Organizations generate a plethora of data on a daily basis. For any organization to have a successful business model, collecting insightful information is imperative. This information can be retrieved from the existing data that the organizations have. Often, it is observed that while segregating data, instead of retrieving the insightful information, organizations retrieve reliable information, which gives them the idea of what they are looking for, but doesn’t help them to understand the importance of the retained information. But, for any organizations to grow and evolve, it becomes imperative that information which gets retrieved is insightful. That’s where the process of Web Data mining comes into the picture.

Just like the manual process of mining which is conducted for extracting minerals, web data mining is the process of extracting information, that determines the patterns, trends, and ideas for accelerating business intelligence, observing business growth, and promulgating strategies that can be deployed for business excellence. Business intelligence is also governed by analysing the inputs installed by the competitors’ company, observing its market share,  and deriving patterns that can accelerate, and enhance business. Hence, integrating business models with web data mining would be crucial for the success of the organizations.

Understanding Web Data Mining

The Web data mining is the process of automatically identifying and extracting information from the available documents and services available online, in order to identify patterns and behaviour of customers towards a product, observe the pattern in the sale of that particular product and for improving the capability of the Search Engine Optimization for identifying useful web pages.

A Successful business model does not only involve scanning through the data available on the web, but also picking out information that is of paramount importance. According to experts, successful web data mining would involve the following applications:

• Scanning through the news articles of the rival companies, for identify the strategic plan of that company.
• Looking through the 10-k filing, so that insights about the product development, sales, and purchases can be derived, and comparing it with the company’s sales, purchase and product models, so that difference can be observed.
• Automatically finding and analyzing the government rules and regulations
• Identifying and tracking conferences with locations and organizations.

Web Data Mining using Web Content

Web content mining, extracts information from the web documents, by analyzing the text, videos and images, using machine learning and natural language processing. Web content mining would help in identifying that information that is driving the content of the rival company to be successful. By analyzing the blogs put out by the rival company, or accessing the videos and images, the organizations will have an idea about the quality and speciality of the products offered by the competitor. This will drive them to chalk out strategic processes that will upscale their products.

Web Structure Mining

Web structure mining extracts the information from the structure of the data available online. By analyzing the data available online, organizations will be able to track down the nodes, hyperlinks, and web graphs, which will be helpful in determining the link between different commercial products. By analyzing the hyperlinks in the content of the rival company, necessary information like the market performance can also be retained.

Web Usage Mining

With the help of the machine learning model, web usage mining extracts information from the large sets of data, to understand customer behaviour.

Hence for any successful business model, it becomes paramount that insightful information is readily available, so that data-driven decisions can be made.

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What’s the best way to help your city thrive after a global pandemic? For Mark Gannon, director of business change and information solutions at Sheffield City Council, the answer might lie in big data.

“We want to give the organisation and the city the greatest opportunity to bounce back from COVID. Part of that focus is around using digital and data,” he says.

Gannon aims to create an organisation that’s driven by data. Working alongside counterparts in other public sector organisations, he wants to combine public data sources and create new insight into some of Sheffield’s most-pressing health and wellbeing issues, helping the city’s councillors make timely and cost-effective decisions in a post-COVID age.

“So when we make policy decisions, they’re based on intelligence rather than gut instinct. We’re a political organisation, so obviously there are going to be politicians that will always have views. But I think if we can inform those views with data and intelligence, that will be good,” he says.

The council already has a small business intelligence (BI) team that Gannon says is “doing some really clever stuff”, such as helping social workers to maximise their engagement with citizens to understand the complexity of cases. The aim right now is to think about how big data can create further insight-led improvements for people across the city.

A crucial element will be the establishment of an Office of Data Analytics (ODA) for Sheffield, a cross-city initiative that aims to draw in information from a range of institutions, including health and care organisations, the police, and the city’s two higher education establishments, the University of Sheffield and Sheffield Hallam University.

“We want to use all the enthusiasm, data and intelligence that we have to create a gearing effect from that combination to get more than the sum of its parts. So that’s a really exciting conversation and it means we’ve got an opportunity to do something in our response to COVID that could actually move the city forward,” he says.

The ODA will be research-led: while a physical office might follow at some stage, the project will operate – like so many other workplaces right now – as a virtual office.

Gannon is helping to develop the terms of reference for the ODA, while his team puts together some candidate projects. The aim is to identify these projects through workshops that can help to create benefits for the city and its citizens.

