Data Science for Business

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1 IBM Global Business Services February 2017 White Paper Data Science for Business How to extract greater business value from your data through a robust approach to data science.

2 2 Data Science for Business Contents 2 Defining the data scientist: Skills required; tools; roles and responsibilities; data science as a team sport 5 Data science projects: How data science projects are different to traditional IT; agile methodology; CRISP-DM 7 The industrialization of data science: How to scale; experimentation to industrialization Summary Organizations across all industries have accumulated large volumes of data and are continuing to do so, but effectively extracting value through data science is a challenge. Identifying a diverse team of data experts, applying an effective project methodology, and choosing the appropriate deployment approach will help to achieve this goal. This paper provides guidance for business leaders who want to understand how an informed approach to data science can improve their operational performance. Defining the Data Scientist The term data scientist is not a particularly new one. It was coined in 2008 by D.J. Patil, and Jeff Hammerbacher, then by the respective leads of analytics at LinkedIn and Facebook. 1 But nine years later, it is clear that there is still no consensus on what actually makes a data scientist. Most data scientists you talk to would agree that the single master of the universe type of data scientist does not exist (or they are at least extremely rare). When we consider the main skill sets of a data scientist (computer science, mathematics and statistics, and subject matter expertise) this is perhaps unsurprising. Computer science Machine learnning Unicorn Math & statistics Traditional software Traditional research Subject matter expertise Figure 1: Key data science skill sets.

3 IBM Global Business Services 3 The data scientist who claims to be a master of all these fields is either a genius or someone who is being a bit generous with the definition of master. While some data scientists are undoubtedly experts in implementing Big Data platforms and coding using languages like Pig, Hive and Scala, they will likely be less familiar with advanced mathematical and statistical techniques, or they will have limited real knowledge of the industry or industries in which they operate. It is perhaps more useful to consider the roles that make up a data science team that combines all of the data science skills rather than trying to find individual unicorns who possess all these skills themselves. A data science team can be broken down into three main roles: Data Scientist The key word here is scientist. These are people who have advanced knowledge in mathematics, statistics and research techniques. They are capable of working directly with the business team (and by this I mean the department or organization requesting the services of the data scientist) to define or clarify the business problem and turn it into a data experiment (or series of experiments). They will have an excellent understanding of the different modelling techniques that can be applied to a given problem (such as segmentation, classification, association, boosting, and bagging) and will be able to design an experiment that can combine these techniques into a logical approach to tackle the business problem. They will also be highly skilled in programming languages or statistical software packages tailored to data science (for example, R, Python, SPSS, and SAS). Visualization and story-telling competencies are equally as important as the more technical elements. Data scientists need to be able to not only prove that the analysis is robust, but also convince a non-technical audience of the value of the analysis/model and justify why it should be incorporated into business processes or systems. Data Engineer The data engineer is more focused on setting up and working directly with the data infrastructure (such as databases, Hadoop clusters, and stream computing). That is not to say they are not capable of creating predictive models, but rather that they are more focused on providing the data platform from which the data science experiments can be designed and performed. They will be experts in identifying and extracting the most useful data from the diverse sources available, organizing it into a coherent structure and then performing initial analysis, aggregating where necessary. When it comes to deploying models, the data engineer will work closely with the data scientist to implement the chosen model, potentially recoding some of the model to optimize the performance in a production environment.

4 4 Data Science for Business Data Analyst While the data analyst will have a good grounding in mathematics, statistics and computer science, their strengths will lie in their deep understanding of the industry and subject area and the associated data. They will be able to help the data scientist understand the data and guide them in the potential avenues of investigation and, most importantly, how the development of models can be incorporated into business processes. They will also be able to assist the data scientists by undertaking some of the more basic analysis of the data (for example, data cleaning, feature creation, trend analysis, and KPIs) and will take ownership of the data model upon which any data science project is based. People with this set of skills are also sometimes referred to as a citizen data scientists. 2 An effective data science team will have a good mix of these three skill sets, and clearly the number and mix of the roles (and people) required will depend on the project being carried out. For example, a proof-of-concept project will be carried out principally by data scientists because the emphasis here is on experimentation. However, when a concept is proven and the decision has been made to deploy a model and integrate it into business processes and systems then the emphasis will shift toward the data engineer and the analyst. The data scientist will continue to support development and improve the model identified during the proof-of-concept as they are moved into production, whereas it would be unrealistic to expect a data analyst or data engineer to play a significant role in defining a proof-of-concept as they are likely to lack experience in mathematics and research techniques. It is also worth noting that one person could perform multiple roles within a data science team, but the depth of their expertise may be limited in certain areas. The data science team will also need to interact with those in more traditional delivery roles (such as the data architect), particularly when moving from experimentation toward implementation and integration with business processes. The key point is that data science is a team sport requiring different disciplines to work together toward a common goal, understanding and extracting actionable information from large, messy sources of structured and unstructured data.

