Yasuto Tamura

Illustrative introductions on dimension reduction

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“What is your image on dimensions?”
….That might be a cheesy question to ask to reader of Data Science Blog, but most people, with no scientific background, would answer “One dimension is a line, and two dimension is a plain, and we live in three-dimensional world.” After that if you ask “How about the fourth dimension?” many people would answer “Time?”

Terms like “multi dimensional something” is often used in science fictions because it’s a convenient black box when you make a fantasy story, and I’m sure many authors would not have thought that much about what those dimensions are.

In Japanese, if you say “He likes two dimension.” that means he prefers anime characters to real women, as is often the case with Japanese computer science students.

The meanings of “dimensions” depend on the context, but in data science dimension is in short the number of rows of your Excel data.

When you study data science or machine learning, usually you should start with understanding the algorithms with 2 or 3 dimensional data, and you can apply those ideas to any D dimensional data.
But of course you cannot visualize D dimensional data anymore, and that is almost an imaginary world on blackboards.

In this blog series I am going to explain algorithms for dimension reductions, such as PCA, LDA, and t-SNE, with 2 or 3 dimensional visible data. Along with that, I am going to delve into the meaning of calculations so that you can understand them in more like everyday-life sense.

This article series is going to be roughly divided into the contents below.

  1. Curse of Dimensionality (to be published soon)
  2. PCA, LDA (to be published soon)
  3. Rethinking eigen vectors (to be published soon)
  4. KL expansion and subspace method (to be published soon)
  5. Autoencoder as dimension reduction (to be published soon)
  6. t-SNE (to be published soon)

I hope you could see that reducing dimension is one of the fundamental approaches in data science or machine learning.

Data Science in Engineering Process - Product Lifecycle Management
Siemens Advanta Consulting

How to develop digital products and solutions for industrial environments?

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The Data Science and Engineering Process in PLM.

Huge opportunities for digital products are accompanied by huge risks

Digitalization is about to profoundly change the way we live and work. The increasing availability of data combined with growing storage capacities and computing power make it possible to create data-based products, services, and customer specific solutions to create insight with value for the business. Successful implementation requires systematic procedures for managing and analyzing data, but today such procedures are not covered in the PLM processes.

From our experience in industrial settings, organizations start processing the data that happens to be available. This data often does not fully cover the situation of interest, typically has poor quality, and in turn the results of data analysis are misleading. In industrial environments, the reliability and accuracy of results are crucial. Therefore, an enormous responsibility comes with the development of digital products and solutions. Unless there are systematic procedures in place to guide data management and data analysis in the development lifecycle, many promising digital products will not meet expectations.

Various methodologies exist but no comprehensive framework

Over the last decades, various methodologies focusing on specific aspects of how to deal with data were promoted across industries and academia. Examples are Six Sigma, CRISP-DM, JDM standard, DMM model, and KDD process. These methodologies aim at introducing principles for systematic data management and data analysis. Each methodology makes an important contribution to the overall picture of how to deal with data, but none provides a comprehensive framework covering all the necessary tasks and activities for the development of digital products. We should take these approaches as valuable input and integrate their strengths into a comprehensive Data Science and Engineering framework.

In fact, we believe it is time to establish an independent discipline to address the specific challenges of developing digital products, services and customer specific solutions. We need the same kind of professionalism in dealing with data that has been achieved in the established branches of engineering.

Data Science and Engineering as new discipline

Whereas the implementation of software algorithms is adequately guided by software engineering practices, there is currently no established engineering discipline covering the important tasks that focus on the data and how to develop causal models that capture the real world. We believe the development of industrial grade digital products and services requires an additional process area comprising best practices for data management and data analysis. This process area addresses the specific roles, skills, tasks, methods, tools, and management that are needed to succeed.

Figure: Data Science and Engineering as new engineering discipline

More than in other engineering disciplines, the outputs of Data Science and Engineering are created in repetitions of tasks in iterative cycles. The tasks are therefore organized into workflows with distinct objectives that clearly overlap along the phases of the PLM process.

Feasibility of Objectives
  Understand the business situation, confirm the feasibility of the product idea, clarify the data infrastructure needs, and create transparency on opportunities and risks related to the product idea from the data perspective.
Domain Understanding
  Establish an understanding of the causal context of the application domain, identify the influencing factors with impact on the outcomes in the operational scenarios where the digital product or service is going to be used.
Data Management
  Develop the data management strategy, define policies on data lifecycle management, design the specific solution architecture, and validate the technical solution after implementation.
Data Collection
  Define, implement and execute operational procedures for selecting, pre-processing, and transforming data as basis for further analysis. Ensure data quality by performing measurement system analysis and data integrity checks.
Modeling
  Select suitable modeling techniques and create a calibrated prediction model, which includes fitting the parameters or training the model and verifying the accuracy and precision of the prediction model.
Insight Provision
  Incorporate the prediction model into a digital product or solution, provide suitable visualizations to address the information needs, evaluate the accuracy of the prediction results, and establish feedback loops.

Real business value will be generated only if the prediction model at the core of the digital product reliably and accurately reflects the real world, and the results allow to derive not only correct but also helpful conclusions. Now is the time to embrace the unique chances by establishing professionalism in data science and engineering.