He gives the example of the Urban Flows Observatory, which is a massive data-gathering project at the University of a Sheffield that draws information from sensors across the city. The initiative collects information, such as data on energy use, climate change and pollution, that relate to the physical process that take place within the city.

Gannon says the council is talking to the university about how it might combine this data with its own information on social concerns, such as inequality and health and wellbeing. The aim is to create new insight on the urban area of Sheffield.

“So there’s various conversations taking place and, because of COVID, the need to have quick access to data and intelligence is really driving a conversation where people get to see why this insight so important,” he says.

Gannon and his public sector peers in Sheffield are keen to make the ODA work on its own terms – and that means open data and application programming interfaces (APIs). While the council has had offers from external providers that are keen to sell their data products, Gannon says the aim is to not become reliant on proprietary tools.

Rather than create data lakes of stored information, the ODA – and the council’s other data initiatives – will aim to collect and analyse information once its data scientists understand the questions that need to be answered. Gannon hopes this open approach will create projects that deliver data-led benefits that people can believe in.

“I think there’s a massive trust issue with this stuff,” he says. “People tend to not trust government with their data, so we’re going to spend quite a bit of time upfront on the public trust element. The aim is to do everything in the open, so we publish all of our plans, working-out, algorithms; everything we use, we’ll make it public so people can see what we’re doing.”

Sheffield’s desire to make the most of its information should be welcomed. Data science is still at a nascent stage in UK local government, according to University of Oxford researchers. They suggest that there is enormous potential for the use of big data to be expanded and to help with the delivery of better services to citizens.

More efficient forms of service delivery will be critical in a post-COVID age, with estimates suggesting UK councils will face significant funding cuts due to a £1.2bn hole in their finances from the coronavirus pandemic. Gannon recognises the scale of the challenge ahead and says big data can help the council to understand the value of its interventions.

“The issue for local government and public sector is going to be budgets – they’re going to be significantly reduced,” he says. “So we’re going to have to think about how we make sure that when we do intervene, that that’s going to have the impact on key areas like health and social care.”

Gannon gives the example of identifying and then tracking vulnerable adults through the health and social-care system. Information is often held in stove pipes by different public sector organisations, which means tracing an individual’s journey – and the impact of interventions – is difficult in normal times, never mind during a global pandemic.

“The bit that’s often missing is about understanding the impact of your work on vulnerable adults. So we’ve got a prevention strategy, but what we don’t have is the data to demonstrate that the prevention is having an impact. We want to track the interventions that we’ve made, and then tweak the interventions as required,” he says.

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Business intelligence (BI) is nothing new to enterprises that have been relying on data processing and analysis to deliver insightful reports that reflect business performance.

These tools are a great match for enterprises that value the data their operations generate.

BI software and programs work together to turn data into actionable insights that can drive better business decisions and market strategies and, ultimately, drive revenue as a result.

Combined with the masses of external data amassing every second – whether that’s customers’ feedback and experience, competitor intelligence, seasonal buying habits, or otherwise – businesses can have a huge amount of data at their disposal.

While BI systems draw specific data from pre-defined sources to turn them into insights, big data technologies capture data from a variety of sources in real-time, regardless of their formats or structure.

Unlike BI, big data doesn’t answer critical questions that enterprises have, but rather, it provides them with new information that can prompt new questions that enterprises haven’t thought of asking.

Once enterprises have a better idea of what they want to find out, they can turn to BI tools to deliver the insights, and even make predictions.

Business impact

Since big data came into the picture, BI becomes just that much more conducive.

Through big data, BI can now deliver insights that enable businesses to better understand their customers, improve marketing techniques, make personalization possible and identify issues and opportunities that emerge in real-time.

Marketing strategies no longer have to a shot in the dark. With data varying from customer feedback to their journey records, enterprises can market products with a clear understanding of what customers want and need.

Enterprises can then leverage data alongside real-time market analysis data to develop and deliver new and improved products and services.

Big data technologies can help businesses augment their operations. Data doesn’t have to be externally sourced, it can come from internal systems and programs as well.

Siloes between teams and hiccups in workflows can be detected in real-time, allowing enterprises to redesign their processes effectively.

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One of the great promises of continuous intelligence (CI) is in its ability to provide decision support (augmenting the work of a human) or decision automation in reaction to real-time events. To realize the benefits CI can deliver in such applications requires predictive analytics and artificial intelligence (AI) algorithms that derive actionable information. However, a key to achieving maximum benefits is to embed sophisticated analytics technologies into normal business operations and processes.

