Mary Willcock is a data scientist on our Baker Tilly Digital team with experience in advanced data analytics solutions. She uses a business-first approach to data, diving deep into organizations to ask the right questions and prepare innovative tests that discover true gaps in business intelligence. Mary joined Baker Tilly through the acquisition of Talavant, a Wisconsin–based data analytics management consulting firm.  here What led you to working for Baker Tilly’s consulting team?
I have always been interested in the way that business and technology interact together. While I took a traditionally winding career path to discover the data science and advanced analytics space, when I did I realized advanced analytics really captures both data science and advanced analytics on a day to day basis. I love the use of technology and the ever-increasing opportunities we have because of technology, but we always have to ground them in the business and make sure we’re properly leveraging technology to solve the real problems we are encountering in the business. What do you enjoy most about working for Baker Tilly Digital?
Baker Tilly Digital provides services to a wide variety of industries as well as departments within those industries. For one project, we might work with an IT department, another project the marketing department, then the next project a manufacturer or a business in the retail industry. Interacting with a variety of industries provides the opportunity to learn different ways to problem solve. By seeing such a broad variety of projects, if I learn something that one industry is doing, I can transfer that knowledge over to the next business to provide tangible business solutions.  Due to the pandemic, what obstacles and/or opportunities are clients seeing?
Specifically around advanced analytics and AI (Artificial Intelligence), a lot of the models and previous data that we have been using no longer serve the current problem. Patterns that were true one to two years ago might not be true anymore due to the pandemic. For example, how customers decided to purchase goods during a retail season in 2020 will not mirror how they purchased in previous years. In many industries, it is common to use simple forecasting and intuition to make future business decisions. When our intuition is based on past patterns that are no longer accurate in our current pandemic economy, we need to leverage bigger tools and better technology to help address these problems.
Machine learning and AI are extremely powerful because they can identify these patterns that exist beyond where the human brain can identify and calculate. Since machine learning is able to analyze more data and zero down onto very particular patterns within that data, the opportunity to use these toolsets to find patterns about the way the world works now during this pandemic and make sure we attune our business appropriately is vast.  What does the collaboration with AWS mean for Baker Tilly Digital? 
Amazon Web Services provides a robust framework to implement any type of data services. As Baker Tilly Digital collaborates with AWS, we get access to AWS’s cutting edge tools to leverage for all of our clients to better serve and guide them as we address these tough problems, such as the pandemic.   What opportunities can clients take advantage of with this collaboration?
When considering digital solutions, a cumbersome tool can be the downfall of an otherwise successful project. The collaboration between AWS and BT Digital provides this opportunity to pair excellent tools with a robust solution to make sure our solutions are successful from start to finish. Describe what the inaugural Deep Racer master classes, events and offerings can provide clients and prospects. 
Baker Tilly Digital has some very knowledgeable professionals in the advanced analytics and AI space. Our 
professionals will draw on their entire breadth of knowledge, concepts broader than just deep learning, expanding on tools one might engaged with during an AWS collaboration.  Baker Tilly Digital professionals will be hosting masterclasses to share their knowledge and provide attendees the opportunity to learn a robust set of concepts to apply a toolset that can address their most pressing business challenges.  Did you take up any hobbies during quarantine? / What do you like to do outside of the office? Typically, my hobby outside of the office is that I play the cello. I play in a community symphony orchestra and have a wonderful time. Unfortunately, quarantine has kept me away from playing with other people. As a family, my husband, son, and I have been re-watching "The Office" and "The Great British Baking Show."
 

Baker Tilly principal Mary Willcock discusses technology trends, her team’s recent collaboration with AWS and how Baker Tilly Digital’s inaugural DeepRacer event will bring organizations together to learn about AI and machine learning.

Baker Tilly Digital

DeepRacer League Overview

Analytics

Accelerate your digital transformation with AWS

Big Data

Data Visualization

Mary Willcock discusses technology trends and AWS collaboration

Client background: SKYGEN USA provides TPA and SaaS benefit administration solutions for dental and vision payers. SKYGEN USA is dedicated to transforming the delivery of health benefits through innovative, technology-enabled solutions that drive down the cost of care while promoting better outcomes for all Americans. By developing and employing software solutions, SKYGEN USA provides unprecedented levels of automation and cost savings – creating the most efficient health plans in the country. The business challenge: SKYGEN USA had a mixture of on premise and cloud-based infrastructure with applications that required modernization to support their business growth. SKYGEN USA needed to assess the opportunity of moving its core applications to a cloud-based infrastructure and IT operating model. Their core offering is a large business automation platform that includes many instances of client-specific customization.  To serve all of its clients with the full benefits of the system, SKYGEN USA wanted to ensure all aspects of the platform and its customizations would be optimized to leverage the benefits of a modern cloud infrastructure. SKYGEN USA faces two major issues: Each SKYGEN USA client is on their own “instance” of an Enterprise System; over time clients have added custom objects to support their business needs – resulting in the need to support multiple versions of the system The need to seamlessly push enhancements to their clients on a regular basis, without requiring substantial client resources to support UAT – resulting in longer lead times for product releases SKYGEN USA was looking to understand the benefits, risks, steps and timing required to migrate the application to a new next gen cloud platform. The new cloud platform needed to support SKYGEN USA’s future strategy of rapidly developing new, smaller and more cost effective capabilities and products. Key questions SKYGEN USA was looking to answer are:  What infrastructure and policies do we have today and how would this change if we migrated our applications to the cloud? What are the key business benefits to cloud migration? When we migrate, how would we do it and what process and timeframe would cause the least disruption? The Baker Tilly approach: In conjunction with AWS, Baker Tilly Digital utilized a well-architected review to develop an IT Cloud Strategy for SKYGEN:   The well-architected review allowed Baker Tilly Digital to dive into all facets of SKYGEN USA’s current IT infrastructure and identify gaps based on best practices. The team then connected the findings from this review to SKYGEN USA’s business growth objectives and recommended a cloud modernization approach that both aligned to best practices and supported strategic growth goals. Modernization efforts began with a deep dive into the current core system, removing all customization, while in tandem, initiating the cloud architecture design to support this next generation platform and cloud native micro services.  Developing SKYGEN USA’s strategic roadmap included technical and non-technical critical work streams to maximize growth potential. The technical work streams included migration to the cloud and determining how to deliver products to the market faster. The non-technical work streams support platform modernization and product innovation, while defining product strategy and governance policies and incorporating changes to management processes to ensure lasting growth.   The cloud modernization approach provided a significant opportunity for SKYGEN USA to integrate a more modern CICD (continuous integration (CI) and continuous delivery (CD approach to application development, allowing for more frequent, smaller and incremental software changes)).  The business impact: The new cloud focused strategy is projecting a ~50% growth in new product revenue and product development efficiencies. Adoption of modern cloud technology also allows SKYGEN USA to: React quickly to changing market needs and realize product based revenue much quicker Eliminate upfront investment needs and pay infrastructure costs only for what is used  Design, test, develop and deploy new products between 40% - 60% faster than today Lower costs to support, run and maintain new modular products by 30% - 40% Recognize a 50% - 60% reduction in deployment costs and about a 70% reduction in deployment time Leverage a micro services architecture to enable a modular and product strategy driven product portfolio Access a large pool of customers with specific unmet needs, or opportunities in adjacent markets  Improve current client experiences with the removal of client specific customization and versions 

