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Data Collection Strategy in the Exacting Informational Environment

Data Collection Strategy in the Exacting Informational Environment. Seminar on Statistical Data Collection (Geneva, Switzerland, 25-27 September 2013). By Nana Aslamazishvili. Agenda. Introduction Existing Practice and Strategic Vision What is the best way to define what to collect?

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Data Collection Strategy in the Exacting Informational Environment

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  1. Data Collection Strategy in the Exacting Informational Environment Seminar on Statistical Data Collection (Geneva, Switzerland, 25-27 September 2013) By Nana Aslamazishvili

  2. Agenda Introduction Existing Practice and Strategic Vision What is the best way to define what to collect? SebStat as a new data production architecture Streamlining the statistical production Communication issues Lessons learned Concluding remarks and future plans

  3. Introduction “Change in all things is sweet”. Aristotle Following the international statistical standards, NBG produces the majority of the country’s most important official statistics: Financial Sector Statistics (Monetary and Financial Statistics, Financial Soundness Indicators, Interest Rates Statistics); External Sector Statistics (BoP, IIP, External Debt Statistics, International Reserves Statistics, Exchange rates Statistics).

  4. Introduction Our Achievements: Participation in the Data Dissemination InitiativesFigure 1

  5. Existing Practice and Strategic Vision “Sound strategy starts with having the right goal”.Michael Porter In General, data can be produced in many ways and each mode has a different cost structure, advantages and disadvantages. It is worth emphasizing, however, that traditional reporting mode, used at the NBG, leaves much to be desired. NBG’s current data collection and production system might be characterized as: • Excel spread sheets based reporting, • Decentralized between different units, • Unsupported data validation and processing by the compliant IT technologies, and hence • Unaccompanied with a centralized data warehouse. Decentralized and multi-dimensioned datasets led to weaknesses of appropriate metadata.

  6. Existing Practice and Strategic Vision “Sound strategy starts with having the right goal”.Michael Porter In order to define the main framework of our strategic vision on NBG’s statistical business process transformation we had to focus on following complicated questions: • Statistical needs: What would be more comprehensive set of data according to the international standards we want to have from financial institutions? • Collection strategy: What kind of mode would be more reasonable to change excel spread sheet based reporting over to web-based collection, and how to collect different data by the standardized way to ensure centralized collection? • Streamlining: How to optimize standard Statistical Business Process Model in order to reduce reporting burden as well as data validation and production costs?

  7. What is the best way to define what to collect? Statistical needs, in terms of quality, availability, and analytical usefulness of financial and external sectors data, in both in-country and international context are clearly formulated in worldwide recognized manuals and guidelines, such as SNA, BoP, MFSM, FSI, and so on. To be in conformity with international standards and integrally linked to other macroeconomic statistical systems, as well as to FSI and macroprudential analysis, we decided in favour of balance sheet data structure for financial institutions, describing the unified framework for comprehensive analytical consideration.

  8. What is the best way to define what to collect? “Statistics is the grammar of science”.Karl Pearson The ideal way to collect data for balance sheet purposes can be easily foundin a System of National Accounts fundamental idea regarding analysing flows and stocks, reflected under the question: “Who does what, with whom, in exchange for what, by what means, for what purpose, with what changes in stocks?” “Answering these questions for all economic flows and stocks and operators in a given economy would provide an enormous amount of information describing the complete network of economic interrelations“ (2008 SNA, Chapter 2. p.2.8). In this very vision is based our approach in a part of data collection from the financial institutions.

  9. What is the best way to define what to collect? What is the most comprehensive data requirements in line of international guidelines? Figure 2 shows a structural breakdown requirements for financial data analytical purpose, used by the balance sheet approach, we are focus on Figure 2 Financial/Nonfinancial Instruments In national/foreign currency By resident sectors: Central bank Other depository corporations Other financial corporations Central government Local government Public non-financial corporations Private non-financial corporations Other resident sectors Non-residents Instrument Figure 3 Financial/Nonfinancial Instruments In national/foreign currency By resident sectors: Central bank Other depository corporations by subsectors Other financial corporations by subsectors Central government Local government Public non-financial corporations by type of economic activities Private non-financial corporations by type of economic activities Other resident sectors households NPISHs Non-residents by countries by sectors by type of economic activities Currency breakdown Residency To deeper in-country analysis structural breakdown was broadened Sectorization Residency

  10. SebStatas a new data production architecture: a. Collection strategy One of the challenging question we arose at the very beginning was: What kind of mode would be more reasonable to change excel spread sheet based reporting over to web-based collection, and how to collect different data by the standardized way to ensure centralized collection, on one hand, and automation of whole statistical business process, on the other?

