A Smarter Library: Using Data Analytics to Improve Resource Management and Services at NLB

Transcription

1 A Smarter Library: Using Data Analytics to Improve Resource Management and Services at NLB By Germin Ong 4 September Civil Service College

2 A Smarter Library: Using Data Analytics 2 ABOUT THE AUTHOR Germin Ong is Senior Researcher at the Institute of Public Administration & Management (IPAM), Civil Service College, Singapore. This article was written with inputs from the National Library Board (NLB). ABOUT THE INSTITUTE Institute of Public Administration & Management (IPAM) builds public service capabilities in service management, HR management, public finance and law, and foundational competencies through research, curriculum design, programme development and delivery. In IPAM's programmes, public officers are equipped with managerial and operational competencies through different learning approaches including classroombased learning, facilitated group discussions, learning journeys, self-reflection and e-learning. ABSTRACT Data analytics can be a valuable tool for helping government agencies to deliver better public services. It can provide important insights into consumer behaviour and better predict demand for goods and services, thereby allowing for better resource management. In a bid to transform itself into a smarter library, the National Library Board (NLB) initiated a comprehensive Big Data programme in 2012 that employed advanced data analytics. Since the implementation of the programme in July 2014, NLB has continued to gain new insights into its users and their borrowing behaviour and better predictive capabilities, which enable resources to be optimised and public service delivery to be more effective. KEYWORDS Data Analytics; Resource Management; Public Service Delivery DISCLAIMER This case study is intended for class discussion only and not to illustrate effective or ineffective management.

3 A Smarter Library: Using Data Analytics 3 A Smarter Library: Using Data Analytics to Improve Resource Management and Services at NLB Introduction Data analytics has become one of the most important technology trends over the last decade. 1 According to IBM, data analytics is the systematic use of data and related business insights developed through applied analytical disciplines (e.g., statistical, contextual, quantitative, predictive, cognitive and other [including emerging] models) to drive fast-based decision making for planning, management, measurement and learning. Such analytics can be descriptive, predictive or prescriptive. 2 Descriptive analytics Today, most organisations use descriptive analytics to organise and turn historical data into useful and actionable information. Analytic tools such as financial and operational dashboards are used to describe and understand this data. 3 However, the insights generated can be limited. For instance, they will not be able to tell the user what might happen in future. Predictive analytics Organisations with more mature data analytic capabilities tend to make use of predictive analytics. By using historical data to understand the past, they attempt to predict future events by modelling scenarios using simulation and forecasting. 4 For example, management compile and summarise information to find out prior and existing trends and patterns, so as to predict future trends, such as future demand for a particular type of product IBM. (2011). The 2011 IBM Tech Trends Report: The Clouds are Rolling In Is Your Business Ready? Accessed, 28 July James W. Cortada, Dan Gordon and Bill Lenihan (2012). The Value of Analytics in Healthcare: From Insights to Outcomes. Accessed, 8 Oct Ibid. Ibid.

4 A Smarter Library: Using Data Analytics 4 Prescriptive analytics Organisations can also tap on prescriptive analytics techniques such as optimisation to determine the course of action to take. By understanding how certain actions lead to certain outcomes, they are able to identify the best course of action to take based on existing data. This will help to influence optimal future outcomes. For example, the system may be able to recommend the optimal number of staff to deploy in order to achieve the desired outcomes. Figure 1: Types of data analytics. Source: Adapted from Cortada et al. (2012) In Singapore, the Government has been advocating and encouraging public agencies to use data analytics to improve public service delivery and manage resources. Together with the Civil Service College (CSC), the Infocomm Development Authority (IDA) of Singapore has been driving the adoption of analytics and these capabilities within the public sector. 5 In the last few years, many government agencies have embraced and developed their data analytics capabilities to enhance their operations and their abilities to serve the public. One such agency is the National Library Board of Singapore (NLB), which was recently awarded the Best Practice Award in Resource Management at the 2014 Excellence in Public Service Awards for its initiatives in using data analytics to better meet library users needs. 6 This case study will provide a brief overview on how NLB uses these three types of data analytics to improve resource management and service delivery in its libraries. 5 6 Infocomm Development Authority. (2014). Data & Analytics. Landscape/Technology/Data-and-Analytics. Accessed, 29 July National Library Board. (2014). Excellence in Public Service Awards Accessed, 29 July 2014.