SKYGEN USA had a mixture of on premise and cloud-based infrastructure with applications that required modernization to support their business growth. SKYGEN USA engaged Baker Tilly Digital to assess the viability of moving its core applications to a cloud-based infrastructure and IT operating model. 

Healthcare & Life Sciences

Health Plans

Cloud Strategies

SKYGEN USA engages in a strategic cloud assessment with Baker Tilly Digital

John Runte, a principal in the consulting practice of Baker Tilly, joined the firm in 2002. John has an extensive consulting background and currently leads several teams within the consulting practice including Baker Tilly Digital, Enterprise Transformation Services and Growth Strategies. Prior to joining Baker Tilly, he consulted in a global accounting, tax, and consulting firm. What led you to working for Baker Tilly’s consulting team?
Wow, can’t believe I was part of the team that formed our management consulting group 18 years ago given the demise of Arthur Andersen. There were four of us (and many more who quickly followed) to start our consulting team here at Baker Tilly. It’s been fun to see our team go from small to over 850 management consultants in the present day. What do you enjoy most about working for Baker Tilly Digital?
What I love most is the constant push of innovation and watching the next generation take the reins and deliver on really innovative and complex client initiatives. I also enjoy solving complex client problems and leaving our clients in a better position than when we arrived. We offer a number of different services and solutions that bring a lot of diversity to our delivery so every day is something new. Services range from program management, project management, change management on the transformation services side to technically advanced digital solutions including custom application development and advanced analytic solutions.  Our digital labs offering is unique and focuses on proof of concept oriented solutions where we take tough business problems and we ultimately solve them using technologies in a proof of concept way prior to moving those towards production type environments. Overall, it’s the constant diversity at Baker Tilly Digital that I love. Due to the pandemic, what obstacles and/or opportunities are clients seeing? 
Since March, there have been a number of industries and businesses doing extremely well and there are others that have been challenged. We have seen some organizations that want to move forward and leverage the pandemic and the crisis for digital / transformative change. They are focused on digital applications to reduce the manual footprints within their organization. 
Everyone is focused on data. Many organizations back in March were caught with a “where exactly are we within our finances perspective, or, where are we at from an inventory perspective given stress in our supply chain.” All of these questions are certainly prime for data / analytics solutions.  We do a significant amount of work in that space and the pandemic has brought to light the number of digital use cases within organizations. I’d say the crisis pushed organizations to take action on those opportunities.  What digital strategy and technology trends do you expect to see in 2021?
Ongoing workflow enhancements so that everything that can be electronic can be migrated to digital / electronic channels. Many person-to-person workflows are being considered for digital / technical change to eliminate the in-person human interactions. 
Even when we come back to a new normal, there will be a push to utilize digital/collaborative technologies. There will be a significant focus in electronic commerce for those who have not embraced it. There will also be a significant focus on alternative supply chain tracking, inventory management techniques and even leveraging AI/ML solutions to improve operations and the overall customer experience. What does the collaboration with AWS mean for Baker Tilly Digital? What opportunities can clients take advantage of with this collaboration?
AWS has been a strategic platform and partner for us. We are doing advanced portal and data solution projects as well as electronic commerce related solutions on the AWS platform. The partnership with AWS gives us a standard platform to think about different use cases and business problems by industry. It allows us to leverage the AWS capabilities and services to quickly solve business problems. 
AWS have a significant number of services. A great example is the propensity module. They have an algorithm that is set to deal with propensity and a great example is we have used their model to develop a propensity to pay application. It’s a predictive view of those customers and those vendor partners that have the ability to pay; certainly a critical capability during the pandemic and early economic crisis associated with the pandemic. 
We believe that leveraging these canned AWS services allow us to build strategic and game changing type applications for organizations. We are exactly where we want to be from a digital development perspective. Describe what the inaugural Deep Racer event can offer for clients and prospects.
The DeepRacer event is a league which consists of a series of events. What we are trying to do is bring organizations together to learn about AI and machine learning. The event starts with a number of preparatory sessions including masterclasses on data science and predictive analytics. This thought leadership is available for organizations to participate in prior to the race.  We also will be creating social connections for organizations that want to collaborate with each other. 
The actual event is a race that is about using AI, machine learning and deep, predictive algorithms that allows a car to race around multiple tracks. The league we’ve established consists of three different races. In-between these races we will be hosting masterclass sessions to help these organizations learn real-world machine learning solutions. We will also be conducting industry and/or functional thought leadership “fireside chats” which will lean towards industry themes and trends for the market.
I am really looking forward to this event! It should be a fun and interesting way to bring organizations together. If this league goes well, we will be connecting organizations and peer groups with similar interests in order to collaborate and share information, key learnings and AI use cases.  Did you take up any hobbies during quarantine? / What do you like to do outside of the office?
Outside of the office, I am an avid outdoorsman. Over quarantine, I spent a lot of time fishing with my family. It was fun to watch my 20-year old daughter gain interest since there wasn’t anything else to do! She really embraced the sport of fishing. That’s where we spent the majority of our time. I also took in a round of golf or two, but I always say you can’t be a good fisherman and a good golfer at the same time. They both take too much time to be consistently good!   

