What should you do with your data?

What should you do with your data?

The fans of a computer in a data center

Collecting data isn’t an issue anymore. Everyone does it. But no one really knows what to do with it. You know you should do something with it… but what? The generally accepted wisdom about data is that it’s useless until it actually does something. It’s fantastic if a bunch of data wizards apply an obscure algorithm to a data set that surfaces some unique insight. But it’s useless if that insight isn’t acted upon. It’s usless until that hard-won insight is applied to make some difference in your organisation.

Let’s look at a few ways you can operationalise this valuable resource.

Make better decisions

We all want to make better decisions. But it’s no good trying to make marketing decisions with software error reports. Data needs to be relevant to the problem you’re trying to solve. To be relevant, data from across the organisation need to be accessible.

This has a few implications for using data to make better decisions:

  • Data needs to be combined from data stores across the organisation into coherent places - this is hard and something even the most advanced companies struggle with
  • Your data reporting needs to be in a format that people can:
    • Easily access (no highly technical interfaces)
    • Easily understand
    • Easily share
    • Know exists
  • People need to be empowered to make decisions using that data
    • If a customer support rep has access to a bunch of data and can see a customer’s problem but doesn’t have the power to rectify it, what’s the point of them having access to the data in the first place?

This list has a lot of underlying requirements and implications such as management practices and technical infrastructure and the willingness to change it if needs be. But enabling better decisions reduces uncertainty which is nothing to be sniffed at in an uncertain world.

Innovate products, services, and processes

One of the beautiful things about data is the patterns it surfaces. These patterns are what fuel AI prediction and enhancement machines. And in turn this fuels innovation.

Some examples of how AI has innovated existing products/services/processes or generated new ones:

  • Spam filters for email that learn which messages are most likely to be spam/nefarious
  • Chatbots for support
  • Dynamic pricing based on demand (Airlines are good at this)
  • Fraud detection used by banks
  • Search / newsfeed algorithms

Informationalise products, services, and processes

Big B2B companies like Slack are working on integrating AI into their core product to enable users to ask questions like “Who should I talk to about [subject]?” This makes their product more valuable to the people using it and more likely they’ll continue to use it.

This is a trend that’s happened across the board as more data has become available. Take car dashboards as an example. As cars have become more computerised more data has become available. This data has made its way onto the dashboard you see when you’re behind the wheel. Now car companies are working to use data to completely remove the need for humans to drive.

Informationalising a product is arguably the easier thing to do with data: many products and services generate some kind of data the end user would be interested in. This can easily be presented with a simple analytics dashboard.

Data depends on your needs

Above are some examples and food for thought on how you can actively use the data you’ve got. The fact is this: how you might use data will be different from even your closest competitor because it’s unique to you. You might surface some insight that no one else has. This is how data becomes your competitive advantage and keeps you ahead of everyone else.

Need help figuring out what you can do with the data you’ve got? We can help.

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