Adopting Data Science in a Business Environment
While most organizations understand the importance of data, far fewer have figured out how to successfully become a data-driven company. It’s enticing to focus on the “bells and whistles” of machine learning and artificial intelligence algorithms that can take raw data and create actionable insights. However, before you can take advantage of advanced analytics tools, there are other stops along the way, from operational reporting to intelligent learning.
Digital transformation is dependent on adoption. But adoption and proficiency of new technologies can be disruptive to an organization. Mapping a data journey provides awareness and understanding of where your organization is to ultimately get where you want to go, with enablement and adoption of the technology throughout. Without the clarity provided by a data journey, your organization won’t be positioned to successfully deploy the latest technology.
Here are the four elements of an effective data journey.
Determine Your Roadmap
As with any trip, your data journey requires a roadmap to get you from where you are to where you want to go. Before you can get to your destination, the first step is to assess where you are.
Most organizations begin with a focus on operational reports and dashboards, which can help you glean business insights from what happened, including how many products were sold, how often and where. They can also identify where problems exist, and deliver alerts about what actions are needed.
Ultimately, most want to get to the point where analytics tools can help with statistical analysis, forecast, predictive analytics and optimization. Armed with machine learning, manufacturers want to understand why something is happening, what happens if trends continue, what’s going to happen next and what’s the best that can be done.
Capture Data and Build Processes and Procedures
Once you know where you want to go, it’s important to capture the data that is essential in helping you achieve your business goals. Manufacturers capture tremendous amounts of data, but if the data you collect doesn’t solve a business need, it’s not vital to your data processing priorities.
This phase of your data journey isn’t just about what data you collect, it’s also about your data strategy: how you collect the data, pre-process it, protect it and safely store it. You need to have processes and procedures in place to handle data assets efficiently and safely. Questions such as how you can leverage the cloud to gain access to data management tools, data quality and data infrastructure need to be answered.
Make Data Accessible to Business Users
Today, data – and business insights about that data – need to be accessible to business users. This democratization of data makes it possible for business users from procurement to sales and marketing to access the data that’s imperative for them to do their jobs more effectively.
In the past, data was the domain of specialists which often caused bottlenecks in operations while they analyzed the data. In this phase of the data journey, it’s important to consider data management tools that can consolidate and automate data collection and analysis.
Once data is removed from silos, it makes it possible for data to be analyzed by more advanced analytics and data science tools to glean business insights that can propel your success.
Change Company Culture for Full Adoption
A data culture gap is a common barrier to the adoption of advanced data analytics tools for many companies. When employees who are expected to use the data and insights don’t understand the benefits data can bring to decision-making it can create a roadblock. Your company won’t be data-driven until your team embraces a data-driven culture and starts to use the data intelligently.
If you want to get the most out of the advanced data analytics tools that are available today and use data intelligently in your organization, you must first develop a solid foundation.
First, you must be clear where you are in your organization’s data journey with a roadmap. Then create effective data processes, procedures, and collection methods, as well as identify what data management and analytics tools can support your initiatives. Finally, your team is key to adopting advanced data analytics tools, so be sure they are trained and understand how these tools can empower them. Once you have a solid analytics foundation, you’re ready to put machine learning to work to drive your collective success.
Author: Michael Simms