Baker Tilly principal John Runte discusses digital application change due to the pandemic, his team’s recent collaboration with AWS and how Baker Tilly Digital’s inaugural DeepRacer event will bring organizations together to learn about AI and machine learning.

John Runte discusses technology trends and AWS collaboration

Client background: Federal Home Loan Bank of Chicago (FHLB Chicago) is a government-sponsored enterprise that provides secured loans and other support services to about 760 members throughout Illinois and Wisconsin. With over 300 million in revenue in 2019, FHLB Chicago is one of the first financial services companies to migrate to AWS/cloud. Business challenge:  Being one of the first financial institutions to go all in on Amazon Web Services (AWS) in 2015, FHLBC embraced public cloud computing early on. However, as they continued to innovate and leverage more advanced technologies, it was time to take a step back and re-evaluate the security, governance, and cost optimization of their current environment.   The Baker Tilly approach:  Baker Tilly worked with FHLBC to perform a comprehensive IT audit focusing on security and highlighting any identified risks. With their IT infrastructure on AWS, there was opportunity to engage the BT Digital team to look beyond the scope of the audit and assess overall performance utilizing the well-architected framework. The framework consists of 5 core pillars including, operational excellence, security, performance efficiency, reliability, and cost optimization. Recognizing that FHLBC was further along in their cloud journey, Baker Tilly adapted the approach to hone in on the areas that would drive the highest value. In doing so, Baker Tilly provided recommendations rooted in best practices but tailored to FHLBC’s current stage of migration. These include the following:  Successfully stand up a Cloud Center of Excellence using hub and spoke model and implementing policy as code to effectively govern service adoption Centrally govern AWS accounts using a multi-account structure to provide transparency in where costs are being incurred and consolidate billing Optimize AWS spending by right-sizing EC2 instances based on current capacity needs and assessing the impact of hidden data transfer costs  The business impact:  Through the assessment of current state, identification of high-value areas, and alignment to AWS best practices, Baker Tilly uncovered opportunities for FHLBC to optimize their costs and allow for continued innovation as they scale. As FHLBC outlined their business growth plan, their technical priorities were made clear in how they can improve their AWS environment to meet the innovation needs of the business at the lowest cost.

Federal Home Loan Bank of Chicago (FHLBC) engaged Baker Tilly Digital for an assessment of their current cloud technology. Baker Tilly Digital identified and implemented best practices that aligned with AWS's platform

Federal Home Loan Bank of Chicago strengthens cloud computing technology to drive innovation

Dave DuVarney is a principal on our Baker Tilly Digital team with nearly 20 years of experience in advanced data analytics solutions. He uses a disciplined, process-driven approach and enhanced data insights to help businesses transform. Dave joined Baker Tilly through the acquisition of Talavant, a Wisconsin–based data analytics management consulting firm where he served as President. What led you to working in the data analytics world?
I actually fell into it. I worked for a niche data and analytics company where I did application development. I’ve always been interested in process improvement and measuring that improvement, so I quickly gravitated towards this kind of work. That work drove me closer to the data side of things. 
 What do you enjoy most about working for Baker Tilly Digital?
I love the breath of experiences of Baker Tilly Digital team members. The broader we get the more experiences we have and the richer type of solutions we can offer to our clients. 
 Due to the pandemic, what obstacles and/or opportunities are clients seeing?
From an opportunity perspective or just a general awareness, people are recognizing that when the world is changing around you quickly, you cannot wait for information. The days of getting the month-end financial report are not quick enough to actually make insightful decisions and move forward. You need that information in your fingertips all the time. 
 What digital strategy and technology trends do you expect to see in 2021?
In the near term, we’re seeing a lot of push in the areas of data governance. Organizations have a good handle on how their data is defined and managed, as well as being accurately completed. It is very important for that right now. We will continue to see an interest in machine learning, so we are seeing clients go a level deeper from their analytics and driving from that prescriptive nature to predictive.
 What does the collaboration with AWS mean for Baker Tilly Digital?
Our collaboration with AWS gives us a strong channel partner to both help us drive adoption into the market and give us a broader reach across the number of clients. As a partner, it gives us excellent resources to lean on as we work to increase our skills and experience with their platform. 
 What opportunities can clients take advantage of with this collaboration?
Our clients can have access to those subject matter resources in a more direct connection within that vendor. We can help bridge that gap between what the business is looking for and what the vendor can provide.
 Describe what the inaugural DeepRacer event and the masterclasses can offer for clients and prospects.
The DeepRacer league offers a community where those with similar interests can work on solving a problem in a fun and enriching environment. The masterclasses are clearly there to help individuals increase their skills, learn from both Baker Tilly Digital & AWS experts and dive a level deeper into the subjects we are offering classes on. 
 Did you take up any hobbies during quarantine? / What do you like to do outside of the office?
Even before quarantine, I always enjoyed staying active and exercising. In the beginning of quarantine when gyms weren’t open, I had to build a makeshift gym in my backyard. I had a rock and a towel to swing around. It seemed to work pretty well! During quarantine, I stumbled onto a guitar in my sister’s basement and started taking lessons. I’ve kept up with it since June! I’m terrible, but it gives me something to work on in the evenings. 

Baker Tilly Digital principal Dave DuVarney discusses technology trends, opportunities stemming from the pandemic, the collaboration with AWS and what the inaugural DeepRacer event can offer to attendees.

