At this point skills will be unavailable or underdeveloped. The focus is on sourcing talent, either internally or externally. Find people with the initiative to build and expand the analytics program in the organization from the ground up. Attracting people at this stage should be a careful process, because the first data scientists are for a large part responsible for the early success of the program. Early success will convince leadership to invest and scale, while early struggles may lead to termination of the program. Early success also spurs the interest of other employees in analytics, and a cultural shift to a more analytical culture.
Source talent internally or externally to build an analytics program.
Focus on early success to gather momentum.
Analytics evolves from showing numbers to calculating numbers, and the required skills evolve with it. Data mining and analytical skills are the most important skills in this phase. The current team has to be trained for more advanced analysis and the creation of analytical models that can be used for predicting future events based on new incoming data. In addition to basic statistics, advanced knowledge of data and data mining techniques is necessary. The retention of current talent is also a priority, because it can take a lot of resources to bring newly hired employees up to speed. A small team in the beginning means that a few individuals will have critical knowledge about the systems. Expanding the team will spread the critical knowledge and help overcome a sudden departure.
Train or attract data scientist to dive deeper into the data.
Retain talent with critical knowledge.
Introduce employees to basic data literacy skills.
As analytics continues to evolve, the required skills evolve with it. We move from basic data mining to more advanced concepts, such as machine learning. Machine learning is the science of training a computational model from past experience, to be able to match future events. We can use the large amounts of data we are collecting about customer behavior to attempt to predict future behavior. If we are able to predict future behavior, we can better anticipate the needs of the customer and act accordingly. To be able to do this, we need to have the knowledge to build and train machine learning models. The performance of the models is determined by the quality of the data and the model. A lot of parameters can be tuned, and sometimes intricate knowledge about specific techniques is necessary to build a performing model. The required knowledge and education level depends on how accurate the model has to perform. There are diminishing returns in the amount of effort required to improve the accuracy of a model. Netflix launched a public contest with a \textdollar1,000,000 prize to improve their Cinematch algorithm, an algorithm that predicts how a user would rate a movie \cite{bennett2007the}. Where a trivial algorithm has a error score (root mean square error) of 1.0540, Cinematch already improved this by roughly 10\% to 0.9514. It took 3 years and the combined effort of many experts on the subject to improve this score by another 10\% to 0.8567. Now for Netflix this improvement can be worth the \textdollar1,000,000, but it is important to always closely guard if the return will outweigh the additional effort. Humans do not always act rational, and you do not have perfect information about all the decision factors. It is therefore not always possible to achieve a high prediction accuracy.
Use new big data sources in machine learning models.
Use learning analytical models to predict user behavior.
Use the predicted user behavior to optimize your offerings.
Data science skills will continue to be needed to analyze the data and build analytical models. In addition to this, it is necessary to bring analytical talent to the rest of the organization. Departments, across the organization, should be creating their own roles that analyze and improve the department. They might still rely on a central analytics department to facilitate the technical side, but also start to take on some of the burden themselves. It is beneficial to combine domain knowledge with an analytical background. Business-IT skills come into use, to capitalize on the opportunities of analytics in new products and services. To build a data-driven culture all new hires across the organization can be assessed for their knowledge analytics and data. To get the most out of your data and analytics efforts it is beneficial that the whole workforce is data-literate to some degree.
Train employees to be more comfortable with basic data analytics
Democratize and distribute basic analytics across the organization, bring it closer to the work floor
Bring in the skills to optimally align the business and IT to capitalize on new business opportunities made possible by analytics
Mature data-driven organizations house a diverse set of skills. Large organizations, on the leading edge of data science, are often ahead of academic research in fields such as machine and deep learning. They tend to hire academic researchers, with a PhD in computer or data science, for advanced analytics applications. To sustain a culture of data-driven innovation, it is beneficial to have people in the organization with an entrepreneurial mindset and business development skills. Human capital is the greatest asset of an analytical company, because they form the machine that turns data into new business.
If applicable, employ computer scientists to develop artificial intelligence features
Develop entrepreneurial en business development skills within the organization to sustain innovation