As the saying goes, “Give a man enough rope and he’ll hang himself.” Well, Big Data can also be a really big rope.
Data can be an amazing tool for helping any business understand itself, its processes, its market, and the effectiveness of its marketing – but only if it’s being handled by people and software capable of accurately sifting through all the data. When it isn’t handled properly, well, it’s easy to go astray.
At Velocidi, our specialty is helping our clients get a handle on large amounts of data spread between multiple systems or databases, and accurately pulling insights out of it. Of course, that means we’ve also seen plenty examples of big data gone wrong.
Here are some of the most common mistakes we’ve seen.
Seven Biggest Mistakes You Could Be Making With Your Data
1. Not consolidating databases early
We understand that not every SMB has the budget for a major investment into database and analytics software right off the bat. But the fact is, with each month that passes where you’re still using multiple interfaces and databases, you’re going to have a data problem. It’s a problem which will only continue to grow larger and harder to fix the longer it goes uncorrected.
To be able to properly leverage data, it needs to be in a single central system – or at least be part of a system capable of pulling data in from multiple sources. As long as you have multiple discrete databases which can’t talk to each other, you’ll be at a major disadvantage. Sooner or later, you will need to consolidate, and that will only get harder the longer you wait.
2. Not having well-defined, measurable, timely goals
When we ask our clients “What are your goals for your data analysis?” it’s far too common for us to hear answers like, “To grow our business!” or “To increase sales!”
That’s not a goal. That’s a mission statement.
A goal is definable and measurable, such as “To increase sales 15% year-over-year.” You need to have a core metric (sales), a well-defined target (15%), and a timeframe (yearly). Picking the right goals at the start of a project gives you something to aim for, as well as helping you find checkpoints along the way. Obviously, this is a simple goal as an example, but if someone using it has gone six months and they’ve seen an 8% sales increase, they would know they’re on the right track.
3. Disregarding customer data
It’s all too easy for a company to get so wrapped up in its own internal hopes, dreams, and goals that they end up completely forgetting to make sure they’re still producing a product that customers want. For a fine example, just look at the still-simmering controversy about the video game company Electronic Arts over-monetizing their latest Star Wars title to the point where it bordered on gambling. EA undoubtedly had internal sales/profit targets they were aiming for but went about achieving those goals in a way that their customers very vocally rejected.
That’s a worst-case scenario to be sure, but one which is entirely possible. Never become so obsessed with your internal numbers that you tune out the customers. Always keep an eye on consumer trends. If you know when the winds are shifting, you’ll be able to change course before bad publicity forces the change.
4. Under- and over-personalization
Right now, customer personalization is one of the trickiest balancing acts in data. On one hand, buyers are so desensitized to mass-market messaging that they will very often tune out a message that doesn’t seem to be aimed at them. On the other hand, if you leverage your data too hard and get too personal, it seems creepy or intrusive.
Just look at that infamous incident where Target accidentally “outed” a pregnant teen before her own family knew!
This goes hand-in-hand with point 3. You need to know your audience extremely well to know where the sweet spot is. When you find that sweet spot with just the right level of personalization, you’ll be creating messaging and content that’s far more effective than any other.
5. Buying social media numbers
Let’s keep this one short: Numbers such as how many followers you have on Facebook or how many hits your website gets are pure vanity numbers. They have virtually no impact on your bottom line, particularly if (as is so often the case) they’ve been obtained through less-than-organic methods.
Grow your social media organically. Get people to follow you because they like you, not because you paid for some data farm in Russia to add you to their network. Organic followers promoting you to their friends are where the real juice is.
6. Not adjusting the internal culture
Big Data is everybody’s business. It’s not something that just one person, or one department, can take over. A switch to a data-based outlook could easily require a substantial shift in your internal processes, workflow, and philosophy.
As one example: In the past, sales and marketing were usually considered distinct departments, with relatively little overlap. Occasionally, they were even somewhat at odds. That doesn’t work with big data. Sales and marketing need to be working side-by-side, contributing to a shared data pool, each making insights known to the other.
If possible, get R&D in there as well so they can make products based on the insights sales and marketing have to customer needs.
7. Relying entirely on data
It might sound odd for us to say, but there truly is a limit to how much big data can do on its own. There is absolutely still a need for human insights, instincts, and nonlinear problem-solving. The difference is, with big data, you often have a method of testing those insights. You don’t have to fly blind, hoping that your clever guesswork is correct. You can look for evidence in the data.
Big data is full of great opportunity – but be smart in how you use it.