A new report from Gartner indicates that we’re on the way. In its latest “Magic Quadrant for Analytics and Business Intelligence Platforms,” Gartner noted that we are in a period where machine learning (ML) and business intelligence (BI) are converging.

Specifically, BI solutions providers now routinely are making ML features and functionality available to businesses within their BI offerings. For many businesses, this is a logical next step on the journey from descriptive analytics and reactionary BI to predictive and prescriptive analytics and pro-active BI.

One way to look at the market is that in the past, BI solutions helped businesses transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. In most cases, BI looked at what happened and made sense of that information. For example, BI typically would be used to:

• Analyze customer buying patterns and sales trends
• Measure, track, and predict sales and financial performance
• Track the performance of marketing campaigns and more. 

In contrast, predictive analytics, ML, and AI try to predict what will happen next. Is that financial transaction suspect? Will offering this customer free shipping enough to close the sale? Which payment options will more likely get a customer with a past due account to respond?

Gartner believes the two areas (BI and ML) are converging. According to Garter, 40% of ML model development and scoring will be done in products that do not have [ML] as their primary goal by 2022.

Incorporating ML into BI will bring sophisticated analytics to more people, groups, and business units. The thinking here is that ML will be easier to use when it is embedded in an application the user is familiar with and already knows how to use.

Many BI solutions providers are embracing ML in other ways, too. Beyond making integrating ML capabilities into their solutions for enhanced analytics, some are using AI and ML within their offerings. In this scenario, the providers seek to make their tool easier to use by using AI and ML to help prepare data, derive insights, and allow users to understand better and explain their findings.

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Today, when we look around, the technological advances in recent years have been immense. We can see driverless cars, hands-free devices that can turn on the lights, and robots working in factories, which prove that intelligent machines are possible.

In the last four years in the Indian startup ecosystem, the terms that were used (overused rather) more than funding, valuation, and exit were artificial intelligence (AI) and machine learning (ML). We also saw investors readily putting in their money in startups that remotely used or claimed to use these emerging technologies.

From deeptech, ecommerce, fintech, and conversational chatbots to mobility, foodtech, and healthcare, AI and ML have transformed most industry sectors today.

The industry has swiftly moved from asking programmers to feed tonnes of code to the machine to acquiring terabytes of data and crunching it to build relevant logic.

Sameer Dhanrajani, Co-Founder and Chief Executive Officer of AI Advisory & Consulting Firm AIQRATE, says, 

“We are data-rich but information-poor. AI plays a strong role here and it is the engine today. It compels organisations to take AI-driven decisions. Democratisation of analytics and data as a service will lead to the creation of new marketplaces to buy and sell advanced analytics algorithms; the virtual algorithm taking decisions based on heuristic data. AI will eventually start mimicking the human mind. The error rate is already drastically reducing.”

The machine market in India

A subset of artificial intelligence, machine learning allows systems to make predictions and crucial business decisions, driven by data and pattern-based experiences. Without humans having to intervene, the algorithms that are fed to the systems are helping them develop and improve their own models and understanding of a certain use-case.

According to a study carried out by Analytics India and AnalytixLabs, the Indian data analytics market is expected to double its size by 2020, with about 24 percent being attributed to Big Data. It said that almost 60 percent of the analytics revenue across India comes from exports of analytics to the USA. Domestic revenue accounts for only four percent of the total analytics revenue across the country.

The BFSI industry accounts for almost 37 percent of the total analytics market while generating almost $756 million. While marketing and advertising comes second at 26 percent, ecommerce contributes to about 15 percent.

At present, the average paycheck sizes of AI and ML engineers in India start from Rs 10 lakh per annum and the maximum cap often crosses Rs 50 lakh per annum.
According to a report by Great Learning, an edtech startup for professional education, India is expected to see 1.5 lakh new openings in Data Science in 2020, an increase of about 62 percent as compared to that of 2019. Currently, 70 percent of job postings in this sector are for Data Scientists with less than five years of work experience.

Career roadmap in data analytics

Shantanu Bhattacharya, a data scientist at Locus, had told YourStory earlier about the phenomenon, and opined that it is wrong to look at machine learning as a tool or a career path, and that it is only a convenient means to develop training models to solve problems in general.

