How artificial intelligence can be used for potential business value.

The use of the artificial intelligence market is expected to grow to $390.9 billion by 2025, and industries within the space show a similar trend that is automotive AI, for example, is expected to grow by 35% year over year, and manufacturing AI will likely increase by $7.22 billion by 2023. However, according to top industry analysts, most (about 80%) of AI projects stall at the pilot phase or proof-of-concept phase, never reaching production. In many cases, this is due to a lack of high-quality data. Ethical and responsible AI continue to be obstacles for many companies, which often lack the resources or internal talent to build unbiased models in a time where AI is making increasingly impactful decisions. Companies also face an uphill battle with scaling and automation.

Believe in Your Data

The main factor of using artificial intelligence confidently in one’s business is to understand the value of data. People need high-quality training data to launch effective models. So, defining the data strategy upfront, including what the data pipeline will look like, will be crucial to success. Many data scientists and machine learning engineers say that about 80% of their time is spent wrangling data.

• The first step of the process is to collect data. One must start with a clear strategy for data collection. They should think about the use cases they are targeting and ensure that their datasets represent each of them. They must have a clear plan for collecting diverse datasets. Implement the data annotation process would require a diverse crowd of human annotators. The more accurate their labels are, the more precise their model’s predictions will ultimately be. Various perspectives will enable the user to cover a broader selection of use and edge cases. At the data collection and annotation phase, it’s critical to have the right plan for tooling in place. Be sure to integrate quality assurance checks into your processes as well. Given that this step takes up most of the time spent on an AI project, it’s especially helpful to work with a data partner in this area.

• The next step of the process is to train data. Feeding the ML machine with the right data is a vital step. It affects the characteristics of the machines as well as achieving accuracy in the result.

• Once the model reaches the desired accuracy levels, it is ready to launch. Post-deployment, the model will start to encounter real-world data. The user should continue to evaluate the model’s output; if it fails to output the correct data, a loop that data back through the validation phases. It’s helpful to keep a human-in-the-loop to manually check a model’s accuracy and provide corrected feedback in the case of low-confidence predictions or errors.

The Ones Who Tried and Won

In 2017 John Deere acquired Blue River Technologies, and together they’re poised to revolutionize pesticide use. Their AI models use drones and computer vision algorithms to identify weeds on farms. Doing so enables pesticides only to be sprayed on the weeds, rather than all crops in a field. Spending on pesticides was around $20 billion per year, but these efforts, it is expected to lead to a 90% reduction in pesticide costs. The methodology for this AI project is precise image segmentation. This method requires labeling data at the pixel level to determine which component of an image is weed versus crop. As one might imagine, the annotation process is very complex and involved. It requires both a comprehensive tooling interface and human levelers with a deep level of expertise in segmentation.

Use of AI in other Businesses

The manufacturing industry is using AI to automate logistics and supply chains. Nokia, for example, uses machine learning to alert an assembly operator when quality deviates. Specifically, if there are inconsistencies in the production process. AI may also monitor and track packages as part of a smart factory monitoring system, reducing lead time and preventing overstocking, or it can monitor throughput and downtime, highly impactful factors from a cost perspective. There are many automotive AI trends worth highlighting, including automation and safety, voice assistance, and personalization, among others. Self-driving cars are perhaps receiving the most fanfare, as these have the power to most dramatically change our daily lives.

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Business owners will need to accept the fact that technology is constantly evolving. It’s also a good thing since the latest tools can improve efficiencies, impact the bottom line, and resolve complex issues.

Your relevance hinges on your capacity to adapt to the ever-changing tech landscape. At any rate, it’s always to start incorporating big data across core processes and using it to compete better against other industry players. The challenge is knowing how to plan and implement a strategy for aligning your business with existing situations. Here are a few tips to help you get past that:

1. Lay it out

In making sure you get the most from your big data implementation, it’s important to draft a strategy that aligns with your business’s overall goals. It helps to identify short-term and long-term objectives and set benchmarks for each one.

