Search

Tag: big data

Understanding Shoppers Using Big Data

The emergence of big data has taken many industries by storm. The consumer packaged goods (CPG) industry has been at the forefront of big data technology. The success of any CPG company is dependent on the ability to take advantage of changing consumer trends better than the competition. Below are ways in which CPG companies are using big data and analytics in order to drive growth and understand their customers.

Grocery Store Decisions
While shoppers walk through grocery aisle’s looking for the product that best meets their needs, many people are unaware of the logistical and strategic methods behind shelving products in the grocery store. The ability to analyze and leverage massive datasets is important for CPG companies when they’re competing for shelf-space and customer awareness. Retailers and distributors gather consumer insights and measure actual point-of-sale (POS) data for strategic decision making related to production, distribution, and promotion.

Marketing Strategies
Staying up to date with point of sale data allows CPG companies to reconcile inventory after each transaction and adjust inventory numbers based on high or low sales. Additionally, actual point of sale data gives a company an opportunity to make a general customer profile. Valuable information such as names, addresses, phone numbers, past purchases and order history are recorded to make a personalized product recommendation based on a customer profile.

Social Media
The growth of social media and digital technology has given consumers a larger platform to express their opinions on brands. This has helped CPG brands to continually evolve and change based on direct feedback from their customers. The gathering of information from social media platforms, known as social intelligence, can help CPG companies understand their different customer segments. Sentiment analysis tools can be used by extracting data from Twitter, Facebook, Pinterest, and Instagram to get an in depth understanding of what customers care about.

Practical Value
In 2012, L’Oréal, used social media analytics to understand their brand perception in the digital world. By mining and filtering social media data, L’Oréal analysts were able to track their customer “Voice of Beauty” program. This data was highly useful as L’Oréal USA was able to use consumer insights to engage with their customers. Social media analytics used by L’Oréal enabled the company to develop a stronger bond with their customers and uncover customer sentiments in real time to improve the product development process.

With Synaptik’s social listening tools, companies have the advantage to track conversations around specific phrases, words, or brands. Sign up for a 30 minute consultation and we can show you what customers are saying about your products and services across multiple social media channels online (Facebook, Twitter, LinkedIn, Pinterest).

The Two Ways Big Data Helps Developing Nations

Big data tools have become a national conversation in across U.S. enterprise as businesses try to leverage their existing data into a competitive advantage. Companies in industrialized nations have used big data tools for complex processes like consumer personalization that create a unique experience for each individual. While big data tools create advantageous situations for businesses, they can also provide cost effective solutions for governance in developing nations. Although Big Data will not be able to create resource parity with wealthier countries, developing nations can harness the power and potential of Big Data to alleviate issues caused by challenges facing health care and tourism.

1. Improvement in health care
Healthcare is a difficult commodity to receive due to cost and geographical locations even in industrialized countries like the United States. Traditional healthcare data includes vital statistics and hospital administration statistics. But with advances in technology, healthcare providers can see medical records, mobile phone and purchase records, GPS, social media, and more. The increase in mobile phones usage among developing nations has presented an opportunity to improve the delivery of healthcare. India’s personal identification programme is an example of big data technology tools in healthcare. In 2010 the government in India issued cards and identification numbers to all of its citizens. The cards, identification numbers and biometric information gave opportunities to monitor health and social data including, electronic medical records and information on health insurance for low-income families. While this was an ambitious project for a developing country like India, the opportunity for success provided a foundation for collecting health statistics.

2. Improvement in tourism
In our previous post, we highlighted how tourism is changing in the information age. For developing nations, tourism can help generate revenue that can be transferred via taxes into essential services. Any improvements in the tourism industry could be highly beneficial for the overall economy of a developing nation. In Mexico, BBVA Bancomer, BBVA Data & Analytics, and SECTUR worked together in order to analyze digital footprint data of visitors in Mexico. Some of the findings of this study are listed below:
– National tourists used their credit cards for trips while the international tourists used their cards for entertainment
– The highest percentage of spending is generated by U.S. tourists, followed by visitors from Argentina.
– In Cancun and Playa del Carmen, tourist spending was concentrated on Fridays and Saturdays and was more stable during the week
Statistics provided by-https://www.bbva.com/en/bbva-shows-big-data-can-boost-tourism-mexico/

These statistics are only a small portion of the general findings from BBVA Data & Analytics. Businesses and companies in Mexico could use this data to their benefit and design promotions for busier periods of the week (Fridays and Saturdays). Additionally, they are able to anticipate when international tourists are more likely to visit islands or other tourist destinations in Mexico. These type of metrics are highly useful as they can provide new insights that were previously unavailable for businesses in developing countries.

At the current pace of technological advancement, big data tools will continue to increase in importance. Improvements in healthcare and tourism are two of the many ways in which big data technology can be used to benefit developing nations. Big data tools are still relatively new to the industrialized world, therefore leveraging this technology will be a necessary action for developing nations in order to compete in a global marketplace.

Sources:

Tena, M. (2016, December 02). BBVA shows how Big Data can boost tourism in Mexico | BBVA. Retrieved January 29, 2018, from https://www.bbva.com/en/bbva-shows-big-data-can-boost-tourism-mexico/

Wyber, R., Vaillancourt, S., Perry, W., Mannava, P., Celi, L. A., & Folaranmi, T. (2015, January 30). Big data in global health: improving health in low- and middle-income countries. Retrieved January 30, 2018, from http://www.who.int/bulletin/volumes/93/3/14-139022/en/

United States Government . (n.d.). The official U.S. government site for Medicare. Retrieved January 30, 2018, from https://www.medicare.gov/

What you need to know: The Future of FinTech, RegTech and Wealth Management in the Digital Space

The tipping point is here. High tech business intelligence tools with built-in machine learning algorithms and big data inputs were once reserved for the Fortune 500. Now, the FinTech fad has shifted from early stage adopter to mainstream money manager and former technophobes are starting to digitize their businesses from end to end. New low cost, user-friendly self-service tools that can produce rapid-fire insights and on-demand customer service are finally within reach and can provide family wealth managers the brain power they need without the additional headache. Synaptik, True Interaction’s “Plug, Play, Predict” machine platform is already serving companies in the space, providing value more quickly than industry norms.

Reuters white paper on the digitization of wealth management identified three drivers behind the mainstream movement towards FinTech:

– New tools for investment research, risk management, trade processing, compliance, and reporting
– New business models offering better, faster, cheaper variants of existing services in investment management and brokerage
– New marketplaces, new managers, and new financial products that are changing the way capital and risk are allocated

In this blog post, we’ll explore disruptive technologies traditional firms with limited IT expertise can use to beat the market, improve existing services and stay on top of an increasingly complex regulatory environment. By leveraging cloud, open source, big data, Artificial Intelligence, API and Chatbots, companies can create robust digital ecosystems that will win younger clients and increase profits across the board. Companies that continue to resist digital transformation run the risk of becoming less competitive while those that embrace the opportunity will benefit from supplementing talented human capital with technological know-how.

