Big data has become the big thing recently and seems to be all anyone is talking about (besides it being the year of mobile, of course). By "big data," we mean a concentrated effort to gather intelligence about your business so you can accomplish a deeper analysis.
The fanfare isn’t unwarranted, though. Big data allows you to get a better understanding of your business, your customers, and in many instances your competitors.
However, investing in a big data initiative is pointless if your current data is low quality or difficult to manage. For example, if your business still runs predominantly on spreadsheets managed by a few people, you’re probably not ready for big data. It’s important to ensure high accuracy and cleanliness of the data you have now before even thinking about starting down the path of a big data initiative.
In the case of data, accuracy means that calculations produce the same results, even when different systems gather the data. Cleanliness refers to consistency between internal and external data entry.
Here are five red flags that signal your business is not ready for big data:
Your numbers don’t add up and you don’t know why.
For example, your production numbers say you should have 20,000 parts but your inventory shows 5,000 on hand, you’ve definitely got a problem. Are the sales being properly entered into the system and accurately deducting the values from inventory? Is there an issue with a separate system that isn’t tying back to the production system? Or the problem could be also reversed, if data entry in the production floor system is counted and entered into the system incorrectly. In either case, you need to confirm that your process is functioning before you can determine how it relates to big data sources.
You have no idea where to get the information you need.
If someone asks for a simple report and its impossible to get that for them, you’re not ready for big data. For example, the Marketing department wants to do a trend analysis of sales by customer type for the last five years. In building the report, you notice that the production system only holds the last three years of sales information. You know the other data is stored in offline storage but you don’t know how to get that information for the report. The data can’t be retrieved and used quickly, making a simple request difficult to fulfill.
You can’t source your data because it comes from multiple spots.
Say you are a healthcare facility and you need a list of all the patients that have had a certain treatment. You’re able to pull a list from both the medical records and the billing records, but come up with different lists. Some patients show up in the medical files and not billing, and vice versa. If your data is clean and accurate these lists should be the same. If not, it’s a sign you have a data problem.
You’re entering data and don’t know why.
People need to understand the purpose for the work they’re doing. If they don’t know why the data is important or who will use it, they’re less likely to care and may skip it or put in less effort. This leads to procedural issues. If you don’t care about something or know why it’s used, you won’t see the purpose or relevance. It’s important to communicate why clean data entry is important to get the best results possible.
Your data entering is uncontrolled.
Bad business processes around data management are a sure sign you aren’t ready for big data. For example, if you don’t require your sales team to enter sales data into a CRM but you still run reports on that information, your data output will not be very useful. If project managers don’t require their team to add their billable hours by a given time, but you move forward to create an invoice to a customer, it will not be accurate and could impact finances. These types of inconsistencies may seem small but can lead to larger problems in the future.
If you notice any of these problems in your business, it’s time to look at how to improve the processes and data you already have before leaping into anything as important and complicated as a big data initiative.
Have more questions about big data? Get in touch.
Photo Credit: Patrick McAndrew