Author: Joe Sticca

The NCAA Division 1 Men’s Basketball Tournament is unlike any other event in sports. Widely known as March Madness, the 68 team single-elimination tournament has become one of the most famous annual sporting events in the United States. The unpredictability and excitement of each game draws millions of viewers each year. Experts and fans are amazed by the improbable victories of smaller schools over traditional powerhouses (2018 marked the first year a number 16 seed defeated a number 1 seed). However, advances in modern computing have uncovered new patterns and insights to make informed predictions on games.

In Comes Google!
At the end 2017, The NCAA announced Google Cloud as its official cloud provider. Additionally, the NCAA agreed to migrate 80 years’ worth of competition data across to the Google Cloud. This partnership recognized Google Cloud as the official cloud sponsor of the NCAA Tournament and provides analytics and machine learning tools for interested fans and bracket makers. These platforms are highly efficient and will analyze NCAA competition data that will allow fans to search, compare, and analyze team and player statistics.

Practical Value
Traditional basketball statistics (Three-point percentage, free-throw percentage, point differential, etc.) are normally used to determine outcomes and the strengths of individual teams. However, these statistics fail to incorporate historical trends that could be crucial to win your bracket pool. Google Cloud’s data integration uncovered new insights that go beyond the boxscore such as:
• Teams wearing blue have the most Final Four appearances
• PAC-12 teams are best at winning tight games.
• Teams with cat mascots have caused the most upsets.

Machine Learning
The first round of the NCAA tournament is very unpredictable as only 164 of the 18.8 million brackets on ESPN.com were perfect after round one in 2017. A competition hosted by the NCAA and Google Cloud challenged participants to build and train machine learning models to forecast the outcome of games. While still new to the market, machine learning models have predicted these games with relative success. Data scientist’s at MarketWatch used statistics from all the first round teams from 2001-2017 to develop a machine learning algorithm. When they tested the algorithms with the 2017 first round data, it had a 75% success rate.

Summary
While machine learning and data analytics are not immune to error, the ability to analyze and identify patterns in large datasets provides a unique opportunity for businesses and individuals to uncover new and valuable insights.

Synaptik, True Interaction’s Data Management and Machine Learning platform makes it easy to connect with, or build classical statistical models, custom machine learning models, algorithms, and even neural nets to boost your workflow with automated analytics. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

Data is central to your business. So how can you ensure that your small or medium sized business is maintaining its data in an accurate and consistent manner? Doing so is called data integrity. Data integrity, a central part of information security, involves storing and managing data in a precise and dependable way. Maintaining data integrity is common practice in large and enterprise organizations. But what can your small or medium sized business do to ensure data integrity despite limited access to time and resources?

1.) Establish data entry protocols
Maintaining data integrity begins at data entry. Creating data entry protocols aligned with business processes can serve as the first line of protection from unclean, inaccurate data. But protocols are just the beginning. Ensuring the on boarding and continuous development of staff will need to be a constant priority. In addition, protocols will require updates when systems or demands from systems change.

2.) Set rules for data validation
Errors are bound to occur and compile despite the most robust of data entry protocols. However, even the most minor errors can threaten data integrity. In response, database administrators should use validation rules to control data entered into company databases. For example, administrators can restrict permissions of certain individuals from altering entered data. This provides an added layer of quality assurance and security when maintaining data integrity.

3.) Automate data cleaning
Maintaining clean data is essential to ensure data integrity. As mentioned, processes to keep data clean may involve establishing rules for data input and scheduling data health checkups. However, these measures are not impervious to generating unclean data. One option is to select and set up a tool that automates data cleaning processes. Doing so reduces time and energy spent by team members ensuring data integrity.

In short, the data integrity of your small or medium-sized business would substantially increase following a systematic approach to data entry, validation, and cleaning. Fortunately, there are a number of platforms available that can help automate some or all of these processes for your business for a reasonable fee.

This article is about maintaining data integrity. True Interaction built SYNAPTIK, our Business Process Automation Platform, specifically to make manage core business processes like data cleaning to enhance the data integrity of digital systems. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

Selecting an optimal best-of-breed system represents one of the most critical decisions for any company looking to purchase new software. A best-of-breed system is one that best meets the need of a specific business function. This might be Salesforce (Customer Relationship Management), Quickbooks (Accounting) and ADP (human resources).