Courtesy of PWC

Beat the Market

Big name hedge funds and investment firms deploy AI to comb through the internet for new investment opportunities. The elusive “super-algo” can swallow huge amounts of information from news reports, databanks and social media platforms and quickly optimize portfolios to profit from microscopic ripples and seismic shifts in the market. While private family wealth managers have relied on traditional methods and experience to pinpoint good investment opportunities, machine learning can provide the edge they need to compete in a volatile world. Now, building data ecosystems that provide real-time information and time series data on company performance and consumer trends no longer requires a Ph.D. in data analytics or computer science.

When considering investment management software, companies should look for some key features including scenario simulation, modeling, portfolio rebalancing, performance metrics, yield curve analysis and risk analytics. Your software should also be flexible, adaptable and able to ingest structured and unstructured data. The costs of professional investment programs range from $1300 to $8000 but as the market matures costs are likely to go down.

Money Management on Demand

Wealth management firms have relied on traditional relationship-driven business models for decades. But the personal touch that keeps more senior clients happy may repel the next generation. To attract younger clientele, companies need to invest in on-demand, low-touch digital customer service models that provide better transparency and more autonomy to their clients. Creating a flexible digital strategy that allows different client segments to engage with their portfolio independently and with their advisor as little or as often as they want is key to success. EY’s report “Advice goes virtual” looks at the range of innovative wealth management models that are now available and highlights firms that have struck the perfect balance between automation and human capital. Companies like Personal Capital, Future Advisor and LearnVest provide digital platforms with phone-based financial advisor services to meet the needs of busy millennials and satisfy the clients that prefer a dedicated human that knows the future they want to make for themselves. EY’s chart on innovations in wealth management sums up the range of digital opportunities that clients are gravitating towards.


Courtesy of EY

Automated Compliance

Since the financial crisis, the cost of compliance has risen steeply. Tech Crunch reports that “the global cost of compliance is an estimated $100 billion per year. For many financial firms, compliance is 20% of their operational budget.” Innovations in RegTech, an offspring of FinTech, can automate certain components of the compliance process and have the potential to dramatically reduce the cost of doing business. The Institute for International Finance (IIF) defines “RegTech” as “the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently.”

Since 2008, the increasing speed of regulatory change has kept wealth management firms in a state of paralysis. Companies are constantly playing catch up and readjusting procedures to meet new requirements. In the not so distant future, integrated RegTech solutions will connect directly with regulatory systems and automatically update formulae, allowing wealth management firms to refocus their resources on revenue generating activities.

Instead of producing lengthy paper reports for regulators, new RegTech solutions can generate and communicate required reports automatically. Instead of scouring hundreds of documents and spreadsheets on a quarterly basis, RegTech solutions will alert compliance managers to risks in real-time so they can be eliminated immediately. The possibilities are endless and the cumbersome and costly task of navigating the increasingly complex regulatory environment will continue to generate more innovations in this field. While RegTech is still in its infancy, small family wealth management firms should start investigating this growing subsector and use this disruptive technology to their advantage.

Traditional wealth management firms that continue to resist the digital revolution will begin to look antiquated, even to their most senior clientele. True Interaction specializes in building and executing digital transformation strategies for companies that don’t have IT expertise. Synaptik, True Interaction’s CMS for data, is already providing firms in the FinTech, RegTech and AdTech spaces with easy-to-use data management, visualization, and deep learning insights. Our experts are providing free consultations to help them assess their needs and start planning their digital future. Schedule your custom consultation here.

By Nina Robbins

Big Data Definition, Process, Strategies and Resources

Are we at the Big Data tipping point?

The Big Data space is warming up – to the point that various experts by now perceive it as the over-hyped successor to cloud. The publicity might be a bit much, however Big Data is by now living up to its prospective, changing whole business lines, such as marketing, pharmaceutical research, and cyber-security. As a business gains experience with concrete kinds of information, certain issues tend to fade, however there will on every relevant occasion be another brand-new information source with the same unknowns awaiting in the wings. The key to success is to start small. It’s a lower-risk way to see what Big Data may do for your firm and to test your businesses’ preparedness to employ it.

In nearly all corporations, Big Data programs get their start once an executive becomes persuaded that the corporation is missing out on opportunities in data. Perhaps it’s the CMO looking to glean brand-new perceptiveness into consumer conduct from web data, for example. That conviction leads to a comprehensive and laborious procedure by which the CMOs group could work with the CIOs group to state the exact insights to be pursued and the related systematic computational analysis of data or statistics to get them.

Big Data: Find traffic bottlenecks?

The worth of Big Data for network traffic and flow analysis is in the capacity to see across all networks, applications and users to comprehend in what way IT assets, and in particular net-work bandwidth, is being dispersed and devoured. There are several tools with which customers can finally see precisely whoever is doing what on the net-work, down to the concrete application or smartphone in use. With this real-time perceptiveness, associated with prolonged term use history, clients can spot tendencies and outliers, identifying wherever performance difficulties are starting and why.

Big Data has swished into any industry and at the moment plays an essential part in productivity development and contention competition. Research indicates that the digital cluster of data, data processing power and connectivity is ripe to shake up many segments over the next 10 years.

Big Data: What type of work and qualifications?

Big Data’s artificial intelligence applications of tools and methods may be applied in various areas. For example, Google’s search and advertisement business and its new robot automobiles, which have navigated 1000s of miles of California roads, both employ a package of artificial intelligence schemes. Both are daunting Big Data challenges, parsing huge amounts of information and making decisions without delay.

A Big Data specialist should master the different components of a Hadoop ecosystem like Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark. They should also get hands-on practice on CloudLabs by implementing real life programs in the areas of banking, electronic communication telecommunication, social media, insurance, and e-commerce.


Image: Erik Underwood/TechRepublic

How can the value of Big Data be defined?

The Big Data wave is altogether about detecting hidden worth in information resources. It characteristically is thought of as a large organization bringing all their different sources of information together (big and complex). Then boiling this data down to, still sizable, however a lot more controllable, data sets. This data can additionally be attacked with advanced systematic computational analysis of data or statistics, machine learning, and all types of out there mathematics. From this, brand new and unforeseen insights can be found.

Experts say that when Big Data programs disappoint, it’s frequently since businesses have not plainly described their objectives, the systematic computational analysis of data or statistics analytics problem they desire to answer, or the quantifications they’ll use to measure success. An illustration of a program with a plainly described and quantifiable objective is a retail merchant desiring to improve the precision of inventory in its stores. That lessens waste and betters profitability. Measuring before and after precision is easy; so is calculating ROI founded on the resulting increased profitability.

Big Data: Who should receive measurement reports?

The boom in the B2B Big Data market (from a sub-$100m business in 2009 to $130bn today) reflects an enterprise-led agglomerate scramble to invest in information mining, suggestive of the California gold rush, accompanied by a similar media buzz. Big Data is one of those specifications that gets flung about lots of businesses – without much of an agreement as to what it means. Technically, Big Data is whatever pool of data that is assembled from more than a single source. Not only does this trigger the technological interoperability problems that make data interchange so thwarting, but it as well makes it hard to know what information is available, what format it’s in, in what way to synthesize aged and brand-new data, and in what way to architect a practical way for end-users to communicate with Big Data tools.

