Most large firms are looking at the possibilities of AI/ML, but data science fails to take things to the next level. Developing a machine learning or data science model can take anything from a few weeks to more than a year. When you start re-architecting the entire ML pipeline with deployment in mind, your efforts may be futile
The gap between organizations that are able to derive value from data science and those that are struggling is widening. Data science is becoming increasingly popular as a function and competence among businesses, but many fail to take full advantage of its potential for business growth because of a lack of understanding.
Most large firms are looking at the possibilities of AI/ML, but data science fails to take things to the next level. Developing a machine learning or data science model can take anything from a few weeks to more than a year. When you start re-architecting the entire ML pipeline with deployment in mind, your efforts may be futile.
Why Corporations aren’t set up for machine learning:
Leadership support means more than money
Managers and business executives expect data scientists to deliver a lot of value in return. One possible approach is for executives to receive some basic data science training. Leaders must allocate resources in the proper way, and comprehend what machine learning models are all about. They must enable data scientists to flourish in their professions.
Lacking access to data
Many businesses are compartmentalized, which means each department has its own data collection methods. Data scientists, on the other hand, frequently require information from other departments. In an era of rapid technological change, businesses will need to step up and establish consistent data structures.
The disconnect between IT, data science, and engineering
There is a fundamental split between IT and data science in many firms. IT tends to put a premium on getting things to function and keeping them stable. Data scientists like tinkering with and breaking things. Engineering isn't considered necessary for data scientists, and engineers may not understand what a data scientist sees.
Reasons Why Big Data Science and Analytics Projects Fail
Data science is becoming increasingly popular as a function and competence among businesses. However, many of them have been unable to consistently generate economic value from their investments in big data, AI, and machine learning. Furthermore, research shows that the difference between businesses that are successful in deriving value from data science and those that are failing is widening.
Let's look at the reasons why big data science and analytics initiatives fail so we can better understand the mistakes firms make while executing lucrative data science projects and learn how to prevent them.
Not having the Right Data
You can't have a data science endeavor without data. However, gathering, creating, or purchasing this data might be difficult. Even if you can acquire access to the data, you'll still have to deal with a pile of problems, including:
- How do you secure the data? -Placing a critical file on a company-wide sharing is a rookie data management mistake. Data security software that constantly identifies critical data and sends it to a secure location allows you to quickly get control of your data.
- Is the underlying data biased? - Bias may manifest itself in analytics in a variety of ways. Including everything from how a subject is posed and investigated to how data is gathered and arranged. Although data scientists will never be able to totally eradicate bias in data analysis, they can make progress. They can employ countermeasures to detect it and reduce problems in the field. Bias in data analysis has a variety of negative consequences, ranging from making poor judgments that directly affect the bottom line to negatively influencing certain groups of persons participating in the analysis.
- Can you ethically and legally use the data for your intended use case? - The ease with which the computer makes data available can lead to serious misuse. It's conceivable that the rules governing who can use the data and for what purpose aren't explicit enough. People in control of the data may fail to enforce the rules or be unable to enforce them. Alternatively, they may not have sufficient control over who has access to the data. Whatever the reason, this can lead to severe problems. - The ease with which the computer makes data available can lead to serious misuse. It's conceivable that the rules governing who can use the data and for what purpose aren't explicit enough. People in control of the data may fail to enforce the rules or be unable to enforce them. Alternatively, they may not have sufficient control over who has access to the data. Whatever the reason, this can lead to severe problems.
- Can you process the data in a timely and cost-appropriate manner? -It's a crucial step before processing that often entails reformatting data, making data corrections, and combining data sets to enrich data. It's a time-consuming process for data professionals or business users, but it's necessary to put data in context in order to turn it into insights and eliminate bias caused by poor data quality.
- Is the data clean? (Probably not in which case…) Can you clean the data? -You can clean data by looking for faults or corruptions, repairing or eliminating them, or manually processing data as needed to avoid repeating the same mistakes. Although software solutions may help with most parts of data cleansing, certain tasks must be completed manually.
