IPT 2015 Sales & Use Tax Symposium

Transcription

1 IPT 2015 Sales & Use Tax Symposium Data Analytics Sell Side 1.00 pm 2.15 pm Tuesday, September 29, 2015

2 Agenda 2 Introductions Session Description Learning Objectives Survey Questions Define Data Analytics Data Analytics with Your Organization Practical Application Case Study Conclusions Q&A

3 Presenters 3 Yannick Einsweiler Director, Data & Analytics State and Local KPMG LLP 51 JFK Parkway Short Hills, NJ yeinsweiler@kpmg.com Patrick McWilliams Senior Director Sales, Use & Value Added Taxes Gap Inc Masthead NE, Suite 300 Albuquerque, NM Patrick_mcwilliams@gap.com

4 Session Description Data Analytics (For Sales) what is new in this evolving field? How can I use it with my day to day work? Why should I spend precious time understanding and deploying this technology? This interactive session will define, explore and encourage the use of various Data Analytics tools and concepts in the transaction tax area. The presenters will provide a detailed explanation of the topic and include a real world case study applying the data analytics principles to problem solve and create new insights. 4

5 Objectives After this session, the participant should be able to: Define Data Analytics Understand the wide world of available data analytics tools and concepts Glean insights from your data Apply these techniques to a real world case study using the power of Data Analytics to unlock your tax process 5

6 6 Survey Questions

7 Question #1 How many Companies have specific Data Analytics functions? o Where is it in the org? 7

8 Question #2 Who is currently using some form of Data Analytics on their sales data to drive behavior or performance of their team? 8

9 Question #3 How many people see the emergence of Data Analytics as a game changer? o o Tax Industry in general? Their shop specifically? 9

10 10 Define Data Analytics

11 E2E Process Data Analytics Data Investigation Mine Raw data At the Source Confirm Data Location Creation Ownership Maintenance Discover New sources Unsuspected data Extraction & Staging Acquire Extract Transfer Load & Format Blend Link sources Confirm integrity Data Analysis Transform Find Patterns Identify Exceptions Format for Consumption Annotate Mark exceptions Identify indicators Graph Analysis Develop Insight Build views by dimension Feedback loop with Client Findings Define bookmarks Write narrative Predictive Analytics Data Assessment Deliverable Information Gaps - Tax-relevant data not captured Process or Technical Data Quality - Inconsistent or aggregated data Data Governance and Control 11

12 Deliver Insight Create interactive dashboards Immediate feedback & flexible ask new questions Share with Stakeholders - let them play with the data to endorse understanding Define Frequency of the data refresh monitor resolution of process, technical and integration issues (tax engine, document coding, data quality, bugs) Make Data-driven decisions Cut the middleman and transform role of IT Use in compliance, audit support and planning e.g. what-if scenarios 12

13 Data Analytics Predictive C O S T Descriptive Inferential Better Source Data Better Statistical Models Machine Learning Level 1: Descriptive and Exploratory : Look for correlations. Use basic statistical significance to understand possible relationships. Ensure tax significance of findings. Level 2: Inferential and Causal: Use much more randomized data sets. Prove correlations vs. causation. Ensure derived causation models can extend beyond samples used in the analysis process. 13 Level 3: Predictive and Mechanistic: Most detailed analysis. Ensures we can trace back almost every possible correlation factor. Use machine learning algorithms to further automate future datasets and provide predictive categorizations.