These will serve as the basis for finding big data solutions that can produce intended outcomes and determining areas that can benefit the most from these solutions. You may also invite key stakeholders in the planning process to help you come up with a roadmap for building your enterprise’s big data infrastructure.

2. Invite top talent

Adapting to big data is not just a matter of using the right platforms and tools. There is also the human factor involved. The success of your big data implementation will depend on the personnel handling the organization’s data engineering requirements, so it’s important that you find people who can match you with the right strategies and tools.

You need specialists who have had experience building and maintaining databases that cater to the unique requirements of an organization. Be specific with the job description and put effort into hiring people who can help navigate your business through the challenges of big data adoption.

You may also need a data scientist who can help turn raw data on internal processes and industry trends into actionable insights that aid decision-making. In case you’re strapped for resources, you can always hire a third-party solutions provider that can help you set up your organization’s big data infrastructure. By augmenting your current tech and IT staff, you will be able to make the adoption process move faster.

3. Provide skill enrichment

In addition to hiring the right people, you also need to train your staff on handling big data platforms. After all, your business won’t fully adapt to current standards if your employees are kept in the dark about big data operations. Resolving this involves organizing internal training programs that will teach them how to use big data in their respective departments.

Along with this, you should also provide your data engineering team with opportunities for skill enhancement on account of big data’s evolving nature. From learning the best practices on data replication to Snowflake to Python, to developing custom platforms, the people in your organization must stay current with changing demands in big data technology.

4. Track your implementations

After setting up essential assets, maintenance and monitoring are still needed to keep the infrastructure up and running. Your data engineering team may do much of the work of identifying recurring issues and preventing bottlenecks from building up.

More importantly, you will want to monitor your organization’s vulnerabilities towards potential threats. As cybersecurity becomes an essential investment, you need to adopt the latest data security models and protocols for protecting cloud storage systems from breaches.

All of this is possible through a robust data security and compliance team within your network. With the large volumes of information passing through the pipeline, it helps to have a dedicated team of specialists to beef up your organization’s defenses, identify risks, and protect your organization’s reputation.

5. Stay aware

The world of tech is continuously evolving, so it’s important for your organization to stay up to date on current events. Catching up with the latest trends and predicting new ones is crucial to keeping your organization afloat. Apart from reading blogs, you as the business owner can represent your organization in tech summits and other events that discuss the impact of big data on your industry. You can also connect with tech advocates in your industry and track what everyone’s talking about online.

The key here is to stay vigilant. You will never know if you are well-equipped to face the next step in the evolution of big data.

Big data presents ample opportunities through which your business can thrive. Focus on implementing these tips so you can stay ahead in this increasingly data-driven world.

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In order to get the maximum out of technology, businesses are adopting data analytics trends

The power of data and analytics is no longer hidden. Today businesses of all sizes, starting from small to medium and big are availing data analytics in their routine to streamline operations. Without data analytics, companies are blind and deaf. Data analytics allows businesses to understand the market and their customers’ preferences and suggests solutions that could yield big profits. A rough estimation suggests that data analytics in business will increase five-fold by 2024 because of the rapid rise in technology adoption. Once upon a time, data analytics was confined to the tech industry. Only IT professionals, data engineers, and top-level enterprise executives got their hands on the technology. But things changed when laymen started embracing artificial intelligence. Today, big data, machine learning, cloud computing, data analytics, and many more technologies are a part of our everyday life. Many companies unveil data analytics in business to optimize business processes, cut costs, increase revenue, improve competitiveness, and accelerate innovation. In order to get the maximum out of technology, businesses should adopt recent data analytics trends. Data analytics trends such as decision intelligence, edge computing, data storytelling, etc are unraveling a world where businesses can understand their customers and address their needs like never before. In this article, Analytics Insight takes you through some of the top data analytics trends that businesses should follow in 2021.

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Top Data Analytics Trends for Business

Moving to Scalable AI

Post the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chain.

Decision Intelligence as the Powerhouse of Decision Making

In modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable data.