Authors

Peter Louis                               

Peter Louis is working at Siemens Advanta Consulting as Senior Key Expert. He has 25 years’ experience in Project Management, Quality Management, Software Engineering, Statistical Process Control, and various process frameworks (Lean, Agile, CMMI). He is an expert on SPC, KPI systems, data analytics, prediction modelling, and Six Sigma Black Belt.

Ralf Russ    

Ralf Russ works as a Principal Key Expert at Siemens Advanta Consulting. He has more than two decades experience rolling out frameworks for development of industrial-grade high quality products, services, and solutions. He is Six Sigma Master Black Belt and passionate about process transparency, optimization, anomaly detection, and prediction modelling using statistics and data analytics.4

Michael Lyamm

AI Voice Assistants are the Next Revolution: How Prepared are You?

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By 2022, voice-based shopping is predicted to rise to USD 40 billion, based on the data from OC&C Strategy Consultants. We’re in an era of ‘voice’ where drastic transformation is seen between the way AI and voice recognition are changing the way we live.

According to the survey, the surge of voice assistants is said to be driven by the number of homes that used smart speakers, as such that the rise is seen to grow from 13% to 55%. Nonetheless, Amazon will be one of the leaders to dominate the new channel having the largest market share.

Perhaps this is the first time you’ve heard about the voice revolution. Well, why not, based on multiple researchers, it is estimated that the number of voice assistants will grow to USD 8 billion by 2023 from USD 2.5 billion in 2018.

But what is voice revolution or voice assistant or voice search?

It was only until recently that the consumers have started learning about voice assistants which further predicts to exist in the future.

You’ve heard of Alexa, Cortana, Siri, and Google Assistant, these technologies are some of the world’s greatest examples of voice assistants. They will further help to drive consumer behavior as well as prepare the companies and adjust based on the industry demands. Consumers can now transform the way they act, search, and advertise their brand through voice technology.

Voice search is a technology to help users or consumers perform a search on the website by simply asking a question on their smartphone, their computer, or their smart device.

The voice assistant awareness: Why now?

As surveyed by PwC, amongst the 90% respondents, about 72% have been recorded to use voice assistant while merely 10% said they were clueless about voice-enabled devices and products. It is noted, the adoption of voice-enabled was majorly driven by children, young consumers, and households earning an income of around >USD100k.

Let us have a glance to ensure the devices that are used mainly for voice assistance: –

  • Smartphone – 57%
  • Desktop – 29%
  • Tablet – 29%
  • Laptop – 29%
  • Speaker – 27%
  • TV remote – 21%
  • Car navigation – 20%
  • Wearable – 14%

According to the survey, most consumers that use voice-assistants were the younger generation, aged between 18-24.

While individuals between the ages 25-49 were said to use these technologies in a much more statistical manner, and are called the “heavy users.”

Significance of mobile voice assistants: What is the need?

Although mobile is accessible everywhere, you will merely find three out of four consumers using mobile voice assistants in their household i.e. 74%.

Mobile-based AI chatbots have taken our lives by storm, thus providing the best solution to both the customers and agents in varied areas – insurance, travel, and education, etc.

A certain group of individuals said they needed privacy while speaking to their device and that sending a voice command in public is weird.

Well, this simply explains why 18-24 aged group individuals prefer less use of voice assistants. However, this age group tends to spend more time out of their homes.

Situations where voice assistants can be used – standalone speakers Vs mobile

Cooking

  • Standalone speakers – 65%
  • Mobile – 37%

Multitasking

  • Standalone speakers – 62%
  • Mobile – 12%

Watching TV

  • Standalone speakers – 57%
  • Mobile – 43%

In bed

  • Standalone speakers – 38%
  • Mobile – 37%

Working

  • Standalone speakers – 29%
  • Mobile – 25%

Driving

  • Standalone speakers – 0%
  • Mobile – 40%

By the end of 2020, nearly half of all the searches made will be voice-based, as predicted by Comscore, a media analytics firm.

Don’t you think voice-based assistant is changing the way businesses function? Thanks to the advent of AI!

  • A 2018 study on AI chatbots and voice assistants by Spiceworks said, 24% of businesses that were spread largely, and 16% of smaller businesses have already started using AI technologies in their workplaces. While 25% of the business market is expected to adopt AI within the next 12 months.

Surprisingly, voice-based assistants such as Siri, Google Assistant, and Cortana are some of the most prominent technologies these businesses are using in their workstations.

Where will the next AI voice revolution take us?

Voice-authorized transactions

Paypal, an online payment gateway now leverages Siri and Alexa’s voice recognition capability, thus, allowing users to make payments, check their balance, and ask payments from people via voice command.

Voice remote control – AI-powered

Communications conglomerate Comcast, an American telecommunications and media conglomerate introduces their first-ever X1 voice remote control that provides both natural image processing and voice recognition.

With the help of deep learning, the X1 can easily come up with better search results with just a press of the button telling what your television needs to do next.

Voice AI-enabled memos and analytics

Salesforce recently unveiled Einstein Voice which is an AI assistant that helps in entering critical data the moment it hears, making use of the voice command. This AI assistant also initiates in interpreting voice memos. Besides this, the voice bots accompanying Einstein Voice also helps the company create their customized voice bots to answer customer queries.