The fluid nature of data science allows people from multiple fields of expertise to come and crack it. Shantanu believes if JRR Tolkien, being the brilliant linguist that he was, pursued data science to develop NLP models, he would have been the greatest NLP expert ever, and that is the kind of liberty and scope data science offers.

He said, “Predefined notions and prior experiences are very poor indicators to define success in data science. It teaches you the ability to define a problem statement in the most explicit way possible. So, it’s equally important to adopt the same mechanism for your mind.”

Needless to say, AI and ML have the scope to exponentially amplify the profitability and efficiency of a business by automating many tasks. And naturally, the trend has spread its wings to the jobs market where the dire need for experts and engineers in these technologies is only going up, and does not seem to slow down.

Thanks to the hefty paychecks and faster career growth, the role of machine learning engineers has claimed the top spot in job portals.

The future of machine learning

Hari Krishnan Nair, the co-founder of Great Learning, says, “With vast quantities of data being generated, the data science vertical is key to mining actionable insights for businesses. This has naturally resulted in professionals acknowledging the scope of this field, and working towards upgrading their skills to meet the demand for data science professionals. 2020 is set to be a big year for data science in India.”

For a country like India, acquiring new skills is not something of a luxury but a necessary requirement, and the trends of upskilling and reskilling are also currently on the rise to complement with the same. But data science, machine learning, and artificial intelligence are those fields where mere book-reading and formulaic interpretation and execution just does not cut it.

If one aspires to have a competitive career in futuristic technologies, machine learning and data science have a larger spectrum of required understanding of probability, statistics, and mathematics on a fundamental level. 

To break the myths around programmers and software developers entering this market, machine learning involves understanding of basic programming languages (Python, SQL, R), linear algebra and calculus, as well as inferential and descriptive statistics.

Siddharth Das, Founder of Univ.ai, an early stage edtech startup that focuses on teaching these tools, says,

“If you look at the United States, every single skilled machine learning professional who graduates from an Ivy League university is picked up by the likes of Facebook, Amazon, Netflix, and Google. Interestingly, a lot of academicians and professors from these universities are also going away to join these high-demanding roles. We cannot print these skills into a person. If we were able to, the world would already be flooded by these professionals.”

For a business world that thrives on data and its leverage, the science around it is where the employment economy is moving towards. While the youth of the country is anxious how rapid their upskilling rate is ought to be, it is no easy mountain to climb to rightfully master the art of data science, which it is often referred to as.

Most professionals say it is a consistent routine of learning for almost six to eight months, to be an expert in this field. During this time, when the industry is almost on the verge of fully migrating to NLP and Neural Networks, which are a significant part of future deep-tech, now is more than a better time to start learning machine learning.

With rapidly changing technological paradigms, predicting how the world is going to run is something close to impossible. And being prepared for anything is the best one can manage with, at the moment.

The post Technologies of the future, but where are AI and ML headed to? appeared first on Artificial Intelligence.

Global Business Intelligence Software Market is forecast to bring about afairly desirable remuneration portfolio by the end of the forecast period.Certainly, the report not only includes a modest growth rate over the forecast time frame but also contains a reliable overview of this business. The study involves overall growth opportunities and valuation currently this market is holding. Additionally, the report involves classified segmentation of Business Intelligence Software market.

Global Business Intelligence Software Market: Key players

Looker
Microsoft
Tableau
Domo
Qlik
Zoho
SAP
Oracle
Cognos
SAS
Information Builders
Yellowfin
TIBCO
MicroStrategy
Targit
InetSoft

Market Segment by Type covers:

Mobile
Cloud

Market Segment by Applications can be divided into:

SMEs
Large Organization
Other

Regional analysis covers:
• North America (USA, Canada, and Mexico)
• Europe (Russia, France, Germany, UK, and Italy)
• Asia-Pacific (China Korea, India, Japan, and Southeast Asia)
• South America (Brazil, Columbia, Argentina, etc.)
• The Middle East and Africa (Nigeria, UAE, Saudi Arabia, Egypt, and South Africa)

Key Highlights of the Business Intelligence Software Market report:
• The key details related to Business Intelligence Software industry like the product definition, cost, variety of applications, demand and supply statistics are covered in this report
• Competitive study of the major players will help all the market players in analyzing the latest trends and business strategies
• Holistic study of market segments and sub-segments will help the readers in planning the business strategies
• Figure Global Production Market Share of Business Intelligence Software market by Types and by Applications in 2019

The report has provided quantitative and qualitative analysis along with absolute opportunity assessment in the report. Also, the report offers Porter’s Five Forces analysis and PESTLE analysis for more detailed contrast studies. Each section of the report has something valuable that helps companies for improving their sales and marketing strategy, gross margin, and profit margins. Using the report as a tool for gaining insightful Business Intelligence Software market analysis, players can identify the much-required changes in their operation and improve their approach to doing business.