5 IBM Global Business Services 5 Data Science Projects As we seek to define the capacities and skills of the data scientist, it is important to also recognize what makes a data science project different from a typical IT or business-led project. First, it is important to mention that it is (or should be) rare to undertake a data science project as a stand-alone initiative. The functional capabilities that can be provided by data scientists (classification, segmentation, prediction, forecasting, and so on) need to be framed within a wider business and technological context and have an identifiable business challenge or issue. Often, statistical models developed during a data science project are integrated within a wider solution that uses the model outputs to provide improved insights to business users or customers. Having said that, it is also worth highlighting that a common starting point for investigating how data science can drive business value is through proof-of-concept or proof-ofvalue projects. Typically, these will be a focused up-front investment targeted at a particular problem or business area where data science approaches are thought to be able to provide value. Clearly in these types of projects the emphasis will be more heavily placed upon analysis and modelling, but even then there needs to be a business element to the project that sets out the use case and the business case to ensure that any models developed can have a practical impact when deployed within the business. A clearly defined objective and business case is essential for maximizing the chance that a data science project will succeed, as without this there is a risk that the analysis or model will not directly tackle the business problem or provide sufficient benefits to the business. When considering a typical data science project, it is important to take into account two of the main characteristics that define a data scientist: their curiosity and their ability to investigate unstructured problems. These are critical to the success of the types of projects that a data scientist will regularly be involved in, which, in general: have many complex and inter-related problems to investigate, incorporate large volumes of structured or unstructured data that is often of variable quality, do not necessarily have a clearly defined scope or objective. Given these factors, it is perhaps unsurprising that data science projects are non-linear. It is practically impossible to specify at the start of a data science project which algorithms (and associated parameters) will produce the best result for a given problem because the results very much depend on the data (particularly the volume available, the variety of variables and features, and its underlying quality). Based on experience we are able to define approaches to test, but we are unable to specify exactly which variables combined with which algorithms will give the desired result. Therefore we experiment, we evaluate and we iterate until we obtain a result that is good enough to present or deploy (depending on the business need). In other words data science projects naturally follow an agile approach (agile in the Software development use of the word 3 ). Test and Learn techniques are also naturally incorporated into data science projects whereby alternate and sometimes competing hypotheses are evaluated in parallel to establish the best model.

6 6 Data Science for Business According to a KD Nuggets survey in 2014, the top methodology for data mining and data science projects is the CRoss- Industry Standard Process for Data Mining (CRISP-DM) 4. This is an excellent methodology that is used extensively within data science projects across all industries, and has a clearly-defined and easy-to-follow structure while still allowing the flexibility to iterate between process steps (as required). This paper will not describe in detail the CRISP-DM methodology as there are many resources already out there 5, but the important points to take away are that: Data is the cornerstone of each of the CRISP-DM process steps. The majority of time on a data science project will likely be spent on data preparation because without well understood and good quality data the modelling will likely be useless. In fact, problems with the data may well be discovered during the modelling phase which may mean that you need to revisit the data preparation. It is likely that some data science projects will not need to be deployed and integrated within IT systems, but the results will nevertheless be of use and will need to be shared with the relevant decision makers. An alternative to the CRISP-DM methodology is the Standard Methodology for Analytical Models 6 which builds upon CRISP-DM to describe how the process of building analytical models actually needs to be closely incorporated into a more business-oriented context. It is important for those working on a data science project to not lose sight of the fact that the project s goal is to produce actionable insights for the business. Figure 2: CRISP-DM Cross Industry Standard Process for Data Mining, an iterative approach to data science. Source: IBM GBS Data Business & data understanding Evaluation Deployment Data preparation Modelling

7 IBM Global Business Services 7 The industrialization of data science Once initial exploratory analysis has been undertaken, and a use case has been deemed suitable for investigation, it is easy to see how data scientists could use the robust methodologies at their disposal to undertake a single data science project. But what if, in fact, your data science project actually consists of a large number of sub-projects or use-cases to be investigated? How you tackle this will depend greatly on the size and structure of your data science team because the larger your team, the easier it is to subdivide and run tasks in parallel. However, assuming that your data science team is relatively small (as is the case in many organizations) then one approach is to break down the project into a series of sprints, a defined period of time within which you will address a clearly defined and tightly bounded problem. The idea is that at the end of the sprint you have undertaken several iterations of the Data preparation, Modelling and Evaluation steps in the CRISP-DM methodology, and that you have a model or analysis that provides results that are good enough for assessment by business users (non data scientists, including both the citizen data scientists and business experts). The results may not be perfect, and certain elements may need further investigation, but that doesn t matter. These activities will simply get added into the backlog for the next sprint. The important point is that it frames the data science project in such a way that it can more quickly provide real insights to the business: either analysis that can be used directly, or models that can be deployed within existing IT systems to augment the business processes. Evaluation Data preparation Modelling T0 T0 + 1 week T0 + 2 weeks Daily meetings with the data science team (1 hour max) Sprint kick off Presentation of progress to date Presentation of the sprint results Next sprint Workshop with the business (3 hours max) Figure 3: The data science approach to organizing project sprints. Source: IBM GBS

