The modern business world is intricately connected and is rapidly generating enormous amounts of data. Businesses all over the world are developing and implementing analytical and statistical tools to get access to meaningful data to help inform business decisions. This is paramount to consistently innovating and expanding. It’s for this reason that areas like Business Intelligence (BI) and analytics is worth an estimated $22.8 billion as an industry in 2020.
A successful enterprise in the digital age needs to be data and analytics driven, and that’s where the field of data science comes in. Most organizations are searching for innovative tools, platforms, and methods to make sense of their business’ data. This helps increase an organization’s productivity and profits, as well as build customer loyalty. By organizing data and running analytics, companies can build market share while saving millions of dollars each year.
Data scientists are at the forefront of this exciting challenge.
What is Data Science?
According to a PeerPaper Report by ‘IT Central Station’, a leading product review site for enterprise technology, "…data science refers to a collection of practices that leverage computer power to extract knowledge or insights from data. Businesses can harness predictive analytics, based on data science, to model behavior based on patterns. Done right, data science delivers value to businesses by enabling them to improve their understanding of operations, sales growth, customer experience, and more.”
Data science includes data mining, machine learning, predictive analysis, and other related fields. It can be defined as a multidisciplinary field requiring knowledge of diverse skills and technologies, such as programming, machine learning, applied mathematics, algorithms, and many more. The advent of “big data” has made the field even more complex, with practitioners in this field requiring knowledge such as distributed computing techniques, algorithms, and architectures.
Data science and machine learning have become just as fundamental to business growth as risk and cost reduction. And the reality is that the process of implementing predictive analytics can be quite complicated, especially when it comes to finding the right mix of analysts and data scientists. An organization’s ability to assess which data will be valuable will likely determine their success with analytics. Companies then need the agility and technical skills to make this data available.
Practical Applications of Data Science
Below are some examples of data science solutions that can benefit businesses:
- Predictive Analysis Regarding Customers – This involves estimating the lifetime value of a customer. This is done using data science, by identifying optimal methods of customer acquisition, customer retention, and up-selling and cross-selling products.
- Social Sentiment Analysis – This is done by analyzing raw data on social media platforms as well as product reviews by customers. This helps companies improve their products’ messaging and merchandise selection.
- Customer Purchase Intent – Using customers’ past purchase patterns, browsing behavior, and demographics, companies can predict who might be a potential buyer, which specific products they are likely to buy, or when the purchase is likely to happen.
- Scam Detection – This refers to a business’ ability to examine financial transactions and the data related to it, like user accounts and IP addresses, enabling them to identify if fraudulent or malicious activities are taking place.
Now that you have a foundational understanding of data science and its applications, let’s look at some of the best practices for optimizing value from data science:
1. Outline business targets
Also known as ‘defining the use case’, this is the most important step in defining the business issues you want to address. It involves data scientists meeting with the relevant stakeholders and decision makers to clearly define the objectives of a new project.
This ensures that the data scientists select the correct methods and resources to meet the requirements of the relevant stakeholders, who will ultimately utilize the results of the data model to make key business decisions.
2. Identify data needs
In the initial phase of the project, data scientists will need to understand what kind of raw data is available to them. They’ll also need to decide if the information contained in the data is sufficient to design the solution for the identified business issues..
3. Select the right tools and techniques
Modern businesses have a wealth of analytical tools to choose from. It is vital that data scientists select the most appropriate tools and techniques to handle the complexity of the organization’s data, as well as current and future volumes of data.. These front-end decisions will empower data scientists and increase the efficiency of their team.
4. Rely on data, not instinct
Data scientists often uncover insights which may seem counterintuitive. It is important to be unbiased and logical while analysing these results, and take the necessary actions to acknowledge them. Data scientists can help to foster a team culture throughout the organization that prioritizes scientific process and allows the data to speak for itself.
5. Protect your data stores
In recent years, there’s been a surge in instances of cyber attacks on organizations all over the world. So it’s become imperative for organizations to put in place adequate safety features. Data encryption, hashing, and two-factor authentication are examples of methods to help protect your data sources from potential hacks.
6. Promote collaboration
Often a new project for one data scientist will be quite similar to a project that another scientist has already explored. That’s why it’s imperative for professionals to share knowledge to prevent teams from wasting time and efforts in starting from scratch. Companies should adopt a culture of collaboration where everyone learns from each other and develops the work done by others in the wider community.
7. Take action on data
Data science isn't just about deciphering enormous chunks of data and uncovering intriguing patterns. Its true value is in its power to enable us to take action based on the results. Organizations should have a clear plan of action to identify the key insights and drivers that their data will influence.
The findings should be presented with clear visualizations that address the original business targets. This is an important step in helping stakeholders to follow through with an action plan.
8. Test and validate
Testing and validating the data sets and results on a regular basis is critical to ensuring the relevance of the models as well as the data itself. It’s also important for a data scientist to ensure the models are ready to be iterated quickly, in line with project requirements. The underlying patterns of a model can change rapidly due to external reasons, rendering any results invalid. So it’s important to undertake regular and thorough testing and validation.
Each data science project will have unique needs and aims. This above list is by no means exhaustive, however these tips and suggestions have been written to help you avoid many of the common mistakes and errors of those who have gone before you.
How to Pursue A Career in Data Science
Modern data science offers many powerful tools that can help businesses to increase their client base, influence purchasing decisions and grow the business successfully. The aim of this article has been to introduce you to the fundamentals of data science and the best practices for securing optimal results with the least amount of headaches. Whether you’re a new or seasoned data scientist, how efficiently you utilize the data around you will determine how you grow in your organization.
Interested in forging a new path with a career in data science? Let’s chat about how Thinkful can help you reach your goals.