What you need to know about data science, how it works, and why it’s important
Data science has been called the future of artificial intelligence, and for good reason. The techniques of data science hold the keys diving deep with data and making effective use of artificial intelligence and machine learning.
Companies and organizations all over the world are using data science to implement sustainable programs and tools to reap data-driven insights in real time. But what exactly does data science mean and how can your organization benefit? Let’s take these one at a time.
What is data science?
Data science is the practical application of advanced analytics, statistics, and machine learning, along with the associated activities like data preparation, which are used to drive value in a business environment. The goal is to extract insights from data, predict what’s coming next, and then decide the best actions to take—and even sometimes perform those actions automatically.
Data science and big data
Data science is a direct outgrowth of big data. You’ve probably heard the term big data, but if you haven’t, it refers to the massive volumes of information created with every digital action taken, whether by a human or a machine (thanks to the Internet of Things). There’s real potential in all of this data, but it’s created so quickly and at such volumes that organizations often aren’t sure how best to use it to their advantage.
That’s where data science comes in. The approaches, tools, and principles of data science can be applied to harness the explosive power of big data.
What does data science involve and how does it work?
The purpose of data science is to create a data product that then produces data insight. There are three major steps involved: data collection, data modelling and analysis, problem solving and decision support.
This is where the task of a data scientist must begin. This part of the process covers everything from finding the data to processing and then cleaning it.
- Raw data: This data may be sourced from several places including social media, sensor data etc. The data may be structured, semi structured or unstructured. It may also be ordinary or big data.
- Processing: The data scientist then tries to figure out exactly what kind of data they’re concerned with and devise a collection scheme to acquire it.
- Clean dataset: After first processing, the data must be cleaned up. This is because data rarely arrives in ordered little rows that are exactly how you want them to be. Instead they arrive in untidy little clumps that must be patiently straightened out and tidied up.
Data modelling and analysis
After preparing the data, a data scientist then figures out how the data can be analyzed to achieve the specific needs of the project or organization. The tasks involved at this stage are:
- Finding the best algorithms: First, the data scientist has to develop and test the best algorithms that will provide the required insights from the data.
- Model development: The data is usually needed to work within a specific framework. This framework is the model. Designing a suitable model takes time, although how long will depend on the needs of the user.
- Model training: After developing the model, it’s time to train it to recognize the specific outcomes that are required. There’s a lot of machine learning to this stage of the process.
Problem solving and decision support
After the model is developed and adequately trained, the data will have been transformed into the business intelligence solution that is required. What remains is to deploy it through the following:
- Communicate report: Of course, the data scientist has to present the data in a form that can be easily understood and communicated. This can often be the most challenging part of the process.
- Data product: This is essentially the finished model that can now parse data and produce insights all on its own.
- Data insights: This is the point of the whole process and when those game changing insights start rolling in, all the work that came before seems totally worth it.
Why is it important?
The business world only grows more competitive every day. In this challenging environment, the marginal gains that are reaped from simple statistical analysis such as what product customers preferred to buy at a particular season are no more enough to drive innovation inspiring insights.
Advances in data science and the increase in big data have led to a lot of possibilities. Organizations can leverage the same sales information and several clumps of originally meaningless big data to tell not only what people would buy at that season but also when they would buy it, how much they would be willing to pay and who they’re buying for.
Obviously, these advances have helped ensure that companies can make smarter business decisions. Netflix mines data to gain insight into what subscribers want to see and they adapt this information to make decisions on which Netflix original series to produce. And you know how much you spent on Netflix last month.
Users recognize that the gains of data science are huge. According to IBM’s Business Tech Trend, nearly 70% of leading companies say analytics are integral to how their organizations make decisions. Again, over 60% of respondents to a 2015 CapGemini study agreed that failing to use big data could lead to irrelevance and loss of competitiveness. Companies that don’t get on board are at risk of being left behind.
How does RapidMiner help make data science work for you?
Getting your organization ramped up for data science is no walk in the park. It’s a very resource intensive process that can take a good while to complete. And that’s for one project. You will likely need to leverage the skills of competent data scientists. But they don’t come cheap, assuming you can find one to hire in the first place.
RapidMiner is a software platform for analytics teams that unites data prep, machine learning, and predictive model deployment.
At RapidMiner, we believe that there are two solutions to this data scientist bottleneck. If you already have a data scientist or data science team on board, let’s make sure that those resources are more productive. RapidMiner delivers a visual workflow designer that accelerates the end-to-end machine learning process for improved productivity.
But if you are hoping to test out data science without a data scientist, we can help there too. Not every company needs a resident data scientist. And not every company can afford to hire one, so let’s empower more people to do the work that data scientists do. Use your internal analytical talent and leverage your domain expertise.
Download RapidMiner Studio for all of the capabilities to support the full data science lifecycle for the enterprise.
Or, try RapidMiner Go right from your browser to explore data, discover insights, and create models within minutes.
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