5 signs your business is in the big data dark ages

Sure, you collect information… but are you collecting the right data for your business?

And are you managing your big data in the smartest way? Here are five signs your big data strategy may be out of touch, and some simple ways to bring your company up to speed in the information age.

1. You’re still using spreadsheets

You’ve got everything organized into neat categories, and all those macros you sweated over in B-school are finally paying off — you can customize data to your heart’s content. What could be wrong with that?

Admit it — even with macros, spreadsheets take a lot of time to create and manage. They're also error prone.

Remember the Fidelity accountant who forgot the minus sign in front of a $1.3billion capital loss that was marked as a gain? Or the spreadsheet error that cost Fannie Mae over $1 billion? How about the data entry error at the 2012 London Olympics that resulted in overselling 10,000 tickets for a swimming event? They are all true, and they were all due to human error.

And while you can update spreadsheets, they can’t update themselves, or provide you with up-to-the-minute results. As your company and your database grow, keeping up can turn into a real headache.

What you can do: Consider switching to a NOSQL database, which can handle enormous volumes of both structured data (e.g., traditional accounting numbers) and unstructured data (emails, video and audio files, etc.). It allows you to add “outside the box” categories without creating a new interface for them, and enables you to scale up with ease as your business expands.

2. You’re not valuing your most valuable customers

When a big-spending customer missed her flight, an airline ticket counter clerk offered to put her on standby — for an additional $75.

Do you think that big spender will fly with them again?

Customer retention is key to growing profits. You need to identify your company’s VIP customers and store the information so that everyone who contacts them can access it.

What you can do: Use this simple formula to calculate customer lifetime value. The four key components needed to develop a comprehensive CLV model are:

• Gross Profit

• Customer Acquisition Cost

• Retention Rate

• Discount Rate

Mix these ingredients together and we get the resulting Customer Lifetime Value formula:

(GP - CAC) * (RR / (1 + DR - RR)[1]

Then store all the data you have about your most valuable customers in a cloud-based app, and make it accessible to the employees who need it.

3. You’re not using big data to improve customer service

If you’re selling online — and who isn’t? — you have the ability to track your customers’ digital movements. And by using information in the right way, you can significantly improve the quality of your customer service. Consider this reaction: a customer reached out to Amazon when he had a problem with his Kindle. Not only was the problem solved quickly, but the company’s unobtrusive use of his basic data provided a personable solution — and assurance of future business.[2]

What you can do: You may not have the resources of an Amazon, but the right vendor partner can help you build the infrastructure to gather, retain and analyze key data about your customers, and personalize their experience in meaningful ways.

4. You’re using your gut — not numbers — to predict customer actions

You’re not alone, but you may be soon. Only 29 percent of executives reported in a recent survey that they are using predictive analytics, yet 66 percent admitted they could lose market share if they don’t get into the game.[3]

What can predictive analytics do? It can teach a car to recognize who’s in the driver’s seat, preventing auto theft. Or it can use floor sensors to determine when someone walks into a room — or the display area of a store.

What you can do: Depending on your business, a number of predictive tools can help you cut costs without spending a bundle yourself.

5. You’re hiring based on current needs, not future ones

Your company likely won’t look the same in five years — and neither will the pool of workers. Consider this example from Harvard Business Review: A company had a lot of entry-level and senior engineers on staff, but few in between. In the meantime, outside data pointed to an upcoming shortage of engineers entering the workforce. Instead of ignoring the brewing storm, the company went on a proactive recruiting binge, hiring 10,000 new mid-level engineers a year and helping solidify their future.[4]

What you can do: Track significant HR metrics (such as employee referral rates by department, resignation rates by department and average worker age/projected retirements) using big data to stay on top of important trends.