5 A Smarter Library: Using Data Analytics 5 Using Data Analytics at NLB NLB oversees an extensive network of the National Library and 25 Public Libraries that are located all over Singapore. It also oversees the National Archives of Singapore. 7 In total, NLB manages over 8.6 million items in its collection and serves the library needs of over 2 million users (based on FY2011 figures). 8 As a government agency, NLB needs to ensure that resources are properly managed and allocated to the right areas so as to enhance public service delivery. These areas include its physical and IT infrastructure, its operating systems, as well as its collection of library materials. However, this can be a challenging task managing the allocation of funds to the many different areas is not easy, particularly when all of them are integral to meeting the public s information consumption needs. The challenge of allocating funds is exacerbated by two strategic factors. First, Singapore s demographics is expected to undergo a radical change in the near future. With the number of elderly citizens poised to triple by , NLB needs to forecast this impact on its libraries as more elderly residents are expected to visit the library to spend their free time and stay active. 10 Second, as Singaporeans are generally receptive to adopting new technologies and accessing digital information online, NLB needs to consider the possible shifting behaviours of its users and how they consume information to better meet their needs. In light of these two factors, determining the right resource management strategy can be difficult. At the same time, NLB recognises that it sits on a wealth of data that can generate a vast amount of information about its library users. To leverage on this unique asset, they have launched a Big Data Programme, which uses predictive and descriptive analytics to derive actionable insights for NLB 11. Through this initiative, NLB hopes to use these insights to enhance its productivity, invest in better service offerings to improve customer satisfaction and better manage its mix of services and resources. Descriptive Analytics: Knowing Your Customers Building a library can be expensive. To ensure good public service delivery and good resource management, NLB needs to ensure that libraries are built and located in the right National Library Board. (2014). About NLB. Accessed, 31 July June Gwee and Neo Boon Siong. (2013). A Library for the People: A Case Study of the National Library Board. Accessed, 29 July National Population and Talent Division. (2013). Population White Paper: A Sustainable Population for a Dynamic Singapore. Accessed, 29 July Lee Kee Siang, Kia Siang Hock, Lau Yi Chin, Heng, Grace, Lim Chee Kiam and Henri Lim. (2014). The Big Data Programme the Journey towards Data-Driven Library Management and Services at the National Library Board of Singapore (NLB). Presented at the 7 th Shanghai International Library Forum. Ibid.

6 A Smarter Library: Using Data Analytics 6 areas. However, determining the possible demand and usage of a library can be strategically challenging. Furthermore, in light of the changing demographics and population growth, planning where to build the next library will not be easy. To overcome such challenges, NLB must make use of its existing data to understand its library users and their borrowing behaviour. In late 2012, it collaborated with the Singapore Land Authority (SLA), an expert on geo-spatial analytics, to generate useful insights about its library users. This 2-phased project was concluded in July 2014; each phase taking about 6 months to be completed. Location-based data and loan records, including data on planning area, resident population, and projected overall dwelling units were analysed to generate insights about the reading preferences of NLB s users, their usage of libraries and whether these libraries would be able to cope with population growth. 12 Figure 2: Using geo-spatial analytics to understand the borrowing behaviour of library users. (Source: NLB) For example, NLB was able to derive fresh insights on library usage patterns of residents living in Clementi when the Clementi Public Library opened in Recent analytics showed that Clementi residents were visiting the libraries more ( 25 per cent) and borrowing more books ( 30 per cent), as compared to the years before the library opened. 13 Using such analytics, NLB was able to generate insights on factors influencing library usage to aid decision making in planning for future libraries Lee Kee Siang et al., op. cit., p. 6. Lee Kee Siang et al., op. cit., p. 6.

