Many organizations see data acquisition and management as a business expense. You may hear questions like, what does it cost to collect and capture this data? What resources do we need to store it? Are analysis, modeling, and other data science projects a priority for us right now? Do we have the budget?
It’s understandable that these companies are only seeing the price tags, especially when it comes to accessing data from outside the organization. But they’re looking at the issue all backwards. Data isn’t a burden, it’s a positive asset: an incredibly precious, valuable resource. Viewing it as anything else holds businesses back from diving into the data, acting on the insights, and getting the visibility that they really need. Seeing your data as one of your best assets, on the other hand, encourages you to build everything else on top of it, from business analytics to data pipelines. This can revolutionize the way you do business – for the better.
One of the great things about data is this: not only does it have enormous potential to make your business more profitable, but the more you use it, the more the asset goes up in value. Unlike limited resources that run out or hardware assets that wear out over time, knowledge and insights breed more knowledge and insights. The more you use your data, the more data you’ll have. Not the other way around.
That’s because properly organizing and using your data creates more insights, more features, and ultimately, more useful, model-ready data. It’s the raw material that keeps increasing and augmenting. It also becomes even more valuable as you combine it with other sources.
Data is incredibly valuable as a resource to help you understand what is going on in your organization (and the wider sector). Even more importantly, it informs your decision-making going forward. You need to see your data as a repository of knowledge and insights that help you predict what’s coming next, reduce risk, and deliver better results.
At the same time, data is only useful if you use it. When you are assessing the value of the information you have, don’t just consider what it’s worth now, as a static asset. Value it in terms of its potential — what you could do with it, how, and to what end. The more specific you can be about the business benefits it could deliver, the better. Often, just the process of elucidating this acts as something of a wakeup call, pushing you to start a) putting your data to good use, and b) improving the way you manage your data assets with their usability in mind.
You don’t need to limit yourself to what you’ve amassed inside the organization, though. Alternative, external datasets drawn from the broader data ecosystem can also provide extremely valuable insights.
That might include government stats and figures, demographic and census data, information from PoS transactions, IoT sensors and satellites, and “natural language” text sources harvested from social media networks, websites, and blogs. The world is your oyster when it comes to data.
This is especially important to bear in mind considering that data does, to an extent, have a use-by date. You need a lot of historical data for effective predictive modeling, but to get an accurate, up-to-date picture, you need a lot of that data to be recent. The chances are that you won’t have a large enough depository of up-to-the-minute data inside your organization — but you may be able to fill in the gaps using external sources.
We mentioned above that data is only valuable when it’s utilized. That means you need a clear plan for how you’ll put this to use — with clear, measurable results. What does a successful outcome look like? How will returns materialize? How can you monetize your data, whether in terms of creating new revenue streams and products or by cutting costs and solving logistical problems internally?
To achieve this, you will need to build the right infrastructure to get the most out of your data. You need a strategy for how you will apply powerful analytics and sophisticated data science techniques to your data to draw out the actionable insights you need.
If you have limited resources to work with, limited in-house data science capabilities or you’re just getting going with a data science team, this also means being really smart and strategic about where you direct your budget. For example, a well-chosen data science platform that automates a lot of the heavy lifting, suggests the best machine learning algorithms for the task at hand and manages your connections to external data will likely deliver real ROI and quickly justify the cost of acquisition – especially compared to the cost of expanding your data science team.
The key is to find streamlined ways to achieve this that reduce, rather than add to, your workload — especially if you’re working with a small team or don’t have dedicated data scientists on the case. Think about how you can improve your data acquisition processes. Sourcing high-quality, complete, standardized datasets from data catalogs rather than jumbled, unharmonized databases is a good start.
While data is an asset, it’s not one that you simply acquire and be done with it. Your data stores require upkeep. Think of yourself as a data steward, rather than a data owner: you should be managing your datasets, ensuring accessibility and accuracy, removing or correcting any obsolete, inconsistent, or inaccurate data as you discover it. Getting the right tools in place will allow you to clean up and harmonize both internal and external data sources quickly and easily, and even increase its value through feature engineering.
Failing to keep your data in tip-top condition reduces its value to the rest of the organization and undermines your ability to use it for analysis and modeling. Make sure you look after this highly precious asset.