In addition to the right applications of tools and methods, suppliers should invest time and manpower in obtaining the capabilities to make systematic computational analysis of data or statistics work for them. This includes crafting a committed group of specialists to supervise Big Data programs, implement and enhance software, and persuade users that those brand new strategies are worth their while. Given the extensive potential in the marketing industry, stakeholders need to create clever methods to manage the Big Data in their audience metrics. The creation of a united public metric standard is a hard, however essential objective, and stakeholders ought to strive to supply complete transparency to users with regard to tracking information as well as opt-out systems.

Robust metadata and forceful stewardship procedures as well make it simpler for corporations to query their information and get the answers that they are anticipating. The capacity to request information is foundational for reporting and systematic computational analysis of data or statistics, however corporations must characteristically overcome a number of challenges before they can engage in relevant examination of their Big Data resources. Businesses may do this by making sure that there is energetic participation and backing from one or more business leaders when the original plan of action is being elaborated and once the first implementations take place. Also of vital significance here is continuing collaboration amid the business and IT divisions. This ought to ensure that the business value of all ventures in Big Data systematic computational analysis of data or statistics are correctly comprehended.

A recent KPMG study showed only 40% of senior managers have a high level of trust in the user insights from their systematic computational analysis of data or statistics, and nearly all indicated their C-suite did not completely aid their current information analytics plan of action. 58% of organizations report that the influence of Big Data analytics on earnings was 3% or smaller. The actual Bonanza appears limited to banking, supply chains, and technical performance optimization – understandably some organizations feel left behind.

Big Data: How much value is created for each unit of data (whatever it is)?

The big part of Big Data alludes to the capacity of data accessible to examine. In the supply chain realm, that could include information from point-of-sale setups, bar-code scanners, radio frequency identification readers, global positioning system devices on vehicles and in cell phones, and software systems used to run transportation, warehousing, and additional operations.

CIOs and other Information Technology decision makers are used to needing to do more with less. In the world of Big Data, they might be able to achieve cost savings and efficiency gains, IT Ops and business intelligence (BI) strategies, exploiting advancements in open source software, distributed data processing, cloud economic science and microservices development.

Consultants who work with businesses on systematic computational analysis of data or statistics projects cite additional supply chain advancements that result from Big Data programs. For example, an online retailer that uses sales information to forecast what color sweaters sell the most at different times of the year. As a result of that data, the company at the moment has its providers create sweaters without color, then dye them later, based on consumer demand determined in near-real time.

Data experts in science and information experts as well as architects and designers with the expertise to work with Big Data applications of tools and methods are in demand and well-compensated. Want an extra edge looking for your following assignment? Get Big Data certified.

Is senior management in your organization involved in Big Data-related projects?

As with any business initiative, a Big Data program includes an element of risk. Any program may disappoint for whatever number of reasons: poor management, under-budgeting, or a lack of applicable expertise. However, Big Data projects carry their own specific risks.

The progressively rivalrous scenery and cyclical essence of a business requires timely access to accurate business data. Technical and organizational challenges associated with Big Data and advanced systematic computational analysis of data or statistics make it hard to build in-house applications; they end up as ineffective solutions and businesses become paralyzed.

Large-scale information gathering and analytics are swiftly getting to be a brand-new frontier of competitive distinction. Financial Institutions want to employ extensive information gathering and analytics to form a plan of action. Data-related threats and opportunities can be subtle.

To support Big Data efforts there are 2 fundamental types of PMOs: one that acts in an advising capacity, delivering project managers in business units with training, direction and best practices; and a centralized variant, with project managers on staff who are lent out to business units to work on projects. How a PMO is organized and staffed depends on a myriad of organizational circumstances, including targeted objectives, customary strengths and cultural imperatives. When deployed in line with an organization’s intellectual/artistic awareness, PMOs will help CIOs provide strategic IT projects that please both the CFO and internal clients. Over time, and CIOs ought to permit 3 years to obtain benefits, PMOs can save organizations money by enabling stronger resource management, decreasing project failures and supporting those projects that offer the largest payback.

Next, get started with the Big Data Self-Assessment:

The Big Data Self-Assessment covers numerous criteria related to a successful Big Data project – a quick primer eBook is available for you to download, the link is at the end of this article. In the Big Data Self Assessments, we find that the following questions are the most frequently addressed criteria. Here are their questions and answers.

The Big Data Self-Assessment Excel Dashboard shows what needs to be covered to organize the business/project activities and processes so that Big Data outcomes are achieved.

The Self-Assessment provides its value in understanding how to ensure that the outcome of any efforts in Big Data are maximized. It does this by securing that responsibilities for Big Data criteria get automatically prioritized and assigned; uncovering where progress can be made now.

To help professionals architect and implement the best Big Data practices for your organization, Gerard Blokdijk, head and author of The Art of Service’s Self Assessments provides a quick primer of the 49 Big Data criteria for each business, in any country, to implement them within their own organizations.

Take the abridged Big Data Survey Here:

Big Data Mini Assessment

Get the Big Data Quick Exploratory Self-Assessment eBook:

https://189d03-theartofservice-self-assessment-temporary-access-link.s3.amazonaws.com/Big_Data_Quick_Exploratory_Self-Assessment_Guide.pdf

by Gerard Blokdijk

Resiliency Tech: A Signal in the Storm

Redundancy is a four-letter word in most settings, but when it comes to emergency management and disaster relief, redundant systems reduce risk and saves lives. Tropical Storm Harvey caused at least 148,000 outages for internet, tv and phone customers, making it impossible for people to communicate over social media and text. In this blog post, we explore innovative ways smart cities can leverage big data and Internet of Things (IoT) technology to MacGyver effective solutions when go-to channels breakdown.

Flood Beacons

Designer Samuel Cox created the flood beacon to share fast and accurate flood condition information. Most emergency management decisions are based on forecasts and person-to-person communications with first responders and people in danger. With the flood beacon, you can find out water levels, GPS coordinates and water movements in real-time. The beacon is designed to have low power requirements and use solar to stay charged up. Now, it will be up to the IoT innovators of the world to turn the flood beacon into a complete solution that can broadcast emergency center locations and restore connectivity to impacted areas.

EMS Drones

The Health Integrated Rescue Operations (HiRO) Project has developed a first responder drone that can drop medical kits, emergency supplies and Google Glass for video conference communication. “EMS response drones can land in places that EMS ground vehicles either cannot get to or take too long to reach”, says Subbarao, a recognized expert in disaster and emergency medicine. “Immediate communications with the victims and reaching them rapidly with aid are both critical to improve outcomes.” – One of These Drones Could Save Your Life – Jan.12.2017 via NBC News.

Big Data Analytics and Business Intelligence

Emergency management agencies and disaster relief organizations have been using crowdsourcing and collaborative mapping tools to target impact areas but poor data quality and the lack of cross-agency coordination continue to challenge the system. Business intelligence platforms that provide access to alternative data sets and machine learning models can help government agencies and disaster relief organizations corroborate and collaborate. By introducing sentiment analysis, keyword search features and Geotags, organizations can quickly identify high-need areas. Furthermore, BI platforms with project management and inventory plug-ins can aggregate information and streamline deployment.