Dave DuVarney discusses technology trends and AWS collaboration

Time series analysis is as vast a topic as time itself, and as such, there is far too much information to cover in one article. This article is the first part in a series to go over the many intricacies of time series analysis. Read part two to learn about time series analysis and how to build a data pipeline into your business.  Authored by Arthur Wang and Kolin Konjura Modern growth in computational processes impacts big data and artificial intelligence (AI) which directly benefit time series analysis. With more data, increasingly efficient computation, and greater processing power capabilities; leveraging time series methods to solve business problems through forecasting and process modeling has increased. Through these improvements, business owners have a unique opportunity to harness their data through new and old time series analysis processes to better anticipate future customer insights and market trends while making business decisions. What are time series?  A time series is data measured in chronological order. The intervals of measurement can be regular or irregular, and can vary in length. For example, time series can be monthly, weekly, daily, or even to the second. Much of the data we collect are time series, such as stock prices, machine telemetry, and temperature readings over an established timeframe. Additionally, granular data can be converted to a time series; for example, individual clicks on a website can be aggregated to measure daily website activity. Time series analysis includes several techniques and models designed for this kind of data. The growth of big data has created increasing opportunities to leverage time series analyses. What is time series analysis? Time series analysis is a set of methods that allow us to understand time series data. This can be as simple as understanding when seasonal trends occur, but usually involves deeper analysis to build a more complete model of the underlying processes. Building and implementing a time series model describes a fundamental correlation within the data.  Being able to identify these correlations generates insight into the data that can be leveraged to forecast and predict future values.  Why use time series analysis? Time series analysis is especially useful for forecasting. In forecasting, time series methods often perform better than simpler methods, such as linear regression or simple extrapolation of historical data. Time series methods capture underlying structures that are common to time series data, such as trend and seasonality (1). These aspects of time series data can violate the assumptions of simpler methods, making the simpler methods potentially less powerful. By accounting for common real-world conditions, time series analysis produces forecasts that are generally more accurate.
Putting time series analysis in action   Imagine a company is researching the household energy levels in various neighborhoods to identify those that do not meet green energy requirements. This company determines that an individual’s daily usage is higher during the day and lower during the night, which produces a cyclical pattern within the data. Now suppose the city the company is researching is in Minnesota. The Minnesota climate is relatively mild in the summers, but unbearably cold in late fall, winter, and early spring. The data may show that household energy levels spike during those cold months, and level out during the summer months. The data then shows cyclical trends throughout the day and seasonal trends throughout the year. Using time series analysis for modeling and predicting this situation would work well.  Now imagine a supply distribution company needs to understand local traffic patterns for their supply lines. The company needs to determine the most efficient times to move their product, avoiding things like rush hour traffic or sporting event traffic. If they are moving product on Fridays, rush hour may start at 2 pm instead of 4 pm or 5 pm. If it’s football season, there could more traffic on Monday and Thursday evenings than there is on Friday. A time series model can help the distribution company find the optimal time to move their product with efficiency. Advantages The nature of time series data can lead to complex and arduous calculations that can make forecasting difficult. Luckily, with modern computational capabilities, implementing a time series forecast can be done with relative ease. Languages such as Python and R have ready-built packages that allow analysts to create and tune accurate time series forecasts with relatively little effort (2). In addition to providing accurate predictive capabilities, time series models have minimal assumptions, are statistically rigorous, and most importantly, are stable. This means that if a large, unexpected event occurs, the model can react and provide insight to solutions around that event. For example, the COVID-19 pandemic has disrupted operations in several industries; time series methods are able to adjust forecasts and analysis with the new climate in mind. Drawbacks As with most advanced techniques, a time series analysis is only as good as the data itself. If the historical data is sparse, then the forecasting will not be fruitful and a machine learning approach technique may be better in that instance. The more complex the data is, the more difficult it can be to implement a time series forecast model. In these instances, it may be that a time series model is only part of the solution (3). Referring back to the supply distribution example previously explored, time series analysis may give recommendations for optimal driving time, but those times could be dependent on when the supply is ready to be shipped, inclement weather, or unforeseen accidents on the road.  The landscape of time series analysis Today, time series analysis is a mix of proven and emerging techniques. Advances in computing have enabled rapid exploration of different models in order to select the one that performs best, rather than painstakingly selecting one up front (4). Greater processing power and developments in graphics processing units (GPUs) have also enabled the application of deep learning to time series forecasting, an approach that has recently outstripped traditional methods (5). Businesses today have a greater range of choices than ever before in how they generate powerful forecasts. Our Baker Tilly Digital professionals are knowledgeable and prepared to assist your business to determine how to implement time series analysis while leveraging your current data and keeping your future business goal at the forefront of every decision.   1.     https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm 2.     Time Series Analysis with Applications in R by Jonathan D. Cryer and Kung-Sik Chan  3.     Time Series Analysis with Applications in R by Jonathan D. Cryer and Kung-Sik Chan 4.     https://towardsdatascience.com/introducing-pytorch-forecasting-64de99b9ef46 5.     https://robjhyndman.com/hyndsight/tscv/

Business owners have a unique opportunity to harness their data through new and old time series analysis processes to better anticipate future customer insights and market trends while making business decisions.