Voice-activated ordering

It is astonishing to see how Domino’s is using voice-activated feature automate orders made over the phone by customers. Well, welcome to the era of voice revolution.

This app, developed by Nuance Communications already has a Siri like voice recognition feature that allows customers to place their orders just like how they would be doing it in front of the cash counter making your order to take place efficiently.

As more businesses look forward to breaking down the roadblocks between a consumer and a brand, voice search now projects to become an impactful technology of bridging the gap.

Ashish Parmar

Business Data is changing the world’s view towards Green Energy

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Energy conservation is one of the main stressed points all around the globe. In the past 30 years, researches in the field of energy conservation and especially green energy have risen to another level. The positive outcomes of these researches have given us a gamut of technologies that can aid in preserving and utilize green energy. It has also reduced the over-dependency of companies on fossil fuels such as oil, coal, and natural gas.

Business data and analytics have all the power and the potential to take the business organizations forward in the future and conquer new frontiers. Seizing the opportunities presented by Green energy, market leaders such as Intel and Google have already implemented it, and now they enjoy the rich benefits of green energy sources.

Business data enables the organizations to keep an eye on measuring the positive outcomes by adopting the green energies. According to a report done by the World energy outlook, the global wind energy capacity will increase by 85% by the year 2020, reaching 1400 TWh. Moreover, in the Paris Summit, more than 170 countries around the world agreed on reducing the impact of global warming by harnessing energy from green energy sources. And for this to work, Big Data Analytics will play a pivotal role.

Overview of Green energy

In simpler terms, Green Energy is the energy coming from natural sources such as wind, sun, plants, tides, and geothermal heat. In contrast to fossil fuels, green energy resources can be replenished in a short period, and one can use them for longer periods. Green energy sources have a minimal ill effect on the environment as compared to fossil fuels. In addition to this, fossil fuels can be replaced by green energy sources in many areas like providing electricity, fuel for motor vehicles, etc..

With the help of business data, organizations throughout the world can change the view of green energy. Big Data can show how different types of green energy sources can help businesses and accelerate sustainable expansion.

Below are the different types of green energy sources:

  • Wind Power
  • Solar Power
  • Geothermal Energy
  • Hydropower
  • Biofuels
  • Bio-mass

Now we present before you a list of advantages that green energy or renewable energy sources have brought to the new age businesses.

Profits on the rise

If the energy produced is more than the energy used, the organizations can sell it back to the grids and earn profit out of it. Green energy sources are renewable sources of energy, and with precise data, the companies will get an overall estimation of the requirement of energy.

With Big Data, the organizations can know the history of the demographical location before setting up the factory. For example, if your company is planning to setup a factory in the coastal region, tidal and wind energy would be more beneficial as compared to solar power. Business data will give the complete analysis of the flow of the wind so that the companies can ascertain the best location of the windmill; this will allow them to store the energy in advance and use it as per their requirement. It not only saves money but also provides an extra source of income to the companies. With green energy sources, the production in the company can increase to an unprecedented level and have sustainable growth over the years.

Synchronizing the maintenance process

If there is a rapid inflow of solar and wind energy sources, the amount of power produced will be huge. Many solar panels and windmills are operating in a solar power plant or in a wind energy source, and with many types of equipment, it becomestoo complex to manage. Big Data analytics will assist the companies in streamlining all the operations to a large extent for their everyday work without any hassle.

Moreover, the analytics tool will convey the performance of renewable energy sources under different weather conditions. Thus, the companies will get the perfect idea about the performance of the green energy sources, thus enabling them to take necessary actions as and when required.

Lowering the attrition rate

Researchers have found that more number of employees want to be associated with companies that support green energies. By opting for green energy sources and investing in them, companies are indirectly investing in keeping the workforce intact and lowering the attrition rate. Stats also show the same track as nearly 50% of the working professionals, and almost 2/3rd of the millennial population want to be associated with the companies who are opting for the green energy sources and have a positive impact on environmental conservation.

The employees will not only wish to stay with the organizations for a long time but will also work hard for the betterment of the organization. Therefore, you can concentrate on expanding the business rather than thinking about the replacement of the employees.

Lowering the risk due to Power Outage

The Business Data Analytics will continuously keep updating the requirements of power needed to run the company. Thus the organizations can cut down the risk of the power outage and also the expenses related to it. The companies will know when to halt the energy transmission as they would know if the grid is under some strain or not.

Business analytics and green energy provide a planned power outage to the companies, which is cost-efficient and thus can decrease the product development cost.  Apart from this, companies can store energy for later usage. Practicing this process will help save a lot of money in the long run, proving that investment in green energy sources is a smart investment.

Reducing the maintenance cost

An increasing number of organizations are using renewable sources of energy as it plays a vital role in decreasing production and maintenance costs. The predictive analysis technology helps renewable energy sources to produce more energy at less cost, thus reducing the cost of infrastructure.