7 A Smarter Library: Using Data Analytics 7 Predictive Analytics: Supporting Procurement Decisions Determining the types and number of books that NLB should purchase is not an easy feat. With limited financial resources, coupled with the changing demographics and reading preferences of library users, NLB must consider leveraging on data to support its procurement decisions. It is also essential that NLB is able to provide a wide and relevant range of collections that can meet the needs of its library users. This is achieved using Demand Analysis (DA), a system that uses predictive analytics. Such analytics go beyond descriptive analytics to predict and forecast future outcomes, using a combination of past data, rules, algorithms and other types of data to create models that can generate useful insights. Based on the analysis of loan patterns and other data, the system is able to forecast user demand for new and existing titles. 14 In addition, demand forecasting methodologies commonly used in other industry environments were adapted. NLB is able to use such insights to procure more useful and relevant collections for library users, thereby improving service delivery from the users perspective. Instead of depending on past processes that rely extensively on the experience and judgments of librarians to select and procure collections, DA offers NLB a more data-driven approach to support their procurement decisions. Figure 3: Demand analysis for an existing title. (Source: NLB) 14 Lee Kee Siang et al., op. cit., p. 6.

8 A Smarter Library: Using Data Analytics 8 Prescriptive Analytics: Improving Resource Management One of NLB s top challenges and priorities is to ensure that its collections are managed effectively and efficiently. Using DA, it is able to forecast the demand for each new and existing title. However, predicting the demand is only the first step. Determining the right quantity and mix of books is another. Allocating the funds to ensure optimal resource management and outcomes is perhaps one of the most critical decisions that the library needs to make periodically. To cater to users of all ages and backgrounds, NLB needs to ensure its mix of collections is optimal. As a government agency with limited funds and resources, NLB s management has adopted analytics as a data-driven approach to allocate and optimise the use of its funds. To ensure such an optimal mix, NLB combines the use of predictive analytics with simulation, optimisation and decision modelling techniques to engage in prescriptive analytics, a more advanced form of analytics which uses models to recommend optimal behaviours and actions. 15 It goes beyond generating insights for decision-making to determining specific actions that can be taken. For example, instead of merely predicting the demand for a specific title, a prescriptive model can recommend the specific action to be taken, such as the exact number of books to procure in order to achieve optimisation of resources and service delivery outcomes. This is achieved by using the Collections Planning (CP) component, a system that is able to perform what-if analysis. Using the DA to provide an aggregated demand forecast to the CP component, NLB makes use of optimisation technology to plan NLB s lending collections determining the category mix in each public library that will maximise the volume of loans made by users. This takes into account the constraints of space, cost and budget, as well as the differing declines in readership in different libraries. Beyond recommending what and how many items to buy each year, it also alerts NLB to low-demand titles that can be removed and replaced by newer and more popular items. 16 With over one million items added to NLB s collection every year, such analytics will help steer the allocation of budget to the right areas, optimising its limited budget for effective public service delivery. More importantly, such analytics are not a one-off investment. They can offer repeatable and continuous improvements. This also enables NLB to meet the needs for good resource management and make value-for-money purchases Thomas H. Daveport. (2013). Analytics 3.0. Harvard Business Review, December 2013, pp Lee Kee Siang et al., op. cit., p. 9. Lee Kee Siang et al., op. cit., p. 10.

9 A Smarter Library: Using Data Analytics 9 Figure 4: Collection planning for 2012 to (Source: NLB) Using Analytics: Key Drivers for Analytics Success Investing in a proper data architecture It is important to plan and have oversight of the IT and data architecture within the organisation. In NLB, millions of users use its systems. It is therefore critical that the dedicated team of solution architects work with all stakeholders and users to come up with a proper architecture that will serve everyone. From the inception of the Big Data Programme, NLB has adopted the strategy to use a suite of strongly managed and controlled Data Foundation Services (DFS), comprising the Enterprise Data Warehouse, Data Marts and such, as the base and bedrock of the Big Data Programme. All the data collected will be housed under the DFS. The various analytics systems will draw the data from the DFS to generate the respective analytics outputs, providing the flexibility for NLB to perform and implement multiple cost-effective analytics solutions based on its diverse needs Lee Kee Siang et al., op. cit., p. 5.

