On September 13, 2023, the D.C. Tax Revision Commission met and evaluated over a dozen tax proposals. Most concerning, the Commission discussed the possibility of implementing a digital advertising tax or a data mining tax.

D.C. Tax Revision Commission

The Council of the District of Columbia established the Commission to comprehensively review the Districts tax code. The Commissions mandate is to make tax policy recommendations on:

By the end of 2023, the Commission is set to submit its slate of recommendations to the Council, along with specific steps for implementing the recommendations, such as draft legislation and regulations.

The Commission previously released reports in 1998[2] and 2014.[3] Each Commissions report made recommendations for changes to the Districts tax system. For example, in 1998, the Commission recommended a 1.5% business activities tax and taxing sales of tangible products to District residents the same regardless of whether they are sold remotely or by District-based businesses. In 2014, the Commission recommended reducing the Districts business franchise tax rate from 9.975% to 8.25%.

D.C. Tax Revision Commission proposals

Throughout 2023, the Commission has met with various tax and fiscal policy experts, as well as community and industry representatives. It has prepared a series of proposals to review and potentially suggest to the Council. On September 13, 2023, the Commission met for the first time to discuss these various proposals. In advance, the Commission released the list of proposals that they would review.

The first proposal on the list was Strengthen and clarify taxation of digital ads and services. The Commission released a proposal paper, elaborating on the topic. The proposal paper specifically listed as options: (1) a digital advertising tax act similar to Marylands enactment; and (2) a tax on the extraction of consumer data by tech platforms in much the same way that states tax the extraction of valuable commodities like fossil fuels or precious metals. The second option would be a per-consumer excise tax that accounts for each user whose data is being mined.

The sordid history of state digital advertising taxes

Maryland became the first and only state in the United States to impose a tax on gross receipts from digital advertising services in 2022. The tax is imposed on gross revenues derived from digital advertising services in Maryland at graduated rates, from a minimum rate of 2.5% to a maximum rate of 10% of such revenues. The Maryland digital advertising generated immediate controversy, with taxpayers challenging the tax in state[4] and federal courts on federal statutory and constitutional grounds.[5] While those challenges have yet to result in a final nonappealable decision on the legality of the Maryland tax, we expect the Maryland Supreme Court to eventually reach the merits and, hopefully, find the tax to be unconstitutional. And while several other states have considered Maryland-style digital advertising tax legislation, those proposals ultimately have been rejected.

With the history of Marylands digital advertising tax in mind, the Commissions proposal paper acknowledged that any potential D.C. tax similar to Marylands tax would likely face similar legal challenges. But the paper noted that it may be able to minimize challenges by, for example, imposing a tax on all advertising (as opposed to just digital advertising). There could then be less risk that the tax would violate the federal Internet Tax Freedom Act. But imposing a broad advertising tax could prove difficult for the Council based on recent experience. In 2020, the Council considered expanding its sales tax base to include sales of advertising services and personal information, as part of its Fiscal Year 2021 Budget Support Act of 2020. Ultimately, the D.C. Council opted not to pursue expanding its sales tax base to these sales due to the adverse impact on local media and press, on top of the likely litigation that would follow if a targeted tax on digital advertising were adopted.[6]

The Commission meeting on September 13th

At the meeting, the Commission discussed the first batch of proposals but without formally voting on them, which will happen in a later meeting. Rather, the meeting was intended to advance discussion in preparation for later formal voting.

The Commissions members were generally interested in pursuing a digital advertising tax or a data mining tax. The members saw these taxes as being worth consideration because of the potentially large amount of revenue they could generate. One member supported the taxes because he saw them as an expansion of the tax base to match changing technology, rather than a tax rate increase.

However, there was hesitancy among the Commissions members because of the ongoing Maryland litigation and potential tax implementation difficulties. The Commissions members noted that the process to pass and implement these taxes could be long, especially as they were interested in whether the Maryland tax would survive legal scrutiny.

The Commission also considered a variety of other proposals. For example, the Commissions members reacted negatively to increasing the general sales tax rate from 6 percent to 7 percent due to regressivity concerns. However, they were supportive of eliminating the motor vehicle excise tax exemption for electric vehicles.

