DECEMBER - 2019CIOAPPLICATIONS.COM9its quality is simply not satisfactory for running analytics2. they do not have proper infrastructure and tools to support computationally expensive big data analytics needs3. their business divisions sit in silos, do not have proper analytics understanding and are not ready to adopt data science and go through challenging change management process4. there is a big mismatch between what business wants and what analytics teams are trying to do and what academia taught them5. there is lack of planning and criteria to successfully identify analytics use casesAs a result, significant portion of data science or Big Data analytics projects fail (according to some surveys it is high as 85%) and many data scientists often find themselves frustrated with the data they deal with as well as with lack of the impact they wanted to create when they took the job. So, at this point you are probably asking "What is then the right process to structure data science / analytics organizations and successfully derive the value from them?" Although the topic itself also brings a lot of controversiesand may require an additional longer article, I would like to briefly offer some good practices that are worth considering (in my humble opinion, there are not best practices yet).To address five challenges mentioned above, at least the following five highly inter-connected teamshave to exist:· Data platform team responsible for Big Data infrastructure including but not limited to data ingestion, data management, data governance, data quality, data architecture and deployment of analytics solutions. This team serves as abridge between IT and Data Science organizations and could sit within IT as well. · Product management team accountable for engaging with business partners or clients, understanding business problems, identifying potential analytics use cases and specifying business requirements. This team serves as a link between business and data science and often plays the role of analytics translator or data storyteller. · Data engineering team performs data wrangling, data preprocessing and building data pipelines. This team works closely with the data platform team and the data science team. · Data Science team accountable for data exploration, identifying analytics use cases (in coordination with product managers) and designing analytical solutions, building initial analytical prototypes, developing full-scale analytics products and deploying them in coordination with the data platform team.· Value acceleration team ensures successful business adoption of deployed analytics products, creating and implementing key performance indicators (KPI) that will quantify and track the value created, proper change management on the business side and advocating for embedding new skills required to support developed analytical solutions.Figure below illustrates six phases of data science lifecycle and how these teams collaborate. It is extremely important to mention that you need to start simple with a few not data science heavy quick wins, and you need to create and maintain a high synergy among all these teams. The creation of these teams has come as a result of lessons learned by non-data driven companies when forming and scaling up the analytics organizations and it represents a major shift from a previous model where data science teams were highly technical and partially detached from the rest of the company.At the end, regardless which strategy of building a data science organization you choose, keep in mind that you need to insist on building a strong commitment for these analytics initiatives from the business, as this is the most critical factor in ensuring the overall success of analytics organization. Good luck in creating the value by solving business problems using data science! It is extremely important to mention that you need to start simple with a few not data science heavy quick wins and you need to create and maintain a high synergy among all these teams
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