While many companies have a high volume of valuable data, few have strategies in place to use it to produce business-critical insights. Without data science, your enterprise is likely only scraping the surface with basic analytics and metrics.
By embracing data science capabilities, you can incorporate data across the entire fabric of your organization, enabling every department to build a data-driven strategy, rethink their processes, and optimize their performance.
In this post, we’ll break core data science capabilities into three major categories and walk you through how to leverage them to make sure your enterprise stays one step ahead of the competition.
How to Build Your Enterprise’s Data Science Capabilities
Building your data science capabilities can be a daunting task. We’re breaking your core focus areas into three parts: people, expertise, and data.
An organization’s people are their biggest asset. So, when trying to infuse data science capabilities throughout the company, your people are where you should start. However, before you get started, it’s important to think about how to best leverage the people you have to formulate your AI dream team. Here are a few examples:
- Leaders: Whether you’re considering C-suite executives or front-line managers, your organization’s leaders will set the tone for how their direct reports embrace data science in their roles, and they have the best insight into what problems are currently plaguing the organization. AI can help solve those problems—but only when leaders are aligned on their goals.
- Data workers: Data scientists, business analysts, and domain experts alike all have one thing in common—they’re already using data in some capacity in their everyday job. These are the team members you’ll need to rely on to bring your machine learning models to life and who will carry your data science capabilities from concept to reality.
- End users: Think plant managers in manufacturing and clinicians in hospitals—these are the people who should be getting the most value from the AI models your teams develop. While they’re usually not involved in model creation and are fairly removed from an org’s data, maybe that should change…
Once you have a better idea of who will be impacted by incorporating data science throughout the org (hint: everyone) and how it will change their daily roles, it’s important to also prioritize creating a data-driven culture. This is easier said than done as over 90% of organizations say culture is the biggest roadblock to becoming a data-driven organization.
To get everyone invested in what data science can do for them, you need to bake data into the organization’s foundation—and show your teams what it can do for them, too.
Transparency is Key
When building a data-driven culture that supports your people, it’s essential you communicate openly and transparently. Make sure you tell your teams:
- How your data science capabilities will positively impact their daily lives
- How data science capabilities will impact their workload, including the expectations they have to learn about a new data science platform or processes you may implement
- Any changes to job titles, daily responsibilities, or growth paths this may cause
Resist the temptation to keep things close to the chest. Put yourself in your employees’ shoes and think about any concerns adopting data science capabilities could cause.
When we say “expertise,” we’re not talking about finding a data science unicorn to champion your data science capabilities (while we definitely wouldn’t complain if that were the case!). Instead, focus on your current workforce, particularly by giving them the skills they need to wield data science tools successfully. In conjunction, make sure you’re leveraging your domain experts, too—they have a wealth of specialized knowledge that will lead to better, more accurate models.
Your domain experts understand better than anyone how their teams’ processes work, what obstacles they face, and where data science could be most helpful. Sharing this knowledge with your data scientists before they start building a machine learning model will lead to more relevant and useful end results. This knowledge sharing and deep, cross-functional understanding is part of what we refer to as upskilling.
Suppose you were adopting a machine learning system to improve a core process, and each of your current team members has a different level of comfort with machine learning. By upskilling your teams to have a basic understanding of AI and ML, they’ll know better where data science can be useful, and where it can’t, allowing them to communicate better with your data scientists. Your upskilling program should focus on:
- Identifying where employees need to bolster their data science knowledge
- Giving them hands-on training
- Implementing easy-to-use tools
- Giving your employees the flexibility to discover new data science interests and strengths
How you put your data to use will most likely involve data governance and management, building your infrastructure or using a platform, and identifying core capabilities. Here’s what each of these entails:
- Designing data governance and management: Data governance involves architecting policies that control how you collect, store, and share data. Data management includes the ways your organization organizes and maintains the integrity of the data you work with.
- Choosing your infrastructure: Choosing a data science platform that best works for your team is no small task. Before you do so, it’s important to consider what functionalities you’re most in need of to produce results, so that you can evaluate platforms accordingly.
- Identifying core capabilities: What are your teams best at? And what can the tool you’ve selected help them improve? With clean, organized data and a top-notch platform at your fingertips, it’s time to determine which capabilities you’d like to leverage first.
Maximize Business Outcomes with Data Science Capabilities
Data science can impact a business’s ability to use data to improve profitability, retain talent, and ensure positive customer experiences. You have loads of data at your fingertips—don’t waste it! By implementing data science throughout your organization, you can analyze ways to boost efficiency and build new revenue streams.
Your data is your most powerful resource, and data science ensures it doesn’t go untapped. By working to establish a data science culture, earning the trust of your team members, upskilling and leveraging existing domain experts, and optimizing how you use your data, you’ll position your organization for success.
Curious to learn more ways to utilize data science throughout your enterprise? We’ve compiled 50 cross-industry customer case studies in 50 Ways to Impact Your Business with AI.