Augmented Data Management to Shorten Data Delivery Time

The next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their architecture.

Edge Data and Analytics at the Core of Operations

The inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.

The Stronghold of the Cloud Continues

Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2022. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track trials.

No more Big Data, Let’s go to Small and Wide Data

For almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.

Automation at its Best

Business outcomes rely on data. But over the past few years, big data is getting more complex. For example, the inflow of data is in various forms like videos, images, documents, texts, files, etc. Besides, there are also two other categories called structured and unstructured data, which makes data processing even more hectic. The only way out of this is by automating the process of data discovery, preparation, and blending of disparate data. Besides, automating the data discovery and analysis process helps analysts focus on high-value-added activities.

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Virtually all businesses today rely on digital technology to drive major operations, including upstream and downstream supply, sales and marketing, recruitment and onboarding, and internal communication. This is a partial list, but a telling one – symbolic of the reasoning behind the statement that “every company is now a technology company.”

The extent to which organizations use technology in the 21st century has a profound impact on growth, profitability, and overall success. As leaders and technologists increasingly work within the same domains, here are the five best practices that should be observed to leverage the skills of both and drive businesses forward:

1. Do not lead with technology in mind.

Technology’s starting point for driving ongoing business success is helping define the complex business problems in need of solutions and employing strategies that will most effectively support growth. The technology itself often plays a big part, but it should never be the primary focus.

Instead, leaders should always put stakeholders first. Customers, investors, and employees are the core building blocks of business, and fulfilling their needs (both stated and unstated) is the main driver of long-term growth and success.

2. Do not allow technology to remain siloed.

Technology has historically been viewed as a back-end function, receiving instructions and carrying them out independently. However, the divide between strategy and technology can be viewed as a fundamental misunderstanding of how to operate in the information age: Digital transformation is not only about implementing more and better technology but also about overlaying all traditional business processes with the power of those technologies offer.

This means it’s vital for executives to drive a genuine paradigm shift where data and software are concerned. Allowing technology to function as a disconnected department or entity limits its vast potential, along with that of the organization it is meant to serve.

3. Use data as the fulcrum for strategic advantage.

Seismic shifts in technology (artificial intelligence, machine learning, and the advent of microprocessing, to name a few) led to a tidal wave of data. Companies in many industries now access billions of unique data points on a daily basis. All of this information can provide insight regarding many important questions, including:

• How are customers interacting with products and perceiving brands?
• Which customer touchpoints are most valuable?
• Which moments are most critical in the customer journey?
• How well are products and services performing throughout their lifecycle?
• What shifts in the market are happening or are imminent, and how might they affect operations?

Answers to questions like these are an integral part of an evolving business strategy. The value of information is compounded by the speed at which it can be processed, and leveraging fast data is now not only a possibility for most organizations but a necessity to stay competitive.

4. Make technology the center of innovation.

Companies that lead in innovation often lead in the marketplace as well. Digital platforms and tools not only allow companies to ask smarter questions but also facilitate fast and easy implementation of ad hoc solutions testing.

When technology was hardware-focused, research and development was an expensive, time-consuming investment. Today, largely due to a shift to cloud infrastructure, it is a quick and highly dynamic operation with the ability to set companies apart in their sector. Multiple solutions can be experimented with simultaneously, providing deep answer sets and generating additional data in the process.

5. Prioritize the problems that arise at the juncture of technology and business.

In many cases, technology alone can do little to alter the trajectory of an organization.

However, it exponentially increases the value of traditional business knowledge by giving leaders the ability to ask and answer more complex questions.

“Business as usual” in most industries today is technologically enhanced and massively empowered when compared to just two decades ago. And yet, much of technology’s potential remains latent. Leaders who wish to activate it should start by understanding that the bottleneck is still fundamentally human. Internal barriers need to be broken down, and clear communication lines must be established between historically siloed teams. When this takes place, the true power of technology’s ability to innovate when it comes to customer experience and drive business growth can be unleashed – and the organizations that implement it will lead us into the next era of business.

