Lack of analytical and management talent
The demand for data scientists is rapidly growing. This function is becoming increasingly important within organizations and its salary is growing to match its importance. Research by the McKinsey Global Institute shows that the lack of analytical and management talent in successfully implementing Big Data is one of the biggest challenges the USA is facing.
The McKinsey Global Institute estimates that there are four to five million openings for data scientists in 2018 alone. The hunt for data scientists has been opened. This jack of all trades in your organization should possess many talents in order to help shape and direct the explosive amount of new possibilities provided by big data. But is this realistic?
The Data Scientist doesn’t exist
The requirements for data scientists regularly include things like a valued university education in a field like math, statistics, or econometrics. Or technical analytical skills, like being proficient with data mining and the associated software. Also, the data scientist should be able to use several programming languages and tools and be able to achieve quick results using them. The data scientist should be a researcher who dares to think outside the box and has inside knowledge of business processes and problems. Finally, they should be an excellent communicator, so they can communicate and explain all the problems and possibilities that come with the data. They should be a champion of data-driven working and innovation. But this desired superman (or woman) clearly doesn’t exist!
A jack of all trades is a master of none
Is it possible to recruit this fabled data scientist? And even if you were to find one, a jack of all trades is a master of none. Two types of thinking combine to create this unrealistic laundry list of requirements and characteristics:
- The old way of organizing where people bet all their chips on a specialist who comes in like a knight in shining armor to make their organization innovate and work data-driven. This way of thinking is a vestige of the second industrial revolution, where radical division of labor resulted in enormous increases in productivity. Nowadays, this conflicts with the type of agile organization where teams make all the difference. In addition, an increasing number of organizations is on the way back to working with roles instead of functions. More generalization in tasks and roles is crucial in order to facilitate dialogue and self-organization, which is required to embrace agility and survive as an organization.
- The second way of thinking is about the desire to break up complex building blocks into easily digestible chunks. For the position of Data Scientist it is common to see an overlap of many different competencies, skills, and characteristics that the company is apparently unable to unite, so they are all combined into this one mythical person. Just check the job openings! This creates a demand for a super specialist that doesn’t actually exist. Besides that, getting such a specialist carries the risk of missing the true dialogue with each other and instead getting stuck in an “ask and we deliver” mindset, instead of having a valuable discussion about the why, how, and what of solutions and improvements. This often leaves the best ideas and the required support to go in a new direction unused.
Are you still thinking like a Fayolist?
Henri Fayol was a French mining director who made an important contribution to organizational science in the early 20th Century. He formulated a number of important principles of organizing that are still common in behavior and vision today. These ideas are fantastic for creating scalable organizations but are less productive for agile, self-steering teams.
Fayol posited fourteen principles of organizing, the following five of which were the most important: specialization, unity of command, formal chain of command, unity of direction, and authority and responsibility. These principles clash with those of agile teams who need to be flexible and sometimes even disruptive. The Fayolistic mindset has often led to management patterns that keeps basing its decisions based on it, sometimes consciously and sometimes unconsciously.
Cheap or expensive teams?
These days, change takes place in the roots of an organization, so only teams can anticipate, analyze, and solve problems quickly and effectively. This requires agile employees, methods that support an agile workflow, and a combination and overlap of knowledge and competencies, including those of a data scientist, which a team or project team should contain.
It’s crucial to determine which competencies require overlap and which do not, in order to prevent making an expensive team comprising nothing but the jack-of-all-trades super specialists. Teammates with different degrees of analytical skills can easily create a rich dialogue about the meaning of data and possible solutions. See also: Does every BI consultant become a Data Scientist?
At the core, we only know a select few groups of competencies. These are task or role-focused, intellectual, emotional, organizational, social/communicative, and finally, developmental competencies. In this agile world, team competencies can be seen as an extra ingredient, although these largely overlap with the social/communicative competencies. As soon as the goal of a project or a team becomes apparent, it can quickly be determined which competencies are required in order to achieve the set goals. It is important to check the importance of dialogue, cooperation, separation, and autonomy in steering and decision-making. Based on these principles it will become apparent which core competencies should be distinct and which should overlap in order to quickly make indicators.
The most important data science skills are relational
Google’s Hal Varian confirms this when talking about data science skills: “The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – that’s going to be a hugely important skill in the next decades.” Most of these skills only make sense in relation to colleagues. Data only takes on meaning in the context of a group of people or between individuals. Data advances and feeds relational processes.
Agile data science: get started quickly!
Do you want to work with data science quickly and effectively? The following suggestions help your organization to quickly reap the rewards of data science:
- Many organizations still lack true agility. Companies and managers often follow the trends of the day and have a tendency to react to everything without an overarching plan for agile working and effectively applying data science. What is the overarching plan and what are your goals?
- Data scientists often operate in organizations with a traditional structure or with managers who think in traditional ways. We often see a large gap between the world of Business Analytics and Data Science on the one hand and the world of business and commerce on the other hand. Because of this, initiatives with good intentions turn out to be failures. A good BI road map and cooperation in an interdisciplinary team can help to shorten this gap. What’s your Data Science road map?
- Data Science is a team effort. Focus on the required knowledge and skills and the overlap within your team instead of pouring all your money into hiring expensive and unrealistic superheroes. You also run the risk of crucial processes stagnating if and when this person leaves your company for the higher salary and better benefits offered by your competitor. Do you have a team with the right skills? And how do these skills overlap?
- Teams that offer the safe space to introduce differing opinions in discourse provide the best opportunities for improvement and innovation. Are you fostering cooperative discourse?
- Knowledge of team processes and innovation processes are equally as important as knowledge of the content. Invest in these core competencies, as they are a crucial part of agile working and innovation. These competencies improve the motivation and the productivity of the team and its members. How strong is your knowledge of team and innovation processes?
- Continuously reflect on the process, cooperation, and everyone’s contribution. This speeds up personal growth and everyone’s learning curve, as well as that of the team – another advantage in relation to the Data Science super specialist. Only feedback within the team makes short, cyclical, iterative learning possible. This reflection process can often be supported by both competencies as well as methods of improvement, such as PDCA. How does your organization foster reflection and cooperation?
- Personal growth, growth of competencies, team growth and organizational goals should often be centralized. How can I, the team, and the organization advance? And who or what is needed for this?
Why Passionned Group?
Passionned Group has a sizeable number of experts on board who have a great deal of knowledge about Data Science, and how to organize teams around it. With our wealth of experience, we can help you avoid a huge number of pitfalls while making rapid progress. Contact us with no strings attached for an orientation meeting.