With 30-plus years of collective experience in big data - cleaning it up, mining it for opportunities, developing customer insights, using it to save money and solve problems, etc. - our team at Alcott has seen a LOT. (We won't say we've seen it ALL, because that would not be statistically accurate, now would it!)
Here are some common data issues and opportunities we help resolve with clients on a regular basis.
1. The data is a mess. The single biggest problem our scientists and analyists encounter in their work is poor data hygiene. Companies - especially large and / or older ones - have a tremendous amount of information about their customers and potential customers, but it isn't maintained in a clean, workable condition. Most common examples: Bad email addresses, duplicate customer files with slightly different names, and out-of-date postal addresses.
2. Data isn't synced well with other data. This problem is similar to poor data hygiene, but in this case multiple data streams are coming from a variety of places that are not housed and maintained in a way that can be maximized for full return. This can cause companies to communicate the wrong offers to customer segments, or not notice when a customer has moved in the sales funnel.
3. Data isn't warehoused in a way that allows it to be best leveraged. By "data warehousing", we mean the server and software in which the data is stored. If you're using a series of Excel sheets, you'll have significantly less ability to harness the data than you would if you were housing everything on a MySQL server, for example. We can help get you there if you aren't already, and we can add insight and value to data already being warehoused appropriately.
4. Data models are out of date or lacking important additional information. We recommend rebuilding your models once every 2-3 years, or more often depending on the volatility of your industry. In addition, we recommend appending additional datasets to your existing customer data to help spot opportunities and weaknesses. We've helped clients find places where they're spending too much money on marketing and others where they aren't spending enough. Our lead data scientist Phil Lenzini is regularly able to identify opportunities to add additional datasets to clients' existing information. And every time he does this, the model is improved.
5. Data isn't segmented. It's not just smaller companies who have a hard time segmenting data appropriately. Sometimes the largest organizations have such a crush of data that they don't have the resources or time to keep segments robust and dynamic. Or they're segmenting well, but the process is too manual and wastes time. (Or the flip: They've automated the process so well that they're missing key insights and unable to quickly respond to opportunities.)
We are total geeks about data and would love to work with your teams to make the best, most valuable sense of your company's data. Drop us a line for a free consultation.