The report provides comprehensive information to identify market segments that help to improve the quality of business decision-making based on demand, sales, and production based on application-level analysis and regional level. Further, the report has been analyzed graphically to make this report more effective and understandable. The experts have constructed the detailed study market 2019 in a structured format for better analysis.

Chapters involved in Business Intelligence Software market report:
Chapter 1: Market Overview, Drivers, Restraints and Opportunities, Segmentation overview
Chapter 2: Market Competition by Manufacturers
Chapter 3: Production by Regions
Chapter 4: Consumption by Regions
Chapter 5: Production, By Types, Revenue and Market share by Types
Chapter 6: Consumption, By Applications, Market share (%) and Growth Rate by Applications
Chapter 7: Complete profiling and analysis of Manufacturers
Chapter 8: Manufacturing cost analysis, Raw materials analysis, Region-wise manufacturing expenses
Chapter 9: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10: Marketing Strategy Analysis, Distributors/Traders
Chapter 11: Market Effect Factors Analysis
Chapter 12: Market Forecast
Chapter 13: Business Intelligence Software Research Findings and Conclusion, Appendix, methodology and data source

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Over the past few decades, we’ve witnessed an increasing number of computers beating humans at activities we thought (or perhaps hoped) we would always have the upper hand in. It all started in 1997, when IBM’s Deep Blue beat the chess world champion, Garry Kasparov, in match play. By the mid-2000s, AI was consistently beating chess grandmasters in almost every game-playing context. Naturally, AI developers moved on to more complex games to test increasingly sophisticated algorithms, and in 2016-17 we saw computers beating humans in a variety of games – from the ancient game of ‘Go’ to Texas Hold-Em Poker.

Whilst its impressive to see these technologies in action against gamemasters, the goal has never been to make a great chess or Go-playing program. The ultimate objective is to advance AI by building smarter machines that can learn, adapt, and think for themselves.

Then and now – the rise of smart machines

As machines are getting smarter, they are being given more complicated jobs – jobs that were once strictly the domain of humans. Computers are learning to drive vehicles, read medical test results and make treatment recommendations, write news articles, and manufacture goods.

However, we need to remember that technology like AI and Machine Learning (ML) are only as smart as the data being looking at. Where these systems can truly start to evolve is through continuous feedback and corrections. This is why organisations need to look at whether they have a data strategy, data literacy strategy and a culture of data trust in place, before they can start employing techniques to help assist users to be more effective in their jobs. Data literacy is a skill I’m particularly passionate about that can help us better understand the information being produced by machines and enable us to act on it.

The real benefit of AI requires humans

In many fields, AI will provide a fantastic opportunity for workers to reduce the time they spend on mundane tasks, i.e. data entry or filing, allowing them to free up time for thinking creatively, or to focus on more strategic activities that deliver higher value to an organisation and to an individual’s self-worth.

In my field, Data Analytics and Business Intelligence (BI), for example, we are already seeing how a cognitive engine can be used for routine data analysis and recommending visualisations. It’s the kind of field that seems perfect for AI – virtual mountains of data at the disposal of AI programs to learn from, analyse and find the most reliable algorithms to make recommendations. What we’re increasingly seeing is that when humans work with AI, it provides even better opportunities to amplify our brainpower with machine intelligence for faster, smarter and bolder discoveries.

So, what can each of us do to future-proof our roles and the wellbeing of our teams? Focus on your strengths. Humans have the ability to problem-solve in an interdisciplinary style – something that most AI doesn’t do well. We’re naturally flexible and can do different types of work and think in creative ways. Rather than trying to beat machines at their own games, the better move is to merge our strengths with AI. A partnership, rather than “us vs. them” scenario.

Machines will bring their speed and processing power to the table as we use our creativity and non-linear thinking – together we can solve the business problems of today and tomorrow.

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Big Data was not created to be big by premeditation. Instead, it is a logical result of the accumulation of data related to consumer habits, tastes, and expectations, as well as the growing sharing of this data, plus worldwide communication, media storage, and entertainment.