Supply Chain Network Optimization

Supply Chain Strategy, Planning & Optimization

Supply Chain & Manufacturing Consulting

Supply Chain Analytic & Financial Solutions

Supply Chain Resilience

A guide to time series analysis

Authored by Logan Wille Natural Language Processing is as broad a topic as words themselves, and as such, there is far too much information to cover in one article.  This article is the second part in a series discussing the value of Natural Language Processing. Read part one here.  Between phone calls, electronic communications, or written words, the prevalence of language is evident. Given our reliance on a digital business environment, understanding the context of words is even more important for operations to succeed. Businesses that can extract information from these types of data in a quick and systematic way will fully be able to harness customer insights and market trends while making business decisions. Leveraging natural language processing and Amazon Comprehend in your business operations can help your organization grow in the current digital landscape.   What is Natural Language Processing? Natural Language Processing (NLP) is a category of artificial intelligence designed to listen, read, understand, and generate natural language. The most common use case is natural language understanding; where a model is designed to read a piece of text and extract information be it categorizing the text, extracting the entities discussed in the text, or the sentiment of the text. NLP’s biggest value in business is its ability to gain insights into unstructured data that previously would be too much of a burden to assess.  What is Amazon Comprehend? Amazon Comprehend is an Amazon Web Services product that provides a pre-trained, machine learning NLP model that includes out of the box natural language understanding and custom model creation to help businesses uncover insights from their written text data. These out-of-the-box implementations are:  Entity extraction Key phrase Language identification Personally identifiable information Sentiment Syntax These out-of-the-box models require zero training and only requires formatting the text correctly and uploading to AWS. This allows for a simple path to analyzing unstructured and unlabeled text data.  By leveraging Amazon Comprehend, businesses can organize and sub-divide unstructured text data to begin structuring it with relevant meta-information on the text, like companies mentioned, or general sentiment. Some types of information it can extract includes; personal information from text, such as addresses or phone numbers, and log this information into a database. After extracting the data, businesses can begin digging into the valuable insights and explore areas of their business they need to re-evaluate based on current client and market trends.  Amazon Comprehend custom models In addition to the out-of-the-box implementations, Amazon Comprehend allows for custom NLP models to fit specific business application. Currently, Amazon Comprehend offers either classification or entity extraction for these custom models. The custom classification can organize articles into user defined groups, such as if a piece of text is fiction or non-fiction. These groups can be either separate or overlapping:  If separate, no one text can belong to two different categories  If overlapping, a text can belong to one category or several categories  The choice of category and if the grouping is overlapping or not, is chosen at the time of data labeling. Amazon Comprehend limits the maximum number of categories to 100.  Entity extraction finds, classifies, and extracts entities in a text that belong to a defined category. Utilizing Amazon Comprehend the user can create custom categories to extract, such as any person mentioned in the text who is identified as a data scientist. Another example of this would be to identify a device that is mentioned in a text, and Amazon Comprehend would extract the devices mentioned and tag it. This powerful tool can find important pieces of information contained in a text without requiring a human to read every line of the text, reducing the time a human must sift through documents and ultimately saving a business precious man hours.  Overall, custom models take a bit more work than the out-of-the-box implementations, and a decent amount of data is required. A key requirement to develop a custom model is labeled data, that is, text entries with the specific pieces of information that are to be extracted identified in each entry. This labeled data is what is used to train the custom model and identify the desired outputs for the unlabeled data. The volume of labeled data depends on a few things, the task at hand, the accuracy required, and the subtly of the differences of the text.  Once the labeled data is obtained, the process of training Amazon Comprehend is straightforward. The properly formatted data needs to upload to AWS’s S3 and a new Amazon Comprehend customized model can be created. After the training is complete, metrics will be provided to assess the performance of this custom model. To run this custom model on new unseen data, either an endpoint can be created that is accessed through the AWS console or API, or a batch operation can be created.  In the realm of machine learning and AI, natural language processing is at the forefront of innovation. These techniques provide computers a new level of understanding language. Analyzing the immense amounts of data produced and implementing tangible business solutions can feel like an overwhelming task. Our Baker Tilly Digital professionals are knowledgeable and prepared to assist your business through an evaluation of your current NLP processes, an ideation session or helping you determine if Amazon Comprehend applications are a good fit for your organization’s goals and objectives.  

Leveraging natural language processing and Amazon Comprehend in your business operations can help your organization grow in the current digital landscape.  

Utilizing Amazon Comprehend for Natural Language Processing applications

Authored by Mary Willcock Natural Language Processing is as broad a topic as words themselves, and as such, there are far too many use cases to cover in one article. This article is the first part in a series discussing the value of Natural Language Processing. Read part two here.  The importance of language is visible throughout every conversation, electronic communication, or written word you encounter in your day. Text analysis has long offered the ability to translate text into meaningful insights in easily formatted reports, such as word clouds and key word frequency. However, this kind of text analysis has no fundamental knowledge of language; it addresses text as if letters and words are numerically interchangeable. Given our digital environment, understanding the context of words is vitally important for businesses to operate on a daily basis. Leveraging natural language processing into business operations can help your organization grow in the current digital landscape.   What is Natural Language Processing?
 
 NLP is a process for computers to learn language and perform in depth analysis of text along with generating new text. This field of artificial intelligence (AI) gives computers the ability to read and understand language, providing the opportunity for large-scale analysis, real time automation, and a variety of industry specific applications.  
Why is NLP a big deal? NLP begins to understand language and extract meaning. While humans are still better than computers at language, computers are far better at sorting through large volumes of data with speed and consistency.  Language is nuanced and complex. The same word can take on completely different meanings depending on the context. The complexity increases even more if the word does not translate well between languages. At a fundamental level, computers work only with numbers and are designed to be logical with limited contextual aspect, making it difficult for computers to learn language. Sometimes text can be converted to numbers to identify similar groupings of words, other times text has been painstakingly coded out to account for each unique use case. However, the last 2-3 years have produced incredible, fast moving improvements in the realm of computers understanding and processing language. Advancements in access to computational power have unlocked the ability to teach language to computers to the extent that some claim these models portray common sense[1].  Most notable in recent tools is Google’s open source release in November 2018 of the model Bidirectional Encoder Representation from Transformers (BERT)[2]. BERT is a neural network-based NLP tool that  Google has built to understand phrases in its search engine. BERT finds meaning in language and contextually understands when different phrases are the same even if completely different words are used.  Early models attempting to address language could not understand the difference between the word “bank” in the phrases “Deposit the check at the bank” and “On the bank of the river”. Even more difficult are words like “to” and “for”, where the meaning of the word in a sentence can change drastically. Advancements in the availability of computational power in combination with novel techniques as found in BERT have recently provided models where the meaning of text can be understood.  How do businesses use NLP? Every business needs to understand how its brand and products are perceived. Between direct customer reviews and indirect social media mentions, there is a lot of information out there. If the business is trying to increase brand awareness, assessing social media mentions of the business over time can provide insight into changes in brand awareness as well as the sentiment surrounding the brand. These techniques can be similarly applied to understand the broader market and the way the brand and products are perceived – allowing the business to increase strengths and reduce weaknesses based on this natural language processing informed market research. Understanding trends in news articles provides key insights to the business.    The most recent advancements in NLP provide summaries of entire articles that are so clear and accurate that the computer generated summaries cannot be distinguished from human generated summaries[3]. News articles provide valuable insights to market research, brand awareness, and competition analysis, and these insights can be gathered at scale with natural language processing techniques. Analysis of news articles requires a different technical approach to social media analysis and provides robust findings. Automation of this analysis ensures a high volume of data can be analyzed, successfully setting the stage for data driven strategic decisions. Excellent customer service has always differentiated good businesses from great ones. Prior to implementing chat bots, customer service came at the costly price of human interaction. While there is always a time and place for the human touch, chatbots provide the opportunity to automate customer support at low cost. While we have all experienced frustration from a phoneline chatbot, their value on websites and in virtual marketplaces is prevalent when examining customer service. Utilizing chatbots to support the sales process has delivered significant business value, increasing total sales and orders while maximizing ROI. Reaching new customers while improving retention, chatbots provide an improved customer experience where the results speak for themselves.  Natural language processing is at the forefront of new and exciting techniques in the realm of machine learning and AI. These techniques unlock a new level of understanding language through computers, allowing for a robust analysis on: the meaning of the language, enhancing a business’s customer service offerings and leveraging news articles and social media for business insights around product, brand, and market perception. Sifting through the vast amounts of data produced and implementing business solutions based on these insights can feel very daunting. Our Baker Tilly Digital professionals are knowledgeable and prepared to assist your business through an evaluation of your current NLP processes, an ideation session or helping you implement natural language processing into your organization.  References https://arxiv.org/abs/2008.03945 https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html

The importance of language is visible throughout every conversation, electronic communication, or written word you encounter in your day. Text analysis has long offered the ability to translate text into meaningful insights in easily formatted reports, such as word clouds and key word frequency.