Choosing a best-of-breed software solution can have a number of advantages:

1. Getting the best product for the job
Best-of-breed systems will address business needs in the most complete way. For instance, Salesforce provides solutions for most of the business challenges faced by salespeople. When it falls short, applications that sync with Salesforce can further tailor to any needed experience.

2. Working with specialized vendors
Each vendor will likely understand your business pain points better. Quickbooks, for instance, provides financial management solutions to tens of millions of small businesses. Their support teams are likely well equipped to answer questions regarding specific business challenges because they have seen them before.

3. Receiving continuous updates
Most best-of-breed systems need to continuously grow to meet customer demands. That’s how they maintain their best-of-breed status! Current updates to improve software will likely lead to better experiences for customers.

However, best-of-breed systems also have a number of disadvantages:

1. Increasing complexity
Using Salesforce, Quickbooks and ADP for your business? Get ready to manage the complexity of multiple systems and multiple vendors. Doing so can require a tremendous amount of time and energy from your team.

2. Training
Managing complexity is one thing; learning how to get the most out of all these systems is another. Many best-of-breed systems require users to upskill and keep up their understanding of individual systems. Training becomes more complex if you work in an industry with high turnover.

3. Data Integrity
This drawback is critical. With multiple systems, you must manage multiple sources of data. How is this data stored? Can it be organized in a way that produces actionable insights for a given team? Answering these questions are critical before selecting new best-of-breed software solutions.

One potential method of improving the data integrity from multiple best-of-breed systems is to integrate all the data in one safe, well-organized location such as a data management platform.
True Interaction built SYNAPTIK, our Business Process Automation Platform, specifically to make manage core business processes like data cleaning to enhance the data integrity of digital systems. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

Private equity firms and hedge funds should be thriving in the age of information. With thousands of alternative data options, these companies can select which ones are most valuable for their business. However, many private equity firms and hedge funds do not rely on quantitative methods for assessing investment viability. According to the Financial Times, only 62% of hedge funds are investing in machine learning while only 54% are investing in big data initiatives. Should the almost 40% of hedge funds seek potential use cases for either, below are some examples:

Tracking industries
Tracking specific industries represents a major task for people within hedge funds and private equity firms. Hedge funds may have one person or a desk in charge of monitoring and forecasting specific items within an industry. Similarly, private equity funds may specialize in purchasing companies in specific industries such as retail or food service.

Tracking companies pre-purchase
The critical work of private equity funds is researching, bidding on and purchasing companies for their portfolio. To a lesser extent, hedge fund analysts want to monitor the success of certain companies prior to investment or divestment

Track sales
Tracking company sales can signal the health of specific companies. Hedge fund analysts will want to know the sales numbers and projections of the products under their watch. These numbers will likely include cyclical time series data. Perhaps more importantly, private equity firms who have already purchased companies need to monitor how their adjustments (if any) have affected total revenue.

In summary, digitization use cases abound for hedge fund and private equity companies. These use cases include:
1.) Data ingest for millions of data points on companies and industries
2.) Analytics dashboards with visualization capabilities
3.) Data science analysis of industry and/or company data
4.) Low-code apps with user-friendly graphic user interfaces (GUIs) and backends storing proprietary algorithms

Synaptik, our data management and machine learning platform, is currently providing data ingest and management services to a billion-dollar hedge fund. Learn more about our process by contacting us at www.synaptik.co

Individuals who follow the energy industry are aware of the decreasing market share of peakers. All plays on words aside, peakers are natural gas-fired power plants that provide energy grids with extra surges of electricity during peak usage hours. Today, lithium ion batteries are emerging as a growing source of competition in this 1.1-billion-dollar industry. These batteries can ease the usage of natural-gas power plants by storing energy during the day (i.e. from solar panels) for usage at another time.

However, lithium ion battery facilities are still up to 35% more expensive to manage and operate that natural gas peakers. Added costs involve battery construction, maintenance, and disposal. Technological advances will likely make a dent in these added costs over the next decade. Yet there are ways for lithium ion battery providers to begin reducing costs today in order to make them more price-competitive with peaker plants. These use cases involve:

1.) Predicting spikes and modeling unpredictability
Today, the power grid relies on lithium ion batteries primarily to provide short bursts of electricity. These short bursts stabilize the voltage and frequency of the grid, and last only several seconds at a time. In other words, batteries focus more on short-term duties in the current electrical grid. Lithium ion battery companies can better control their costs regarding usage and design if they are able to better predict spikes and uncertainty in order to deliver the grid the power it needs at specific times.