Smart emergency management systems must be flexible, redundant and evolve with our technology. At True Interaction, we believe that traditional private sector business intelligence tools and data science capabilities can help cross-agency collaboration, communication and coordination. Our core team of software developers is interested in teaming up with government agencies, disaster relief organizations and IoT developers to create better tools for disaster preparation and relief service delivery. Contact us here if you are interested in joining our resiliency tech partnership!

Improving the Fan Experience through Big Data and Analytics

As consumer electronics companies produce bigger and better HD televisions, sports fans have enjoyed the ability to feel the excitement of the stadium from the comfort of their own homes. Broadcast companies like NBC, FOX, CBS and ESPN have further enhanced the viewing experience by engaging fans on social media platforms and producing bingeworthy content. The downside of high ratings are stagnating stadium attendance levels.

With the convenience of the at-home viewing experience, how can professional sport leagues bring fans back to the stadium? In a 1998 poll conducted by ESPN, 54% of fans revealed that they would rather be at a game than at home. However, when that poll was taken again in 2012, only 29% of fans wanted to be at the game.

Now, professional football teams are betting big data can provide insights that will help them get fans back in the seats. For instance, The New England Patriots have partnered with data science experts to better understand the needs of their fanbase. By investing in big data and high-power analytics tools, the New England Patriots are uncovering new insights on consumer behavior such as in-store purchases, ticket purchase information, and click rates – information that will help them optimize marketing and sales tactics.

While most Patriot games do sellout, there are instances where season ticket holders do not show up. With tools from Kraft Analytics Group (KAGR), The New England Patriots can access data from every seat in the stadium to see who will be attending and how many season ticket holders came to the game. By tracking all of this data the New England Patriots are able to uncover insights into their fanbase that were previously unknown. Robert Kraft, owner of the New England Patriots, was asked about fan turnout and how valuable it was for the team.

“If somebody misses a game, they get a communication from us and we start to aggregate the reasons why people miss one, two, or three games. At the end of the year, I can know everything that took place with our ticket-holders during that season. It’s incredibly valuable to adjust your strategy going forward depending on what your goals are.“-Robert Kraft, Owner of the New England Patriots

Many teams are also turning to IoT (Internet of Things) solutions to optimize their fan experience. With IoT solutions, devices can be connected to the internet with a click of a button. Professional sports teams have taken advantage of these opportunities by using platforms such as iBeacon. This app uses bluetooth connections in order to connect with mobile devices to create a new type of stadium experience. With this technology connecting to concession stands and areas around the ballpark, fans can find the closest pizza discount and the shortest bathroom line.

Beacon Stadium App
Beacon Stadium App-Courtesy of Umbel

IoT stadiums will eventually become the new norm. The San Fransisco Giants have become leaders in the revolution. Bill Schlough CIO of the San Fransisco Giants commented on this trend,

“Mobile and digital experiences are paramount to our fan experience,” according to Schlough, “and they have played a role in the fact that we’ve had 246 straight sellouts.”

Schlough and the Giants organization have taken an active role to offer their fans a unique viewing experience. Cell phone coverage was introduced in the early 2000s, and in 2004 they introduced a plan to make AT&T Park a mobile hotspot. With WiFi antennas across the stadium, fans have the ability to watch videos and use social media to interact with other fans in the stadium.

As owners and cities continue to spend billions of dollars for new stadiums, meeting consumer demand will be crucially important in a digital world. Teams like the New England Patriots and the San Fransisco Giants have already started using technological tools like analytics and the Internet of Things in order to cater to the needs of their fans. With more innovators in the tech industry, other sports teams will likely follow the path of the Patriots and Giants in order to provide a memorable experience at the game for their customers.

With Synaptik’s social listening tools and easy data management integration, companies have the advantage to track conversations and data around secific topics and trends. Sign up for a 30 minute consultation.

Contributors:

Joe Sticca, Chief Operating Officer at True Interaction

Kiran Prakash, Content Marketing at True Interaction

Sparking Digital Communities: Broadcast Television’s Answer to Netflix

In the late 1990s and early 2000s network television dominated household entertainment. In 1998, nearly 30% of the population in the United States tuned into the NBC series finale of “Seinfeld”. Six years later, NBC’s series finale of the popular sitcom “Friends” drew 65.9 million people to their television screen, making it the most watched episode on US network TV in the early aughts. Today, nearly 40% of the viewers that tuned into the “Game of Thrones” premier viewed the popular show using same-day streaming services and DVR playback. The way people watch video content is changing rapidly and established network television companies need to evolve to maintain their viewership.

While linear TV is still the dominant platform amongst non-millenials, streaming services are quickly catching up. As young industry players like Hulu, Netflix and Youtube transform from streaming services to content creators and more consumers cut ties with cable, established network broadcasters need to engage their loyal audience in new ways. The challenge to stay relevant is further exacerbated by market fragmentation as consumer expectations for quality content with fewer ad breaks steadily rise.


Courtesy of Visual Capitalist

One advantage broadcast television still has over streaming services is the ability to tap into a network of viewers watching the same content at the same time. In 2016, over 24 million unique users sent more than 800 million TV related tweets. To stay relevant, network television companies are hoping to build on this activity by making the passive viewing experience an active one. We spoke with Michelle Imbrogno, Advertising Sales Director at This Old House about the best ways to engage the 21st century audience.

“Consumers now get their media wherever and whenever it’s convenient for them. At “This Old House”, we are able to offer the opportunity to watch our Emmy Award winning shows on PBS, on thisoldhouse.com or youtube.com anytime. For example, each season we feature 1-2 houses and their renovations. The editors of the magazine, website and executive producer of the TV show work closely together to ensure that our fans can see the renovations on any platforms. We also will pin the homes and the items in them on our Pinterest page. Social media especially Facebook resonates well with our readers.“– Michelle Imbrogno, Advertising Sales Director, This Old House

Social media platforms have become powerful engagement tools. According to Nielsen’s Social Content Ratings in 2015, 60% of consumers are “second screeners” – using their smartphones or tablets while watching TV. Many “second screeners” are using their devices to comment and interact with a digital community of fans. Games, quizzes and digital Q & A can keep viewers engaged with their favorite programming on a variety of platforms. The NFL is experimenting with new engagement strategies and teamed up with Twitter in 2016 to livestream games and activate the digital conversation.

“There is a massive amount of NFL-related conversation happening on Twitter during our games and tapping into that audience, in addition to our viewers on broadcast and cable, will ensure Thursday Night Football is seen on an unprecedented number of platforms.”-NFL Commissioner Roger Goodell ,”

With social media optimization (SMO) software, television networks can better understand their audience and adjust their social media strategy quickly. Tracking website traffic and click rates simply isn’t enough these days. To stay on trend, companies need to start tracking new engagement indicators using Synaptik’s social media intelligence checklist:

Step 1: Integrate Social Listening Tools

The key to understanding your audience is listening to what they have to say. By tracking mentions, hashtags and shares you can get a better sense of trending topics and conversations in your target audience. Moreover, this knowledge can underpin your argument for higher price points in negotiations with media buyers and brands.