Moreover, data analytics will make green energy sources more bankable for companies. As organizations will have a concrete amount of data related to the energy sources, they can use it wisely on a more productive basis

Escalating Energy Storage

Green energy sources can be stored in bulk and used as per requirement by the business organizations. Using green energy on a larger basis will even allow companies to completely get rid of fossil fuels and thus work towards the betterment of the environment. Big Data analytics with AI and cloud-enabled systems help organizations store renewable energies such as Wind and Solar.

Moreover, it gathers information for the businesses and gives the complete analysis of the exact amount of energy required to complete a particular task. The data will also automate cost savings as it can predict the client’s needs. Based on business data, companies can store renewable energy sources in a better manner.

With Business data analytics, the companies can store energy when it is cheap and use it according to the needs when the energy rates go higher. Although predicting the requirement of storage is a complicated process, with Artificial Intelligence (AI) at work, you can analyze the data efficiently.

Bundling Up

Green energy sources will play a pivotal role in deciding the future of the businesses as fossil fuels are available in a certain limit. Moreover, astute business data analysts will assist the organizations to not only use renewable energy sources in a better manner but also to form a formidable workforce. The data support in the green energy sector will also provide sustainable growth to the companies, monitor their efforts, and assist them in the long run.

Tap London

Customer Journey Mapping: The data-driven approach to understanding your users

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Businesses across the globe are on a mission to know their customers inside out – something commonly referred to as customer-centricity. It’s an attempt to better understand the needs and wants of customers in order to provide them with a better overall experience.

But while this sounds promising in theory, it’s much harder to achieve in practice. To really know your customer you must not only understand what they want, but you also need to hone in on how they want it, when they want it and how often as well.

In essence, your business should use customer journey mapping. It allows you to visualise customer feelings and behaviours through the different stages of their journey – from the first interaction, right up until the point of purchase and beyond.

The Data-Driven Approach 

To ensure your customer journey mapping is successful, you must conduct some extensive research on your customers. You can’t afford to make decisions based on feelings and emotions alone. There are two types of research that you should use for customer journey mapping – quantitative and qualitative research.

Quantitative data is best for analysing the behaviour of your customers as it identifies their habits over time. It’s also extremely useful for confirming any hypotheses you may have developed. That being so, relying solely upon quantitative data can present one major issue – it doesn’t provide you with the specific reason behind those behaviours.

That’s where qualitative data comes to the rescue. Through data collection methods like surveys, interviews and focus groups, you can figure out the reasoning behind some of your quantitative data trends. The obvious downside to qualitative data is its lack of evidence and its tendency to be subjective. Therefore, a combination of both quantitative and qualitative research is most effective.

Creating A Customer Persona

A customer persona is designed to help businesses understand the key traits of specific groups of people. For example, those defined by their age range or geographic location. A customer persona can help improve your customer journey map by providing more insight into the behavioural trends of your “ideal” customer. 

The one downside to using customer personas is that they can be over-generalised at times. Just because a group of people shares a similar age, for example, it does not mean they all share the same beliefs and interests. Nevertheless, creating a customer persona is still beneficial to customer journey mapping – especially if used in combination with the correct customer journey analytics tools.

All Roads Lead To Customer-centricity 

To achieve customer-centricity, businesses must consider using a data-driven approach to customer journey mapping. First, it requires that you achieve a balance between both quantitative and qualitative research. Quantitative research will provide you with definitive trends while qualitative data gives you the reasoning behind those trends. 

To further increase the effectiveness of your customer journey map, consider creating customer personas. They will give you further insight into the behavioural trends within specific groups. 

This article was written by TAP London. Experts in the Adobe Experience Cloud, TAP London help brands organise data to provide meaningful insight and memorable customer experiences. Find out more at wearetaplondon.com.

Kaylae Matthews

5 Applications for Location-Based Data in 2020

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Location-based data enables giving people relevant information based on where they are at any given moment. Here are five location data applications to look for in 2020 and beyond. 

1. Increasing Sales and Reducing Frustration

One 2019 report indicated that 89% of the marketers who used geo data saw increased sales within their customer bases. Sometimes, the ideal way to boost sales is to convert what would be a frustration into something positive. 

A French campaign associated with the Actimel yogurt brand achieved this by sending targeted, encouraging messages to drivers who used the Waze navigation app and appeared to have made a wrong turn or got caught in traffic. 

For example, a driver might get a message that said, “Instead of getting mad and honking your horn, pump up the jams! #StayStrong.” The three-month campaign saw a 140% increase in ad recall. 

More recently, home furnishing brand IKEA launched a campaign in Dubai where people can get free stuff for making a long trip to a store. The freebies get more valuable as a person’s commute time increases. The catch is that participants have to activate location settings on their phones and enable Google Maps. Driving five minutes to a store got a person a free veggie hot dog, and they’d get a complimentary table for traveling 49 minutes. 

2. Offering Tailored Ad Targeting in Medical Offices

Pharmaceutical companies are starting to rely on companies that send targeted ads to patients connected to the Wi-Fi in doctors’ offices. One such provider is Semcasting. A recent effort involved sending ads to cardiology offices for a type of drug that lowers cholesterol levels in the blood. 

The company has taken a similar approach for an over-the-counter pediatric drug and a medication to relieve migraine headaches, among others. Such initiatives cause a 10% boost in the halo effect, plus a 1.5% uptick in sales. The first perk relates to the favoritism that people feel towards other products a company makes once they like one of them.

