Technology
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 and often multi-dimensional perspectives of a trend or event. While that can lead to incredible results, it can also lead to spurious data correlations.
First, let me state that I am not a Data Scientist or Statistician, and there are definitely people far more expert on this topic than myself. 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 are both 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, it is likely that most of those apartments or condos 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. It is likely that even without high-caliber data experts and specialists, you will be able to identify correlations and trends that can provide you and your company with 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.
There’s a story in there – I just know it…
I was reading an article from Nancy Duarte about Strengthening Culture with Storytelling, and it made me think about how important a skill storytelling can be in business and how it can be far more effective than just presenting facts and data. These are just a few examples. You probably have many of your own.
One of the best salespeople I’ve ever known wasn’t a salesperson at all. It is Jon Vice, former CEO of the Children’s Hospital of Wisconsin. Jon is very personable and has the ability to make each person feel like they are the most important person in the room (quite a skill in itself). Jon would talk to a room of people and tell a story. Mid-story, you were hooked. You completely bought what he was selling, often without knowing what the “ask” was. It was an amazing thing to experience.
Years ago, when my company was funding medical research projects, my oldest daughter (then only four years old) and I watched a presentation on the mid-term findings of one of the projects. The MD/Ph.D. giving the presentation was impressive, but what he showed was slide after slide of data. After 10-15 minutes, my daughter held her Curious George stuffed animal up in front of her (where the shadow would be seen on the screen) and proclaimed, “Boring!”
Six months later, that same person gave his wrap-up presentation. It was short and told an interesting story that explained why these findings were important, laying the groundwork for a follow-on project. A few years later he commented that his initial presentation became a valuable lesson. That was when he realized the story the data told was far more compelling than just the data itself.
A few years ago, the company I work for introduced a high-performance analytics database. We touted that our product was 100 times faster than other products, which happened to be a similar message used by a handful of competitors. In my region, we created a “Why Fast Matters” webinar series and told the stories of our early Proof of Value efforts. This helped my team make the first few sales of this new product and change the approach the rest of the company used to position this product. People understood our value proposition because these success stories made the facts tangible.
I tell my teams to weave the thread of our value proposition into the fabric of a prospect’s story. This makes us part of the story and makes this new story their own (as opposed to our story). This simple approach has been very effective.
What if you not selling anything? Your data tells a story – even more so with big data. Whether you are analyzing data from a single source (such as audit or log data) or correlating data from multiple sources, the data has a story to tell. Whether patterns, trends, or correlated events – the story is there. And once you find it, there is so much you can do to build it out.
Whether you are selling, managing, teaching, coaching, analyzing, or just hanging out with friends or colleagues, being able to entertain with a story is a valuable skill. It is also a great way to make many things more interesting and memorable in business. So, give it a try.
Getting Started with Big Data
Being in Sales, I have the opportunity to speak to many customers and prospects about many things. Most are interested in Cloud Computing and Big Data, but often they don’t fully understand how they will 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, 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 finding things difficult. 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 time stamp 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 will have 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, and data privacy will all become far more important in the near future! This is just the beginning for Big Data.
The Power of Simplicity
“If you can’t explain it simply, you don’t understand it well enough.” – Albert Einstein
In the first part of my career, I didn’t care much for consultants. My experience was that they would come in, tell you what to do, and then leave victoriously while we were stuck trying to implement something that just wouldn’t work. They seemed to make everything seem so complex—often to justify their cost.
Then, I met an amazing consultant who shared something valuable with me. He explained what he believed differentiated a true consultant from a contractor (something I wrote about a decade later in a Tech Republic article). He then made me aware of the Einstein quote above. This was one of those pivotal moments in my career.
For many years, I have met many interesting people. Some seemed to try to intentionally obfuscate even the easiest things to make themselves seem brilliant. Others took such a circuitous route that you sometimes forgot what you were trying to understand and fix. And sometimes explanations were just so tangential that the main point was lost entirely. There are likely many reasons for these experiences – some intentional and many not. The real lesson learned is that it wasn’t just consultants who can be incomprehensible and that clear and comprehensible communication is critical to effectiveness.
Just think about the power of a well-crafted “elevator pitch” when you meet someone new or the ability to quickly explain how your company differentiates itself from the competition (making you the more interesting, better, or safer choice in your prospect’s mind). Or being able to articulate your business strategy in a way that people understand (and can explain to others), which also interests them enough to want to learn more and become part of making that happen.
The best consultants, as do the best employees, managers, executives, and business owners, have this ability to explain something simply. While this is only one attribute of success (likability, powers of persuasion, integrity, luck, etc. are others), it is something that can be taught, developed, and consistently applied.
The power to “explain it simply” is the power to make a difference through better understanding.
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