Wasp Barcode Technologies: The Barcode Solution People
When Big Data is Too Big: Making Your Analytic Numbers Count
You’ve probably heard the buzz phrase “big data” tossed around quite often in business circles. So much that perhaps you’ve kind of tuned it out. You likely have some sort of tracking systems in place, whether it’s for employee productivity, marketing analytics, or inventory management. You’re also probably doing the best you can do with what you have. Is that enough?
Or maybe you have so much information, you don’t know what to do with it all.
When your small business regularly collects accurate, real-time data, it benefits your overall operations Why? Because getting a handle on the essential analytics for your industry and business helps overcome what
nearly half of all small businesses struggle with, according to the
2016 State of Small Business Report: growing revenue and overall profits.
Without structure, big data is unusable.
Big data can become so overwhelming, that it sits around and “is just a mass of unrelated information that would take years to comprehend, and even then, may not yield any insights,” according to
an article in Fierce CIO.
Especially if you’ve
invested in an automated inventory management system, your company has access to a lot of analytics. This is good. Don’t let it get to the point of being too much of a good thing, with those numbers ending up unused and without any benefit to your bottom line. Without structure, the data can become a hot mess of random, unusable information that could take years to comprehend.
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The first key to making analytics count is making your data relevant. This is information that is, no question, critical to the issues at hand. What does that mean for you? Well, that’s up to you to decide. Every business operates uniquely and has its own synergy. So what is important to the plant down the street could look very different to your business.
Deciding what is most important won’t necessarily be easy, either. It might mean sifting through thousands of reports and working your way down from there. Even though it sounds foreboding, the hard work will pay off in the end. Having a keen focus on relevant analytics will help you
At the very least, you’ll want an overview of your particular market, including what customers are looking for, what’s trending, and what your competition is doing. From there, you can hone in on the information that will help your company deliver on-point products that go with the trends of the day and give you a leg up against the other guys. You can even stretch your data to take a look inwardly at your business operations. The data you gather can also help your own people, from the back office to the front lines of sales, get a better grasp of the today’s marketplace and to successfully respond to it.
Should you look back or ahead?
Both. There are two types of helpful data you should be concerned with: hindsight and foresight. Each can be used to your advantage, but you must understand how to differentiate and handle each in unique ways.
Hindsight data is just as it sounds, it tells how the company has done in the past. It consists of solid numbers. You can analyze and compare to see what has worked and what hasn’t. It’s important to note that hindsight numbers need to be run again and again against a common set of metrics. This will give you a better understanding about the health of your company. There are a number of
important analytics that can turn into profits, including cycle times, inventory levels and turnover, write offs and write downs.
Foresight data gives an outside-in view of the company, giving you a snapshot of what could happen down the road, and what your company needs to do about it. It’s all about patterns and trends and gives you an idea of how your company measures up to the market at large. This information is largely unstructured, such as behaviors, buying patterns, customer likes and dislikes, etc. For example, you might find some of this data on the company social media accounts.
Pace your data analysis
As just mentioned, hindsight and foresight data are comprised of completely different types of numbers and should be treated differently. The same goes with how fast you can make decisions stemming from the big data:
- Hindsight decisions happen faster because you’re dealing with hard and fast numbers, which you’ve been measuring for a while through the automated data you get through inventory control.
- Foresight decisions are a different story. They are essentially hypotheses that are drawn up after facts are reviewed, developed, and tested.
Foresight decisions can be tricky because you don’t want to make a mistake that may cost your company more money if you’re too off track. Since you’re essentially guessing at what might happen in the future, you want those guesses to be as educated as possible. So take as much time as possible with as much big data as possible.
Either way, in order to generate pertinent data,
you can’t use 30-year-old technology like spreadsheets or even pencil and paper inventory management methods.
Barcode systems can be customized to fit your business size and needs. They not only make inventory management more efficient and accurate, but the data you receive helps you make better business decisions.
Case study
The Bay Area Air Quality Management District was
struggling with tracking highly sophisticated replacement parts for its air quality management stations. They used an Excel spreadsheet, which was useless because it was rarely accurate. It would often take technicians up to three hours to call around to find the parts they needed. This wasted time and money. After upgrading to an automated inventory management system, the company now has visibility into the quantity of each part he uses every month. Access to reliable data enables them to negotiate better prices for inventory items. This move also improved customer service, which translates into happier customers who will be more likely to do business with them again.
The analytics gathered from improved inventory management has saved the company time to the tune of nearly $30 thousand dollars. Add to that better use of inventory dollars, and those factors equal more revenue and increasing profits year over year.
What challenges do you experience with your company’s analytic data?