  11. SebStat as a new data production architecture: b. Data Structure Defining Approach Frequency Who? For what purpose? By what means? Currency With whom? Does what? In exchange for what? With what changes in stocks? source frequency instrument currency residency sector Additional info on loans collateralize Interest rate Data value = BBB.MM.F04000.GEL.RR.S14000.L010000.LC3.18.00.824012.00.20130531.

  12. SebStat as a new data production architecture: b. Data Structure Defining Approach As a result, the key recording criteria for all financial/nonfinancial instruments are formed as follows: • Status of instrument (assets/liabilities); • Data type (Stock/flow); • Maturity; • Currency denomination; • Additional information in case of loans (loans categories, specific loan products, etc.); • Loans collateralization (for FSI purpose); • Ranging of loans/deposits; • Counterpart characteristics: • Residency • Sector • Type of economic activity. These criteria are intended for monthly financial and statistical data family (FIM). However, the same approach to the data structure definition is used in case of other data families, which are formed for identifying other statistical data under the NBG mandate.

  13. SebStat as a new data production architecture: b. Data Structure Defining Approach Scheme 1. SebStat Conceptual Framework FIM – monthly financial and statistical data; FID - daily balance sheets data; MTR – money transfers statistics; FEX – foreign exchange transactions; BPC – bank payments cards statistics. Special Code Lists, created for each statistical domain (Data Family) allow establish clear rules for data definition. By the SebStat concept, each data family includes set of specific keys, identifying specific variables.

  14. SebStatas a new data production architecture: b. Data Structure Defining Approach One of the main advantage of the SebStat is that there is no need to compose questionnaires for data submission. NBG’s data requirements are presented in form of list of keys, identifying specific data, intended for submission. Next three slides illustrate how the web-based submission is organized and how the data requirements look like.

  15. SebStat as a new data production architecture: c. Web-site support Data Requirements Methodology Data Uploader Data Uploaded CodeLists News Data Families FIM - Monthly financial and statistical indicators FID - Daily financial indicators FEX - Foreign Currency Operations MTR - Money Transfers BPC - Payment cards

  16. SebStat as a new data production architecture: c. Web-site support An example of data requirements for Loans The required data structure Keys, identifying the required data

  17. SebStat as a new data production architecture: c. Web-site support An example of XML files intended for submission to the NBG

  18. Streamlining the Statistical Production Streamlining: How to optimize standard Statistical Business Process Model in order to reduce reporting burden as well as data validation and production costs?

  19. Streamlining the Statistical Production 1 Specify needs 2 Design 3 Build 4 Collect 5 Process SebStat conceptual and IT architecture includes appropriate tools for designing final outputs; data collection and processing modes are premeditated by the standardized way Standardization of data structure makes easy to identify new requirements within the existing mode Well structured raw data through the relevant classifications simplifies processing procedures and makes it more efficient 6 Analyze 7 Disseminate 8 Archive 9 Evaluate Of course, when whole statistical business process is automated, technically is makes easy to define archive rule and implement it. Well established algorithm for producing reliable time series and other outputs makes more efficient and time saving output products; there is more time for the promotion of dissemination products and improvement of communication with users SebStat concept itself connotes its step-by-step implementation, by financial institutions, on one hand, and by statistical domains, on the other. It is worth emphasizing, however that standardized procedures, established initially allows to solve related challenges by the standardized way and speed up evaluation process significantly. SebStat makes flexible enough to prepare draft outputs, redesign existing ones and use other illustration materials (graphs, diagrams, etc.) Consequently we streamlined GSBPM significantly:

  20. Communication Issues FAQs practice was implemented in order to justify specific issues arose in the particular banks In order to ensure awareness of our new ideas and plans regarding statistical business process transformation we carried out 7 joint meetings during the 2011-2013 with banks representatives E-mail or telephone communication is common for prompt reply Skype or video conference is used for inter-country (Ukraine, Turkey) communications, when respondent-bank’s foreign partners are involved in the SebStat Implementation processes In order to resolve specific reporting related problems of individual banks we carried out more than 30 meetings with single banks professionals and IT experts To ensure success of the SebStat project we needed to change our communication strategy dramatically.

