so later, I received a call explaining that my suggestion might work, with a request for another in-person meeting and additional funding. Conceptual Expansion at its best! That led to a new research project and positive results (see http://onlinelibrary.wiley.com/doi/10.1002/art.21384/pdf).
You never know where the next good idea might come from, so why not make it easy for people to share those ideas.
By the end of 2007, I had designed an architecture based on SOA (service-oriented architecture) using open-source products that would do most of what I needed. Then, in 2008 Google announced the “Project 10^100” competition. I entered, confident that I would at least get an honorable mention (alas, nothing came from this).
Then, in early 2010 I spent an hour discussing my idea with the CTO of a popular Cloud company. This CTO had a medical background, liked my idea, offered a few suggestions, and even offered to help. It was the perfect opportunity. But, I had just started a new position at work, so this project fell by the wayside. That was a shame, and I only have myself to blame. It is something that has bothered me for years.
It’s 2013, and far more tools are available today to make this platform a reality, and something like this still does not exist. I’m writing this because the idea has merit, and I think there might be others who feel the same way and would like to work on making this dream a reality. It’s a chance to leverage technology to potentially make a huge impact on society. And it can create opportunities for people in regions that might otherwise be ignored to contribute to this greater good.
Idealistic? Maybe. Possible? Absolutely!
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