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Organisations often block their own path towards scaling by delaying innovations purely out of their loyalty to their core businesses. Unfortunately, fixed organisational structures and legacy operating models result in frailty, disabling the sight of potential market changes. Enterprises hesitate to build products or services with new technology as they are unsure of whether the growth rates would satisfy their shareholders.

Today, to compet in a VUCA environment, businesses and their change agents must consider relooking and reconsidering how they engage and conduct business with their customers and leverage innovative technologies to enhance or introduce products/services to the market.

With rapidly changing consumer buying patterns and preferences, enterprises are increasingly focusing on digitization to evolve their business models, they are being mindful of the efficiency of business operating units, which enables them to pinpoint areas that need additional focus or restructuring quickly. Some of the possible initiatives organisations can opt for are:

• Growth hacking:Organizations need to be agile and iterative in their approach to keep up with the changes in the industry. Having a growth mindset can enable management, embrace challenges, show resilience while working through obstacles, and bounce back from impediments sooner, leading to overall higher achievement. Organizations backed by a growthhacking mindset will be ableto foster innovation and generate higher financial returns.
• Retooling organization:Companies should ‘avoid putting all eggs” in one basket regarding technology infrastructure. The focus should be on adopting technology like building a Lego structure, where individual components are replaced if the scalability and validity for the current and future needs of the business aren’t met.
•  Adopting new age innovation rather reinventing: Applications of AI is evolving within the industry at a fast paced, which enables organizations to evaluate quickly, adapt, pilot and scale within the organizations. Reinventing AI would mean wastage of resources of time, instead organization precious resources can be spent on identifying the right opportunity to evaluate and scale the benefits of AI.The global COVID-19 pandemic has crushed standards and redefined how business is conducted, affecting most enterprises in some way or another. At the same time, enterprises were already leveraging data science and AI in the past few years.  A significantly greater number of organizations are now looking for ways to harness them to reinvent themselves. Key-focused areas remain in strategy building, decision-making and governance setup, business planning and budgeting, funding decision making, managing performance and company culture, risk management, and more.For businesses, resiliency will become even more significant than efficiency as they move forward and data science will help companies maintain. For instance, retail stores and restaurants that were more dependent on brick-and-mortar sales before the pandemic had to make drastic changes to survive and sustain. While some were forced to shut shop, the rest kept steering ahead with new business models to adapt and thrive. Data science helped companies stabilise their organisations, build new processes, establish new communication channels and workflow, adapt to the remote working environment, recognise (and adapt to) changing consumer patterns and identify the emerging trends by using AI and machine learning.Traditionally, legacy companies used to focus only on their core business. With the new wave of transformation and new opportunity post the pandemic, these prominentestablished players are reinventing themselves and creating businesses in new areas with a very different mindset and culture than their traditional organizations.The new digital era demands asignificant change in traditional thinking and focusing on the practical approach of collaboration, competition, and innovationthat can combine data science, AI,and business acumen to conceive, build and bring new digital products to market at scale.

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How to make data work for your small business.

There’s an appetite for data science amongst businesses of all sizes. While buzz is one thing, the fact remains that very few – especially SMEs – have actually employed it to capitalise on the competitive advantage it brings.

Some that do make the leap end up disillusioned if it doesn’t deliver what they’d hoped. There can be a number of reasons for this, but the main one is that the business simply isn’t ready for data science.

Let’s first be clear on where the value in data science lies. While reporting processes are great, they are generally limited to revealing only what has already happened. The real value in data science lies in being able to shift focus to what will or should happen and crucially, being able to take action to change the outcome.

Data science can help businesses build strategies that factor in revenue growth over time, rather than chasing short-term gains. However, before making any investment, it’s worth asking some key questions and replying honestly. Otherwise, any efforts can end up being a flirtation rather than a lasting relationship.

So, consider the following questions; each one that you can answer ‘Yes’ to puts you one step closer to being data science ready:

Do you have sufficient historical data across each of your data sources? 

