In Iowa, a farmer uses tractor-mounted computers to help make decisions about his plantings, while Monsanto, the world’s largest seed company, estimates that data-driven planting advice to farmers could increase worldwide crop production by about $20 billion a year, or about one-third the value of last year’s U.S. corn crop, according to a February 25, 2014 Wall Street Journal article. (Subscription required.)
Some say the technology, based on algorithms and human experts who crunch the data and send it directly to farmers and their machines, trumps the development of mechanized tractors in the early 20th century, which changed farming, forever.
Data changes everything.
Off the farm, 2013 will be remembered for many things. For those of us that are tethered to an office—resolved to accept a rather sedentary daily routine—2013 may be remembered as the year of the “fitness tracker.”
In a nutshell—or a fob, wrist band or wearable disc—high-tech fitness trackers measure everything from overall movement and exercise to diet and even sleep patterns. Fashionable or sporty, these futuristic devices can be worn on your waist, dropped in your pocket, clipped to a lapel, or strapped to your wrist or ankle. They work with smart phone apps and websites to help you set goals, then motivate you to achieve them by quantifying them and displaying your every move in a clever daily report card, which you will be obsessed with, at least for the first month—I will guarantee it :-).
Spoiler Alert: This post is not a review of Fitness Trackers…or tractor technology. Rather, the intent is to introduce a simple way to analyze some data your law firm is storing, which otherwise is likely useless.
Simplicity is a beautiful thing.
Data analysis, once the domain of bean counters and back office geeks, is today allowing even math deficient executives—counting myself among them—to solve business, and personal, challenges.
To wit, the data reported by many fitness trackers is fairly easy to access, analyze, interpret and act upon. (For those new to data analytics this sequence sums up the entire reason why any data is analyzed—to gain access to benchmarks, attach goals, and develop action plans, efficiently.) For example, if your fitness tracker “point” total at 6 p.m. falls below your set daily point goal, simply get up and move. (Sorry, this does not mean that you should walk to the nearest Starbucks to consume a whole milk, caramel macchiato.)
Simplicity is a beautiful thing. And that’s the problem with law firm data—its ugly.
Proprietary silos and data analysis.
The typical law firm’s proprietary silos—finance, marketing, business development, facilities, IT, and HR— have disparate data; some is numerical, while other is text-based or code. To respond to an enterprise question or problem, you need a standard of measure for this data. That’s what most Fitness Trackers do. Instead of independently reporting calories, duration, distance, or intensity—each a disparate measure—they assign points. By accumulating points, weighted according to each aspect of activity and its contribution to the whole, at end of the day you can easily see what you have achieved, mark your progress and adjust your activity. Ultimately, over a longer period of time, tracking daily fitness data will produce tangible results.
While various fitness trackers define “points” differently, the overall idea is that a point system gives end users something easy to analyze at the end of the day in order to build action steps for tomorrow. And that is the benefit of developing a practical, yet simple approach to data analysis.
- Identify a question you want answered, a process improved, a problem solved
- Select the data that is most applicable
- Give that data a common value that can be understood by the people involved–those you are counting on to come up with an answer or solution
- Apply that data to actions
- Get results
Data needs context.
A number is just a number until you give it context. This is the first principle of data.
For example, 12 is just a number until you give it context—the 12th of April, 12:00 PM, or a dozen (12) donuts. A spreadsheet from Google Analytics or a report from your time and billing software will always include a header row that puts the raw numbers into context. But data analysis needs more than context, it needs meaning, or synchronicity—a causal line may connect events; they may also be connected by meaning. Numbers with “meaning” will inform purpose and deliver a road map for action. That is what a data analyst does, creates meaning from numbers in context, in case you are wondering.
[Because I am most experienced with auditing and analyzing web data I’ll default to that illustration.] So, even if I am looking at a Google Analytics spreadsheet that tells me Visits Per Day, Page Views Per Visit and Visit Duration—context—there is nothing embedded in that data that allows me to put them together into something meaningful and thus actionable. That’s where a point system comes in handy.
I can’t tell you exactly how to define your point system, as there are many ways to do that, but the idea is two-fold:
- What business problem do we want this data solve—a goal?
- With that in mind, what is the assumed contribution/impact of each piece of data? Assign a weight to each point, accordingly.
Simply stated, you’ll need a problem—a purpose—and a point system. Data is not rocket science when approached this way. You may be surprised. You can get to an action plan without taxing contortions.
Can you trust the data?
After a month long love affair with my stylish fitness tracker, we have settled into a love/hate relationship—it is delightful; blends with both my business and sporting attire, and it is helpful—but too often it is unreliable. While the data that my tracker collects is supposed to motivate me toward fitness goals, how motivating is it when you spend two hours on the tennis court playing your heart out in competitive doubles only to find that you didn’t get any credit? The answer: Not very. Unfortunately this has happened to me several times since donning the fashionable device. Fortunately, my fitness is not mission-critical—not yet, I still have a few more years of blissful youth. But in business, having accurate data to work from can be.
Experienced data analysts know that not all data can be trusted. Consider that most of the data in the law firm environment is entered by humans. Humans make mistakes. Also, it is entirely possible that even automated data collection, such as Google Analytics, could be tainted through human error such as neglecting to filter out the law firm’s own ISP (internal or administration visits).
When embarking on a data analysis project, evaluating the data at its source is important. If you don’t know what you’re looking for, get help. Tip: If you even suspect the data is corrupt in any way, when reporting your analysis to your stakeholders, always “suggest” or “approximate.” If this is the case, going forward, take extra effort to identify the gaps or the cause of them in order to fix them and collect better data.
To this point, a good data analysis will start with benchmarks that allow you to see where you began and ultimately measure your progress. If you find the data you are working with is blemished, you’ll want to start over. Fix the problems and get at least six to 12 months of qualified data before basing any critical goals or action plans on your data.
What else can you be doing?
The last time I looked, the Chief Analyst position does not exist in most law firms. Of course the CFO is looking at finance data and the CMO or CBDO may be looking at ROI and new business data, respectively, but rarely is anyone looking after the big picture. And that’s okay for now; just understand that a dedicated analyst is probably something the CEO/chairman/managing partner should consider where enterprise wide questions can be answered with data.
Frankly, having an analyst on board, whether in house or contract, is likely one of the most efficient ways to develop your strategic plans–or almost any plan, including marketing plans, client service team plans, etc. But it is not enough that the analyst be savvy with numbers, they need to know the legal industry and your law firm and client base, specifically—a topic for another day. [Have experience hiring data analysts? Implementing data analysis? We'd love to hear your story in the comment below.]
A Parting Story
Sleek and attractive, I chose the Misfit Shine fitness tracker because it is the most stylish, with a casing that is waterproof up to 50 meters—I spend a fair amount of my leisure time in and around salt water so the waterproof feature was important to me. While shopping in the Apple Store, I learned from the friendly sales associate that former Apple exec John Scully was one of the founding investors in the Misfit Shine’s development. I considered the Misfit Shine a safe choice and paid the $125 entrance fee to the own the experience. To wit, I’m reaching my modest daily goal four out of seven days a week—minus the unreported tennis matches; rewarded only by a real time victory. Not bad, considering I’m a desk jockey. But because I now know there’s room for improvement—thanks to the fitness tracker’s easy to follow data reporting—I can tackle the challenge appropriately and look forward to tangible results!