Mosaic Data Science, a leading strategic data analytics consulting firm, works with a number of different partners to deliver cutting-edge machine learning, artificially intelligent, prescriptive and predictive analytics solutions.
Our blog post, How to Make the Most of your Data Science Dollar, examines how firms can take advantage of data science consulting firms. Here is a snippet.
Data-science projects are investments. Like any other investments, some are more rewarding than others. And like other investments, they generally come in portfolios. You shouldn’t pick data-science projects independently, because they often have common requirements for data, skills, and infrastructure that the organization currently lacks. Careful gap analysis may reveal opportunities to enable several projects by filling a common set of gaps, thereby making the projects as a group more attractive than they would otherwise appear individually (counting their costs repeatedly). Thus choosing an optimal portfolio of data-science projects is itself an optimization problem, a kind of data-science problem. In part because an organization’s goals (objective functions) and resources (constraints) change frequently, this particular optimization problem is a good candidate for a greedy multi-period solution, where you attack the most valuable projects first, and re-evaluate each time a project nears completion.
Opportunity cost is a second reason to treat data science as a portfolio-optimization problem. When data scientists approach strategic data analytics projects one at a time, it’s all too easy for them to indulge their analytical perfectionism by devoting disproportionate resource to achieving the best possible project outcome. This may maximize one project’s value at the cost of depriving other projects of adequate resources, so that the total value of the project portfolio is not maximized—even if the portfolio is well chosen. Analytics managers should be explicit with their teams that the goal is not to perfect any single project, but to maximize the value of the whole project portfolio; and management should structure their teams’ reward systems accordingly.