Future meetings and next steps

The Commission currently has scheduled five additional proposal review sessionsSeptember 19th, September 26th, October 10th, October 20th, and October 24th. These sessions will shape which recommendations the Commission will make to the D.C. Council. Of particular interest for the September 19th meeting, the Commission likely will discuss a business activity tax proposal, which may resemble the Texas franchise (margin) tax and the Oregon corporate activity tax. Eversheds Sutherland will continue to follow these review sessions that will color the next decade of District taxation.

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D.C. Tax Revision Commission considers digital advertising tax and ... - Lexology

Long gone are times in business when all the data you needed was in your little black book. In this era of the digital revolution, not even the classical databases are enough.

Handling big data became a critical skill for businesses and, with them, data scientists. Big data is characterized by its volume, velocity, and variety, offering unprecedented insights into patterns and trends.

To handle such data effectively, it requires the usage of specialized tools and techniques.

No, its not simply lots of data.

Big data is most commonly characterized by the three Vs:

All the big data characteristics mentioned impact the tools and techniques we use to handle big data.

When we talk about big data techniques, they are simply methods, algorithms, and approaches we use to process, analyze, and manage big data. On the surface, they are the same as in regular data. However, the big data characteristics we discussed call for different approaches and tools.

Here are some prominent tools and techniques used in the big data domain.

What is it?: Data processing refers to operations and activities that transform raw data into meaningful information. It tasks from cleaning and structuring data to running complex algorithms and analytics.

Big data is sometimes batch processed, but more prevalent is data streaming.

Key Characteristics:

Big Data Tools Used: Apache Hadoop MapReduce, Apache Spark, Apache Tez, Apache Kafka, Apache Storm, Apache Flink, Amazon Kinesis, IBM Streams, Google Cloud Dataflow

Tools Overview:

What is it?: ETL is Extracting data from various sources, Transforming it into a structured and usable format, and Loading it into a data storage system for analysis or other purposes.

Big data characteristics mean that the ETL process needs to handle more data from more sources. Data is usually semi-structured or unstructured, which is transformed and stored differently than structured data.

ETL in big data also usually needs to process data in real time.

Key Characteristics:

Big Data Tools Used: Apache NiFi, Apache Sqoop, Apache Flume, Talend

Tools Overview:

Data provenance tracking

Extensible architecture with processors

Supports data provenance

Extensible with a wide range of processors

Parallel import/export

Compression and direct import features

Parallel import/export

Incremental data transfer capabilities

Reliable and durable data delivery

Native integration with Hadoop ecosystem

Fault-tolerant architecture

Extensible with custom sources, channels, and sinks.

Broad connectivity to databases, apps, and more

Data quality and profiling tools

Graphical interface for designing data integration processes

Supports data quality and master data management

What is it?: Big data storage must store vast amounts of data generated at high velocities and in various formats.

The three most distinct ways to store big data are NoSQL databases, data lakes, and data warehouses.

NoSQL databases are designed for handling large volumes of structured and unstructured data without a fixed schema (NoSQL - Not Only SQL). This makes them adaptable to the evolving data structure.

Unlike traditional, vertically scalable databases, NoSQL databases are horizontally scalable, meaning they can distribute data across multiple servers. Scaling becomes easier by adding more machines to the system. They are fault-tolerant, have low latency (appreciated in applications requiring real-time data access), and are cost-efficient at scale.

Data lakes are storage repositories that store vast amounts of raw data in their native format. This simplifies data access and analytics, as all data is located in one place.

Data lakes are scalable and cost-efficient. They provide flexibility (data is ingested in its raw form, and the structure is defined when reading the data for analysis), support batch and real-time data processing, and can be integrated with data quality tools, leading to more advanced analytics and richer insights.

A data warehouse is a centralized repository optimized for analytical processing that stores data from multiple sources, transforming it into a format suitable for analysis and reporting.

It is designed to store vast amounts of data, integrate it from various sources, and allow for historical analysis since data is stored with a time dimension.