- Do you know whether the data drifts over time? -Data-drift is described as a difference between the data used to test and verify the model before it was deployed in production and the data used to deploy it in production. Data drift may be caused by a variety of variables, one of which being the time dimension. If you look at the graphic below, which depicts high-level steps in the construction of a machine learning model, you'll see that there's a substantial time gap between when data is collected and when the model is used to forecast actual data. Depending on the problem's complexity, this gap might last weeks, months, or even years. Drift can also be caused by mistakes in data collection, seasonality, and other variables, such as how the data is obtained.
With all of these issues (and more), it's no wonder that "a lack of acceptable quantity and quality of training data remains a critical development concern," according to a 2020 International Data Corporation poll.
Data management is worthy of its own essay (or book), but here are a few brief tips:
- Have internal protocols including policies, checklists, and reviews to enforce proper data usage.
- Never assume data is clean. Assume it is dirty unless proven otherwise.
- Build production-grade cloud-based systems for data pipelines which include pro-active alerts and notifications to let you know if something looks off.
- Invest in data and cloud engineers to build these systems (which lead us to the next point…).
Management Problems
Many times, in data science, models do not make it past the proof-of-concept stage. Lack of fundamental data literacy at senior levels leads to data science being overlooked frequently. Business intelligence and software stacks, in many cases, provide clearer value to an organization.
Technical Challenges
Most IT-driven firms are just unfamiliar with the tools and hardware required to properly install data science models. Choosing the proper challenge and pursuing the correct answer is one of the most important aspects of data science. Projects in data science are frequently more difficult than the commercial value they are supposed to deliver.
Data Collection Issues
Most businesses have a lot of data, making it tough to deploy a model into production. Data is always in diverse formats, structured and unstructured, video files, text, and photos. Unstructured or unformatted data may take the majority of the time to clean.
Incompatibility With Enterprise Systems
Data scientists use programming languages such as Python, which may or may not be compatible with the languages used in production systems. It takes a long time to recode, retest, and test the model before distribution. This procedure might take months, and by the time it is ready for manufacturing, it may be obsolete.
Conclusions
Data-driven initiatives have become critical on the route to success for an organization as a result of evolving technology. Data is a significant asset in every organization, regardless of size. The key to a successful data-driven initiative is to overcome these obstacles to the greatest extent feasible. In today's market, there are a plethora of technologies for extracting meaningful patterns from unstructured data.
Now that we have a basic overview of where organizations are lacking while implementing the data science projects, let’s dig deeper into the specific reasons why most of the projects cannot reach the production level.
Siloed Data - When it comes to creating a new model, data is crucial. It is the most important source of energy for the project. Unfortunately, the majority of data in real issues is siloed, dispersed across several databases in various formats. Not only is it difficult to acquire such information, but it is also time-consuming to change it and then store it in a central area for simple access.
Controlling the quality of data, on top of everything else, is a problem since bad or erroneous data might cause your model to backfire, making things much worse. As a result, businesses must exercise extreme caution. Surprisingly, the majority of firms that are new to data science fail at this point. It takes months to gather all of the data in one place, and even if it is done effectively, the project expenses have already risen to the point where continuing is a danger.
Integration of Solutions with Key Business Needs - No matter how many actionable insights you extract from data, if you can't properly integrate them into your business hierarchy, they're doomed. The data science team's findings are needed both internally and outside, but only through the right channels. When your team has achieved the intended outcomes, they must communicate them with other departments. So, if you're one of the ones who's creating the data but not channeling it properly, make sure you start doing so as soon as possible so that the people who need it can get their hands on it.
Another consideration is the level of communication between the technical and non-technical departments. While data scientists are primarily concerned with model correctness, non-technical individuals are frequently concerned with other metrics, such as insight or financial advantages realized. This lack of congruence frequently leads to miscommunication within the hierarchy, and it must be handled appropriately through appropriate channels. Furthermore, the data should be communicated in a way that non-technical teams can understand and with as little jargon as possible.
Reference: https://www.datascience-pm.com/project-failures/