If your team is going to be successful with its data efforts, you will need to establish a structure that encourages the best practices for data science.
If you are reading this and it is 2021, then you are aware there is a data wave and now more than ever, organisations now have an increased need for mining, storing and analysing large chunks of data. And as a result, this has increased investments in data science efforts across businesses, and also a rise in the demand for Data Scientists.
No one wants to do a below-the average-type-of- job. So we’ve made it easy for you. It does not matter the stage your business is in with your data science efforts, these six tips will help you get the best out of it.
Here are 6 best practices to help you get the most out of your data science investments:
Best Practices for Data Science
- Use the Right Tools
- Be Data-driven
- Establish a CoE
- Avoid Unclear Semantics
- Practice Model Validation
- Be Security Conscious
Data science continues to provide companies with a way to transition from reacting to being proactive. This is why many companies are investing in data scientists and expensive high-end technology. It helps gain meaningful insights from their data and improve their value stream.
1. Define Business Goals and Establish a CoE:
Perhaps the most crucial step in your data science journey is creating a business case for each data science initiative in your organisation. Defining a use case helps outline the project's expectations and it gets the management on board.
Leaving out this step might result in deploying models that do not serve your company's goals.
It is also essential to establish a Center of Excellence around your firm's data science efforts. A CoE team provides leadership, direction, research and support during the implementation process. Establishing a COE and creating a business case steers the project in the right direction. Get project managers to work with your data scientists and create reports to help track benefits realisation.
2. Use the Right Tools
There are a lot of analytics and data science tools available for companies to choose from. Selecting the right tool will directly impact the success of your data science project. Have you ever worked with slow internet and a computer system that lags when you want to take a quick action, even if you force yourself to finish the task, you would have exhumed more energy that you normally should.
Besides saving time, when selecting your tools, you also need to choose one with the future in mind. This means ensuring the set of tools selected are dynamic enough to handle increasing data streams and complexity.
The importance of choosing the right tool at each stage in your data science journey cannot be overstated. It will help your teamwork seamlessly and further drive value. With Voyance, you get the right set of tools needed at each stage in your data science process. Our data science platform provides you with quick insights and analytics to help you kickstart your data science journey. The cost of good work should not focus on only the technology aspect, how about the humans behind it? Are they doing more with little time?
3. Be Data-Driven:
Your company's decision-making process should be driven by data. Hire data scientists with strong domain knowledge and good communication skills to help uncover insights that translate into value for your business. You will encounter scenarios where insights extracted by your data analysts are hard to believe. At times like this, you need to keep an open mind and be willing to understand these results.
4. Be Security Conscious:
Every day, Cybercriminals are creating more ways to breach your organisation's data storage. A company that does not believe they can be hacked, is living in denial. Cyber criminals are rampant and your business needs to be protected. Statistics show that cyber-attacks are estimated to cost about $11.4 million every minute in 2021 and that is a lot of money! Thus, every organisation must devise measures to secure its infrastructure from cyber attacks. These attacks could be in the form of malware, DDoS (Distributed Denial of service), phishing and SQL Injection attacks
Use mechanisms like hashing, multi-factor authentication, data encryption and other strategies to protect your data from possible attacks. It’s better to be proactive than to be reactive, don’t wait to have a bad experience before security structures are in place.
5. Practice Model Validation:
Model validation is the process of ensuring that your model generalises to data it has not seen before. Your models must be built and tested on data that is a good representation of the data that it is likely to see in the future. Poor testing or cross-validation process results in a model that performs well during training but poorly on field data, and this is due to overfitting.
Solve this by using data science tools that automate model testing with strong cross-validation techniques.
6. Avoid Vague Data Semantics:
One of the most critical steps in training a model is feature selection. It is very easy for a data scientist to misinterpret a column's meaning in a table if the columns are not correctly named. Misinterpreting data is a common problem that can lead to algorithmic bias and wrong predictions. Data preparation during modeling can solve this problem, but it is tedious and time-consuming, so avoid it.
One way businesses can avoid problems resulting from poor data semantics is to create a data dictionary. This will provide the data scientist with context and the proper meanings of different columns in the database. You can also create a feature library. Many times, data scientists create new features from the existing ones to improve the predictive ability of machine learning models. So, it is crucial to store a record of these newly engineered features for reusability purposes in the future.
It's a journey, but it's worthwhile
As you start engaging these data science practices, your efforts will begin to yield more benefits. To reap these benefits, you need to treat data science as a core competency and create a robust business plan with technical infrastructure and adequate sponsorship. This will help your business improve its process of building and deploying models to drive revenue growth.
Also, understanding that engaging data science is a journey and not a destination helps. It’s a marathon not a sprint. It's an iterative process and every breakthrough is dependent on the previous iteration.