However, location data applications related to health care arguably require special attention regarding privacy. Patients may feel uneasy if they believe that companies are watching them and know they need a particular kind of medical treatment. 

3. Facilitating the Deployment of the 5G Network

The 5G network is coming soon, and network operators are working hard to roll it out. Statistics indicate that the 5G infrastructure investment will total $275 billion over seven years. Geodata can help network brands decide where to deploy 5G connectivity first.

Moreover, once a company offers 5G in an area, marketing teams can use location data to determine which neighborhoods to target when contacting potential customers. Most companies that currently have 5G within their product lineups have carefully chosen which areas are at the top of the list to receive 5G, and that practice will continue throughout 2020. 

It’s easy to envision a scenario whereby people can send error reports to 5G providers by using location data. For example, a company could say that having location data collection enabled on a 5G-powered smartphone allows a technician to determine if there’s a persistent problem with coverage.

Since the 5G network is still, it’s impossible to predict all the ways that a telecommunications operator might use location data to make their installations maximally profitable. However, the potential is there for forward-thinking brands to seize. 

4. Helping People Know About the Events in Their Areas

SoundHound, Inc. and Wcities recently announced a partnership that will rely on location-based data to keep people in the loop about upcoming local events. People can use a conversational intelligence platform that has information about more than 20,000 cities around the world. 

Users also don’t need to mention their locations in voice queries. They could say, for example, “Which bands are playing downtown tonight?” or “Can you give me some events happening on the east side tomorrow?” They can also ask something associated with a longer timespan, such as “Are there any wine festivals happening this month?”

People can say follow-up commands, too. They might ask what the weather forecast is after hearing about an outdoor event they want to attend. The system also supports booking an Uber, letting people get to the happening without hassles. 

5. Using Location-Based Data for Matchmaking

In honor of Valentine’s Day 2020, students from more than two dozen U.S colleges signed up for a matchmaking opportunity. It, at least in part, uses their location data to work. 

Participants answer school-specific questions, and their responses help them find a friend or something more. The platform uses algorithms to connect people with like-minded individuals. 

However, the company that provides the service can also give a breakdown of which residence halls have the most people taking part, or whether people generally live off-campus. This example is not the first time a university used location data by any means, but it’s different from the usual approach. 

Location Data Applications Abound

These five examples show there are no limits to how a company might use location data. However, they must do so with care, protecting user privacy while maintaining a high level of data quality. 

Halley Johnson

5 Things You Should Know About Data Mining

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The majority of people spend about twenty-four hours online every week. In that time they give out enough information for big data to know a lot about them. Having people collecting and compiling your data might seem scary but it might have been helpful for you in the past.

 

If you have ever been surprised to find an ad targeted toward something you were talking about earlier or an invention made based on something you were googling, then you already know that data mining can be helpful. Advanced education in data mining can be an awesome resource, so it may pay to have a personal tutor skilled in the area to help you understand. 

 

It is understandable to be unsure of a system that collects all of the information online so that they can learn more about you. Luckily, so much data is put out every day it is unlikely data mining is focusing on any of your important information. Here are a few statistics you should know about mining.

 

1. Data Mining Is Used In Crime Scenes

Using a variation of earthquake prediction software and data, the Los Angeles police department and researchers were able to predict crime within five hundred feet. As they learn how to compile and understand more data patterns, crime detecting will become more accurate.

 

Using their data the Los Angeles police department was able to stop thief activity by thirty-three percent. They were also able to predict violent crime by about twenty-one percent. Those are not perfect numbers, but they are better than before and will get even more impressive as time goes on. 

 

The fact that data mining is able to pick up on crime statistics and compile all of that data to give an accurate picture of where crime is likely to occur is amazing. It gives a place to look and is able to help stop crime as it starts.

 

2. Data Mining Helps With Sales

A great story about data mining in sales is the example of Walmart putting beer near the diapers. The story claims that through measuring statistics and mining data it was found that when men purchase diapers they are also likely to buy a pack of beer. Walmart collected that data and put it to good use by putting the beer next to the diapers.

 

The amount of truth in that story/example is debatable, but it has made data mining popular in most retail stores. Finding which products are often bought together can give insight into where to put products in a store. This practice has increased sales in both items immensely just because people tend to purchase items near one another more than they would if they had to walk to get the second item. 

 

Putting a lot of stock in the data-gathering teams that big stores build does not always work. There have been plenty of times when data teams failed and sales plummeted. Often, the benefits outweigh the potential failure, however, and many stores now use data mining to make a lot of big decisions about their sales.

 

3. It’s Helping With Predicting Disease 

 

In 2009 Google began work to be able to predict the winter flu. Google went through the fifty million most searched words and then compared them with what the CDC was finding during the 2003-2008 flu seasons. With that information google was able to help predict the next winter flu outbreak even down to the states it hit the hardest. 

 

Since 2009, data mining has gotten much better at predicting disease. Since the internet is a newer invention it is still growing and data mining is still getting better. Hopefully, in the future, we will be able to predict disease breakouts quickly and accurately. 