Much like the real world, we need to have enough context to predict whether our decisions will have a successful outcome or not.

Training a data science model is somewhat like training a puppy. ‘Good behaviour’ – in this case accuracy and performance – should be rewarded to encourage repetition.

Historical behavioural data and outcome data are the only treats a machine learning model needs, the more it has and by learning the relationship between the two, it gets better at making predictions.

However, the data used for training the algorithm must be representative over time in order that it can discern ‘business as usual’. So, data gathered over the course of the pandemic won’t offer a meaningful basis for data science. If all you have is anomalous data, the goalposts are constantly changing.

Also, the devil is in the detail so ensure your CRM system doesn’t overwrite point-in-time customer data to provide just the current snapshot of your customer database.

The same is true for stock data – you need to be aware of stock-outs if you want to distinguish between when there was ‘no stock’ versus ‘no demand’ as one example.

Is your data infrastructure solid?

Not all data is created equal. Making sense of it across all sources – both from the cloud and data that’s held in internal systems – means ingesting them all into a centralised data warehouse as a single point of truth to be used by all internal departments.

The expression ‘garbage in, garbage out’ holds true when we talk about data science models. Ensuring they can decipher data successfully requires ‘cleaning’ it by standardising formats, de-duplicating it and so-on.

Most importantly, there needs to be a common ID that makes it possible to join up data across different sources. For example, if we want to combine a customer’s transaction behaviour, browsing patterns and engagement with email marketing campaigns, we need an identifier.

Do you have access to expertise?

We often hear data is the new oil, but oil has limited use until it is refined. Extracting data’s true potential requires a willingness to invest in data scientists and technologies. Since data science is highly specialised, it cannot be palmed off on the marketing, or the IT team. It’s not fair on them and is very likely to doom any project to failure.

Any sources of inconsistency or friction points in the data will surface false positives and can lead to ‘data drift’, a scenario in which fundamental changes in the way customers behave over time can render models ‘stale’ and the performance degrades.

Consequently, both the data and the models need to be constantly monitored and occasionally retrained. This takes time, expertise and engineering behind the scenes.

Do you know what specific challenges you need to solve?

Advances in data science and machine learning are still a long way from ‘artificial general intelligence’. Their most successful use cases will have quite specific applications, where decision making processes are manual and available data is being under used in that process. In the simplest terms, you have to ask the right question(s).

Most typically, these applications centre around factors like lead generation and conversion, customer acquisition and retention, and stock control. So, data science has to be grounded in strategic thinking and ideally should be cumulative to build across departments and agreed business-wide goals. So, consider how you’ll be able to keep expanding on each set of results by asking the right follow-up questions.

It helps to adopt a ‘walk before you can run’ approach by focusing on the most pertinent problems or use cases first. If you have enough pointed use cases, these naturally come together to form a solution which delivers tangible value. Otherwise, you’re just doing data science for the sake of data science.

Do you have a data culture?

A data culture is one in which all key stakeholders within the business are comfortable enough to trust in the data and outputs from predictive algorithms. The stumbling block is typically internal politics.

Some teams jealously guard ‘their’ data. It’s perhaps ironic that data democracy requires everyone to get over the politics and recognise shared goals over individual or departmental ones.

Doing data science right requires time, investment and ongoing management, it often needs a change in perspective. A data culture must be led from the top and requires the Senior Leadership Team to recognise the benefits of full transparency from a shared data source. If you’re serious about data science. strong leadership is a prerequisite.

Data science is by no means a fad nor a nice-to-have, it will become increasingly business critical in a crowded market. If you are new to data science, the good news is that there is usually a lot of low-hanging fruit, you just need to know where to look for it.

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Companies should develop a data science strategy to drive business intelligence.

Business intelligence is not a luxury anymore but a necessity today. The rapid adoption of disruptive technologies has enabled companies to enhance growth and agility. Data fuels businesses in the current scenario and it enables companies to gain intelligent insights. Hence, data science is an important part of regular business processes. Different companies use different methods to optimize data and make better decisions. This is often called a data science strategy. The significance of a data science strategy is immense in driving growth and staying close to the customers.