Key Characteristics:

Big Data Tools Used: MongoDB (document-based), Cassandra (column-based), Apache HBase (column-based), Neo4j (graph-based), Redis (key-value store), Amazon S3, Azure Data Lake, Hadoop Distributed File System (HDFS), Google Big Lake, Amazon Redshift, BigQuery

Tools Overview:

What is it?: Its discovering patterns, correlations, anomalies, and statistical relationships in large datasets. It involves disciplines like machine learning, statistics, and using database systems to extract insights from data.

The amount of data mined is vast, and the sheer volume can reveal patterns that might not be apparent in smaller datasets. Big data usually comes from various sources and is often semi-structured or unstructured. This requires more sophisticated preprocessing and integration techniques. Unlike regular data, big data is usually processed in real time.

Tools used for big data mining have to handle all this. To do that, they apply distributed computing, i.e., data processing is spread across multiple computers.

Some algorithms might not be suitable for big data mining, as it requires scalable parallel processing algorithms, e.g., SVM, SGD, or Gradient Boosting.

Big data mining has also adopted Exploratory Data Analysis (EDA) techniques. EDA analyzes datasets to summarize their main characteristics, often using statistical graphics, plots, and information tables. Because of that, well talk about big data mining and EDA tools together.

Key Characteristics:

Big Data Tools Used: Weka, KNIME, RapidMiner, Apache Hive, Apache Pig, Apache Drill, Presto

Tools Overview:

What is it?: Its a graphical representation of information and data extracted from vast datasets. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to understand patterns, outliers, and trends in the data.

Again, the characteristics of big data data, such as size and complexity, make it different from regular data visualization.

Key Characteristics:

Big Data Tools Used: Tableau, PowerBI, D3.js, Kibana

Tools Overview:

Big data is so similar to regular data but also completely different. They share the techniques for handling data. But due to big data characteristics, these techniques are the same only by their name. Otherwise, they require completely different approaches and tools.

If you want to get into big data, youll have to use various big data tools. Our overview of these tools should be a good starting point for you.Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.

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Working with Big Data: Tools and Techniques - KDnuggets

The Art Deco facade of the original Toronto Stock Exchange building is seen on Bay Street in Toronto, Ontario, Canada January 23, 2019. REUTERS/Chris Helgren/File Photo Acquire Licensing Rights

Sept 11 (Reuters) - Canada's main stock index rose on Monday, snapping a four-day losing streak, as technology and mining shares benefited from a pickup in investor sentiment ahead of U.S. inflation data this week.

The Toronto Stock Exchange's S&P/TSX composite index (.GSPTSE) ended up 108.11 points, or 0.5%, at 20,182.76.

"It looks like a risk-on day when you see tech rallying, you see some of the base metals moving and the Canadian dollar is rallying as well," said Philip Petursson, chief investment strategist at IG Wealth Management.

The Canadian dollar extended its recovery from a five-month low as an increase in risk appetite weighed on the safe-haven U.S. dollar (.DXY).

Wall Street also notched gains as investors awaited U.S. inflation data on Wednesday.

"The inflation data later this week out of the United States will probably bring a bit of a dampener on the rally," Petursson said. "I expect inflation to move higher on a year-over-year basis and that might just reignite thoughts that the Fed isn't done (raising interest rates)."

Money markets expect the Federal Reserve to leave its policy rate on hold at a policy decision later this month but the following meeting in November is seen as a closer call.

The Toronto market's technology sector rallied 1.6%, helped by a gain of 14% for BlackBerry Ltd (BB.TO), recouping much of last week's sharp decline.

Heavily weighted financials also advanced, rising 1%, and the materials group, which includes precious and base metals miners and fertilizer companies, added 1.2% as copper prices jumped.

Energy was a laggard, falling 1.9%, as the recent rally in oil lost some momentum. Oil settled down 0.3% at $87.29 a barrel.

Reporting by Fergal Smith in Toronto and Khushi Singh; Editing by Tasim Zahid and Sandra Maler

Our Standards: The Thomson Reuters Trust Principles.

The rest is here:

TSX snaps losing streak, led by tech and mining shares - Reuters

By Davis Bell.

I love talking to people who work in different industries than the one in which I work tech because they see the world differently, which enables me to learn new things from them that I can apply to my work.