 

With new data mining techniques and research in the medical field, there is hope that doctors will be able to narrow down problems in the heart. As the information grows and more data is entered the medical field gets closer to solving problems through data. It is something that is going to help cure diseases more quickly and find the root of a problem.

 

4. Some Data Mining Gets Ignored

Interestingly, very little of the data that companies collect from you is actually used. “Big data Companies” do not use about eighty-eight percent of the data they have. It is incredibly difficult to use all of the millions of bits of data that go through big data companies every day.

 

The more people that are used for data mining and the more data companies are actually able to filter through, the better the online experience will be. It might be a bit frightening to think of someone going through what you are doing online, but no one is touching any of the information that you keep private. Big data is using the information you put out into the world and using that data to come to conclusions and make the world a better place.

 

There is so much information being put onto the internet at all times. Twenty-four hours a week is the average amount of time a single person spends on the internet, but there are plenty of people who spend more time than that. All of that information takes a lot of people to sift through and there are not enough people in the data mining industry to currently actually go through the majority of the data being put online.

 

5. Too Many Data Mining Jobs

Interestingly, the data industry is booming. In general, there are an amazing amount of careers opening on the internet every day. The industry is growing so quickly that there are not enough people to fill the jobs that are being created.

 

The lack of talent in the industry means there is plenty of room for new people who want to go into the data mining industry. It was predicted that by 2018 there would be a shortage of 140,000 with deep analytical skills. With the lack of jobs that are being discussed, it is amazing that there is such a shortage in the data industry. 

 

If big data is only able to wade through less than half of the data being collected then we are wasting a resource. The more people who go into an analytics or computer career the more information we will be able to collect and utilize. There are currently more jobs than there are people in the data mining field and that needs to be corrected.

 

To Conclude

The data mining industry is making great strides. Big data is trying to use the information they collect to sell more things to you but also to improve the world. Also, there is something very convenient about your computer knowing the type of things you want to buy and showing you them immediately. 

 

Data mining has been able to help predict crime in Los Angeles and lower crime rates. It has also helped companies know what items are commonly purchased together so that stores can be organized more efficiently. Data mining has even been able to predict the outbreak of disease down to the state.

 

Even with so much data being ignored and so many jobs left empty, data mining is doing incredible things. The entire internet is constantly growing and the data mining is growing right along with it. As the data mining industry climbs and more people find their careers mining data the more we will learn and the more facts we will find.

 

Edward Huskin

How Finance Organizations Are Dealing with The Growing Demand for Instant Response Times

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The financial industry is one of the most innovative industries that has evolved at an incredibly fast-paced over the past decade. Finance is a complex industry that requires a delicate balance between optimal convenience and security. 

With security being the most important aspect, the role of AI has increased in importance and various financial organizations are taking strides to innovate unique solutions to meet the growing demand for faster and instant response rates. 

In a recent study, it was found that automation and digital intelligence save US banks over $1trillion on an annual basis. From a world perspective, more countries in different parts of the world are adopting AI tools to meet the growing demand for instant response time.

The client experience

Despite the fast rate of digital integration into various industries, clients still want to feel a personal connection to a brand experience. The advances in machine learning have allowed for a vast improvement in personalized services using customer data. This feature uses AI tools to better understand and respond to client needs. 

A feature of this nature allows financial organizations to develop improved products and increase speeds in response rates. The client not only experiences faster service but also gains access to products that are relevant to their needs and interested.

The improved customer experience has also improved by eliminating the need to go to the physical office of a financial institution to solve a problem. The incorporation of chatbots for customer service allows clients to easily solve queries remotely. 

A recent example is the Bank of America’s chatbot, known as Erica, who is accessible at all times of the day is currently used by a million people. This eliminates having to deal with human assistants meaning that it is easier to access solutions. Customer service is on the areas that allow financial institutions to thrive and the client is increasingly demanding optimal customer service. 

Improved security and fraud prevention 

More financial organizations are making use of biometric data to record customer data. Some financial institutions have decided to replace passwords, thus simplifying client verification. Despite the simplicity, it offers a higher level of security beyond a simple pin code. 

In the future, clients are anticipated to simply use their biometrics to access their funds at an ATM or the bank. Another aspect of improving response times to limit cybercrime and prevent fraud by easily identifying client patterns. The knowledge of client patterns allows clients to be contacted in the event of unusual activities. 

Disruption from startup innovation

The term disruption has transformed into a positive term in the past decade because disruptors have created technology that speeds up and streamlines payments, product maintenance for clients and increasing the value chain. 

Financial institutions are finding ways to work collaboratively with disruptors and innovative FinTech companies to create improved technology-driven solutions. The culture of disruption has allowed financial institutions to deliver more innovative money management solutions and simple avenues to process transactions with minimal delays. 

Disruptors generally evolve at a rapid pace and are also becoming institutions that are becoming standalone financial service providers. The expanded competition only creates room for a wide range of institutions to choose from dedicated to solving client problems. 

Using robotics to eliminate the risk

The growing alliance between financial services and technology companies focused on AI allows the financial industry to have a better understanding of consumer patterns to develop products relevant to them. 

The joy of incorporating AI tools means that the client does not have to resort to interacting with a bank teller to solve an issue. The integration of AI tools is a good way to ensure that tasks are performed with minimal human error and eliminate hurdles that arise due to inaccuracies. 