Why A Strategy?

Creating an effective data science strategy is not as simple as it sounds. Our world is getting smarter every day and businesses need to stay on the competitive edge to achieve success. A data science strategy or data strategy will enable the company to reach the right data, metrics, and data resources with ease and better accessibility. Developing a strategy will need a company to first define its goals, it can be a larger and measurable goal like generating more revenue. The next step is to find the right data resources suitable to the business goal. The executives need to clearly define the questions that they want the data insights to answer so that the company does not end up following unuseful and wrong data. A clearly articulated business strategy can ease the process of developing a data science strategy.

Building A Strategy

As mentioned above, the initial step would be to define measurable goals and find the right data resources. Next is identifying the project infrastructures by understanding which technologies to use, should everything be developed internally or outsourced, etc. The company should also decide the data storage platform and the desired form in which you would like to get insights like visuals, charts, reports, and more. Building a data science strategy also involves deciding the algorithms and technological models that should be used. This includes AI, machine learning, statistical inference, and making clear if these algorithms need to be transparent and explainable.

Another most important step is constituting a data science team. Hiring data scientists can be a bit difficult today as the role is in high demand. The company should analyze if it is going to build an in-house data science team or hire experts from outside. Collecting and storing data will create regulatory obligations that need to be met. Companies should consider data governance as an essential component to avoid data becoming a risk and liability. For this, the data science strategy should include compliance, security measures, and privacy policies in place.

Once all these steps are accomplished, a company can then use data analytics to process the huge amount of data and get actionable insights through disruptive technologies. Data and analytics are the crucial part of business intelligence today and intelligent insights are the only way to understand the audience better and create personalized services.

Data science and machine learning go hand in hand. Machine learning, a subset of AI, is an effective and widely used tool to deliver data analysis and insights. Machine learning models can be fed with data and this will enable the machines to learn from these data and improve from past patterns and risk behaviors. Ensuring data quality often becomes a challenge and integrating machine learning into the data strategy can help overcome it. Machine learning can accurately detect errors with minimal human intervention. Machine learning and data science strategy intersect and this is the current business intelligence scenario. Hence, companies should have a data strategy in place to enhance growth and efficiency.

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You’ve heard about Artificial Intelligence but can it really be used in the casino business? After all, doesn’t the casino already have a house edge?

However, even before the pandemic, major casinos which cost millions of dollars to operate were experiencing stiff competition. Several casinos in Atlantic City were closed, and many Las Vegas casinos were on a tight financial rope.

Now that the pandemic has happened, and casinos throughout both Europe and the US are facing fiscal challenges, many casino operators are looking to Artificial Intelligence to ensure the casino remains in the black.

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that uses computers to accomplish tasks that would normally require human intelligence.

Of course, when we talk about computers accomplishing tasks that mimic human intelligence, we’re not talking about simple tasks like exchanging 5 dimes and two quarters to make a dollar’s worth of change, or a vending machine sending 20 cents in change after you buy a bag of peanuts.

No, Artificial Intelligence is used in manufacturing robots, creating smart chat boxes for websites, and operating as a virtual travel assistant, and a thousand other tasks.

So how are casinos using Artificial Intelligence?

One thing is for sure and that is the gambling games will remain the same. Playing blackjack will still be playing blackjack. New Zealand casinos pokies will still be the one-armed bandits they have always been, with of course some progress in technology that comes with time.

However, one of the cardinal rules of casino managers is that it is significantly easier and more profitable to target a return gambler than it is to court a never seen before gambler.

What’s the solution? Artificial Intelligence to the rescue.

The single biggest use of Artificial Intelligence is to provide a database of players, what their preferred method of gambling is, the amount of money they spend on various games as well as how much time they spend in casino gambling.

Naysayers will say that is impossible to do but remember, there are eyes in the sky everywhere at physical casinos. By using artificial intelligence to survey casino security footage, plus information used on casino player cards, casinos have more information than ever before.

