The Role of Data Stewards and Data Scientists

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1 SPECIALIST REPORT Data Stewards & Data Scientists The Role of Data Stewards and Data Scientists Dr. Wolfgang Martin, Analyst Even before the age of Big Data, the question of responsibility for different types of data and its respective quality in companies was always more than simply important - because information is the foundation of all decisions. It has always been a matter of supplying the right customer with the right product, at the right location, at the right time, in the right quantity, and with the right invoice. This was, and still is the target of information management. But the importance of information management is not always acknowledged. However, when information is not correct, fully-complete and available promptly, it will lead to process interruptions and delays, cancelled orders, returned deliveries, standstill, and irritation from the customer side. It is also the root cause of many more uncomfortable and annoying issues. Failure to have correct information available costs large amounts of money, negatively affects turnover, and causes other additional costs. Processes can only run [efficiently] when the information they require is absolutely correct. Information management is crucial, Data Ste wa rd: Successful and effective data stewards reside in the business, are visible, respected and influential they must have the vision to understand the importance of data quality to the overall business objectives, as well as the impact of quality issues on downstream business processes. The leader of the steward team, i.e. the corporate sponsor for data quality, will serve as the ultimate point of decision on issues and conflicts occurring across stewards and teams. Ted Friedman, Gartner Group1 but the question remains as to who is responsible for it and who is answerable when the information provide is not correct. 1) See accessed 19 Feb

2 The Tasks of Data Stewards Data stewards are becoming increasingly responsible for tasks involved in information management, and for safeguarding data quality in companies. They work within the context of information governance to define rules and processes used in information management. They also have to ensure that all information is correct and consistent throughout the entire company, and that it is managed and used correctly. Data stewards make sure that information governance is observed and implemented by everybody involved in company activities; whereby this should not be seen as an enforcement measure, but instead as being service support for specialist departments, involving different activities. Data stewards should play a leading role in the development of data definitions, support data profiling, discover errors in data and calculate the consequences of such errors. They should propagate the correct use of data and ensure data security. They monitor compliance with the rules, as well as the quality level of the data; and are involved when the priority of data quality measures is being discussed. Of course, the tasks of data stewards should also be given priority within the framework of the company strategy and company targets. For example, a software company should focus the work of its data stewards on the customer data used for sales and marketing processes currently in the company focus (such as is practiced by the German company Uniserv GmbH). A general focus on customer data would be an unclear target and is therefore mostly unsuccessful. Organisation of Data Stewards Data stewards can start work in the company even when no elaborate information governance yet exists. A small mixed team from specialist departments and IT can begin by deciding upon data definitions, information management processes and user rules. The persons involved should be good communicators and intermediaries, as well as being a capable negotiator. They must not necessarily be delegated into the team full-time, but should be given sufficient opportunity to perform the activities necessary as a data steward. Recruitment of new employees for these tasks has proven less effective, because to be able to work efficiently and successfully, a data steward must know the company extremely well, including all of its informal information channels. The team builds a network within the company and identifies a dedicated contact person in each individual specialist department and IT. It also has the task of establishing a functioning partnership between specialist departments and IT. Its organisation is best made by linking contact persons from specialist departments and IT together to form a Competence Centre. This competence centre has a sponsor from senior company management, and has dual leadership; an employee from the specialist department acting as a primus inter pares, and a separate staff member from IT. It has its own budget, is responsible for the program, and develops its own methodology - that of information governance. Big Data Analytics Data stewards are responsible for company data and/or (in the respective magnitude) for data from specialist departments. Now, big data comes along. How does the management of big data influence the tasks and targets of data stewards up until now? Let us first take a look at the demands of big data analytics. As opposed to traditional analytics, big data requires new skills and roles which (from an organisational point of view) are best located in an expanded BI competence centre. Major companies such as Amazon, ebay, Facebook, Google, Twitter, etc. have been involved with big data for a long time, and have created new roles, which when combined together form a big data team. The five most important roles in such a team can be found in a proposal from McKinsey & Company2. Although this report deals more with big data marketing projects, it is written in such a way that it can also be applied to other types of big-data projects. 2) See the Five Roles You Need on Your Big Data Team blog in the Harvard Business Review (July 2013) on_your_bi.html?utm_source=socialflow&utm_medium=tweet&utm_campaign=socialflow

3 MANAGING A COMPETENCE CENTRE Illustration: A competence centre can be organised in different ways and therefore can be allocated with different rights and levels of assertion. The illustration shows above the organisational culture, and beneath its architecture. A start can be made with simple collection and marketing of best practices. In the next step, standards are set and communicated. Finally, proactive use of the standards is promoted (either virtually or centrally), and central services are made available for project support.