NLP AI Technology has also worked towards assisting financial institutions make informed decisions by developing different useful apps. For example, there are apps that use NLP to gather data on influencers, marketers and blog posts, that data is then used to advise financiers on how to invest. There is also other software that helps digitize financial documentation processes using NLP and that is just a few examples amongst quite a few.

Taking advantage of the sharing economy 

A recent innovation in finance has been the recognition of the power of a shared economy which has been realized in industries such as transport and hospitality. The client is always looking for fast means to meet their needs and the cheapest possible options. 

The rise of digital currencies and the decentralized model have shown banks that people respond to a system that allows for decentralized asset sharing. 

With the rise of cryptocurrency, financial institutions have also started exploring the potential of employing blockchain to create a system that presents a public ledger and improve internal operation within an organization to deliver at high speed. 

Moving infrastructure to the cloud

Financial institutions are growing more and more to use the cloud to manage their operations and this allows for easier management. Financial institutions realize the importance of automating processes such as data management, CRM, accounting and even HR. 

Using analytical tools allows for the fast-tracking of data gathering and delivering solutions to clients. This allows functions like client payment, statement generation, credit checks and more to become automated and more accurate. 

Once again, the issue of cybersecurity is forefronted in machines ‘taking over’ and the concern stems from the fact that the software is being sourced from third parties and requirements in the industry are highly sophisticated. 

The rapid growth of data-driven solutions has placed pressure on financial institutions to work with trustworthy service providers or develop inhouse data management systems to avoid third-party interactions. 

Conclusion

The language of convenience is one that is universal; everyone wants everything to work faster, be delivered to their doorstep and accommodate their needs. The financial industry is no exception to these expectations from customers. Finance organizations are taking the leap into incorporating AI tools to partly manage operations because it simplifies monitoring, reporting and processing large volumes of data. 

The sophistication of analytical tools ensures that issues are resolved before they become larger issues that are beyond an organization’s control. It is certainly exciting to see how financial industries and organizations will transform in 2020 to incorporate tech tools to streamline security and operations. 

Signavio

Stop processing the same mistakes! Four steps to business & IT alignment

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Digitization. Agility. Tech-driven. Just three strategy buzzwords that promise IT transformation and business alignment, but often fade out into merely superficial change. In fact, aligning business and IT still vexes many organizations because company leaders often forget that transformation is not a move from A to B, or even from A to Z––it’s a move from a fixed starting point, to a state of continual change.

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Mit den richtigen Prozessen zum Erfolg: vier Schritte zum Business-IT Alignment

 

Within this state of perpetual flux, adaptive technology is necessary, not only to keep up with industry developments but also with the expansion of technology-enabled customer experiences. After all, alignment assumes that business and technology are separate entities, when in fact they are inextricably linked!

Metrics that matter: From information technology to business technology

Information technology is continuing to challenge the way companies organize their business processes, communicate with customers and potential customers, and deliver services. Although there is no single dominant reorganization strategy, common company structures lean towards decentralizing IT, shifting it closer to end-users and melding the knowledge-base with business strategy. Business-IT alignment is more than ever vital for market impact and growth.

This tactic means as business goals pivot, IT can more readily respond with permanent solutions to support and maintain enterprise momentum. In turn, technological advances and improvements are hardwired into current and future strategies and initiatives. As working ecosystems replace strict organizational structures, the traditional question “Which department do you work in?” has been replaced by, “How do you work?”

But how does IT prove its value and win the trust of the C-suite? Well, according to Gartner, almost 20% of companies have already invested in tools capable of monitoring business-relevant metrics, with this number predicted to reach 60% by 2021. The problem is many infrastructure and operations (I&O) leaders don’t know where to begin when initiating an IT monitoring strategy.

Reach beyond the everyday: Four challenges to alignment

With this, CIOs are under mounting pressure to address digital needs that grow and transform, as well as to renovate the operational environment with new functions. They also must still demonstrate how IT is meeting a given business strategy. So looking forward, no matter how big or small your business is, technology can deliver tangible and intangible benefits (like speed and performance) to hit revenue and operational targets efficiently, and meet your customers’ expectations of innovation.

Put simply, having a good technological infrastructure enriches the culture, efficiency, and relationships of your business.

Business and IT alignment: The rate of change

This continuous strategic loop means enterprises function better, make more profit, and see better ROI because they achieve their goals with less effort. And while there may be no standard way to align successfully, an organization where IT and business strategy are in lock-step can further improve agility and operational efficiencies. This battle of the ‘effs’, efficiency vs. effectiveness, has never been so critical to business survival.

In fact, successful companies are those that dive deeper; such is the importance of this synergy. Amazon and Apple are prime examples—technology and technological innovation is embedded and aligned within their operational structure. In several cases, they created the integral technology and business strategies themselves!

Convergence and Integration

These types of aligned companies have also increased the efficiency of technology investments and significantly reduced the financial and operational risks associated with business and technical change.

However, if this rate of change and business agility is as fast as we continually say, we need to be talking about convergence and integration, not just alignment. In other words, let’s do the research and learn, but empower next-level thinking so we can focus on the co-creation of “true value” and respond quickly to customers and users.