Natural Language Processing for your business 

Embarking on a digital journey can feel like a daunting endeavor with many opportunities for innovation and a multitude of solutions to consider. Successful transformation requires forward thinking in both market trajectory and advanced technology; igniting the power to reimagine the status quo and elicit positive change in not only your business outcomes, but also the people you serve.  Baker Tilly Digital is excited to announce our collaboration with Amazon Web Services (AWS). Leveraging AWS cloud services, Baker Tilly will enhance the value we offer through digital solutions and services, elevating our ability to advise clients across the entirety of their business.  So why AWS? “From the beginning of our journey with AWS, we [Baker Tilly] were impressed with their commitment to continuous innovation with new product releases, their dedication in seeking to understand and support customer needs, and the quality and cost-effectiveness of their products and services”. Baker Tilly Digital and AWS are working together to enhance the strategic approach and technical solutions that support customers through their digital transformation journey by providing a complete cloud services and management portfolio that will give enterprise customers fast, flexible access to the cloud. 
About Baker Tilly Digital  The Baker Tilly Digital practice combines strategic market knowledge and advanced technical expertise that enable us to work with your organization across multiple digital needs. Our practice both uncovers and addresses the digital challenges your business is facing, provides informed perspectives on market trajectory based on experience, and works with you to determine the future vision for business optimization and customer value. Our service offerings and solutions encompass five pillars that are critical for any digital strategy.  Baker Tilly Digital aligns our service offerings around five key pillars that allow us to help organizations foster their digital transformation and growth. The five pillars are:  Data focuses on capturing the right data, at the right level, while enabling the distribution of insights in a secure and visually appealing way. Baker Tilly Digital works with companies to develop the best approach to leverage and optimize their data, while providing the analytical aptitude to derive valuable insights for growth.  The Cloud services consists of the Baker Tilly Digital team assessing everything, from the opportunity for migration, to developing cloud-based solutions, to full scale cloud transformation. Baker Tilly Digital assists companies through their implementation, migration and integration of current and future digital assets.  Build focuses on application design, development, and modernization. Baker Tilly Digital helps companies to envision current and future applications that better align with business objectives and sees them through to production.  Assess, while multifaceted, provides an audit or assessment of the current digital landscape, with the ability to hone in on areas related to architecture, application performance, governance and digital maturity. Baker Tilly Digital learns a company’s biggest challenges and business goals to better cater our approach to deep dive into the areas that provide our clients the greatest value.  Transform, often follows assessments of the current digital environment, works with the end in mind by understanding a company’s desired future vision. Baker Tilly Digital helps to define the optimal path forward considering technology, product strategy, governance and operational design. Combining Baker Tilly Digital’s strategic lens in assessing and developing digital capabilities aligned to business goals, with AWS and knowledge within the digital world, together, we provide a robust suite of service offerings that can assist any organization with digital transformation. If you would like to learn more about Baker Tilly Digital or our AWS collaboration, please contact us.

Baker Tilly Digital is excited to announce our collaboration with Amazon Web Services (AWS). Leveraging AWS cloud services, Baker Tilly will enhance the value we offer through digital solutions and services, elevating our ability to advise clients across the entirety of their business.

Preparing for PPP: CPA Business Funding Portal

Baker Tilly Digital announces strategic collaboration with Amazon Web Services (AWS) to accelerate digital transformation 