14 Tools Review Unstructured data storage and manipulation platforms: Hadoop/Java; R ; Python etc. SAS / IBM SPSS : All inclusive platforms Strong Text Mining (TF/IDF) Term frequency relationship with inverse document frequency Capture needed data elements reporting/metrics 14 Self-Service because Tax has unique data needs Data Extraction: Queries or leverage built-in functionality (e.g. for SAP, use DART) Data Integration: Alteryx, Talend Data Visualization: Tableau, Qlikview, Spotfire (Published reports, delivery method) Each of these tools can be sensitized and tax logic can be stored

15 Data Analytics within Your Organization 15

16 Data Analytics within Your Organization 16 Data Analysis vs. Analytics Data analysis is inherent to the tax function and has always existed Analytics is a term commonly used to methodize and systemize the process of data collection to consumption It is not a science and not only about technology! User base of Analytics Everyone from tax analyst to C-level, to all tax-relevant stakeholders: same tool, same data, different objectives Think how Excel is used today! Enablers of Analytics IT to make all data available for use in a controlled environment Best practice: Tax Technology person/team A techy tax professional who understands how tax-relevant data is created Leverage technology to streamline data management, increase (and control) automation learn about data sources and interactions Suggest new measures and dimensions that can help compliance, operations, defense or planning out-of-the box thinker

17 17 Practical Application

18 Practical Application: How do I make this real? How do I use this technology to improve my team s performance? What can I do myself? At my own desk? What would make a difference for my Company? I need a REAL solution.. Something tangible! 18

19 Possible Uses (Sales) Trends in Sales Data o o Drivers why are trends what they are Differences what are the root causes of any change Tax Research word mention; relevant topics; deliver the answers o PR news, Legislative articles on sales tax holiday, new bills Tax Flagging (Core data element for tax process) o o Original assignment of flag on new items Maintenance of flags on existing items o Nutrients, Ingredients, Medicine/Drug/Supplement Labels, Juice Content Refund - Recoup remitted tax on bad debt / write-offs 19 Data Quality ensure good data keeps flowing Compliance o o o Obsolete or incorrect location (tax jurisdiction) codes Highlight exposures or exceptions + control: revenue without tax determination (i.e. new product offerings) Accuracy of exemption certificates: scope, validity

20 Practical Application SST Definition: Example Tax Flagging Process Candy means a preparation of sugar, honey, or other natural or artificial sweeteners in combination with chocolate, fruits, nuts or other ingredients or flavorings in the form of bars, drops, or pieces. Candy shall not include any preparation containing flour and shall require no refrigeration. It is a FOOD but different treatments by state for Sales Tax. Classic example of a failure point in the tax process when it is coded improperly. Key Tax Decision Company Item Master Tax Category Code Mapping Exercise Provider Item Master Provider Content 3 rd Party 1,000,000 Items 55 Categories 55 Categories Tax Calc Made 20

21 21 Case Study

22 Case Study Test Project: Automate Tax Flagging using Big Data and Analytics Problem Statement: Products, and especially Food products, are constantly changing and laws are becoming more sensitive to ingredients. (i.e. Juice content, sugar grams, flour) Tax systems and business processes require greater granularity and speed Companies remain liable for ensuring they tax appropriately The risk associated with tax flags is large and growing Challenge: How can I automate this laborious (expensive) task? How can I maintain a large set of previously tax flagged products? How can I ensure my flagging is correct and stays current regardless of the number of products and/or flag combinations? 22 Must 1. Assign new flag but also 2. confirm assignment of existing fleet

23 Case Study Background: Retailers have a large set of products that have been coded or flagged for sales tax. In some cases millions of items. The typical process is a one and done mentality. Once an item has been tax flagged it does not get re-validated or changed unless an event has occurred. (i.e. Audit, Law Change) 23 Tax Flagging Complexity: 10,000 tax jurisdictions All companies must do some form of this activity The number of flag combinations is growing; product offerings are expanding digital and physical markets are merging Marketplace vendors: vendors are using others sites to drive sales and could utilize the host tax process Expanded Offerings: Companies are expanding their offerings because they are not limited by shelf space in a retail store i.e. tickets, rentals, small food items, services

24 Case Study Real Example: Rice Krispy Treat Is it a candy or a food? Urban Myth: Rice Krispy Treat binding ingredient was changed; flour was the main ingredient but now it is rice powder (see SST definition) Manufacturers actively reducing ingredients and changing current ingredients (binders, colors, fat, rice powder in place of flour) Real Solution: Yesterday: Manually managed, tracked, assigned and maintained tax flags ad hoc Today/Tomorrow: BIG Data Technology can supplement what our tax teams do today. Use these new tools to create a process that text mines any changes in existing product labels. Also suggest tax flag assignments for new items 24 **Tax Team moves from data input to data validator ; increase impact and lessen hours committed**