2.) Predicting lengths of peak.
Lithium Ion batteries will need to provide more than short bursts of electricity in the future in order for them to encroach further on peaker market share. In time, analysts will need to understand how lithium ion batteries can provide more sustained levels of energy over several hours. This will allow lithium ion batteries to serve as more of a replacement to peakers, and less of a stop gap measure.

3.) Modeling battery lifecycle
As stated earlier, lithium ion batteries are up to 35% more expensive than natural-gas peakers for use by energy grids. Batteries may become even more expensive if the federal government repeals its 30% investment tax credit for these facilities. The ability for battery facilities to monitor real-time battery expenditure to the second and store these millions of data points for the use case of optimization analysis could lead to the engineering of batteries better equipped to meet the needs of the energy grid at a reduced cost.

Synaptik, True Interaction’s data management and machine learning platform, is currently exploring ways to better serve the energy industry. To learn more, please schedule a conversation at www.synaptik.co

Lawyers and law firms must rely on the clearest information possible in order to best serve their clients. Federated searches represent one way that firms can become more adept at meeting this goal. These searches allow users to analyze hundreds and thousands of data points about individuals and companies by pulling information from select database APIs (i.e. The Department of Justice, the Securities and Exchange Commission (SEC)) within seconds. However, not all lawyers and law firms would require such powerful tools. Instead, they should consider employing a federated search product if:

1.) If A Quick Google Search Is Not Comprehensive Enough
Google searches are a powerful tool. In many cases, Google search is enough to provide lawyers with information about individuals that are not central to a case. Google searches are likely used to investigate all individuals of interest, but these searches may not be all that is needed to understand the situational context of the case.

2.) If a Private Investigator (P.I.) Is Not Needed
Private investigators generate a significant amount of business from lawyers and law firms. They represent the ultimate background check. Private investigators scour through personal records and documents for the person of interest. In addition, they will often trail persons of interest for several days and observe their behavior. For most cases, a P.I. will only be asked to research one or two persons of interest. This is because hiring a P.I. is expensive for firms, and only necessary in specific situations.

3.) If A Firm or Lawyer Can Afford It
Federated Search capabilities come at a price tag. A federated search can cost several thousand dollars a month for creation and maintenance depending on the number of agents. Large and medium sized firms (> 35 lawyers) have enough staff and generate enough revenue to consider whether this type of service fits their needs. However, smaller firms and individual shops would probably not have enough revenue or need to justify the cost of federated search capabilities.

So what is the optimal use case for lawyers and law firms utilizing federated search capabilities? In short, when medium-to-large law firms need information on individuals greater than the specificity provided by a Google search but not as comprehensive as a private investigator.

Synaptik, True Interaction’s Data Management and Machine Learning Platform, helped one organization save $34M a year using is federated search capabilities. To learn more, please contact us at www.synaptik.co

Big data and analytics have become increasingly relevant in recent years. Simply put, Big Data is a term that describes the large volume of data-both structured and unstructured- that business collects on a daily basis. How people and organizations use this large volume of data is becoming more and more important in the modern marketplace. For the tourism industry, Big Data has huge potential as it can track information on human activity and preferences that will benefit companies and their customers.

Industry Outlook

The tourism industry is a major contributor to the U.S economy. In 2016, the U.S. travel and tourism industry generated over $1.5 trillion in economic output and supported over 7.6 million U.S. jobs. Additionally, according to the U.S. Department of Commerce, international travel to the United States should grow by 3% annually through 2021. This demonstrates how tourism benefits the U.S. economy on a national and local level as restaurants, hotels, ride services and many other businesses generate significant revenue from tourism.

Big data and technological trends in tourism

1. Personalization
While many people associate a personalized consumer experience with tech giants like Netflix and Amazon, companies in the tourism industry should not be deterred from creating a more personalized experience. Making travel arrangements (flights, lodging, activities, etc.) can be a time-consuming process. For many working men and women, time is a valuable resource and a personalized experience would simplify a customer’s ability to find the right travel package.

2. Mobile Friendly Platforms
Across the world, smartphone usage is rising and future metrics indicate that this trend will only continue. Companies are developing their marketing strategies and platforms in order to reach consumers on their mobile devices. Mobile big data can be utilized by companies in the tourism industry to create personalized marketing campaigns. An increase in mobile internet traffic will force companies to develop a user-friendly platform for customers interested in traveling.

