The seven problems data scientist don’t want to solve
Boards and CEOs of leading companies are scrambling to to get ‘data’ on their strategic agenda and to enhance their data and analytics capabilities so they can grow, innovate compete.
They’ve identified, data and analytics done right can create deep customer intimacy, an efficient supply chain, automation of operations, new revenue lines and a strategic moat (or barrier for their competitors).
Among the many other topical subjects trending (e.g. Hadoop, Real-time, NoSQL, Cloud, Chief Data Officer, Internet of Things etc) is the emergence of the role of the Data Scientist.
Over the past 24 months Contexti has worked with over 37 enterprise and government customers across Asia Pacific and we have seen first hand the success and failure of establishing and promoting the Data Scientist role. Before a Data Scientist can be successful and even after they have delivered their brilliance (insights) there are a number of problems (tasks) that need to be addressed.
While the Data Scientist can assist in solving these problems, it’s not something they want to do, nor is it something they should be asked to do (it would simply be a misallocation of resources).
Here are the Seven problems Data Scientist don’t want to solve and a potential opportunity for you to step up to make an impact;
(1) Get Data on your strategic agenda: For some organisations, ‘data’ is still treated as a cost of business. To enable the Data Scientist to work their magic and truly make an impact they need the right mandate and sponsorship right from the top. This means data is treated as a Strategic Asset (not a cost) and therefore features on the companies Strategic Agenda. Opportunity for General Managers and Business Unit Leaders to step up to take the message to the C Level and Board.
(2) Align projects to strategic outcomes: It’s too tempting to kick-off an experimentation side project without the right business case or alignment to strategic outcome. This wastes everyones time, including the Data Scientists. This is different to well designed pilot project. Like all strategic projects, big data analytics projects must be prioritised (for value, impact and risk) and aligned to strategic outcomes. Opportunity for Project Directors to step up with best-in-class business case development, project and portfolio management expertise.
(3) Create the right team for data initiatives: Successful Data initiatives need more than just a Data Scientist. You need to consider subject matter experts, data engineers, process analysts, change managers to name a few. Depending on your objective you may also consider front line staff or product owners who may influence or be impacted by your data initiative. All these stakeholders must be engaged and be on the journey in order to reach the right outcome Opportunity for Human Resource Managers to step up and take charge with team design and engagement.
(4) Architect and engineer data platforms: Data Scientists need access to a suitable data platform that houses and supports various data formats, volumes and from different sources. The platform should be flexible enough to meet for the various analytics use-cases and be scalable to meet the processing intensity when moved into ‘production’. Opportunity for Enterprise Architects, Engineers, Database Administrators to step up into Big Data Platform Architecture, Engineering and Support function.
(5) Ingest data (real time, batch): The value of big data is enhanced when a Data Scientist can work with data from multiple sources, made available in a common location so new insights can be attained. Ingesting the data into the data platform is a tricky task as data can arrive in batch or in real-time in different formats, from different locations and at different times. Opportunity for Software Engineers to step up and solve the data ingestion challenge.
(6) Actions and test insights: Your Data Scientist has found an amazing insight. Someone now needs to action the insight (e.g. send the new offer to customer, start the new business line, change a process to reduce cost etc). Further, someone must measure the effectiveness of the insight when applied at-scale. What other positive or negative impacts does it create? Opportunity to Business Analysts to step up to action and measure insights from the Data Scientist.
(7) Manage change and embed insight: Once the insights found by the Data Scientist has been actioned and tested for scale they need to be embedded into Business As Usual processes. This will invariably have an impact on people, process, systems, customers or partners. This will require process redesign, impact assessment, communication and stakeholder management. Opportunity for Process Consultants and Change Managers to step up and manage the change required to embed new insights.