Step 2: Conduct a Sentiment Analysis

Deciphering a consumer’s emotional response to an advertisement, character or song can be tricky but sentiment analysis digs deeper using natural language processing to understand consumer attitudes and opinions quickly. Additionally, you can customize outreach to advertisers based on the emotional responses they are trying to tap into.

Step 3: Personality Segmentation

Understanding a consumer’s personality is key to messaging. If you want to get through to your audience you need to understand how to approach them. New social media tools like Crystal, a Gmail plug-in, can tell you the best way to communicate with a prospect or customer based on their unique personality. This tool can also help you customize your approach to media buyers and agents.

By creating more accessible content for users and building a digital community around content, television networks can expect to increase advertising revenue and grow their fan base. With Synaptik’s social listening tools, companies have the advantage to track conversations around specific phrases, words, or brands. Sign up for a 30 minute consultation and we can show you what customers are saying about your products and services across multiple social media channels online (Facebook, Twitter, LinkedIn, etc.).

Contributors:

Joe Sticca, Chief Operating Officer at True Interaction

Kiran Prakash, Content Marketing at True Interaction

by Nina Robbins

Real Estate: Climate-proof your Portfolio

The real estate industry is built on the power to predict property values. With sea levels on the rise, smart investors are thinking about how to integrate climate science into real estate projections. Complex algorithms and regression models are nothing new to developers and brokerage firms but the rapidly evolving data ecosystem offers breakthrough opportunities in resiliency marketing, valuation and forecasting.

In Miami, investors are starting to look inland for property deals on higher ground. According to a New York Times article by Ian Urbina, “home sales in flood-prone areas grew about 25% less quickly than in counties that do not typically flood.” To get in front of the wave, real estate investors and appraisers need to regularly update their forecasting models and integrate new environmental and quality of life data sets. Third party data can be expensive but as municipal governments embrace open data policies, costs may go down.

Today, no fewer than 85 cities across the U.S. have developed open data portals that include data on everything from traffic speed to air quality to SAT results. Real estate professionals are using data to do more than just climate-proof their portfolios. With high-powered business intelligence tools, businesses can turn this rich raw data into better insights on:

Home Valuation

Zillow, an online real estate marketplace is leading the charge on better home valuation data models. The company’s ‘zestimate’ tool is a one-click home value estimator based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. Now, they’ve launched a $1 million dollar prize competition calling on data scientists to create models that outperform the current Zestimate algorithm.

Design

According to the Census Bureau, in 1960, single-person households made up about 13% of all American households. Now, that number has jumped to 28% of all American households. Additionally, a survey by ATUS cited in a Fast Company article by Lydia Dishman revealed that the number of people working from home increased from 19% in 2003 to 24% in 2015. The rapid rate of technological change means a constant shift in social and cultural norms. The micro-apartment trend and the new WeLive residential project from WeWork are signs of changing times. For developers, the deluge of data being created by millennials provides incredible insight into the needs and desires of tomorrow’s homebuyers.

Marketing

Brokerage firms spend exorbitant amounts of money on marketing but with big data in their pocket, real estate agents can narrow in on clients ready to move and cut their marketing spend in half. According to this Wall Street journal article by Stefanos Chen, saavy real estate agents use data sources like grocery purchases, obituaries and the age of children in the household to predict when a person might be ready to upsize or downsize. This laser-sharp focus allows them to spend their marketing budget wisely and improve conversion rates across the board.

In today’s competitive marketplace, real estate professionals need a self-service data management and analytics platform that can be applied to any use case and doesn’t require advanced IT skills. Synaptik is designed to adapt to your needs and can easily integrate quantitative and qualitative data from websites, social media channels, government databases, video content sites, APIs and SQL databases. Real estate is big business and better intelligence mean better returns. Sign up for a demo and find answers to questions you didn’t even know to ask.

By Nina Robbins

New York Civic Tech Innovation Challenge – Finalist

The Neighborhood Health Project is a 360° urban tech solution that takes the pulse of struggling commercial corridors and helps local businesses keep pace with competition.

New York City’s prized brick-and-mortar businesses are struggling. With the rise of e-commerce, sky high rents and growing operational costs, the small businesses that give New York City Streets their distinctive character face mass extinction.

This year’s NYC Department of Small Business Services Neighborhood Challenge 5.0 paired nonprofit community organizations and tech companies to create and implement tools that address specific commercial district issues. On June 15th, community-based organizations from across the city from the Myrtle Avenue Brooklyn Partnership to the Staten Island Economic Development Corporation, presented tech solutions to promote local business and get a deeper understanding of the economic landscape.

The Wall Street Journal reports that “the Neighborhood Challenge Grant Competition is a bit like the Google Lunar XPrize. Except rather than top engineers competing to put robots on the moon, it has tiny neighborhood associations inventing new methods to improve business, from delivery service to generating foot traffic.”

Synaptik, the Manhattan Chamber of Commerce and the Chinatown BID were thrilled to have their Neighborhood Health Project chosen as a finalist in this year’s competition.

The Neighborhood Health Projects aims to preserve the personality of our commercial corridors and help our small businesses and community at large adapt to the demands of the 21st century economy. By optimizing data collection, simplifying business engagement and integrating predictive analytics, we can get a better understanding of the causes and effects of commercial vacancies, the impacts of past policies and events and create an open dialogue between businesses, communities and government agencies.

“With Synaptik, we can provide small businesses user-friendly tools and data insights that were previously reserved for industry heavy weights with in-house data scientists and large resource pools” said Liam Wright, CEO of Synaptik.

The Neighborhood Health Project Team was honored to have had the opportunity to share the stage with such innovative project teams. “It is great to see civic organizations take an innovative role in data intelligence to serve community constituents and local businesses. We came far in the process and hope to find alternative ways to bring this solution to New York City neighborhoods ” said Joe Sticca, Chief Operating Officer of Synaptik.

By Nina Robbins

Big Data – The Hot Commodity on Wall Street

Imagine – The fluorescent stock ticker tape speeding through your company stats – a 20% increase in likes, 15% decrease in retail foot traffic and 600 retweets. In the new economy, net worth alone doesn’t determine the value of an individual or a business. Social sentiment, central bank communications, retail sentiment, technical factors, foot traffic and event based signals contribute to the atmospheric influence encasing you company’s revenue.

NASDAQ recently announced the launch of the “NASDAQ Analytics Hub” – a new platform that provides the buy side with investment signals that are derived from structured and unstructured data, and unique to Nasdaq. Big Data is the new oil and Wall Street is starting to transform our crude data material into a very valuable commodity.

What does this mean for the future of business intelligence?

It means that businesses that have been holding on to traditional analytics as the backbone of boardroom decisions must evolve. Nasdaq has pushed big data BI tech squarely into the mainstream. Now, it’s survival of the bittest.

An early majority of businesses have already jumped onto the Big Data bandwagon, but transformation hasn’t been easy. According to Thoughtworks, businesses are suffering from “transformation fatigue – the sinking feeling that the new change program presented by management will result in as little change as the one that failed in the previous fiscal year.” Many companies are in a vicious cycle of adopting a sexy new data analytics tool, investing an exorbitant amount of time in data prep, forcing employees to endure a cumbersome onboarding process, getting overwhelmed by the complexity of the tool, and finally, giving up and reverting to spreadsheets.


