Data science and artificial intelligence are hot media topics. An expert talking about the capabilities of predictive analytics for business on a morning TV show is far from unusual. Articles covering AI or data science in Facebook and LinkedIn appear regularly, if not daily.

Due to a surfeit of information about AI and big data on the Internet, companies can assume that data analysis is the solution for most of their data-related issues.

For instance, we had such a case in our work. An OTA that uses flight information from the Innovata dataset contacted our data science team with a request to analyze it to extract data. Innovata, which was incorporated by the FlightGlobal news and information site, is a leading provider of historical, current, and future schedule data for more than 900 airlines worldwide. Our clients considered working with large datasets a big data problem. While the dataset is indeed large, the problem that the OTA was solving didn’t require data analysis, only data retrieval. Our data science team delegated the project to a software architect instead of a data scientist.

So, before planning to use data science or AI for your business, find out whether it’s the technology you need. You can answer this initial question by isolating and specifying your problem.

In this article, we’ll discuss what factors to consider and steps to take to team up with a competent data science or AI vendor.

1. Match your problem with possible solutions

In general, data science and AI solutions entail gaining valuable insights using available data. Some companies approach data science vendors to build products whose key functionality is centered around machine learning. For instance, this may be an application that transforms speech into text. Other organizations may want to develop a custom analytical and visualization platform to be in control of their operations and make strategic decisions based on the insights.

In the broadest sense, you can apply data science to gain insights about your business and improve your operational efficiency, or you may want to deliver AI-based applications to your end-customers. In the former case, the end-consumer would be your company, in the latter — your customers.

Customer-facing apps and fraud detection

Customer-facing applications powered by machine learning algorithms solve your customers’ problems. People may use these products for their daily activities or to do work tasks faster and easier.

These are examples of customer-facing solutions that need data science engagement:

• Virtual text and voice assistants (e.g. Mezi travel assistant or Expedia chatbot)
• Recommendation engines for eCommerce and over-the-top media service providers (e.g. Amazon and Netflix recommendation systems)
• Price prediction engines (e.g. Fareboom or Kayak fare predictions)
• Apps for conversion of speech into text (e.g. the IBM Watson Speech to Text or Voice Assistant by QuanticApps)
• Sound recognition and analysis applications (e.g. Do I Grind healthcare app)
• Image editing applications (e.g. Prisma app)
• Image recognition apps and features (e.g. credit card recognition with the Uber app)
• Real-time visual and voice translators (e.g. Google Translate app or iTranslate Voice)
• Document classification apps (e.g. Knowmail)

There’s also a group of fraud detection products that employs both data science and traditional programming techniques to build.

Business analytics: business intelligence and statistical analytics

Business analytics (BA) is the exploration of data through statistical and operations analysis. The purpose of BA is to monitor business processes and to use insights from data for making informed decisions.

Business analytics techniques can be divided into two groups — business intelligence and statistical analysis. Companies with business intelligence (BI) expertise analyze and report on historical data. Insights into past events allow companies to make strategic decisions regarding current operations and development options. Statistical analytics (SA) allows for digging deeper while exploring a problem. For instance, you can find out why customers prefer booking from OTAs rather than from your hotel site this week or whether or not a particular user buys products after reading an email about current deals.

Business analytics can be used for:

• Data management
• Dashboards and scorecards development
• Big data analysis
• Price, sales, or demand forecasting
• Client analysis
• Sentiment analysis on social media
• Risk analysis
• Market and customer segmentation
• Customer lifetime value prediction
• Upsell opportunity analysis, etc.

Business analytics allows for solving problems of various complexity, from simple reporting to advice on measures for risk mitigation or operations improvement. And to address these problems, you may use different types of analytics. There are four analytics types: descriptive, diagnostic, predictive, and prescriptive.