One example of this from the accounting profession: Matthew May, one of the co-founders of Acuity, a national accounting firm, recently stated during a panel discussion at an accounting conference that his firm had learned a lot of the processes for lead generation and prospect data mining from a software company located near his firm. Matthews firm borrowed the tactics of this software company to successfully grow its database of leads

I thought it might be useful for folks in the accounting industry to hear a few lessons from the tech world that can be applied to accounting:

How to attract and retain top talent in a labor constrained market:

The accounting industry is facing a significant shortfall of qualified workers. This enables employees to be choosy about where they work, which means in turn theyre after more than just competitive pay and benefits. For managers who began their careers a decade or more ago, this requires changing their paradigm of what employees want. This is a challenge we in the tech industry have faced for quite some time, especially for technical roles like engineering and product management. Some tech companies thought the answer was simply perks Massages! Ping pong! Bring your dog to work! But in my experience, what workers actually want are career development, opportunities to learn and upskill, talented co-workers, hard problems to solve, and a culture of openness and collaboration. Time spent talking to employees and recruits to figure out what matters to them and how you can deliver those things is time well spent.

Quantitative data is key to success.

Clear and real-time analytics are essential to the health of a tech company, accounting firms, and really any business. Without the right numbers, its impossible to see exactly what is happening from an operations and workflow standpoint in any business and that is no exception in your firm or in a tech company.

Two types of metrics to consider from the technology space are the development of a common set of OKRs (objectives and key results) plus key performance indicators (KPIs) which should be used to drive the strategic vision of the business and/or accounting firm.

One of the reasons that quantitative data is so vital is that it removes the ambiguity of the actions necessary. For example, you may think theres a problem based on qualitative data (client issues, staff complaints, and workflow misfires) but you also need to back up assumptions with actual objective metrics as well. In addition, having numerical objectives and key results will make you much more focused. The same goes for KPIs.

If you need some guidance on which KPIs to track, consider the following: client profitability, client retention, new client growth rate, job profitability, employee productivity, and engagement or service profitability.

Focus on the customers you want.

When developing apps and technology solutions, the most successful companies focus on the ideal client profile (ICP). Its important to drill down on the very specific characteristics of your ICP and to cater towards them, rather than trying to be all things to all people and get distracted by people who arent your ICP.

By understanding and aligning your product and services with your ICP, you can allow your team to truly create exemplary client experiences and uncover new ways to make your firm better. Regularly asking for feedback and being open to the good, the bad, and the ugly is another best tech company practice you can use.

Simplify and streamline your tech stack.

From my perspective as the leader of a technology company, this may seem counterintuitive, but when it comes to tech I do believe less is more. I liken appropriate technology usage in a business as a road with a cliff on both sides.

On one side of the road is a cliff formed in response to highly manual processes that do not leverage the powerful technology now available to accounting firms and other businesses. On the other side of the road the cliff is formed when a company has too many solutions. Firms on this side of the road are spending a ton of time and money on technology for the sake of it without getting the full return on investment out of it. If you go too far in one direction or the other it becomes a disaster.

The important takeaway here is to practice proactive technology hygiene where you can evaluate what is working and what is not, then take action to make changes as you see fit and match your tech stack to the objectives you are trying to meet.

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Following these four lessons from tech companies when it comes to your firm is a positive step toward building an optimum practice playbook. Just like a tech company, accounting firms struggle with staffing, streamlining tech, establishing OKRs and tracking formal KPIs, all while trying to deliver a seamless and secure client experience. Its a tall order, but the more we can learn from each other and leverage different business strategies the stronger well all be in the future.

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Davis Bell is the CEO of, Canopy, a leading cloud-based practice management platform for tax professionals. He has held leadership roles in strategy, operations, and finance at a succession of SaaS companies. Hes passionate about building innovative software that delights users.

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Lessons From the Tech World that Apply to Accounting Firms - CPAPracticeAdvisor.com

Purism introduced the new Librem 11 tablet running secure PureBoot and Linux kernel-based PureOS. Librem 11 is made for individuals, organizations, government agencies, law enforcement agencies, and businesses that need security and privacy with powerful portability.

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Librem 11 offers powerful features that include:

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Librem 11 tablet sets new standard for privacy and security with ... - Help Net Security