Using AI tools such as predictive analysis, the casino’s marketing department is more armed than ever before.

The object, of course, is to be able to analyze who are the most valuable players to the casino. And while a lot of popular lore centers on ways that the casino attracts high-value “whales” to gamble at the casino, the AI focus is on the more affluent younger generation.

Why is younger better?

One thing learned from the pandemic is that older gamblers are fickle while younger gamblers in their mid-20s to 30s are easier to attract.

For the next few years at least, casino managers have decided that younger gamblers are their bread and butter.

Using Artificial Intelligence, marketing departments are very eager to attract these younger gamblers for a return trip. In addition, the major players in the casino staff are paying attention to what facilities and shows attract the younger crowd.

Customer service is greatly improved where AI is involved and is very useful in areas such as nearly instantaneous check-in to avoid long 2-hour waits.

Security

Security is also greatly enhanced using Artificial Intelligence.

The use of facial recognition prevents gamblers who have been banned by the casino for activities such as card-counting at Blackjack from returning, and potential trouble makers are also screened out.

Conclusion

It seems that Artificial Intelligence is going to be a big part of everyone’s lives, and casinos are no exception. AI can be very useful not only for the casino but to provide a better experience for the player.

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Most of the big data analytics we perform centers around what we expect it to be

“Why is the product unsuccessful? Did we not plan it after going through big data analytics?” asked a company executive. This is not the first company or the first time such confusion has happened. If we take a close look around, most of the data analytics we perform centers around the concept we expect them to be, leading to massive setbacks. Yes, it is true. Even though we praise big data for being the accelerator of every decision, we can’t deny the fact that it can be misleading at times.

Big data is more than just structured and unstructured data. It is seen as a base ingredient of all decision-making processes. For the past two decades, ever since mobile phones came into existence and technology evolved exponentially, It became a critical part of every business operation. Big data in business is a very common substance that executives and employees used to get insights into their market performance. Technology experts are all praises about data, with many touting it as the best thing that has happened to humankind. But the truth is a little twisted. When data is used correctly, it opens the door to double or triple-fold the revenue in minimum time. Unfortunately, it can also be misleading, draining the company’s efforts to go down the gutter. A report by Blazent, an IT intelligence company unravels many findings of big data disadvantages. It shows that around 42% of executives state that misuse of data can impair revenues and 39% said this can be deteriorating for correct decision-making. Henceforth, this article takes you through how big data is misused and what can be done to patch the gap.

Drawing an example from political endeavor

Political circle, especially, presidential elections were heavily relying on big data outcomes. Of course, curiosity didn’t let us be silent. It almost became a custom to know the result through pre-poll analysis. But if we look back at the records, they were not always right. Most recently, the 2016 election that gave Hillary Clinton a 90% chance of victory ended up making Donald Trump the President of the United States. This could either be because of a crack in the data or the data itself was faulty. The big data analytics clearly depicts the fact that human nature as of yet, cannot be reduced to a series of ones and zeros.

Moving on to big data in business and its disadvantages 

Businesses are increasingly relying on big data today. Starting from making simple decisions on marketing and promotion to big ones like where to invest and how to gain more revenues, literally, everything revolves around data. Unfortunately, business executives are unaware that technological innovation is a double-edged sword. If it is not used for good intent, It can wreak havoc.

Datasets are huge and are spread across many disparate locations and diverse forms. Henceforth, business organizations are unaware of whether the data is clean, accurate, manageable, and usable. Besides, some of the data are also manually entered into the system, prompting human errors. While such mismatched data are processed together, it leads to serious negatives and misleading outcomes. However, companies, unaware of the datasets condition take the result as everything and proceed with it.

Businesses are increasingly relying on algorithms to sort company issues. Brian Bergstein of MIT Technology Review suggests that the growing reliance on big data in business is creating a corporate bubble of overconfidence. But why are algorithms unreliable? Even though algorithms are computer-based, they have their own form of risk since they are ‘created by people and they contain interferences and assumptions coded in.’ These coded-in values shape the outputs like computer-generated predictions, recommendations, and simulations.