Big Data manages a lot. Everything, to some people, but whatever your level of technological prowess, the worldwide system of networks and data centers that make up Big Data likely manages something important to you.

It is true that Facebook, Google, and the other big companies rely on certain types of predictive algorithms to work through their massive amounts of data. These are AI systems that require technical and financial resources that small businesses can’t typically afford.

While often associated with large companies, Big Data can also benefit small businesses that want to tap into new development opportunities.

And these businesses don’t need to make heavy investments in infrastructure or human resources to do so. There are various solutions that make Big Data accessible to almost any business. This is the “how” question.

On the other hand, for the “why” question, Big Data can be integrated into BtoC and BtoB strategies of businesses to help them address at least five key challenges.

5 Ways Big Data can Improve Your Small Business:

1. Customer UX (User Experience)

Big data reveals information about customers and their shopping habits that can be used to understand customer behavior and ensure a better experience for them and the business through marketing, sales, and customer service.

Offering a personalized experience for customers provides a competitive advantage to small businesses that translates into revenue and growth.

In a recent research report, the Boston Consulting Group (BCG) expects businesses that create personalized customer experience to increase their revenue by 6 – 10%.

What’s one example of Big Data making this a possibility? We’re sure you’ve seen ads for Squarespace, Wix, and more. These tools can help you build attractive web spaces for your business no matter how small.

2. Predictive Marketing

Understanding the behavior of customers now will help you anticipate the evolution of their needs and tailor effective marketing campaigns accordingly.

If traditional marketing strives to understand consumers to bring them the product that best meets their needs, predictive marketing, thanks to Big Data, goes beyond product or service recommendation.

Predictive marketing is about knowing customers’ desires before they even think about it, and to propose a personalized service that evolves according to the behavior of the customer.

There are web-based tools you can use to do just that. Check out what’s trending on Buzzsumo in relation to your products and services to see who’s attracting the most attention in your neck of the woods.

3. Product Innovation

The two first points above converge to make the often risky endeavor of product innovation, well, less risky.

Big Data also helps brands improve their existing products and/or services (customer UX) or innovate and create new ones to meet future needs (anticipation and predictive marketing).

User data analytics (knowing which features make customers tick and which don’t, detecting problems and discovering new opportunities) allow businesses to improve product adoption and accelerate innovation. If you use a web page for part of your business, check out Google Analytics to see which parts of your page are attracting the most attention.

Tech companies have the ability to design, develop and roll out new attractive products at short periods of times thanks in a large part to Big Data.

4. Decision Making Process

Big Data is changing the paradigm of decision making and can bring much more than Business Intelligence in this regard.

Traditional decision-making processes require internal expertise. However, having a dedicated BI center is not within the reach of all businesses, but if they take advantage of Big Data, it is accessible.

For example, Big Data, via the internet and advanced search engines, allows you to seek out your competitors. From there, you’re able to understand the best ways to stand out to consumers that may have otherwise just stuck with the competition.

A data-centric company is a company that knows how to fructify data and use it to manage every aspect of business.

Big Data enables smaller companies to make informed decisions and choose the right investment to create more wealth.

5. Get Ready for Insights-Driven Business

Competition is getting increasingly intrusive, and you should make use of your own user data before someone else does!

The majority of business leaders plan on taking advantage of Big Data and you should be one of them if you don’t want to be left behind.

In a study entitled Big and Fast Data: The Rise of Insight-Driven Business by Capgeminiand EMC², 75% of business leaders said they will be using Big Data to transform, reorganize and restructure their businesses.

Per the report, 64% of respondents said non-traditional players were getting into their territory, and 53% expect increased competition enabled by data.

The post 5 Ways to Improve Your Small Business Using Big Data appeared first on Artificial Intelligence.

While Machine learning has been on the technology agenda now for as long as twenty years, it is only in more recent times that its potential benefits in field service management terms have been more widely understood.

Machine learning is an offset of artificial intelligence (AI), whereby AI can learn to do a task without needing to be programmed by a human—the AI learns to do the task itself. There has been growing interest in AI in recent years, with technology leaders, including Elon Musk and Mark Zuckerberg, utilising machine learning to enhance existing technology.

The excitement for machine learning in service organisations has increased profusely, even though its powerful benefits are not completely understood by the masses yet.

Organisations around the world are now beginning to see machine learning as a ‘predictions-as-a-service’, whereby all manner of macro and micro environmental data—such as weather patterns and a specific technician’s capabilities — can be seamlessly connected and analysed to provide accurate predictions based on history.

