Critical Reflections on Whether to Outsource Big Data and Analytics

As the data piles up, managing and analyzing these data resources in the most optimal way become critical success factors in creating competitive advantage and strategic leverage. To address these challenges, companies are hiring data scientists. The “data scientist” job profile is relatively new and combines a unique skill set consisting of a well-balanced mix of quantitative, programming, business, communication, and visualization skills. It speaks for itself that these individuals are hard to find in today’s job market. Universities are jumping on the big data and analytics bandwagon and offering various masters programs in big data and analytics to close the gap.

The shortage of skilled talent and data scientists in Western Europe and the U.S. has triggered the question of whether to outsource analytical activities. This need is further amplified by competitive pressure to reduce time to market and lower costs. Companies need to choose between building the analytical skill set internally, either at the corporate or business level, outsourcing all analytical activities, or going for an intermediate solution whereby only a portion of the analytical activities are outsourced. The dominant players in the outsourcing analytics market are India, China, and Eastern Europe, with some other countries (e.g., Philippines, Russia, and South Africa) gaining ground as well.

Various analytical activities can be considered for outsourcing, including the heavy-lifting grunt work (e.g., data collection, cleaning and preprocessing), provisioning of analytical platforms (hardware and software), training, and education, as well as the more complex analytical model construction, visualization, evaluation, monitoring, and maintenance. Companies may choose to grow organically and start by outsourcing the analytical activities step by step, or immediately go for the full package of analytical services. It goes without saying that the latter strategy has more risk associated with it and should thus be more carefully and critically evaluated.

Despite the benefits of outsourcing analytics, it should be approached with a clear strategic vision and critical reflection with awareness of all risks involved. First of all, the difference between outsourcing analytics and traditional information and communications technology (ICT) services is that analytics concerns a company’s front-end strategy whereas most ICT services are part of a company’s back-end operations.

Another notable risk is the exchange of confidential information. Intellectual property (IP) rights and data security issues should be clearly investigated, addressed, and agreed upon. Moreover, all companies have access to the same analytical techniques, so they are only differentiated by the data they provide. Hence, an outsourcer should provide clear guidelines and guarantees about how intellectual property and data will be managed and protected (e.g., using encryption techniques and firewalls), especially if the outsourcer collaborates with various companies operating in the same industry sector.

Consider the example of Bank A, which invested in state-of-the-art data quality solutions, whereas bank B did not. Outsourcer XYZ can now use the high-quality data from bank A to build high-performing analytical credit risk models, and sell those to bank B as well, thereby diluting the competitive advantage of bank A. This danger is further amplified by the many mergers and acquisitions witnessed in the outsourcing sector. Furthermore, many of these outsourcers face high employee turnover due to intensive work schedules, the boredom of performing low-level activities on a daily basis, and aggressive headhunters chasing these hard-to-find data science profiles. This attrition problem seriously inhibits the continuity of the partnership and a long-term, thorough understanding of a customer’s analytical business processes and needs.

Another often-cited complexity concerns the cultural mismatch (e.g., time management, different languages, and local versus global issues) between the buyer and outsourcer. Exit strategies should also be clearly agreed upon. Many analytical outsourcing contracts have a maturity of 3 to 4 years. When these contracts expire, it should be clearly agreed upon how the analytical models and knowledge can be transferred to the buyer thereof to ensure business continuity. Finally, the shortage of data scientists in the U.S. and Western Europe will also apply, and might even be worse, in the countries providing outsourcing services. These countries typically have universities with good statistical education and training programs, but their graduates lack the necessary business skills, insights, and experience to make a strategic contribution with analytics.

Given the above considerations, many firms currently adopt a partial outsourcing strategy, whereby baseline, operational analytical activities such as querying and reporting, multidimensional data analysis, and OLAP are outsourced, whereas the advanced descriptive and predictive analytical skills are developed and managed in-house.


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