4 In particular, the McKinsey approach identifies roles, instead of job titles, thus enabling the necessary expertise and job description to be envisaged. It relates to the five following roles: Data Hygienists ensure that data is cleansed and that it remains clean throughout its entire life cycle. Data profiling and data cleansing commence at the start of the project when the first data is collected. All team members involved are those who want to use this data. Data Scientists take over the organisation of the data and the creation of analytic models within the project framework. This includes checking, alteration and replacement of models as necessary, as well as communicating the results of the analysis to the managing board and the rest of the company. Campaign Experts have to interpret the results and implement appropriate actions based upon them. This includes prioritising channels and establishing campaign stages. Data Explorers search the big data universe to find the data needed for the project. They then have to prepare and condition the data ready for the project, because most incoming data was never created, stored or properly organised for analytical examination, and is therefore unsuitable for an analysis. Business Solution Architects are responsible for consolidating identified data and preparing it for analysis. The data must also be given a structure for the enquiries expected. Data required in minute or hourly intervals must also be promptly updated. The roles of Data Explorer and Campaign Expert require expertise, such as provided by cognitive scientists and behavioural economists. Such expertise is necessary to identify precisely what data is important for the project and what is not. It is also of great help with the interpretation of results and their relevant implementation. This is why McKinsey s role model is so important. Once all tasks and roles are thoroughly understood, it will be clear what experts are needed for the project

5 The Roles of Data Scientists We should now take a closer look at the role of data scientists. These are employees usually found more in the business intelligence field, with a more detailed profile as follows: Technical Expertise: In-depth knowledge of natural sciences or engineering is necessary, because they form the foundations for working successfully as a data scientist. Future data scientists should be sought amongst persons with this background, and then tested for additionally required abilities. Problem Consciousness: The ability to break down a problem in testable hypotheses. Data Scientists & Data Stewards: a Dream Team? How does a business intelligence competence centre with its data scientists, and an information management competence centre with its data stewards, work together? The interface is formed by data hygienists, who adopt the tasks of data stewards in big data initiatives. Here we can already see the difference in the work of data stewards. In the company, data stewards are the performing organ of information governance applied at the same level through- out the company, for all company data. The accuracy and completeness of data in big data initiatives is re-established for each new project. There is no continuous data governance; instead, it is mostly decided by data scientists for each individual project. Data scientists turn big data into big value, delivering products that delight users, and insight that informs business decisions. Strong analytical skills are given: above all a data scientist needs to be able to derive robust conclusions from data. Daniel Tunkelang, Principal Data Scientist, LinkedIn Communication: The ability to explain complex issues by using anecdotes and simple-to-understand and easily communicable subject matter. This can mean that some big-data projects run without any data governance at all. Here, data scientists often say that cleansing data from different big data sources could influence the analysis results; arguing that the data could be falsified due to data stewards making wrong assumptions about its quality. Also, important information absolutely necessary within the framework of data discovery can be concealed in rogue entries. The background idea is the well-intended thought, that in big data projects, data must serve the purpose of the project; whereas in a company, data must serve the long-term company strategy. Creativity: The ability to view and deal with problems from an alternative point of view, i.e. thinking out of the box. Conclusion: The task of data stewards is the management of company data, and/or data from specialist departments in compliance with the frame requirements of information governance. Their target is to provide specialist departments with high-quality, readily-available, consistent data. They are also responsible for ensuring that information governance remains aligned to current company strategy. Data scientists have the task of transforming big data into big value. They are responsible for the methodology of big data analytics, as well for communicating analysis results to the managing board and the rest of the company. Data stewards play a role in big data initiatives too, i.e., that of a data hygienist, who is now focussed on the project targets, and not on company strategy. In big data analytics they play the role of a SWAT team3, i.e. a tactical, special team, and not the strategic role as in the company. In this respect, they are again a service provider, but do not act independently. Indeed, they are called in by big data scientists to support big data initiatives whenever necessary. 3) SWAT (Special Weapons and Tactics) is a term [also] used in American marketing terminology

6 SPECIALIST REPORT Data Stewards & Data Scientists Dr. Wolfgang Martin Dr Wolfgang Martin is an internationally recognised, independent analyst and expert in the fields of: BI / PM (Business Intelligence / Performance Management), Analytics and Big Data Business Process Management, Information Management and Governance SOA (Service-Oriented Architecture) CRM (Customer Relationship Management) His specialist fields are the reciprocal effects of technological innovation in business and the resultant organisation, corporate culture, business architectures and business processes. UNISERV GmbH Rastatter Str. 13, Pforzheim, Germany, T: F: , E: info@uniserv.com, Uniserv GmbH, Pforzheim, All rights reserved