10 A Smarter Library: Using Data Analytics 10 Figure 5: NLB s Big Data architecture. (Source: NLB) Proof of concept The use of the proof-of-concept principle is widely practised at NLB. Teams will come up with ideas and determine whether these are feasible or not, before proceeding to work and develop prototypes. This practice encourages teams to explore, develop and generate better ideas internally at the conceptual stage prior to the developmental stage. This helps NLB to manage project risks and ensure resources are allocated to the right areas. This phased approach enables management to be more informed about the benefits and feasibility of the projects, so that they will be able to give their utmost support to ensure their eventual success. The right mind-set To effectively utilise analytics and exploit data, organisations must consider building up data analytic capabilities across the entire organisation. This requires senior management to embrace and view analytics as a valuable tool for solving problems and identifying opportunities. 19 At NLB, this was undertaken as part of a wider strategic initiative to leverage on Big Data technologies the Big Data Programme. It is recognised across all levels of NLB that Big Data can be a valuable tool. Manpower and resources are also dedicated and allocated for building up analytic capabilities. 19 Dominic Barton and David Court. (2012). Making Advanced Analytics Work for You. Harvard Business Review, October 2012, pp

11 A Smarter Library: Using Data Analytics 11 Developing analytic competencies One of the most important resources an organisation needs to develop and succeed with analytics is analytic competencies. To effectively use data analytics, employees must acquire and develop their analytics skills. They will require such skills in order to explore and work comfortably with data to develop analytic solutions. Given today s increasing complexity in operations, business problems are rarely solved by any single department in an organisation. Rather, people from different departments may need to come together as a team to collaborate and develop an effective analytics solution. Opportunities to help public officers develop and enhance their analytics capabilities are readily available in Singapore. For instance, the Civil Service College has put together a series of analytics training courses in foundational data analytics and operation research training courses for public officers to arm themselves with basic data analytics skills. Overcoming Challenges Garbage in, garbage out One of the key challenges that data analytics professionals face is that they do not have good data to work with. In many organisations, they either do not collect the data, have incomplete data, or collect the wrong data. To obtain useful insights and results from data analytics, organisations must give sufficient consideration to the data source and the types of data required. At NLB, the data analytics and business users work together in teams to derive data analytics solutions. It is essential that business users, domain experts in the operational aspects of the business, provide and contribute useful insights to build the right models for analysis. In most cases involving complex analytics, the teams may need to recreate analytics models multiple times before they are able to derive accurate and useful results. Given that NLB is one of the few pioneer libraries to try out new and innovative analytics solutions, they would not have been able to purchase or use ready-made solutions. It is therefore essential that the teams must have the patience and perseverance to overcome the multiple possible hurdles they may face in their analytics journey. Management support must be vital to ensure successful completion of analytics projects. Using collaborations to succeed Many executives may view the perceived lack of data analytics capability as a challenge. This can hinder the adoption of analytics within an organisation. To overcome this, employees within any given organisation must be ready and willing to collaborate with each other. Such collaborations can take place internally, as well as outside of the organisation. Departments without the necessary IT or data capabilities can also consider collaborating and forming cross-functional teams to build on each other s strengths. Public agencies can also consider working with either private sector organisations or other public agencies with the necessary expertise. For example, NLB collaborated successfully

12 A Smarter Library: Using Data Analytics 12 with SLA, which has the expertise in geo-spatial analytics to generate useful insights about NLB s customers. Public agencies can also consider collaborating with Institutes of Higher Learning (IHLs). Conclusion Data analytics can be a valuable tool to help public agencies deliver better services to the public. It can provide important insights into consumer behaviour and better predict demand for goods and services, thereby allowing for better resource management. The Big Data Programme and data analytics initiative have enabled NLB to take a data-driven approach to transform itself into a smarter library that optimises its limited resources and provides better library services. However, neither Big Data nor data analytics can solve all problems. They are only as useful as the people working with them. 20 In light of the evolving social landscape and increasing citizen expectations, it is paramount that public agencies take greater and immediate actions to build and leverage on data analytic capabilities as part of their transformation journey. ***end*** 20 Ethan Rouen. (2012). Big Data won t solve your Company s Problems. Accessed, 31 July 2014.

13 A Smarter Library: Using Data Analytics 13 DISCUSSION QUESTIONS 1. How did the use of data analytics help NLB use to improve resource management? 2. Which type of analytics could be replicated and be used in your agency? 3. Which key driver(s) for analytics success is/are the most challenging to achieve? How can agencies achieve them? 4. Discuss what you could do to leverage on data analytics in your own team/organisation?

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