“There is a gap and struggle with business operations between spreadsheets, enterprise applications and traditional BI tools that leave people exhausted and overwhelmed, never mind the opportunities with incorporating alternative data to enhance your business intelligence processes.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Now, the challenge for data management platforms is to democratize data science and provide self-service capabilities to the masses. Luckily, data management platforms are hitting the mark. In April, Harvard Business Review published results of an ongoing survey of Fortune 1000 companies about their data investments since 2012, “and for the first time a near majority – 48.4% – report that their firms are achieving measurable results for their big data investments, with 80.7% of executives characterizing their big data investments as successful.”

As alternative data like foot traffic and social sentiment become entrenched in the valuation process, companies will have to keep pace with NASDAQ and other industry titans on insights, trends and forecasting. Synaptik is helping lead the charge on self-service data analytics. Management will no longer depend on IT teams to translate data into knowledge.

“Now, with the progression of cloud computing and easy to use data management interfaces with tools like Synaptik, your able to bring enterprise control of your data analytics processes and scale into new data science revenue opportunities.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Synaptik’s fully-managed infrastructure of tools makes big-data in the cloud is fast, auto-scalable, secure and on-demand when you need it. With auto-ingestion data-transfer agents, and web-based interfaces similar to spreadsheets you can parse and calculate new metadata to increase dimensionality and insights, using server-side computing, which is a challenge for user-side spreadsheet tools.

By Nina Robbins

Securing The Future Of ROI With Simulation Decision Support

EDITOR’S NOTE: This article is about how to approach and think about Decision Simulation. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

EXCERPT

Simulation is simply the idea of imitating human or other environmental behaviors to test possible outcomes. It is obvious a business will want to take advantage of such Simulation technologies in order to maximize profits, reduce risks and/or reduce costs.

Simulation decision support is the backbone of many cutting edge companies these days. Such simulations are used to predict financial climates, marketing trends, purchasing behavior and other tidbits using historical and current market and environmental data.

Managing ROI

Data management is a daunting task that is not to be trusted in the hands of lose and unruly processes and technology platforms. Maximizing profit and/or reducing risks using simulated information will not be an automatic process but rather a managed task. Your business resources should be leveraged for each project needing long term ROI planning; computer simulations are just some pieces to the overall puzzle. Simulation decision support companies and platforms are not exactly a dime a dozen but should still be evaluated thoroughly before engaging.

Scaling Your Business

Modern software platforms exist to assist in the linear growth of your business initiatives. Algorithms have been produced thanks to years of market data and simulations in order to give a clear picture to your expectations and theories. Machine learning has also been rapidly improving over that past several years, making market simulations even more accurate when it comes to short and long-term growth. There is no lack of Algorithms or libraries of Data science modules, it is the ability to easily scale your core and alternative data sets into and easy to use platform that is configured to your business environment. Over the last several years these Data Science platforms, such as Synaptik.co, has allowed companies with limited resources to scale their operations to take advantage of decisions simulation processes that were once too expensive and required specialized, separate resources to manage.

Non-tech Based Departments Can No Longer Hide

All branches of companies are now so immersed in software and data that it is difficult to distinguish the IT and non-IT departments. Employees will plug away at their company designated computing resources in order to keep records for the greater good of the corporation. These various data pools and processes are rich in opportunities to enable accurate business simulations. In turn, simulation findings can be shared with different departments and partners to enrich a collaborative environment to amplify further knowledge for a greater propensity for success. It is no joking matter that big or small companies will need simulation decision support processes to ensure they not only stay competitive but excel in their growth initiatives.

Data and Knowledge Never Sleeps

In 2016, the Domo research group produced data visualizing the extent of data outputted by the world. By 2020, the group predicts that we will have a data capacity of over 44 trillion gigabytes. This overwhelming amount of data available to the average human has companies on their toes in order to grasp the wild change in our modern world. The data produced is neutral to the truth, meaning accurate and inaccurate ideas are influencing the minds of your customers, partners and stakeholders. Scaling profits and reducing risk will become an increasingly involved activity, which gives us another reason to embark on Decision Simulation processes to deal with the overwhelming amount of data and decisions needed in this fluid data rich world.

EDITOR’S NOTE: This article is about how to approach and think about Decision Simulation. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

By Joe Sticca

Shocking? Predictive Analytics Might Be Right For Your Future

EDITOR’S NOTE: This article is about how to approach and think about Predictive Analytics. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

EXCERPT

“What is marketing?” Isn’t it the attempt to sell products and services to people who are most likely to buy them? Would you be shocked to learn that Predictive Analytics is useful for completing sales? We have a tendency to think of our processes/departments and data in silo-ed terms. Though, with today’s platforms it is critical to harness insights across silos as well as bring in “alternative data”.

How is your Data Management? Can your sales and marketing staff use your data sets to up-sell products or services?” Data management is the biggest barrier as well as the biggest opportunity to surpassing internal KPIs.

Know Your Customer.

“Have you ever heard of someone lamenting about things they should have done as youth to be successful adults?” They might read a good book and suggest “they could have written that themselves.” They think that the path to success is “obvious.” Simply know everything about your customer and provide him or her with valuable products or services. That is the secret to success. “But how do you get to know your customer?” The answer is Data Management and Predictive Analytics.

What Do You Know?

Customer Relationship Management (CRM) software has become very popular because it allows you to accumulate, manage and act upon client data. This can be an automatic data management system. You could even review past buying habits and automatically send an email for a hot new product, which might be appealing. Up Selling can increase your profits per customer. CRM is Business Analytics – giving you a deeper understanding of who your customer is, what he wants and where he is going. “Why do you think so many websites want to place cookies on your computer?” They want to track your behavior and anticipate your next buying action.

When Did You Know It?

“If you don’t know what your customer bought yesterday, how will you know what they will buy tomorrow?” The most agile business understands their customer in real-time. The Twitter world is about immediate gratification. People want to say “Hi,” see your pictures and plan your future together within the first 3 seconds, you meet. The profitable business knows the answers before the customer asks them. Predictive Analytics might be right for your future because it gives you the power to anticipate consumer buying trends and or behaviors across channels (Social, video, mobile, etc.). Your competitor might already be using these Business Analytics; you might be leaving “money on the table.” Sign up for a discussion, demo or strategy session today Hello@TrueInteraction.com.

EDITOR’S NOTE:This article is about how to approach and think about Predictive Analytics. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

How Alternative Data Can Transform Your Business Intelligence

EDITOR’S NOTE: This article is about harnessing new sources of Alternative Data. True Interaction built SYNAPTIK, our Data Management, Analytics, and Machine Learning Platform, specifically to make it easy to collect and manage core and alternative data/media types for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design

Big data has been commonly described over the last few years through properties known as the “3 V’s”: Volume, Velocity, and Variety. If you are a human being just about anywhere in the world today, it’s patently obvious to you that these three dimensions are increasing at an exponential rate.