8 8 Data Science for Business This agile method ultimately has the goal of accelerating the implementation of data science insights into business processes, but simply carrying out an experimentation does not ensure that the insights and models will be deployed; we also need to consider how to move from a data science sprint to deployment. There are several approaches to doing this, and the best option will depend on the resources available and the organisation s capacity and appetite for change, but we can break this down into two main options: Staged Deployment Through a staged deployment approach, the idea is to define either a period of time or a number of subjects to be studied. During the high level design phase the time constraints and the scope of the project can be combined to define a number of sprints that will make up the experimentation phase of the project. 2.1 Data analysis, extraction, and preparation 1. High level design of the experimentation + business + data understanding 2.2 Iterative design and development 2.4 Summary report and final decision 3. Deployment 2.3 Iterative validation by key business users Experimentation phase Figure 4: A staged approach to deploying data science insights and models. Source: IBM GBS

9 IBM Global Business Services 9 It is important to recognize that the data science sprints will likely also be supported by a separate work stream with the goal of extracting, consolidating and preparing the data needed for the data science project (a role typically performed by a data engineer). In addition, throughout the experimentation, the data science team will be supported by key business users (subject matter experts) to help the data scientists make business sense from the data and to also assess the results of each sprint. At the end of the experimentation phase it is critical that the team is able to make a compelling case for deployment in a non-technical and business-oriented style. Often one of the main sticking points for the deployment of statistical models is not their value, but rather their complexity and the ability of non-statisticians to be able to understand them (and, inversely, the ability of data scientists to be able to explain them). This is where the visualization and story-telling skills of the data scientist come into play. Once this hurdle has been passed, the data science team can now start to set out the steps required to deploy the model or analysis. The team that undertakes the initial deployment will most likely be an evolution of the project team present during the experimentation phase (with greater input from data engineers, and data and solution architects), but it could also be passed on to an existing technical delivery organization if there are sufficient technical skills in the programming languages and tools used in the data science solution. Agile Deployment Agile deployment is similar to staged deployment in that it will begin with a high level design phase (sometimes referred to as Sprint 0 ) where the number and scope of the sprints is defined. However, from that point onward the goal is getting analyses or models deployed as quickly as possible and integrated into existing business processes: a minimum viable product. In order to be able to do this, the project will need to be organized somewhat differently to a classic data science project (which tends to follow a staged deployment approach), and the team will either need to be empowered to make decisions on whether an analysis or model is deployment ready or the decision makers will need to be integrated into an extended team. This is critical, as at the end of each sprint a decision is needed on whether: The sprint has concluded and no deployment or follow up analysis is required. The time allocated to the sprint needs to be extended. The outputs of the sprint are ready to be deployed as a recurrent analysis/report. This applies to sprints that are not aimed at producing results that will directly impact a business process or IT system. The deployment is, therefore, generally fairly straightforward. The model or analysis is ready to be incorporated with existing IT systems and business processes.

10 10 Data Science for Business Sprint Dev 1 Decision End of Sprint Extend the Sprint Sprint Dev 1b Sprint 0 High level design Sprint to be deployed as a recurrent analysis Sprint Dep 1 Sprint 2 Sprint 3 Sprint to be integrated with Production IT systems Sprint Dep 1 Figure 5: An agile approach to deploying data science insights and models. Source: IBM GBS It is also possible to organize the data science team in such a way that experimentation and development, and deployment are run in parallel, with separate teams focusing on these two different aspects. This may also be more efficient in terms of getting the most out of your data scientists who could focus more on the experimentation and hand off the responsibilities of deployment to a team made up of data engineers and data analysts. This will, of course, depend on the size, scope and desired time-to-market of the project. The approach described in this paper can be applied to any industry or subject area where data is sufficient in volume and quality to merit a data science project. Data is central to this and is ultimately the main reason why data science projects are inherently agile. However, this should in no way constrain data science to the laboratory (or proof-of-concept stage). Data is a resource that businesses cannot ignore, and the methodologies and resources exist to be able to exploit them.

11 IBM Global Business Services 11 About the author Rob Worsley Senior Managing Consultant Advanced Analytics Leader, France IBM Global Business Services (GBS) References 1 Thomas H. Davenport and D.J. Patel, Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review (October 2012) hbr.org/2012/10/data-scientist-the-sexiest-jobof-the-21st-century 2 Bernard Marr, How the Citizen Data Scientist will Democratize Big Data, Forbes (April 2016) forbes.com/sites/ bernardmarr/2016/04/01/how-the-citizen-data-scientist-willdemocratize-big-data/#447c1c The Agile Alliance, Agile 101agilealliance.org/agile101/ what-is-agile 4 George Piatetsky, CRISP-DM, still the top methodology for analytics, data mining, or data science projects, KD Nuggets article (October 2014) kdnuggets.com/2014/10/crisp-dm-topmethodology-analytics-data-mining-data-science-projects. html 5 IBM SPSS Documentation on CRISP-DM ftp://public.dhe. ibm.com/software/analytics/spss/documentation/ modeler/14.2/en/crisp_dm.pdf 6 Olav Laudy, Standard methodology for analytical models, Wikipedia olavlaudy.com/mediawiki/index. php?title=standard_methodology_for_analytical_models

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