Enhanced predictions. Dynamic forecasting. Increased profitability. Improved efficiency. Data science is the master key to unlock an entire world of benefits. But is your business even ready for data science solutions? Or more importantly, is your business ready to get the full ROI from data science?
Let’s look at the overall market for some answers. Most organizations have increased their ability to use their data to their advantage in recent years. BCG surveys have shown that the average organization has moved beyond the “developing” phase of data maturity into a “mainstream” phase. This means more organizations are improving their analytics capabilities, data governance, data ecosystems, and data science use cases. However, there’s still a long way to go until they are maximizing the value of their data.
So, yes there is a level of functional data science that many organizations are exploring and capable of reaching. Yet if you want to leverage data science to deliver faster and more complete insights (and ROI), your business needs to ensure that the proper data infrastructure and the appropriate internal culture exists.
The following 8 tips will help you to ensure your machine learning projects, predictive analytics, and other data science initiatives operate with greater efficiency and speed. Each of these tips will require an upfront investment of time and money, but they are fundamental in making sure your data science produces the ROI you want.
Lay the Right Foundation with Accurate, Consistent, and Complete Data
Tip 1: Before Diving into Data Science, Get Your Data in Order
Raw data, left alone, is mostly an unruly mess. It’s collected by numerous systems and end users with an incongruous attention to detail. After it’s gathered, the data is often subject to migrations, source system changes, or unpredictable system errors that alter the quality even further. While you can conduct data science projects without first focusing on proper data governance, but note what ends up on your plate will vary greatly – and comes with a fair amount of risk.
Consider this hypothetical example of predictive analytics in manufacturing. A medium-sized manufacturer wants to use predictive maintenance to help lower the risk and cost of an avoidable machine breakdown (which can easily amount to $22,000 per minute). But first, they need to train a machine learning algorithm to predict impending breakdowns using their existing data. If the data’s bad, then the resulting detection capabilities might result in premature replacements or expensive disruptions.
Tip 2: Aim to Create a Single Source of Truth with your Data
Unifying data from assorted sources into a modern data warehouse or data mart simplifies the entire analytical process. Organizations should always start by implementing data ingestion best practices to extract and import high-quality data into the destination source. From there, it’s critical to build a robust data pipeline that maintains the flow of quality data into your warehouse.
Tip 3: Properly cleanse and standardize your data
Each department in your organization has its own data sources, formats, and definitions. Before your data can be data science ready and generate accurate predictions, it must be cleansed, standardized, and devoid of duplicates before it ever reaches your analytics platform or data science tool. Only through effective data cleansing and data governance strategy can you reach that level.
Tip 4: Don’t Lean on your Data Scientist to Clean Up the Data
Sure, data scientists are capable of cleaning up and preparing your data for data science but pulling them into avoidable data manipulation tasks slows down your analytical progress and impacts your data science initiatives. Leaning on your data scientist to complete these tasks can also lead to frustrated data scientists and increase turn-over.
It’s not that data scientists shouldn’t do some data cleansing and manipulating from time-to-time; it’s that they should only be doing it when it’s necessary.
Tip 5: Create a Data-Driven Culture
Your data scientist or data science consulting partner can’t be the only ones with data on the mind. Your entire team needs to embrace data-driven habits and practices or your organization will struggle to obtain meaningful insights from your data.
Frankly, most businesses have plenty of room to grow in this regard. A recent survey finds that only 31% of organizations characterize themselves as data-driven and 28% have been successful at achieving strategic data usage. Though many of those organizations are still seeing results from big data or data science practices, the overall capacity of their efforts is still stunted.
For those looking to implement a data-driven culture before they forge deep into the territory of data science, you need to preach from the top down – grassroots data implementations will never take hold. Your primary stakeholders need to believe not only in the possibility of data science, but in the cultivation of practices that fortify robust insights.
A member of your leadership team, whether it’s a Chief Data Officer or other senior executive, needs to ensure that your employees adopt data science tools, observe habits that foster data quality, and connect business objectives to this in-depth analysis.
Tip 6: Train Your Whole Team on Data Science
Data science is no longer just for data scientists. A variety of self-service tools and platforms have allowed ordinary end users to leverage machine learning algorithms, predictive analytics, and similar disciplines in unprecedented ways.
With the right platform, your team should be able to conduct sophisticated predictions, forecasts, and reporting to unlock rich insight from their data. What that takes is the proper training to acclimate your people to their newfound capabilities and show the practical ways data science can shape their short and long-term goals.
Tip 7: Keep Your Data Science Goals Aligned with your Business Goals
Speaking of goals, it’s just as important for data-driven organizations to inspect the ways in which their advanced analytical platforms connect with their business objectives. Far too often, there’s disconnection and data science projects either prioritize lesser goals or pursue abstract and impractical intelligence. If you determine which KPIs you want to improve with your analytical capabilities, you have a much better shot of eliciting the maximum results for your organization.
Tip 8: Consider External Support to Lay the Foundation
Though these step-by-step processes are not mandatory, focusing on creating a heartier and cleaner data architecture as well as a culture that embraces data best practices will set you in the right direction. Yet it’s not always easy to navigate on your own.
With the help of data science consulting partners, you can make the transition in ways that are more efficient and gratifying in the long-run.
Need some support getting your business ready for data science? Aptitive’s team of data management, analytics, and data science consultants can help you ensure success with your data science initiatives from building the business case and creating a strategy to data preparation and building models.
Schedule a data science readiness whiteboard session with our team and we’ll determine where you’re at and your full potential with the right game plan.
Dale Hanson is the Data Science Practice Lead at Aptitive with hands on project experience across the entire data science practice. Dale works with clients to help drive strategic insight by implementing, productionalizing, and maximizing ROI for data science projects.