We’ve seen the staggering statistics with regards to Volume and Velocity reported and discussed everywhere:

Big Volume
IDC reported that the data we collectively create and copy globally is doubling in size every two years. Calculated at 4.4 zettabytes in 2014, the organization estimates global data will reach 44 zettabytes — that’s 44 trillion gigabytes — by 2020.
Cisco forecasts that overall mobile data traffic is expected to grow to 49 exabytes per month by 2021, a seven-fold increase over 2016. Mobile data traffic will grow at a compound annual growth rate (CAGR) of 47 percent from 2016 to 2021.

Big Velocity

Facebook’s 1.97 billion monthly active users send an average of 31.25 million messages and view 2.77 million videos every minute.

Twitter’s 308 million monthly active users send, on average, around 6,000 tweets every second. This corresponds to over 350,000 tweets sent per minute, 500 million tweets per day and around 200 billion tweets per year.

Big Variety = Alternative, Non-traditional, Orthogonal Data

These well-touted figures often leave one feeling aghast, small, and perhaps powerless. Don’t worry, the feeling is mutual! So today, let’s get ourselves right-sized again, and shift our focus to the 3rd dimension — of big data, that is — and examine a growing, more optimistic, and actionable business trend concerning big data that is materializing in organizations and businesses of all kinds, across just about any industry that you can imagine, without regard for business size or scope. Let’s examine the explosion of big data Variety, specifically with regards to harnessing new and emerging varieties of data to further inform reporting, forecasting, and the provision of actionable BI insights.

In a pattern similar to online retail’s “Long Tail” — the emphasis of niche products to consumers providing that emerged in the 2000’s — more and more future-leaning businesses are incorporating outside, alternate “niches” of data that differ from the traditional BI data sources that standard BI dashboards have commonly provided.

In a recent interview in CIO, Krishna Nathan, CIO of S&P Global explained that “Some companies are starting to collect data known as alternative, non-traditional or orthogonal.” Nathan further describes Alternative Data as the various data “that draw from non-traditional data sources, so that when you apply analytics to the data, they yield additional insights that complement the information you receive from traditional sources.” Because of the increasing prevalence of data from mobile devices, satellites, IoT sensors and applications, huge quantities of structured, semi-structured and unstructured data have the potential to be mined for information and potentially help people make better data-driven decisions. “While it is still early days for this new kind of data”, Nathan says, “CIOs should start to become familiar with the technologies now. Soon enough, alternative data will be table stakes.”

In The Field

Let’s examine the various applications of these new data sources that are manifesting themselves in parallel with the burgeoning technological advancements in our world today.

VC and Credit

Alternative data is increasingly wielded by VC firms as well as the credit industry to lend insight into backing startups, businesses, and technologies. Many small businesses, especially those with a limited credit history, have difficulty demonstrating creditworthiness and may be deemed as high risk when viewed through the lens of traditional data sources.

However, Experian recently described the growing number number of online marketplace lenders, or nonbank lenders, that have already begun taking a nontraditional approach by leveraging a wealth of alternative data sources, such as social media, Web traffic, or app downloads to help fill the void that a business with limited credit history might have. By combining both traditional and nontraditional data sets, these lenders are able to help small businesses access financial resources, while expanding their own portfolios.

Health

Patient information continues to be collected through traditional public health data sources, including hospital administration departments, health surveys and clinical trials. Data analysis of these sources is slow, costly, limited by responder bias, and fragmented.

However, According to MaRS DD, a research and science-based entrepreneurial venture firm, with the growing use of personal health applications among the public, self-reported information on prescription drug consumption and nutritional intake can be analyzed and leveraged to gain insight into patient compliance and use patterns, as well as into chronic disease management aptitude in between visits to frontline healthcare practitioners. In addition, social media platforms can be used as both monitoring tools and non-traditional methods of increasing patient engagement, as they allow healthcare professionals to interact with populations that under-utilize services. Healthcare organizations can mine social media for specific keywords to focus and project initiatives that track the spread of influenza, zika, or opioid addiction, for example, or even to provide real-time intervention.

Retail, Dining, Hospitality and Events

Several different kinds of data sources can give these industries a bigger picture and aid in both more granular reporting, but also more accurate forecasting. For example, Foursquare famously predicted that Chipotle same-store sales would fall 29 percent after the Mexican chain was hit with E. coli outbreaks, based upon check-ins on their application. The actual decline announced by Chipotle ended up being a spot-on 30 percent. It’s no coincidence that Foursquare recently announced Foursquare Analytics, a foot traffic dashboard for brands and retailers.

In addition, by making use of CCTV or drone imagery, incredible insight can be garnered from examining in-store foot traffic or the density of vehicles in a retailer’s parking lot over time. Today, a combination of Wi-Fi hotspots and CCTV cameras can compile numbers about in-store customer traffic patterns in the same way that online stores collect visitor and click information. For example, by using a modern CCTV system to count the number of people in each part of the store, heatmap analytics can visualize “hot zones” — to help maximize in-store promotional campaigns, and identify “cold zones” to determine how store layout changes can improve customer traffic flow.

Don’t forget the weather! By leveraging a real-time weather data analytics system in order to process historical, current, and forecasted weather data, retailers can predict how shifting demands will affect inventory, merchandising, marketing, staffing, logistics, and more.

Wall Street

You can bet your life that investment firms are early adopters of alternative data sources such as in the Chipotle-Foursquare story mentioned earlier. Consider the incredible resource that satellite imagery is becoming — it’s not just for government intelligence anymore: Because satellite imagery now enables organizations to count cars in retailers’ parking lots, it is possible to estimate quarterly earnings ahead of a business’ quarterly reports. Data analysts can use simple trigonometry to measure the shadows cast by floating oil tank lids in order to gauge the world’s oil supply. By monitoring vehicles coming and going from industrial facilities in China, it’s even possible to create a nascent China manufacturing index. Infrared sensors combined with satellite images can detect crop health far ahead of the USDA. All of this makes a boon for traders and investors.

What About Your Organization?

No matter the size of your business, now is the time to consider upgrading your 2000’s-era BI Dashboards to incorporate alternative data sources — remember, the convergence of IoT, cloud, and big data are creating new opportunities for analytics all the time. Data is expected to double every two years, for the next decade. Furthermore, it is essential to integrate all of these data opportunities with traditional data sources in order to create a full spectrum of analytics, and drive more intelligent, more actionable insights.

The Right Analytics Platform

Legacy data management systems that have not optimized their operations will not be able to process these new and disparate sources of alternative data to produce relevant information in a timely manner. The lack of machine learning mechanisms within these sub-optimal systems will hinder businesses in their knowledge discovery process, barring organizations from making data-driven decisions in real time.

According to Joe Sticca, Senior Executive of Digital Transformation & Data Science for True Interaction, “The most deleterious disadvantage of failing to address these pressing issues… is the careless neglect of invaluable business insight that is concealed in the mass of available data. Now, more than ever, businesses of all size need the ability to do great data discovery, but without necessitating a deep core technical development and data analyst skillset to do so.”

