87% of data science projects never make it beyond the initial vision into any stage of production. Even some that pass through discovery, deployment, implementation, and general adoption fail to yield the intended outcomes. After investing all that time and money into a data science project, it’s not uncommon to feel a little crushed when you realize the windfall results you expected are not coming.
Yet even though there are hurdles to implementing data science projects, the ROI is unparalleled – when it’s done right:
You can enhance your targeted marketing.
Coca Cola is using data from social media to identify their products or competitors’ products in images, increasing the depth of consumer demographics and hyper-targeting them with well-timed ads.
You can accelerate your production timelines.
GE is using artificial intelligence to cut their product design times in half. Data scientists have trained algorithms to evaluate millions of design variations, narrowing down potential options within 15 minutes.
With all of that potential, don’t let your first failed attempt turn you off to the entire practice of data science. We’ve put together a list of primary reasons why data science projects fail – and a few strategies for forging success in the future – to help you avoid similar mistakes.
You Lack Analytical Maturity
Many organizations are antsy to predict future events or decipher buyer motivations without having first developed the proper structure, data quality, and data-driven culture. And that overzealousness is a recipe for disaster. While a successful data science project will take some time, a well thought out data science strategy can ensure you will see value along the way to your end goal.
Effective analytics only happens through analytical maturity. That’s why we recommend organizations conduct a thorough current state analysis before they embark on any data science project. In addition to evaluating the state of their data ecosystem, they can determine where their analytics falls along the following spectrum:
This type of analytics is concerned with what happened in the past. It mainly depends on reporting and is often limited to a single or narrow source of data. It’s the ground floor of potential analysis.
Organizations at this stage are able to determine why something happened. This level of analytics delves into the early phases of data science, but does not have the insight to make predictions or offer actionable insight.
At this level, organizations are finally able to determine what could happen in the future. By using statistical models and forecasting techniques, they can begin to look beyond the present into the future. Data science projects can get you into this territory.
This is the ultimate goal of data science. When organizations reach this stage, they can determine what they should do based on historical data, forecasts, and the projections of simulation algorithms.
Your Project Doesn’t Align with Your Goals
Data science, removed from your business objectives, always falls short of expectations. Yet in spite of that reality, many organizations attempt to harness machine learning, predictive analytics, or any other data science capability without a clear goal in mind. In our experience, this happens for one of two reasons:
1. Stakeholders want the promised results of data science, but do not understand how to customize the technologies to their goals. This leads them to pursue a data-driven framework that’s prevailed for other organizations while ignoring their own unique context.
2. Internal data scientists geek out over theoretical potential and explore capabilities that are stunning but fail to offer a practical value to the organization.
Outside of research institutes or skunkworks programs, exploratory or extravagant data science projects have a limited immediate ROI for your organization. In fact, the very odds are low that they’ll pay off. It’s only through a clear vision and practical use cases that these projects are able to garner actionable insights into products, services, consumers, or larger market conditions.
Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology?
The above use case from GE is a prime example of having a clear goal in mind. The multinational company is in the middle of restructuring, reemphasizing their focus on aero engines and power equipment. With the goal of reducing their six to twelve month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and their budget allocated for R&D.
Organizations that embody GE’s strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.
Your Solution Is Not User Friendly
The user experience is often an overlooked aspect of viable data science projects. Organizations do all the right things to create an analytics powerhouse customized to solve a key business problem, but if the end users can’t figure out how to use the tool, then the ROI will always be weak. Frustrated users will either continue to rely upon other platforms that provided them limited but comprehensible reporting capabilities or stumble through the tool without unlocking its full potential.
Your organization can avoid this outcome by involving a range of end users in the early stages of project development. This means interviewing both average users and extreme users. What are their day-to-day needs? What data are they already using? What insight do they want but currently can’t obtain?
Equally important to determine is your target user’s data literacy. The average user doesn’t have the ability to derive complete insights from the represented data. They need visualizations that present a clear-cut course of action. If the data scientists are only thinking about how to analyze complex webs of disparate data sources and not whether end users will be able to decipher the final results, then the project is bound to struggle.
You Don’t Have Data Scientists Who Know Your Industry
Even if your organization has taken all of the above considerations into mind, there’s still a chance you’ll be dissatisfied by the end results. Most often, it’s because you are not working with data science consulting firms that comprehend the challenges, trends, and primary objectives of your industry.
Take healthcare, for example. Data scientists who only grasp the fundamentals of machine learning, predictive analytics, or automated decision making can only provide your business with general results. The right partner will have a full grasp of healthcare regulations, prevalent data sources, common industry use cases, and what target end users will need. They can address your pain points and already know how to extract full value for your organization.
And here’s another example from one of our own clients. A Chicago-based retailer wanted to use their data to improve customer lifetime value, but they were struggling with a decentralized and unreliable data ecosystem. With the extensive experience of our retail and marketing team, we were able to identify their current state and efficiently implement a machine learning solution that empowered our client. As a result, our client was better able to identify sales predictors and customize their marketing tactics within their newly optimized consumer demographics. Our knowledge of their business and industry helped them to get the full results now and in the future.
Is your organization equipped to achieve meaningful results through data science? Secure your success by working with Aptitive. Schedule a whiteboard session with our team to get you started on the right path.
Ryan Lewis is a Data Science Consultant at Aptitive. Ryan helps companies grow their their businesses through the use of statistics, machine learning, and other cutting edge technologies.