Data Science: Why and How do you Invest in Data?
Understand Data Science at a glance, see practical examples of how it can help your business, start a project in your company, and more!
Data Science is a multidisciplinary mix of data inference, algorithm development, and technology to solve analytically complex problems.
Certainly you have heard this term, or if not, you are about to hear a heap about it from here on.
In this article, we’ll explain the concept and tell you why it’s important to invest in it.
Check it out!
What is Data Science and why is it so urgent to invest?
Data Science is a discipline that studies where information comes from, what it represents, and how it can be transformed into a valuable resource to create business and IT strategies.
A Data Science strategy aims to mine large amounts of structured and unstructured data to, among other things, identify patterns to help organizations control costs, increase efficiency, recognize new market opportunities, and increase competitive advantage.
The Data Science approach basically employs mathematics, statistics, and computer science disciplines and incorporates techniques such as machine learning, cluster analysis, data mining, and visualization.
It is headed by data scientists, specialists who have a combination of analytical skills and technical knowledge (algorithms, coding etc.) as well as experience and business insight. In addition to managing and interpreting large amounts of data, many data scientists also have the task of creating data visualization models that help illustrate the business value of digital information.
The main advantage of Data Science in an organization is the empowerment and facilitation of decision making. Organizations that employ this approach may include quantifiable evidence based on data in their business decisions.
Thus, these data-driven decisions can ultimately lead to increased profitability and improved operational efficiency, business performance, and workflows. In customer-focused organizations, Data Science helps identify and refine audiences.
The main reason to invest in Data Science lies in the power of innovation that this approach can bring. By making business more data-driven and combining specialized tools and professionals, you can:
- increase brand value;
- refine products and services;
- enhance the customer experience;
- acculturate innovation.
What technologies and practices are under the umbrella of Data Science?
Data Science began with statistics and evolved to include concepts / practices such as Artificial Intelligence, Machine Learning, and the Internet of Things, to name but a few.
As more and more information becomes available, companies have been collecting and storing increasingly large quantities through behaviors and purchasing trends registered across their various platforms. With the growth of the Internet, the Internet of Things and the exponential increase in the volumes of data produced, there was a flood of new information – or Big Data.
Once the doors were opened by companies seeking to increase profits and drive better decision making, the use of Big Data began to be applied in other fields such as medicine, engineering and even in the social sciences.
As we have pointed out, a functional data scientist, as opposed to a general statistician, has a good understanding of the software architecture and understands various programming languages. The data scientist:
- defines the problem;
- identifies the main sources of information;
- designs the structure for collecting and sorting the required data.
Highly sophisticated software, within these technologies, are used to:
- model the data.
What pains does Data Science promise to heal in business and what benefits does it offer?
The specific benefits of Data Science vary depending on the purpose of the company and the market segment in which it operates. Sales and marketing departments, for example, can extract customer data to improve conversion rates or create more targeted and efficient digital campaigns.
Banking institutions are mining data to improve fraud detection. Video streaming services have Data Science strategies to determine the interest of their users and use that data to determine which TV shows or movies will be produced.
Data-based algorithms are also used to create custom recommendations based on a consumer’s purchase history. Transportation companies use Data Science tactics to find the best routes and delivery times as well as the best modes of transport for their shipments.
Here are some Business advantages of Data Science:
Improved customer experience
One of the most interesting benefits of Data Science is the ability of sales and marketing teams to understand their audience at a very granular level. With this knowledge, an organization can create the best possible experiences for the customer.
Risk and Fraud Mitigation
Data scientists are trained to identify data that stands out in some way. They create statistical, network, and Big Data methodologies for predictive fraud-prone models and use them to create alerts that help ensure quick responses when unusual data is recognized.
Delivery of really relevant products / services
Another advantage of Data Science is that organizations can find when and where their products sell best. This can help deliver the right products at the right time – and can help develop new products to meet and even anticipate the needs of current and future consumers.
How do you start a Data Science project in your company?
A Data Science project requires a wealth of knowledge and sophisticated tools. The best way is to seek help from consultancies and specialized agencies.
There are, however, some initial steps that can help a lot in this journey. They are:
- Begin by considering how your organization visualizes data: is it viewed passively, as a by-product of the transactions?
- Then look at the data you have and ask what else you’d like. Transactional data is just the beginning. One of the most common and critical types of Business Analytics is customer data. Within this domain, there are categories such as contact data, preference data, and customer telemetry data (that is, what they are doing with the product). Each of these types of data has its own opportunities and challenges, but regardless of how you rate them, collecting customer data across multiple points of contact can provide valuable insights.
- Decide on a data acquisition strategy. Identify the types you want, find out how you’re going to collect them, and determine the metrics to track progress toward your goal, either by improving the quantity, variety, or quality of the data.
- Develop your traditional data analysts in a Big Data skill set or opt for data scientists with experience working with larger datasets.
The Influence of Data Science
Over the past ten years, Data Science has grown quietly to include companies and organizations from all around the world. It is now being used by governments, geneticists, engineers and even astronomers.
Data Science has become an important part of academic and business research. Technically, this includes automatic translation, robotics, speech recognition, digital economics, and search engines. In terms of research areas, Data Science has expanded to include biological sciences, medical care, medical informatics, humanities and social sciences. In short, Data Science now influences the economy, governments, and businesses of all sorts.
This approach adds value to any company that can use their data well. From statistics and insights into workflows and hiring new candidates, to helping senior staff make more informed decisions, Data Science is valuable and must be on the radar of companies that see value in technology and are constantly striving to innovate.
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