One solution path? Cutting-edge fully-managed data and machine learning platforms like Synaptik, that make it easy to connect with dozens of both structured and unstructured data services and sources, in order to gain the power of algorithms, statistical analysis, predictive modeling and machine learning, for a multitude of purposes, and metrics such as brand sentiment, campaign effectiveness and customer experience. Synaptik helps businesses transform via an adaptive, intuitive and accessible platform – using a modern mix of lightweight frameworks, scalable cloud services, and effective data management and research tools. More importantly, it works with non-IT skill sets to propagate better pattern recognition across your organization’s people and divisions.

(infographic by Quandl.com)

by Michael Davison

Evolution of Big Data Technologies in the Financial Services Industry

Our previous post provides an industry analysis that examines the maturity of banking and financial markets organizations. The significant deviations from the traditional business model within the financial services industry in the recent years emphasize the increasing need for a difference in how institutions approach big data. The long-standing industry, so firmly entrenched in its decades-long practices, is seemingly dipping its toes into the proverbial pool of big data as organization recognize that its implementation is integral to a firm’s survival, and ultimately its growth. IBM’s Big Data @ Work survey reports that 26 percent of banking and financial markets companies are focused on understanding the concepts surrounding big data. On the other end of the spectrum, 27 percent are launching big data pilots, but the majority of the companies surveyed in this global study (47 percent) remains in the planning stage of defining a road map towards the efficient implementation of big data. For those organizations still in the stage of planning and refinement, it is crucial to understand and integrate these observed trends within financial technologies that can bolster a company’s big data strategy.

Customer Intelligence

While banks have historically maintained the monopoly on their customer’s financial transactions, the current state of the industry, with competitors flooding the market on different platforms, prevents this practice to continue. Banks are being transformed from product-centric to customer-centric organizations. Of the survey respondents with big data efforts in place, 55 percent report customer-centric objectives as one of their organization’s top priorities, if not their utmost aim. In order to engage in more customer-centric activities, financial service companies need to enhance their ability in anticipating changing market conditions and customer preferences. This will in turn inform the development and tailoring of their products and services towards the consumer, swiftly seizing market opportunities as well as improving customer service and loyalty.

Machine Learning

Financial market firms are increasingly becoming more aware of the many potential applications for machine learning and deep learning, two of the most prominent uses being within the fraud and risk sectors of this industry. The sheer volume of consumer information collected from the innumerable amount of transactions conducted through a plethora of different platforms daily calls for stronger protocols around fraud and risk management. Many financial services companies are just beginning to realize the advantageous inclusion of machine learning within an organization’s big data strategy. One such company is Paypal, which, through a combination of linear, neural network, and deep learning techniques, is able to optimize its risk management engines in order to identify the level of risk associated with a customer in mere milliseconds. The potential foreshadowed by these current applications is seemingly endless, optimistically suggesting the feasibility of machine learning algorithms replacing statistical risk management models and becoming an industry standard. The overall value that financial institutions can glean from the implementation of machine learning techniques is access to actionable intelligence based on the previously obscured insights uncovered by means of such techniques. The integration of machine learning tactics will be a welcome catalyst in the acceleration towards more real-time analysis and alerting.

IoT

When attempting to chart the future of financial technology, many point to the Internet of Things (IoT) as the next logical step. Often succinctly described as machine-to-machine communication, the IoT is hardly a novel concept, with the continual exchange of data already occurring between “smart” devices despite the lack of human interference. As some industries, such as in retail and manufacturing, already utilize this technology to some extent, it is not a far-fetched notion to posit that the financial service industry will soon follow suit. While there are those who adamantly reject the idea due to the industry being in the business of providing services as opposed to things, this would be a dangerously myopic view in this day and age. Anything from ATMs to information kiosks could be equipped with sensing technology to monitor and take action on the consumer’s’ behalf. Information collected from real-time, multi-channel activities can aid in informing how banks provide the best, most timely offers and advice to their customers.

For more information to empower your data science initiatives please visit us at www.Synaptik.co. We pride ourselves to empower every day users to do great data discovery without the need for deep core technical development skills.

Joe Sticca, Chief Operating Officer of True Interaction, contributed to this post.

By Justin Barbaro

Technological Disruptions in the Financial Services Industry

The financial services industry has long boasted a resilient and steadfast business model and has proven to be one of the most resistant sectors when it comes to disruption by technology. In the recent years, however, a palpable change is overturning the ways and processes that have upheld this institution for so long. Organizations within this historically traditional and unyielding sector are realizing the need to not only assimilate into the digital era, but to embrace and incorporate it fully or else be overtaken but others who have opted to innovate rather than succumb under the pressure of this increasingly competitive industry.

Changing Dynamics

The relationship between banks and their customers have drastically changed from the time during which the traditional banking model was formulated. Perhaps more than any other commercial enterprise, banks retained control over the relationships they had with their customers. For the most part, the bank with which an individual was aligned often determined his or her financial identity as nearly all transactions were administered through one’s bank. Moreover, McKinsey reports that, historically, consumers very rarely flitted between different service providers because of the promising image of stability that the industry has worked hard to maintain. While this may have been the case in the past, the relational dynamics between banks and their customers are not the same today as they were nearly a decade or so ago.

Instead of being reliant to a single bank for all financial dealings, consumers have more options at their disposal, which they are taking full advantage of by engaging in transient relationships with multiple banks such as “a current account at one that charges no fees, a savings accounts with a bank that offers high interest, a mortgage with a one offering the best rate, and a brokerage account at a discount brokerage.” Competition between financial institutions is undeniably fiercer than ever, and it turns out that consumers are also being courted by new peer-to-peer services, such as PayPal, that allow those who opt to use these services to conduct financial transactions beyond the traditional banking means and organizations.

Data Growth

The sheer rise in players and competitors within this industry alone is enough to indicate another glaring issue: the sudden growth in volume of financial transactions. More transactions leads to an explosion of data growth for financial service providers, a predicament for which not many organizations are adequately prepared to handle. A study conducted by the Capgemini/RBS Global Payments estimates that the global volume for electronic payments is about 260 billion and growing between 15 and 22% for developing countries. The expansion of data points stored for each transaction, committed on the plethora of devices that are available to the consumer, is causing difficulties in the active defense against fraud and detection of potential security breaches. Oracle observes a shift in the way fraud analysis is being conducted, with it previously being performed over a small sample of transactions but which now necessitates the analysis of entire transaction history data sets.

Timely Insight

Shrinking revenues is one of the most prominent challenges for financial institutions to date, calling for a need to improve operational cost efficiencies. New financial technologies are being developed to address issues like this by leveraging the amount of available data that these financial institutions have access to and monetize it. Traditional Business Intelligence tools have been a staple in the industry for years, but it is usually limited in its capacity. A lot of the available BI tools work well in conjunction with business analysts when they are looking to find answers and solutions to specific conundrum. The key to revamping traditional banking frameworks in order to make it more competitive and agile in the current environment is to build and incorporate processes that are capable of revealing patterns, trends, and correlations in the data. Oracle posits that disruptive technologies in the financial sectors need to be able to do more than report, but also uncover. The technological ramifications of an evolving financial industry with a continuously expanding amount of data and the demand for real-time, data-driven decisions include the ability to detect unanticipated questions and simultaneously provide tangible solutions.

Denisse Perez, Content Marketing Analyst for True Interaction, contributed to this post.

By Joe Sticca