According to Criterio, airline and mobile bookings made up 27% of all online travel bookings worldwide. Additionally, smartphone bookings have seen a significant increase with 33% more bookings year-over-year during the second quarter in 2016. This represents a shift in consumer behaviors as travelers are rarely separated from their smartphones

3. Affordable New Destinations
Before the emergence of the internet and big data technology, travelers mainly explored historic and noteworthy sites like New York City, Los Angeles, Walt Disney World, etc. However, with big data analysis and other technological tools, travel companies have the ability to understand the travel habits and travel patterns of modern consumers. Knowing this type of information can be very beneficial as companies can respond with new offerings and packages that cater to the needs of individual consumers.

Big data and technological tools have been adopted in many industries as consumer data increases in importance. In a highly competitive industry like tourism, leveraging the right customer analytics to gain a competitive advantage will be crucial for the for success of any firm.

Initial Coin Offerings (ICOs), a business fundraising event similar to an initial public offering in which investors purchase company-issued tokens with digital currencies (i.e. Bitcoin) for equity, are increasing in frequency as cryptocurrency become more widely accepted across the globe. (Please see Part 1 of this blog post for a more detailed explanation of the most common forms of ICO tokens). This new system of fundraising holds as much promise as peril for companies seeking to use different ICO methods to attract investors. Below describes the prevailing issues concerning each:

Security tokens: Security tokens may seem like an ideal way for a company to sell stock to outside investors, but they come with one major catch. As described in Part I, security tokens allow for companies to directly sell stock in their companies to investors. The challenge here is that many companies seeking to launch security tokens as investment vehicles do so as an attempt to skirt regulations that traditionally oversee these types of transactions. Ideally, companies seeking to launch these would first seek regulatory approval in order for security token offerings to be deemed legal,, yet this process may appear too timely and costly for many startups looking to security tokens to boost cash reserves. Companies seeking to use security tokens should make sure that they can demonstrate compliance with the Howey Test, the result of a 1934 Supreme Court case that established the four characteristics of an investment contract transaction:
1.) An investment of money
2.) Expectation of profits from the investment
3.) The investment of money is in a common enterprise
4.) Any profit comes from the efforts of a promoter or party

So how can companies sell stock in their company without investors expecting profits (point 2 above) in the form of dividends? That’s a great question. Until it is answered, companies seeking to use security coins need to be particularly careful of regulatory agencies such as the Securities and Exchange Commission that have already warned companies that ICOs attempting to circumvent securities laws may be prosecuted under federal law.

Equity Tokens: Equity tokens, a category of security tokens, are still subject to the regulations detailed above. However, equity tokens could become a robust method for companies to raise early-stage funding by democratizing the opportunity to invest, should a company receive adequate legal approval to use an equity token. However, one of the current limitations of equity tokens is timing. In order to succeed, equity tokens will need to rely on blockchain-enabled smart contracts between the equity issuer (the company) and the equity purchaser (the investor). Smart contracts are legal agreements between two parties that are stored for transparency and posterity on a decentralized blockchain using cryptography code. The challenge here for democratization involves context: if only the most sophisticated contemporary investors understand complex transactions involving the blockchain and smart contracts, will they actually empower the “everyman” investors to participate? Only time will tell, but the not-too-far-off future looks bright for companies looking to legally leverage equity tokens to raise early stage capital if they are able to comply with federal laws.

Utility Tokens: With utility tokens, the possibilities are endless. As described in Part 1, a consortium of major banks are planning to use utility tokens to improve interbank transactions. Other utility coin examples include:
– Brave, a web browser company, has a Basic Attention Token (BAT) that provides advertisers with utility tokens based on user attention.
– Kik, a messaging service, has Kin, a token provided to developers responsible for a transactions their service provided on the platform
– Filecoin, a decentralized storage network, allows network members to earn Filecoins for hosting files on their unused Hard Drive space, which can then be exchanged for used for USD, Bitcoin and other crypto currencies.

One major future challenge with utility tokens involves whether or not they can maintain a competitive advantage for their issuers. For instance, if Amazon monitored Filecoin’s success and decided they would like to replicate their model while providing higher value for their network members at scale, would Filecoin be able to leverage network member engagement with its token to prevent token holders from leaving the network? Industry behemoths have become notorious for replicating competitors products (i.e. Facebook with Snapchat) and it remains highly likely that behemoths may follow suit with successful companies reliant on utility coins to retain network members.

By Justin Barbaro