A good data science platform alleviates a ton of your data-related headaches. It connects to multiple data sources, including external data sources, and offers ETL tools. Augmented data discovery tools help you fill in gaps in your data. Feature engineering boosts accuracy and insights. It supports your machine learning modeling, and may even suggest the most relevant algorithms for your dataset and purpose. It will provide a production environment in which to deploy your model, supporting staging and testing for your machine learning project.
These benefits give you an indispensable competitive advantage. As more and more companies recognize this, they start to realize that a powerful data science platform isn’t just “nice-to-have”; it’s a necessity.
What’s more, these factors mean that calculating the total cost of ownership of any data science platform goes far beyond the price tag. Rather, it’s important to consider what your current pain points are; what data problems are racking up costs or holding you back from generating more income. Once you start to think about these problems as financial barriers that a data science platform can fix, you get a far clearer picture of what investing in a platform would mean for your budget.
Let’s start by looking at some of these common data problems and how they create bottlenecks and costs.
In the age of big data, pretty much every organization is collecting more data than they can ever hope to use in any meaningful way. That doesn’t mean you should stop accumulating so much data; after all, you don’t always know exactly what you’ll need until you need it. But you do need to figure out how to take control of all that data so that it doesn’t overwhelm you.
That means establishing streamlined, effective data preparation strategies, including automating as much of the cleaning, harmonizing, and organizing steps as possible. It means finding elegant ways to connect and combine your various data sources. It means designing data science projects and machine learning algorithms that make the important connections for you, by rendering your key concerns as data science questions.
All of which will be far easier if you have a powerful data science platform at your disposal. One that facilitates the bringing together of multiple data streams, ironing out inconsistencies and incompatibilities as you go. One that helps you navigate and connect the data you need with laser precision.
Perhaps this sounds familiar: you have a ton of data in-house but, to your frustration, most of it is just that tiny bit too out of date to help you make meaningful predictions about the future. Or perhaps your internal data is too narrow, meaning that you really need to supplement it with external sources and augmented data discovery to get a full, nuanced, accurate picture. The right platform simplifies those connections, giving you access to verified, carefully vetted, high-quality external data sources — and making it really easy to flesh out your existing datasets with relevant additional details.
Machine learning projects often have very long lead times, and after all the time you spend getting your model production-ready, you might find that it doesn’t quite deliver the results you need. This can make them really risky from a budgeting and ROI point of view. However, if you’re using a platform that takes a lot of the heavy lifting out of the data preparation and exploration side, and helps you select and apply relevant algorithms automatically, you can trim the fat from the process. This allows you to experiment with and refine your models much faster, reducing the risk of potentially wasted resources.
What’s more, if your data science platform helps you to scan through and tap into relevant, up-to-date, external data sources quickly and easily, you can use this to make insightful, up-to-the-minute, highly accurate predictions which reflect a rapidly changing business context. Meanwhile, using built-in feature engineering tools helps you to identify unexpected connections within the data and rank these features by their relevance and usefulness. This means you can continually and efficiently improve the accuracy of your models.
But now for the million-dollar question: what impact will investing in a data science platform have on your budget?
To answer this, it’s important to think carefully about how the improvements we’ve talked about here will reduce your costs and help you maximize the value of your existing resources and data assets.
Let’s start with cold, hard, direct costs.
How much would you expect to pay to access the data that you really need to make accurate predictions and business-critical decisions? If you were to buy all these external datasets individually, the price would really add up. Especially when you factor in the time and resources you would need to dedicate to making it relevant to your use case and harmonizing this data with your existing datasets. With a data science platform, you should be able to cut out all those costs by connecting to an extensive library of external datasets, all rolled into the price. Plus, with augmented data discovery, you only tap into the specific data you need and you merge this directly with your existing dataset. This means even more savings.
Now consider how a data science platform can extend the value of your existing data assets.
How valuable is the data you own? How could you monetize this — and what price tag would you put on the resulting product? Even if you kept all of this internal, how could you use this data to reveal important insights about your business, highlighting inefficiencies and potentially lucrative opportunities? Again, what would be the financial benefit of fixing these problems?
Now consider that extracting all this value from your data is contingent on having the right technology, i.e. a robust data science platform. Unless you take the plunge, you can cross out the value estimation of your data and write a big fat zero in its place.
And what about the missed opportunities if you don’t upgrade?
The world in 2021 will be very different from anything we could have predicted in 2019. While some of your historical data will be relevant, a lot of it will be of limited use when making predictions for the coming year. To really understand the business context we’re heading into, you need a way to seize on the most recent, accurate data.
That demands external data and augmented data discovery. It involves finding ways to maneuver quickly and with confidence to beat the competition. It means making use of tools like feature engineering that will help you make vital connections and identify emerging trends and patterns at speed.
The bottom line is this. When it comes to working a data science platform into your budget, the real question isn’t “how can we afford this” but rather, “how can we afford not to?”