Granular strategies

Without this granular strategy, companies may spend too much on technology without ever solving the business challenges they face, simply due to differing departmental objectives, cultures, and incentives. Simply put, business-IT alignment integrates technology with the strategy, mission, and goals of an organization. For example:

  • Faster time-to-market
  • Increased profitability
  • Better customer experience
  • Improved collaboration
  • Greater industry and IT agility
  • Strategic technological transformation

Hot topic

View webinar recording Empowering Collaboration Between Business and IT, with Fabio Gammerino, Signavio Pre-Sales Consultant.

The power of process: Four steps to better business-IT alignment

While it may seem intuitive, many organizations struggle to achieve the elusive goal of business-IT alignment. This is not only because alignment is a cumbersome and lengthy process, but because the overall process is made up of many smaller sub-processes. Each of these sub-processes lacks a definitive start and endpoint. Instead, each one comprises some “learn and do” cycles that incrementally advance the overall goal.

These cycles aren’t simple fixes, and this explains why issues still exist in the modern digital world. But by establishing a common language, building internal business relationships, ensuring transparency, and developing precise corporate plans of action, the bridge between the two stabilizes.

Four steps to best position your business-IT alignment strategy:

  1. Plan: Translate business objectives into measurable IT services, so resources are effectively allocated to maximize turnover and ROI – This step requires ongoing communication between business and IT leaders.
  2. Model: IT designs infrastructure to increase business value and optimize operations – IT must understand business needs and ensure that they are implementing systems critical to business services.
  3. Manage: Service is delivered based on company objectives and expectations – IT must act as a single point-of-service request, and prioritize those requests based on pre-defined priorities.
  4. Measure: Improvement of cross-organization visibility and service level commitments – While metrics are essential, it is crucial that IT ensures a business context to what they are measuring, and keeps a clear relationship between the measured parameter and business goals.

Signavio Says

Temporarily rotating IT employees within business operations is a top strategy in reaching business-IT alignment because it circulates company knowledge. This cross-pollination encourages better relationships between the IT department and other silos and broadens skill-sets, especially for entry-level employees. Better knowledge depth gives the organization more flexibility with well-rounded employees who can fill various roles as demand arises.

Get in touch

Discover how Signavio can lead your business to IT transformation and operational excellence with the  Signavio Business Transformation Suite. Try it for yourself by registering now for a free 30-day trial.

Signavio

Scaling Up Your Process Management

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Any new business faces questions: have we found the right product/market fit? Does the business model work? Have we got enough money to keep the doors open? Typically, new businesses are focused on staying afloat, meaning anything that isn’t immediately relevant to that goal is left until later—whenever that might be!   

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Machen Sie mehr aus Ihrem Prozessmanagement

However, most businesses soon realize that staying afloat means finding the most efficient way to deliver their products or services to customers. As a result, the way a business functions starts to move into focus, with managers and staff looking to achieve the same outcome, in the same way, over and over. The quickest route to this? Establishing efficient processes. 

Once a business has clarified the responsibilities of all staff, and identified their business process framework, they are better able to minimize waste and errors, avoid misunderstandings, reduce the number of questions asked during the day-to-day business, and generally operate more smoothly and at a greater pace.

Expanding your business with process management

Of course, no new business wants to remain new for long—becoming firmly established is the immediate goal, with a focus on expansion to follow, leading to new markets, new customers, and increased profitability. Effectively outlining processes takes on even more importance when companies seek to expand. Take recruitment and onboarding, for example. 

Ad hoc employment processes may work for a start-up, but a small business looking to take the next step needs to introduce new staff members frequently and ensure they have the right information to get started immediately. The solution is a documented, scalable, and repeatable process that can be carried out as many times as needed, no matter the location or the role being filled. 

When new staff are employed, they’ll need to know how their new workplace actually functions. Once again, a clear process framework means all the daily processes needed are accessible to all staff, no matter where the employee is based. As the business grows, more and more people will come on board, each with their own skills, and very likely their own ideas and suggestions about how the business could be improved… 

Collaborative process management

Capturing the wisdom of the crowd is also a crucial factor in a successful business—ensuring all employees have a chance to contribute to improving the way the company operates. In a business with an effective process modeling framework, this means providing all staff with the capability to design and model processes themselves. 

Traditionally, business process modeling is a task for the management or particular experts, but this is an increasingly outdated view. Nobody wants to pass up the valuable knowledge of individuals; after all, the more knowledge there is available about a process, the more efficiently the processes can be modeled and optimized. Using a single source of process truth for the entire organization means companies can promote collaborative and transparent working environments, leading to happier staff, more efficient work, and better overall outcomes for the business. 

Collaborative process management helps to grow organizations avoid cumbersome, time-consuming email chains, or sifting through folders for the latest version of documents, as well as any number of other hand brakes on growth. 

Instead, process content can be created and shared by anyone, any time, helping drive a company’s digital and cloud strategies, enhance investigations and process optimization efforts, and support next-gen business transformation initiatives. In short, this radical transparency can serve as the jumping-off point for the next stage of a company’s growth. 

Want to find out more about professional process management? Read our White Paper 7-Step Guide to Effective Business Transformation!