Over the past few years, interest in data science has soared. Many enterprises are already leveraging data science to better understand customers, improve business processes, and achieve their digital ambitions. As the data science market is expected to reach 33.5 billion USD by the end of 2017, the demand for data science specialists is rising. However, according to McKinsey report, by 2018 about 140,000 to 190,000 big data jobs will be unfilled due to the lack of applicants with expertise and experience. That is why many organizations are turning to offshore data science service providers for help striving to build a competitive advantage with the help of professional data science specialists. With our hands-on experience in data science, BI and big data services and delivery of numerous projects across different industries, we understand how challenging it is to find qualified specialists. Therefore, we hope the knowledge we share in this post will help you evaluate and select the right data science service provider to reap maximum benefits from your data assets.
1.Choose the provider that can satisfy your business requirements
The provider you need depends on the type of analytics you need to build, how they will be operationalized, and whether you need ongoing support. The major considerations when evaluating your requirements for offshore data science service provider may include your desired analytics sophistication (machine learning, artificial intelligence, automation) as well as data security requirements (regulatory compliance, on-site versus off-site protocols, certifications). Moreover, data specialists with background experience in related fields such as BI or big data may provide even more valuable insights and create complex analytics systems with the help of the latest methods of data collection, analysis and processing.
2. Consider data science service providers that have experience in your industry
Executives considering outsourcing data science services to offshore organizations often require relevant industry experience. On the whole, every industry has its own set of best practices. One of the main responsibilities of the data science service provider is to have a clear understanding of data-driven processes and experience to provide expertise about these practices and guide the customer in applying them profitably. Industry experience of your trusted data science provider may include excellent track record and a decent portfolio of data science projects in various industries. Availability of a wide array of data driven service offerings is huge plus for any data science service provider. For instance, N-iX data specialists have expertise in market basket analysis, sentiment analysis, predictive and behavior analysis.
Furthermore, providers that have experience in working in a variety of industries like telecom, fintech, healthcare, hospitality, etc. have already built a strong domain knowledge in different verticals. This knowledge may encompass numerous aspects of data analysis and processing and enable them to create complex solutions covering related areas of expertise. So industry experience and fundamental understanding of data algorithms and business processes are crucial for any specialist who deals with collecting, processing and analyzing data.
3. Look for the availability of necessary skills and expertise
When choosing a data science provider, it is essential to pay close attention to the analytic tech stack of the data experts available within the company. For instance, expertise in a variety of programming languages and software packages is necessary to flexibly and efficiently extract, clean, analyze, and visualize data. They may include R, Python, Java, Scala, Hive, Pig, Hadoop, Cassandra, Matlab, Tableau, etc. With the help of these technologies, data experts can build industry use-cases related to workforce, customer and marketing analytics. Additionally, technologies enable them to use various data science techniques like machine learning, statistical computing, mathematical models, and algorithms.
Also it is essential for a data scientist to be able to work with unstructured data, whether it is from video feeds, social media, or audio. Aspiring data scientists should be familiar with multivariate statistics (including regression, principal components analysis and clustering) as well as data-driven predictive model development. What’s more, experience in using SQL, Teradata or Oracle is needed for any qualified data specialist.
4. Analyze the talent pool of data scientists within the country
Availability of different events, communities and courses for developing expertise in data science and related fields is necessary to stay competitive in today’s high-tech and rapidly evolving society. In this respect, Ukraine has recommended itself as a country with a vast number of data experts and rapidly developing tech community. For instance, Applied Sciences Department of the Ukrainian Catholic University has launched an educational initiative Lviv Data Science Summer School. The participants may study state-of-the-art methods and tools of Data Science, Machine Learning, Business Analytics. N-iX senior data experts along with CEO Andrew Pavliv have also participated in the UCU’s data science learning programs. This is not only a great chance to share experience and promote data science within the country but also an opportunity to find aspiring talents.
In addition, Ukrainian community Data Science UA is actively popularising data science in Ukraine by conducting regular conferences, meetups, workshops and courses. They include such events as Data Science UA conference, International AI Ukraine conference, Databases’ past, present and future Meetup, etc. Moreover, such data science communities as AI and Data Science Ukraine, Data Science Group, Data Analytics and others organize regular meetups among the most aspiring data experts. This gives Ukrainian data specialists a possibility to network and learn from more experienced professionals and, as a result, make revolutionary changes in modern technologies.
5. Opt for providers with good problem-solving capacities
Data science isn’t just about creating algorithms, writing code, or visualizing data but also about solving critical problems. Business problems are often vaguely defined, complicated, and have success conditions and dependencies. This means only certain types of model or levels of precision (fraction of positive predictions which are indeed correct) and recall (fraction of the true positives which are found by the model) may solve them. A qualified data science service provider should have enough capacities to productively approach any problem. This includes identifying a situation’s salient features, defining how to frame a question that will yield the desired answer, deciding what approximations make sense, and consulting the right people during the analytic process. Being able to discern which problems are important to solve and identify new ways of leveraging the data is critical for any business.
All in all, data science develops by leaps and bounds thus becoming a critical skill for developers and managers across almost all industries. That is why more and more companies seek for reliable data science service providers as they help them effectively transform raw data into valuable insights. The above-mentioned tips may help you move your business to the next level and effectively leverage data science as a service.