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Big data is game changing for anyone able to harness its awesome power.
But, what if you’re too small for big data? What if big data and its corresponding big price tag are out of reach for your business, department or project? Do you gamble on your instincts, go with your gut and give it your best guess?
I’m not doubting your experience, acumen or business instincts, but please don’t do this. Ever.
Too many variables affect your results, and decisions made without data analysis can have unexpected and significant costs.
What do I Have?
If big data is a glaring searchlight that illuminates every detail, little data is a glowing bulb. If you put enough small lights in a room, pretty soon you can see every corner. The smallest data capability can shed light on the situation, and is always better than guessing or going with your gut feeling. Even people with great instincts can be blindsided by hidden causes or costs. |
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You may not have big data, in-depth analytics or custom algorithms, but you do have facts, figures and feedback that will help you expose what’s really going on – trends, hidden costs, root causes and opportunities.
Maybe the problem you’ve been trying to solve isn’t the problem at all, or that capital investment is going to cost more money than it saves, or you have a problem in one department driving all of that turnover, or perhaps your change strategy will have unintended results and you'll lose more business than you gain.
How Little is Little?
Little data doesn’t require a lot of resources or significant investments. You’ll need:
- A spreadsheet
- Time to identify, and then track and record, available information applicable to your project, strategy or challenge
- Usable data collection parameters (what’s the question, what’s the plan, what are you trying to solve?)
- Time to normalize the information into standard, chartable figures
- Time to read and interpret results
For those who are not data fluent, this may sound like a lot, but once you get a system in place it gets easier. Little data, any data, is critical to effective planning and achieving the best possible results. Make the time, do the research and embrace little data.
How Do you Data?
We are flooded with data across every aspect of our lives with access to more information than any generation ever. The abundance can be beneficial or part of the problem.
How do you sort through the noise to find data that will quantify success or identify the problem? As you embark on your data journey, here are a few navigation points:
1. Data isn’t always numbers
When you read reviews on Amazon, you’re assessing user data to make a decision. You quantify the feedback by looking for trends. If one person said the widget broke in a week, maybe they were doing it wrong. If 50 people report the gizmo rapidly died, maybe it’s a weak product and you need a better option.
Data analysis can do the same thing for your client or employee feedback, even without numbers, or especially without numbers, because the comments are frequently more revealing than a numerical rating.
How do you clearly and accurately quantify feedback in client, employee or user comments? You standardize the data to a limited – preferably 10 or fewer – set of values. Then, sort the responses into the appropriate category. Analyzing standardized values generates quantifiable figures to identify issues or uncover what users value so you know where to invest resources.
Once a trend is revealed, you can drill down into specifics, get a sub-set of granular data, search for root causes or define closer analysis parameters. Think user feedback, satisfaction surveys, exit interviews, client reviews, product feedback, spending habits, and on to infinity.
2. Learn a little bit about statistics
Ensure what you’re looking at is valid and makes sense. If your figures are not statistically valid it can lead you astray. You can get a surprisingly good understanding of statistics with online resources, but taking a statistics course is helpful.
Understanding statistics facilitates accurate data evaluation. For example, comparing attrition between employees from two sources with the total employees who left from each group, 35 from Source A and 57 from Source B provided a misleading and inaccurate conclusion that Source A performed better.
Adding the total employees hired from each, A contributed 80 employees and B provided 320, changed the picture considerably. Source A had a hefty 43% attrition rate, while Source B's was only 18%. The valid analysis revealed that Source B was doing a much better job finding best-fit talent and Source A should be eliminated or refocused with input on why Source B was more successful.
3. Using data is fun – no really, it is
Establishing information collection mechanisms, determining standardization criteria, and getting more familiar with statistics takes effort and time. When you have the system in place the fun begins; assessing data and seeing what’s really going on is enlightening and exciting. Once you get started, it can be hard to stop, addictive even. You might find yourself wanting more and more.
Make sure you don’t get bogged down in data. After you determine what information is valuable for providing a clear picture, set limits and make sure you don’t get so mired in variables that you stop making decisions or taking action. Seeing every corner is fun, but sometimes you only need to light up the spot in front of you to jump in the right direction.
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Let There be Light
The defense of little data is this; don’t avoid using data because you’re a little guy in a big data world. You don’t need to have a super computer, an analytics team or bespoke algorithms to make data work for you.
Data is a tool – a valuable resource that can guide decisions, improve performance and save the day – embrace it, leverage it, use it and get going in the right direction.
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