The biggest trap today’s enterprises run into is treating data science as a siloed, special entity. Most organizations have hired, or plan to hire, data scientists for their organization to help get more insight from their data. However, bringing in data scientists, separating them from the business’s everyday operations, and expecting them to generate impactful results immediately is simply unrealistic.
Data scientists don’t want to be siloed away from the rest of the organization, and for this reason, turnover rate is at an all-time high—only 2% of data scientists have not left their jobs in the past five years.
Why? Because the famous ‘data science skills gap’ doesn’t just refer to a lack of talent in the market. It also refers to the skills gap that exists between technical data scientists and the people running the business. To make data science initiatives successful, organizations need to embrace transformation and strive to create a truly data-driven culture.
Find a data science approach that produces real results
By maximizing the value of your existing people, expertise, and data. Read about the most popular approaches to data science and discover which one works best for your organization.
Head to page 5 of our Buyer’s Guide
Head to page 5 of our Buyer’s Guide
We’ll preface this by saying that if your business just has one or two data science use cases you really care about, a surface-level approach of hiring a few statistical geniuses might technically work. However, Forrester used to write about a ‘Thousand Model Vision,’ where leading companies have thousands of use cases they’ve transformed into successful ML models—you could be one of them. This post will outline ways to achieve true transformation for those who aim to create data-driven value in as many ways as possible.
Don’t Be a Statistic
You’ve likely seen, heard about, or even experienced first-hand a data science project that didn’t quite go according to plan. In fact, less than 1% of machine learning models have their desired impact, and 85% of AI projects were estimated to fail and deliver erroneous outcomes by 2022.
To avoid becoming part of this trend, enterprises need to commit to true data science transformation rather than patching holes in the fabric of the organization. So, what exactly does that look like? Let’s find out.
Three Necessary Components to Bring Data Science Closer to Your Business
True data science transformation involves everyone in the organization, not just C-suite executives who lead the way with resources and budget, or super-technical data scientists who perform the work. We’re breaking it down into three elements—your employees, your company culture, and the data science models you create.
Upskilling Your Employees
In a data science context, upskilling means providing all your employees with the knowledge they need to use data science and machine learning to produce top results for the business. And it works, too, as a recent McKinsey study found that 70% of companies who’ve invested in upskilling report business results that exceed those investments.
Without upskilling, organizations often consist of disparate teams working to solve business problems, with no context of how other teams are operating. At one end of the spectrum, you have business analysts and domain experts who understand the ins and outs of the problems the business is facing, as well as current processes and workflows. At the other end, you have data scientists who have deep statistical knowledge and coding expertise, but very little context about how those models will eventually make an impact on the business.
Upskilling goes both ways, and true transformation doesn’t just mean teaching analysts how to do data science—it requires closing both the context gap and the skills gap.
Upskilling is foundational for successful data science
Want to learn more about what platforms that support true upskilling offer?
Head to page 8 of our Buyer’s Guide
Head to page 8 of our Buyer’s Guide
Improving data literacy and analytics know-how involves a constant sharing of knowledge. Business experts and data scientists should be on the same page, and citizen data scientists should be able to infuse data science work with invaluable business context that helps drive better results, faster.
For many businesses, investing in a transparent, accessible data science platform that everyone can use to participate in DS/ML initiatives is a great place to start. Having a solution that offers code-optional workflows and flexible modeling capabilities means that everyone is working in the same place, toward the same goals.
The importance of upskilling can’t be stressed enough. Think of it this way—just like how data science maximizes the value of your existing data, upskilling maximizes the value of your existing people.
Creating a Data-Driven Culture
Every organization has data. Most organizations have heaps of it. But, is your organization making the most of it, or are you just letting it collect dust?
Less than 0.5% of all data is ever analyzed and used. According to Richard Joyce, Senior Analyst at Forrester, “Just a 10% increase in data accessibility will result in more than $65 million additional net income for a typical Fortune 1000 company.”
Data-driven organizations make decisions based on the data they collect. It might seem simple and intuitive, but letting the data speak for itself can be tough when it goes against intuition and strongly held historical ideals.
Without a strong culture shift, organizations will fail to create real, long-term change. They’ll continue to rely exclusively on siloed data scientists or external consultants, and the rest of the company can continue not being invested in data science or data.
Well, that doesn’t sound ideal.
Instead, with successful cultural transformation, everyone, not just data scientists, invests in becoming more data literate, understands core principles of data science and works to integrate data-driven decision-making into the fabric of the organization. Data supports every department, every process, and every change made going forward.
A few examples of ways to implement a data-driven culture:
- Ensure you have proper data aggregation, management and governance to support all critical aspects of your business
- Teach employees to help themselves by encouraging data and code literacy and providing them with access to the necessary tools and learning platforms
- Encourage your C-suite to lead by example and bring data-driven proof to any and all meetings they run
Becoming more data-driven leads to a more strategic and streamlined way to work, creating a culture where people truly understand the importance of what they do and feel happy to be a part of it.
Generating Models with Long-Term Value
Creating a model can be hard. Creating a model that can sustain their value over time is even harder.
Many organizations start with one high-impact use case that generates value initially, but, without proper maintenance and monitoring, the model is subject to drift and deterioration over time.
One prominent real-world example of model drift, or concept drift, is changing consumer preferences. Models can’t adjust to the changing world unless they’re retrained, and their data sets are updated. Maintaining models isn’t particularly interesting work for data scientists, as they’d rather focus on developing models for new use cases rather than tweaking models that were never built to be sustainable in the first place.
Essential platform features
There are lots of factors to evaluate when choosing a data science platform. Don’t let model maintenance slip through the cracks.
Head to page 12 of our Buyer’s Guide
Head to page 12 of our Buyer’s Guide
All that to say, if your team is just relying on a one-off model to produce long-term results without giving it proper attention and management over time, you’ll be sorely disappointed.
According to a recent RapidMiner-commissioned Forrester report, 82% of organizations say AI/ML is the most (or one of the most) important current investment areas. Organizations who embrace true transformation not only have systems in place to prohibit model drift, they’ll also be focused on embedding models across more areas of the business.
Leading organizations will find more interesting and innovative ways to use data science—today, and in the very near future. In the same study, 85% of surveyed businesses said it was critical for them to reduce risk with AI, 82% looked to reduce fraud and ensure regulatory compliance with data science, 82% planned to use AI to increase operational efficiency, and 80% stated it was very important to drive new customer acquisition with data science—all with the foundational goal of creating successful models for the long term.
Wrapping Up
By aligning your people, processes, and technology around a strong data science initiative, your organization can achieve a true, data-driven transformation guaranteed to generate long-term value. There are benefits to starting now—early adopters are currently seeing a 5.8X return on their data science investments, compared to 3.8X for less mature organizations. In 2-3 years, the expected ROI for early adopters is 9.3X, compared to 5.5X for later adopters.
Don’t wait until tomorrow to deal with problems AI could help you solve today. Check out our Buyer’s Guide to Enterprise Data Science Platforms to learn more about the best approaches to data science, how to effectively integrate AI into your organization, and how to evaluate which data science platform is right for you.
