How to Cultivate Data Science Culture in Your Organization?
It is clear to us by now that the data revolution is changing businesses and industries in unalterable ways. The thing with revolutions is that they rarely move backward. They keep progressing ahead. The rate at which they advance might differ, but it is hard to ignore the movement. It is quite the same with data.
The growing proliferation of the data culture heralds the age of data science upon us.
Today, organizations are increasingly dependent on data to aid decision-making to reach their top market and navigate their red oceans. Doing so is not an option but a matter of long-term survival.
However, as the data dependency grows, so does the need for data scientists. These are the people who work their magic on data using complicated statistical algorithms and get the data to talk in a language we understand. In fact, so important is the role of the data scientist, that this role is not touted as the most exciting career opportunity of the 21st century! There has been a 29% increase in demand for data scientists year over year and a 344% increase since 2013.
However, there is a shortage of data scientists is also well documented. According to August 2018 LinkedIn Workforce Report, more than 151,000 data scientist jobs were going unfilled in the U.S alone.
Building a data science culture – the steps for success
These statistics are hardly surprising. After all, data science is a relatively new business area. Hence, demand will outpace supply owing to the specialized nature of this role. Along with this, we need to consider that there are very few data scientists who can be called ‘experienced’ in the truest sense. But given these challenging circumstances, what can organizations do to leverage this innovative field for business success?
Acknowledge the rise of data science
Do you think Amazon or Netflix would have been the behemoth that they are had they not acknowledged the role of data?
Humans by nature are programmed to be resistant to change. However, in the same breath, we also acknowledge that to resist change is a sure shot recipe to be left behind. So how do we execute change?
Any sustainable change begins with the acknowledgment that change is needed. In the case of data science, it begins with the acknowledgment that data science is the new way of work. As with any technology or change, data science comes with its own learning curve and it will take a little time before we enjoy the tangible results it promises.
Developing the learning culture
A data science culture starts at the top. The top management, the CEO and the key decision makers have to commit themselves completely to this.
The thing is, you cannot import data culture, and you definitely cannot impose it. After all, culture has a human aspect associated with it. So, what can you do?
To begin with, you need to create opportunities for the workforce to interact with data…to play with it…to experience it. You need to educate them on the importance of creating a vibrant data culture. You need to tell them how it is going to impact the workforce, help them do their jobs better, enable them to deliver an impact, assist them to innovate better, and solve pressing problems? And then, you must enable them to experiment with data science.
It is only when we take these calibrated steps in relation to our organization that we can bring about data transformation.
Enable and empower your data scientists
Change is a continuous constant in the technology landscape. Data science is no different.
While some data scientists will rally for Python, some will favor R3. Some will want to work with one statistical model and some on another. As an organization, to establish a data science culture, you have to give your data scientists the flexibility to try out new technologies.
You have to encourage them and enable them to take up new challenges and come up with the impactful solutions. You can only do so if you give them a data science platform that is scalable, and extensible and has the capability to manage large volumes of data, enable complex processing, provide in-memory storage and robust security.
Develop your pool of citizen data scientists
While the role of the data scientist is a niche one, to establish a data culture you have to grow your knowledge experts, the business users, to fit the role of the citizen data scientist. These business users are the people who know what business problems need resolving. They are the people who know what questions to ask the data. They just need to know how to talk to data to ask these questions. But what do they need for this?
The answer is surreptitiously simple. You need to give these citizen data scientists access to a platform that allows them to easily apply data models to the right data to gain predictive insights. They need the flexibility to connect with the data, explore its myriad possibilities and create compelling visualizations without any dependencies.
You need a platform that allows Open Source, algorithms, computation and business users work seamlessly. If the platform has pre-built functions, uses popular algorithms, has toolsets to analyze structured, unstructured data and social data, provides static models for changing data sources, and can capably handle large data volumes, then anyone in your organization can become a data scientist.
It is logical to assume that to build a data science culture, you must first democratize data science. And for that, you need to build your army of citizen data scientists who will become the pallbearers of this culture.