  21. Communication Issues Methodolodical Materials and Technical Tools SebStat Guide National Nomenclature of Types of Economic Activities Bridge Table Instructions for data uploading Examples of XML files for each Data Family Case studies for Loan products identification NBG’s President Decree N350

  22. Communication Issues In order to investigate bank’s short- and long-term needs and challenges they are facing we carried out SebStat Implementation Assessment survey. The results are fruitful enough: Firstly, they show that bank’s expectations regarding SebStat project success are positive, and Secondly, they serve as recommendations for identifying priorities for future cooperation.

  23. Communication Issues However, there are several most pressing problems in the process of SebStat Implementation: • inconsistency between SebStat methodology and existing one within banks (41%); • inadequacy of existing IT technologies at the banks (29%). Probably because of this problem banks expectations regarding reporting burden are slightly negative (53%).

  24. Communication Issues On the whole, banks consider that methodological and technical support from the NBG side is sufficient enough for SebStat implementation (4.29; scope 1-5) and would be still continued in the future. Moreover, 29% of surveyed banks consider that there is a room for future enhancing of SebStatframework.

  25. Lessons Learned Despite a fact, that implementation of a new statistical informational system of NBG takes place in condition of extremely limited resources, it is worth emphasizing, that SebStat is an ambitious project. However, it is successful thanks to number of well-considered points. • Prerequisites: first of all, it should be analysed all prerequisites (existing practice, professional experience and skills, data collection environment, etc.) for changes. • Confidence: to have a clear understanding of problem and ways how to solve it, before introduction of new idea, is essential in order to gain real trust and confident among stakeholders. • Priorities: learn how to develop priorities, especially when resources are limited. It is essential to be sequent in actions. • Working Team: Well organized working team plays important role for project success. Members of the team should be methodologists, analysts, national accountants, subject-matter statisticians (depending on Central Bank’s mandate), bankers, financial accountants, IT specialists, programmers, depending on the nature of problems to be solved.

  26. Lessons Learned • Long-Term Aims: It is essential to focus own initiatives and ideas on a long-lifecycle statistical project; Statistics is too important to be taken lightly. • Cooperation Strategy: Effective cooperation strategy with data providers and users is one of the important priorities of statistical production process organization. • High level management support is essential for project success. It is crucial to understand, that “The only free cheese is in the mouse trap”. It is essential to convince the management that it is the exact. Generally speaking we strongly believe that SebStat is the right direction to right positioning on the modern information market, and our strong expectation is that with SebStat we can produce timely, relevant and competitive statistics.

  27. Concluding Remarks and Future Plans Hence, it was our aim to create innovative informational system able to change dramatically “stove pipe” production into standardized procedures for each step of the data collection and production processes at the NBG. After the pilot round of collection, quality of data and uploading procedures are more than promising. The adoption of the new centralized collection standards makes it easier to establish a flexible statistical data management system not only at central bank, but at the commercial banks also. The main advantage of SebStat is that it makes much easier, less resource-intensive, and less burdensome on data providers.

  28. Concluding Remarks and Future Plans • SebStatas a system creates environment that facilitates enhancement of statistical data collection scope and quality, on one hand, and improvement of statistical capacity in terms of data presentation and dissemination, on the other. • As one of the next step of implementation, other financial institutions will be included in SebStat project. • In a broad sense, SebStat as a statistical informational system connotes formation of the Integrated Meta-Informational System. It is essential to analyzing and interpreting SebStat-related data and to making meaningful decisions. • Creation of the Integrated Meta-informational System will be one of the important achievements of the SebStat project.

  29. Concluding Remarks and Future Plans • It is worth emphasizing, that SebStat, by its concept and architecture, intended to be a system with a long lifecycle. On the other hand, data production now-a-days requires significantly higher skills and professionalism, then before. So, it is crucial to establish a good, continuing knowledge transfer mechanism in order to ensure using of system’s capacity in a workmanlike manner. • Elaboration of comprehensive training materials for data providers, as well as for data producers and users is very important, and its realization is one of important part of our plans in the near future.

  30. Thank you ___________________ For more information: Nana Aslamazishvili Head of Monetary Statistics Division National bank of Georgia naslamazishvili@nbg.gov.ge Tel: (995 32) 240 6251

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