Although many enterprises are beginning to heavily invest in data science activities, only a handful of them are seeing the desired ROI. That’s because it’s hard to do. It’s difficult to shift to a culture of building and scaling data-driven services and products. It’s a challenge to operationalize data science processes and integrate data science into business practices. It’s an uphill battle to put an enterprise’s data “house” in order, to get a complete view of what data exists and eliminate data silos and connect disjointed analytics teams. All of these challenges are among the reasons why most enterprises aren’t getting the return they expected from their data science investments.
However, it is possible to do, with the right approach. If your data science practice isn’t delivering the results you expected, or you are in the process of exploring how a data science capability might fit into your organization, here are a few considerations to take into account to improve your success.
1. Make sure your POC projects can scale
When enterprises begin to dabble in data science, most want to start small, with a proof of concept (POC). This makes sense. However, many data science POCs never make it into production. With the pressure to find more value in big data, many companies rush into doing a POC without first putting strategy, controls or processes in place that will make it easier to build and deploy a successful POC at scale if desired. Without these prerequisites in place, many essential components necessary for scaling the project are not documented as data scientists do their exploration and model building work. While POC projects are experimental, it is also essential to develop a well-defined scope, plan and evaluation metrics at the start with an eye on future production deployments.
2. No data silos
For enterprises to realize value from data science, data science and analytics need to permeate the business enterprise-wide, from the c-suite to marketing to manufacturing. Data science processes need to be operationalized to produce analytical insights that are integrated into day-to-day operations and decision-making. To do this, data must be made more widely discoverable and accessible. Enterprises should consider the concept of the managed data lake or centralized data repository, which is emerging as one of the best tools to enable data scientists to prepare data for analysis and get the most value from it.
3. Balance your data science team
It’s difficult to assemble the right skill sets for a data science team, which should span multiple disciplines and functions. Your most successful data scientists may come from a variety of backgrounds, from physics or bioinformatics to engineering or English. But in general, they should have strong skills in computer science, mathematics and particular domain expertise that brings needed skills to a project.
4. Commit for the long term
Changing culture and process is complicated. Bringing all data, technology tools and stakeholders together is an iterative process that takes time, so it’s important to get buy-in from the top and invest in a sustained effort. Keep in mind that failure is actually an important part of data science, which involves hypothesis, trial and error. The key is to “fail fast,” measure the results, learn from them and keep trying new things.
Data science has quickly become imperative in applying big data to achieve business goals. However, data science is inherently exploratory, iterative, and experiment-driven. Therefore it’s important to put a well-defined structure around your data science practice and invest in the right big data infrastructure in order to build measurable, executable projects and actually realize value from your investments. Let us know how Zaloni can help you with this endeavor.