Finally, one of the biggest setbacks of big data analytics is people’s perceptions. While company executives have a perception on certain products or product developments, the consumers’ viewpoint might vary. But this goes unnoticed when companies focus on delivering their viewpoint to customers without addressing their concerns. Organizations design questions that they want to ask. It is solely on the executive’s perception of what clients needed to answer. They weren’t reflecting on what clients wanted to express. As a result, business takes the wrong path in the name of following big data insights.

What can be done?

Listen to customers. It is the only option to keep away misconceptions. Even though engaging with customers and having a face-to-face or virtual conversation may not be as exciting as compiling big data answers, they reflect on people’s thoughts. When we ask random questions and let them talk, they talk their hearts out and say things that might build the stairs for the organization’s success. For example, Toyota and Adobe are two such companies that go for people’s view than big data decision-making.

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How artificial intelligence in data analytics can help visualise business data?

In an ultra fast-paced digital world, businesses of all sizes produce huge amounts of data that are challenging to keep up with. Such data carries much promise when it comes to analyzing them. Recent technological advances have changed how enterprise analytics perform. There are still some challenges to using data and analytics in many aspects of an organization. However, when using artificial intelligence in data analytics, businesses can produce outcomes far beyond what they can do manually, both in terms of speed and accuracy.

Analytical approaches comprising predictive models have now begun to shift merely to descriptive approaches, which is already beneficial for many users and continues to be valuable. Descriptive analytics has evolved much, making greater use of visual analytics. Despite this, making use of data and analytics to interpret and envisage significant phenomena in businesses is difficult.

Predictive models capitalize on past data and a reasonable amount of expertise to create and predict outcomes. However, the use of past data here limits how and when they can be deployed. Existing data analytics approaches have historically been a bit narrow. They are focused on particular functions or units, even though many business problems and issues cut across functions and units.

Data Analytics Influenced by Artificial Intelligence

Powered by automation and artificial intelligence, the next-generation of enterprise analytics is emerging. Apart from this, the innovation relies on connections across existing information systems and role-based assumptions about what decisions will be made on data and analytics. AI-enhanced software has the potential to assess data from any source and deliver meaningful insights. It can analyze customer data that can be particularly revelatory and disrupt product development while improving team performance and enabling businesses to know what works and what doesn’t.

Artificial intelligence typically refers to the field of data science. It leverages advanced algorithms to power computers to learn on their own. By integrating AI into their data analytics processes, businesses can be able to automatically clean, evaluate, explain and visualize their data.

In an article, Tom Davenport and Joey Fitts wrote that automation in analytics, often called “smart data discovery” or “augmented analytics”, is reducing the reliance on human expertise and judgment by automatically pointing out relationships and patterns in data. The systems, in some cases, even recommend what the user should do to address the situation identified in the automated analysis. Together these capabilities can transform how people analyse and consume data.

Artificial intelligence and automation have made significant advancements in data analytics that were inconceivable a few years ago. Enterprises these days are realizing the benefits of these technologies and using them to examine their data to derive fine-grained insights. AI is now creating new methods for data analysis. Historically, data engineers or analysts have had to use a query or SQL when it comes to analysing data. However, as the significance of data continues to grow, multiple ways to excerpt insights have emerged. Artificial intelligence emerges as a crucial technology, becoming the next step to query or SQL.

Earlier, data and analytics have been discrete resources that needed to be fused to accomplish value. This also required extensive knowledge of what type of data was apt for analysis within an organization. Most data analysts lacked such knowledge in a broader context. However, AI-powered analytics can increasingly provide context. Many key vendors are already using these capabilities in their transactional systems offerings, such as ERP and CRM.

In conclusion, this is just the beginning of data analytics powered by artificial intelligence. As the advances in this technology will continue to evolve, the potential of AI-driven data analytics tools will be striking.

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