And, while all this can be done without any interpretation effort required from professionals working in service-based organisations, the actionable insight it delivers can create significant competitive advantage.

But is the machine rendering man redundant?

Quite the opposite. In fact, the combination of machine learning predictions and operational research carried out by business leaders is providing a deeper and highly valuable level of business intelligence that is enabling more informed strategic decision-making, as well as improved productivity and performance.

For example, with machine learning technicians can become increasingly efficient in a way that makes businesses profitable, while also reducing the time that is spent on some tasks.

So how exactly is machine learning leveraging new opportunities for field service organisations?

When it comes to delivering business value through machine learning, the primary opportunities revolve around better planning and more precise scheduling. Three key examples include:

1. Predicting traffic patterns

Innovative service organisations are now introducing the ability to route engineers and technicians according to predictive traffic patterns. Based on historical data such as bank holiday traffic patterns, this enables technicians to be dispatched to particular jobs when traffic is less congested in those specific locations.

This is already delivering huge time and cost savings, not to mention improvements to the customer experience by reducing delayed arrival times and the need for long ‘wait-in windows’.

Beyond this, ML derived predictions can also provide organisations that deliver repair and installation services to the home with more accurate indications around the estimated duration of specific jobs, enabling scheduling and productivity to be further optimised.

2. Weather forecasting

The UK Met Office holds climate records dating back to 1959, so with 60 years’ of historical weather data, the Met Office has developed a weather forecasting model that enables it to predict weather patterns based on historical information and other seasonal factors.

Similarly, field service organisations are starting to mirror this weather predictions model by adding machine learning capabilities onto their existing management systems.

This is delivering quick time to value by identifying when certain jobs—often those that need to be performed outdoors or at height— should be postponed due to the expectation for poor weather and associated health and safety concerns, as well as time and cost considerations.

3. Preventing customer no-shows

One of the greatest profit drains for businesses operating in a field-based context are customer no-shows whereby a technician travels to a customer’s home at an agreed appointment time only to find there is no one at the property to provide access. Unsurprisingly, this is a key frustration for businesses.

So how can machine learning help with this? It can better predict whether a specific customer is going to be at home or not based on historical data about the specific customer’s track record, the location of their home and a host of other factors relating to the weather and their work situation.

The potential for this actionable insight to eliminate wasted technician time is significant and is expected to provide an increasing source of competitive advantage.

4 ) Sending the right person to the right job

Machine learning can also streamline service offerings by allocating certain engineers to specific jobs. For example, if an engineer frequently installs smart meters into homes, that engineer will become familiar to that job and will inevitably become faster and completing the installations.

Because of this, machine learning software can reallocate that employee for future smart meter installations to speed up job processes. Streamlining business decisions through machine learning can ensure that employees can work on the jobs that they excel at, improving customer satisfaction.

5 ) Predictive maintenance

By leveraging the data that is generated by the Internet of Things, machine learning can anticipate when repairs will be needed and can proactively schedule service without requiring human intervention.

Consequently, machine learning can monitor the status of the equipment and can predict when an issue may arise, allowing engineers to attend to the equipment before the issue is encountered.

By utilising preventive service over reactive service, organisations can prevent costly failures and can stop spontaneous breakdowns that irritate customers and take up engineers’ time.

How the machine is driving customer and employee experiences

From the viewpoint of the customer who needs their boiler or dishwasher repairing, the benefits of machine learning can include a dramatic increase in ‘first-time-fixes’ by ensuring the right parts and technicians are dispatched to them first time.

This is improving overall customer satisfaction and experience levels— something that is becoming increasingly critical in an environment where customers now demand Uber-like service levels and have more choice and sway than ever before.

Similarly, for employees working in field service organisations, machine learning can also improve the overall employee experience and support staff retention levels.

Dispatchers’ day-to-day roles are made easier by the fact that they have fewer decisions to make on their own, and for engineers and technicians, wasted trips can be minimised by reducing instances of customer no-shows or arriving at a job without the requisite parts or skills.

What’s next?

Of course, machine learning remains a new concept for many and questions remain around how best to apply this in a field service context. There is also still work to be done to tie machine learning into existing workflow systems so that future predictions can be more easily integrated, understood and applied.

The organisations that master this ahead of the masses, however, will certainly be able to improve their compliance with Service Level Agreements (SLA) and reap better ongoing business rewards.

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