Authored by Nathan Olson Data governance is as broad a topic as data itself, and as such, there are far too many of these ‘how’s’ to cover in one article. This article is the second part in a series to go over many of the desk level procedures that make up data governance. Read part one here.  For organizations striving to make data-informed decisions, data quality is key. When the accuracy or completeness of data is poor, leaders quickly lose trust and lack confidence to make informed business decisions. An organization’s data assets can only provide value if they are being utilized properly.  The topic of data governance continues to be inaccessible and difficult to define, despite how much airtime it gets in the industry. Given the wide breadth of domains that data governance covers, breaking down data governance into digestible sections is helpful; this article does that by being part two in a three part series. The first article in the series detailed how documenting data assets can provide concrete benefits for a data program. Part two of this series will continue that trend of outlining the ‘how’ of data governance by focusing on how to implement data quality initiatives in a governance program and explore tangible steps an organization can take to increase accuracy, efficiency, and trust in their data assets. Upcycle data assets Data warehouses are systems that combine data together from multiple different sources within an organization. The data from these warehouses are utilized in analytics and reporting purposes for business leaders to make more informed business decisions. Data warehouses can be very fluid, as data is constantly being added, transformed and merged to meet business needs.    Similar to Bob Ross’s painting lessons, “happy little data quality issues” are part of life. There is often a desire to scrap data assets that have data quality issues and start over. If an organization does not have data governance practices in place to address data discrepancies, any new data asset is going to end up with the same issues as the original. To avoid this, set in motion a regular cadence for information management to clean-up their data assets. This “spring cleaning” can include:  Disposing data and objects no longer in use Adding code comments or documentation Updating naming conventions Validating data processing jobs and report Although these activities should be part of normal development, having dedicated time quarterly or annually can make sure issues are addressed. Road mapping data through code  Another solution to reengineering current data models is to implement code maps. Code maps align industry-wide data elements coming from disparate systems into a format that allows data patterns and relationships to become easily identifiable. For example, if a billing system stores US states according to their two-letter abbreviation, and an enterprise resource planning tool (ERP) uses its own ID numbers for each state, combining these sources can represent a data quality risk. Information management can create a code map in the data platform which takes an industry standardized list (start with the International Organization for Standardization (ISO)) and maps each of the different source systems lists to the standard. This scales well as systems are added and removed to a data platform. Testing data integrity and business rules  There are varieties of data accuracy tests that can be developed. The reports these tests produce can identify: Duplicate records where all records are expected to be unique Data elements that do not conform to an expected datatype Data processing jobs that generated warnings or errors Datasets which grew or decreased by over a certain normal threshold All data integrity tests increase transparency into the overall data quality and use automation to surface insights efficiently. Data integrity and business rules are the two primary types of quality tests that can be built. Data integrity tests validate data is complete, up-to-date and consistent with the original data source. For example, there may be a test to verify that a transaction amount in the reporting database is the same value for that transaction in the operational data source. In contrast, business rules verify that data behaves logically relative to the business. Using the same example before, a business rule test may check that no transactions of a certain charge type have negative values. Among the many different data integrity tests that can be built, the most critical is a data consistency test. In this approach, a data report or analysis would compare two or more data sources that should contain the same data. For example, an organization may generate a data query or extract from its ERP on revenue by department for the past calendar year and generate the same dataset from its reporting database. An assigned team or individual should then compare these reports and follow up on any discrepancies immediately.   Setting up business rules in a data platform will build trust with its users. It also offloads the burden of business users manually cleansing the data when they consume it. The information management team will be responsible for implementing these rules, but defining the rules needs to be done in close collaboration with the business.  Data stewards who are close to the business need to communicate statements about the data that should always be true and never be true. These rules can then be built into data processing jobs to fix or eliminate records that do not match. Organizations must be very thoughtful about any scripts that modify or delete data. In most cases, this data manipulation should be done to data which has already been staged in a data warehouse, so that the raw source-of-truth is available as a reference. Additionally, data stewards should think critically if the data warehouse is the right place to fix this data, or if processes or technology upstream of the warehouse should be adjusted so that inaccurate data is never brought in to begin with. Create a culture of quality Business users and information management should understand two fundamental truths about data quality:  It is rare an organization will have zero data quality issues within their data platform  Everyone within an organization will need to collaborate and be transparent to keep the number of data quality issues close to zero Senior leadership and the data governance team should work closely with information management to define metrics to measure the quality of data assets and policies to address how issues are resolved. For example, a metric may be set for a certain data Extract-Transform-Load (ETL) process to succeed without errors 95% of the time; with any processing failures that do occur needing to be resolved by Noon on that day. To support this, information management should build a mechanism to alert stakeholders when failures occur and follow-up on the resolution. These metrics could then be reviewed periodically by the data governance committee and create an open forum for improvements. Another helpful process is establishing a regular auditing process for an organization’s data assets. This should not be a reactive response to a data quality issue, but instead a pre-established process that occurs quarterly or annually. Data stewards or information management can review reports, data assets and other analyses for inaccuracies or out-of-date data. The findings should be reported to the data governance committee with timelines and plans to resolve any discovered issues. Quality Time Data quality is not an all-or-nothing initiative, but an on-going effort through a series of tools and best practices that can improve the efficacy of your data platform. The results of these data governance practices can improve the accuracy and integrity of data assets and generate returns through automation and issue prevention. Even more impactful, these practices can increase the transparency around data quality and increase stakeholders’ trust in the data. As trust in data increases, a more effective and consistent use of data assets and a more data-driven business emerges.  Get going on the 'how' If your data governance initiative does not establish data quality processes to better utilize your data assets, and set up integrity and business rules to create a culture of quality, it is going to fail. Empower your staff to collaborate on the ‘how’ of data governance to make better-informed business decisions. Baker Tilly is here to help you along your data journey, contact us to get going on your 'how.'


For organizations striving to make data-informed decisions, data quality is key. When the accuracy or completeness of data is poor, leaders quickly lose trust and lack confidence to make informed business decisions. An organization’s data assets can only provide value if they are being utilized properly.  Given the wide breadth of domains that data governance covers, breaking down data governance into digestible sections is helpful; this article does that by being part two in a three part series. 