25 Case Study High-level Process: Step 1: Gather your data Step 2: Select your tools Step 3: Draft your logic and process Step 4: Analyze Results Step 5: Tweak your logic and improve your data until you achieve the result desired Simple Big Data/Analytics Project Concept: Use web crawlers and text mining to gather and improve our source data set Use Hadoop to manage a large volume of improved data Use SAS prebuilt coding library to automate the coding process 1. Build a decision tree to assign flags and 2. Build a report that calls out flag exceptions of an existing population 25 Result: Assigned tax flags first pass An actionable list of product issues/exceptions

26 Case Study Step 1: Gather your data We used internal database to start and then were moving to using a combination with the FoodEssentials Database 26

27 Possible Sources of Data Government Third party/industry players Manufacturer sites Internal databases Supply Chain Websites 27

28 Case Study Web Crawlers Hadoop (HDFS) Step 2: Select Tools SAS Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. Let's take a look at what some of those terms mean. 28 SAS is an integrated system of software solutions that enables you to perform the following tasks: data entry, retrieval, and management report writing and graphics design statistical and mathematical analysis business forecasting and decision support operations research and project management applications development Open-source software. Open-source software is created and maintained by a network of developers from around the globe. It's free to download, use and contribute to, though more and more commercial versions of Hadoop are becoming available. Framework. In this case, it means that everything you need to develop and run software applications is provided programs, connections, etc. Massive storage. The Hadoop framework breaks big data into blocks, which are stored on clusters of commodity hardware. Processing power. Hadoop concurrently processes large amounts of data using multiple low-cost computers for fast results

29 Case Study Step 3: Define Logic TF/IDF = Term Frequency/Inverse Document Frequency Core concept of text mining: How often does a certain word or word combination show in a product category of one tax flag VERSUS How often does the same word or word combination show in other tax flags o Remove preselected list of common repeating word combinations o Machine learning logic is literal with multiple decision trees running in parallel don t allow logic tree to narrow down and end back at the beginning 29 Logic Produced Result: Process was able to assign primary tax code and a secondary tax code (Tax Flags) An example a phrase like natural anti-oxidant infusion was used to assign a pseudo juice product a different tax flag because it realized it was not 100% juice

30 Case Study Step 3: Draft Logic Data Sources: Internal External Apply text mining (SAS) TF/IDF Logic hosted by Hadoop Structured and Unstructured Data 30 Restate goal: Automate the assignment of a tax flag based upon data from multiple sources or Find exceptions in an existing population Several iterations are required to tweak the logic to achieve high % success

31 Case Study Step 3: Draft Logic Example of decision tree as displayed in SAS 31

32 Case Study Example of SAS data for the TF/IDF chart for the Antioxidant Infusion product 32

33 Case Study Step 4: Analyze results Pick apart your results to find improved logic Look for failures in the logic tree or terminology used Find patterns and study the failures Beware of logic loops Step 5: Tweak Tweak Tweak Tweak your process until you find what works 33 Trial and error Reviewed results with current coding team Based upon results accuracy adjusted term selection and decision tree to assign different code Achieved less than 80% match first pass Made adjustments and earned a better than 80% match With further logic refining and better data sources we had hoped to achieve a 90+ % first time tax flag assignment

34 34 Conclusions

35 Conclusions Big Data/Data Analytics can transform your work/function Get involved now; earlier the better Learn study, follow the news, read articles, engage your IT folks (get included) Power of these tools is only limited by your own imagination 35 Go for it! Get in the game and get active!

36 Survey Question (Redo) 36

37 Follow Up Question #3 (Redo) How many people see the emergence of Data Analytics as a game changer? Tax Industry in general? Their shop specifically? 37

38 Q&A What do you want to share? 38