Using Data Science to Grow Your Business: 3 Key Areas to Consider

Using data science to grow your business

Data science can have incredible benefits for your business but it’s important to understand that it’s a solution to a problem, not a way to find the problem. It means that if your company has a lot of data that you don’t quite know what to do with, you need to figure out what you are trying to improve or change before hiring a team of data scientists. Data scientists analyze data to find insights but it’s the job of product managers and business leaders to tell them what to look for.

There are a lot of ways how data scientists can bring value to your company, if you are in the process of figuring out exactly how data science can benefit your business, you can consider the following ways of using data science:

  • to build better products
  • to make better decisions
  • to automate repetitive time-consuming processes.

Let’s take a closer look at these three areas.

Using Data Science to Build Better Products

Data science is able to help you bring a better product to your target market in two main ways: you can either customize a product or a service to make it more personal, or you can provide a totally new experience with the product or service.

Today machine learning looks most attractive for businesses in terms of generating real value and enabling breakthrough innovation. There are three main types of machine learning algorithms – unsupervised, supervised, and reinforcement learning. We are going to focus on the first two and provide you with real-world examples of how these algorithms can benefit your product.

The discussion about supervised and unsupervised learning can get complex and technical at times but essentially supervised learning is about predicting an outcome, while unsupervised learning is about identifying a pattern. Both of them can help you deliver better products to your customers by understanding them better.

Unsupervised learning allows you to capture your customers’ preferences, and use the data to anticipate their needs and behaviors in the future. The most common examples of unsupervised learning are Amazon’s recommendations based on what other customers also bought, and Spotify’s recommendations for your playlist based on the songs you’ve already liked or added. To build this type of recommendations data scientists solve a clustering problem, grouping similar users together to form homogeneous clusters.

Supervised learning is typically used for predicting customer behavior. By solving a classification problem machine learning engineers may help you identify satisfied and unsatisfied customers and predict churn. By solving a recommendation problem data scientists are trying to guess things that your customers may be interested in. By solving a ranking problem data scientists help users find the right thing faster when they search.

Supervised learning is also used to enable features such as face recognition, image classification, speech recognition. These features revolutionize customer experience and make tech products more intuitive to use, like telling virtual assistants to schedule a meeting instead of accessing scheduling software to find a time, create an event, and type the details.

The great thing about machine learning is that it is especially helpful in optimizing user engagement and retention, both of which are critical success factors for your product.

Using Data Science to Make Better Decisions

With data science and predictive analytics, in particular, you can predict important metrics and trends for your business which improves your ability to serve your customers or otherwise compete in the market. Most importantly, predictive analytics provide you with the ability to detect issues that can negatively impact your business before they happen or spread.

Predictive analytics is not a new field, but where and how you can apply it has grown rapidly thanks to recent advancements in technology. Predictive analytics today is all about connecting disparate systems and data sets in order to do proper analysis and derive valuable insight out of seemingly chaotic data.

Solutions powered by advanced analytics have a big potential to reduce costs that arise due to failures, bottlenecks, customer churn, etc. A good example of predictive analytics in action is anomaly detection for IOT by Anodot. Using machine learning algorithms Anadot analytics platform keeps machines running smoothly by flagging anomalies in the data. If a machine is starting to show signs that it needs maintenance or repair, algorithms can understand this from small changes in the sensor data. Such proactive maintenance can keep support costs down and customers happy.

Advanced analytics introduces the ability to leverage the power of multiple data sets and to discover connections where none could have been found before. A good example of this is when in 2016 the New York City government was trying to reduce the costs related to legal claims against the city. By putting together the data from all departments and applying advanced analytics, the city found correlations that were not obvious to the human eye. One of them was that the amount of tree-related accidents spiked after a major budget cut was introduced in the Parks and Recreations department.

With the increase in accumulated data (IBM predicted a 42% increase by 2020), advanced analytics will become the norm rather than a way to gain competitive advantage. Here is a list of helpful tips on how to start implementing advanced analytics in your business:

Five steps to implement effective predictive analytics solutions

Using data science to make better decisions
Source: Bersin by Deloitte


Using Data Science to Automate Processes

Automation is one of the hottest trends in modern technology, so let’s discuss how data science can be used for creating automated innovations.

In order to identify growth opportunities that automation can bring to your company you can start by asking yourself:

  • where do people in my company spend a lot of time to make decisions that could be automated, so their skills could be better leveraged elsewhere?
  • what types of data do people in my company normally search for and collect manually and how can this be automated?
  • which tasks in my company can machines perform faster and more efficiently than humans?

Machine learning can free up resources through automatic retrieval, generation or processing of content. This is becoming increasingly important in an age of large information repositories where the data contained does not possess any natural order.

For example, brand managers analyze large collections of images and posts from social media every day trying to figure out how, when, and where people use their product and how their customers feel about the brand. A social media analytics company Brandwatch uses machine learning to automate the process of image detection and analysis. Their product Image Insight helps collect and analyze more images containing your brand. This saves the valuable time of your best people for where it really matters.

As more and more interactions take place through e-mail, chat and other e-channels there is a chance to automate the tasks related to document retrieval, summarization, and classification. AI-powered text analysis is already used in legal discovery, helping to find all the documents that are relevant to a legal case. Searching through millions of emails and PDFs looking for specific names and key phrases is really difficult for humans to do while it’s pretty easy for a cluster of Elasticsearch nodes.

Decisions that normally require a high level of skills and knowledge — such as pricing, loan decisions, risk assessment and so on — can also be handed over to machine learning algorithms, provided that your data is clean and flows well between systems. This enables quicker decision-making process, and the best part is that the model can learn from the outcomes and get better and better with time. However, this differs from full automation because such models will still have the special cases and exceptions that need special review by the human experts from the field.

I hope the examples above will get you thinking about more practical innovations that you can implement within your organization with the help of data science. We’re looking forward to seeing more businesses successfully using machine learning in 2018.

Good luck out there!

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