Consulting

The ‘how’ of data governance – Part two 

Authored by Nathan Olson  Data governance is as broad a topic as data itself, and as such, there are far too many of these ‘how’s’ to cover in one article. This article is the first part in a series to go over many of the desk level procedures that make up data governance. Read part two here.  Having formalized controls around information management will improve data quality, mitigate security risks, better align business and IT, and enable employees to get more out of data analytics. A tremendous amount of effort has been spent justifying the “why” of data governance. As with any large change effort, explaining the “why” is critical for getting buy-in from stakeholders, as well as from the organization at large. The problem is that most books and presentations on the subject stop there. Some resources walk through the process of kicking off a data governance program, including creating a charter, establishing a data governance committee, and appointing data owners and stewards. A full declaration of the data governance mission and vision is only effective if employees know the “how” of data governance. Don’t stop at the 'why' The International Data Corporation has forecasted that “the Global Datasphere will grow from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025” [1].  That is a massive increase. To compete in an environment with this amount of data growth, organizations need to ensure they are effectively tracking where their data assets live and how they relate to each other. Desk level procedures provide a clear understanding of data governance in action on a day-to-day basis. These are tools an organization can choose to implement as part of their data governance program, depending on what it is they’re trying to accomplish. This article will dive into data asset documentation and the practical artifacts to employ to properly manage the ever-expanding data assets while highlighting some of the most impactful tools to implement at your organization: data dictionaries, business glossaries, source to target maps, and data catalogs. Although there is a lot of value to be gained by comprehensive and robust forms of these tools, even basic versions can provide some quick wins on your data governance journey. Go meta It’s possible to generate returns in a relatively short amount of time by creating a data dictionary. A data dictionary is a central metadata (data about data) repository, containing definitions, relationships to other data, data source information, data formats, and recommended use cases. A technical resource like this can be utilized by anyone interacting directly with a database, data lake, or primary data source. Research has shown that “80% of a data scientist’s valuable time is spent simply finding, cleaning, and organizing data, leaving only 20% to actually perform analysis” [2]. Having a metadata repository available can reduce an analyst’s time collecting data and allow more time for analysis. By standardizing data assets in one place, it becomes easily searchable, and provides necessary context and meaning. Most SQL Server platforms have native data dictionary functions built in, which are accessible at no cost to anyone with SQL knowledge. If there’s a relatively small amount of data and an organization is looking to build a dictionary with user-generated fields, such as definitions and use cases, this can be done easily in Excel. If your organization is looking for a more robust solution, which requires less manual maintenance, it is recommended to use a third-party vendor tool, which can interface directly with data assets.  Getting down to business For non-technical users, creating a business glossary is extraordinarily helpful. A business glossary is a central reference of business terms and definitions, which is valuable to an organization because it empowers business users to have an active role in data management as the magnitude and complexity of data platforms increase. By enlisting the help of the business in creating a glossary, long-term ownership is established as data stewards are formally recognized. While there are third-party vendors that offer business glossaries, it is recommend to start with a  simple spreadsheet that tracks the definitions, data domains and owners for each term. The option to transition to a more advanced solution when running into maintenance and scalability issues is always possible. Listed below are tips for getting started on the Business Glossary: Start with a small set of terms (15-20) from a single data domain area, then grow the reference one department at a time Take industry standardized definitions as a starting point, then adjust them to the organizations’ specifications Include common acronyms in the glossary.
-This gives new employees a reference and helps distribute the glossary as a valuable resource Make this accessible to everyone at the organization X marks the spot The single most valuable piece of documentation to empower the information management team is a Source to Target Map (S2T). The S2T is a blueprint for a data platform which stores all of the information and describes how data gets from the source systems into the end-point data solutions. This document includes data lineage, how to join data sources, conversion and business rules, ETL frequency, and important metadata to the transformation. Having a Source to Target Map will help technical resources get the information they need faster and will free up time for the individuals who have been storing and relaying the information to everyone who calls or emails them. A Source to Target Map can take many different forms and will vary based on the data platform. It is recommended to have two levels of documentation:  A diagram, which could be created in a graphics application that shows a high-level view of how the different data sources, ETL tools, and reporting solutions feed into each other. This is helpful to anyone who wants to understand how the data platform works and it will quickly prove its worth.  An excel document, which is a more detailed resource that outlines the transformation path of individual fields and their metadata, business rules, and data types. For any data engineer working on the ETL platform or business analyst looking to improve their understanding of the data, this will be a go-to treasure trove of information. One to rule them all The three tools covered so far are straight-forward, focused purposes that can deliver specific value. A data catalog is a central directory, which can help any business user find data assets and understand how they should be used. Catalogs can offer an impressive amount of functionality to standardize, communicate, and maintain data assets. Many Data Catalog applications allow for advanced searching, metadata tagging, in-app communication and collaboration with other staff, in-app querying and reporting, direct integration with data sources and visualization software, and data profiling. They can also be leveraged to manage the other tools talked about – data dictionaries, business glossaries, and Stage to Target Maps. A catalog is expansive in what it can offer and how it can be implemented and should be the flagship data asset documentation tool for any advanced data governance program.             There are a multitude of vendors which offer Data Catalog tools and software. A recommended approach is to start small and build out as a foundational Data Catalog. Even something as simple as a spreadsheet, which lists various business-facing data sources (reports, data marts, files) with descriptions of how to access and use them can provide a lot of value to business users. Get going on the 'how' If your data governance initiative does not have executive support, a clear framework of roles and a guiding vision, it is likely going to fail. Empowering analysts to leverage the ‘how’ of data governance more efficiently will help business users make better decisions. These will empower your analysts to leverage the data platform more efficiently and help business users make better decisions. Baker Tilly is here to help you along your data journey, contact us to get going on your 'how.'    References https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists


Having formalized controls around information management will improve data quality, mitigate security risks, better align business and IT, and enable employees to get more out of data analytics. A full declaration of the data governance mission and vision is only effective if employees know the “how” of data governance.

The 'how' of data governance

Baker Tilly Digital and Amazon Web Services (AWS) are working together to enhance the strategic approach and technical solutions that support customers through their digital transformation journey by providing a complete cloud services and management portfolio that will give enterprise customers fast, flexible access to the cloud. 

Baker Tilly Digital and AWS are working together to enhance the strategic approach and technical solutions that support customers through their digital transformation journey by providing a complete cloud services and management portfolio that will give enterprise customers fast, flexible access to the cloud. 

Data
Data focuses on capturing the right data, at the right level, while enabling the distribution of insights in a secure and visually appealing way. Baker Tilly Digital works with companies to develop the best approach to leverage and optimize their data, while providing the analytical aptitude to derive valuable insights for growth. 

Cloud
The Cloud services consists of the Baker Tilly Digital team assessing everything, from the opportunity for migration, to developing cloud-based solutions, to full scale cloud transformation. Baker Tilly Digital assists companies through their implementation, migration and integration of current and future digital assets. 

Build
Build focuses on application design, development, and modernization. Baker Tilly Digital helps companies to envision current and future applications that better align with business objectives and sees them through to production. 

Assess
Assess, while multifaceted, provides an audit or assessment of the current digital landscape, with the ability to hone in on areas related to architecture, application performance, governance and digital maturity. Baker Tilly Digital learns a company’s biggest challenges and business goals to better cater our approach to deep dive into the areas that provide our clients the greatest value. 

Transform
Transform, often follows assessments of the current digital environment, works with the end in mind by understanding a company’s desired future vision. Baker Tilly Digital helps to define the optimal path forward considering technology, product strategy, governance and operational design.

Race teams, start your engines ...
To celebrate our global partnership you are invited to join us for the inaugural Baker Tilly Digital and AWS DeepRacer League!

Contact a Baker Tilly Digital specialist today

Baker Tilly Digital and Amazon Web Services (AWS) are working together to enhance the strategic approach and technical solutions that support customers through their digital transformation journey.

Join the fun with industry peers, Baker Tilly leaders and technologists in a virtual race league powered by Amazon Web Services (AWS)

You are invited to join as many events as you have time available. We are offering weekly master classes, technology showcases, and office hours. Below you will find more information on the types of events being offered throughout the program.

Register your team now to compete and race in the Baker Tilly and AWS DeepRacer league.