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	<title>Watson Analytical Concepts</title>
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		<title>How And Why You Should Use AI And Machine Learning To Enhance Business Intelligence</title>
		<link>https://watsac.com/how-and-why-you-should-use-ai-and-machine-learning-to-enhance-business-intelligence/</link>
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		<dc:creator><![CDATA[sabtain]]></dc:creator>
		<pubDate>Mon, 09 Nov 2020 07:25:00 +0000</pubDate>
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		<guid isPermaLink="false">https://watsac.com/?p=2416</guid>

					<description><![CDATA[Let’s face it: The greatest value from collecting and analyzing data is not revealed by understanding what happened yesterday, but in deciding what to do tomorrow. That capability has been available for years — if your business could spend enough time and money on the pursuit. Today, ultrapowerful predictive analytics is coming to a wider &#8230;<p class="read-more"> <a class="" href="https://watsac.com/how-and-why-you-should-use-ai-and-machine-learning-to-enhance-business-intelligence/"> <span class="screen-reader-text">How And Why You Should Use AI And Machine Learning To Enhance Business Intelligence</span> Read More »</a></p>]]></description>
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									<p>Let’s face it: The greatest value from collecting and analyzing data is not revealed by understanding what happened yesterday, but in deciding what to do tomorrow. That capability has been available for years — if your business could spend enough time and money on the pursuit.</p><p>Today, ultrapowerful predictive analytics is coming to a wider spectrum of enterprises than ever before. Driven by the open-source movement in response to the explosion of big data, midmarket companies and even SMBs now have access to the same AI-driven BI solutions that previously were largely available only to multinationals.</p><p>AI and machine learning technologies are transforming BI and giving decision-makers “aha” moments like never before. Today it’s possible for any company to not only gather information, but also to instantly derive insight and, perhaps even more importantly, reliably apply that insight to future business activity.</p><h3>Finding Outliers</h3><p>Using BI in conjunction with AI and machine learning is how data analysts can really contribute to business success. Senior executives aren’t always sure what analytics can provide, or they don’t know what potential resides in their data. Analysts can help by using automation to uncover anomalies, expose critical situations and enhance strategic deliberations without bias.</p><p>Especially as companies embrace digital transformation, AI and machine learning are becoming increasingly vital. Companies are seeking to streamline operations and embrace new revenue models such as direct to consumer through digital transformation. They need to understand the efficacy of their processes end to end; many times, this isn’t possible using antiquated manual data analysis techniques.</p><p>It’s critical for decision-makers to rapidly see the telltale signals in their data that will impact their business. Analysts, for their part, shouldn’t have to spend 80%–90% of their time manually searching through data. Machines should do the heavy lifting: number-crunching, correlating and trendspotting.</p><p>Here’s a real-world example. An Asian aerospace company, a global manufacturer of turbine jet engine blades, uses AI-enhanced alerts to identify anomalies in its production processes. By sifting through millions of data points each day, the system does the work that used to require 16 trained professionals. Today, only two people are required to review the output.</p><p>The objectivity of AI and machine learning can be invaluable in situations where assumptions might cloud judgment. A digital marketer might believe it has a great advertising campaign based on global web traffic measures. What AI can uncover, however, is patterns that indicate troubling activity. By slicing data by country, region, city or even neighborhood, AI can see, for instance, where a media channel might be engaging in click fraud. That kind of activity would typically not be discovered without automated signaling because the money involved isn’t dramatic.</p><h3>Telling Stories</h3><p>Data is amazingly helpful to any business. But many organizations have gotten into the habit of simply pushing data at people without explaining the significance of the numbers. Instead of creating a compelling narrative, they supply team members with analytics dashboards and expect those individuals to draw the correct conclusions.</p><p>AI and machine learning, as a part of BI, have the ability to significantly improve this situation. For analysts, the ability to instantly spot trends and identify outliers in huge amounts of data points helps them see the larger picture and gain perspective on broader issues impacting the enterprise. They can then create the critical stories that bring clarity, shape opinions and have a real impact.</p><p>The core value of data professionals in a world overrun with disjointed and often obscure statistics is not to simply summarize what happened. Instead, they should apply their knowledge of larger issues in the real world — competitors’ ad campaigns, socioeconomic factors, production line issues and so on — to drive organizational understanding.</p><h3>Choosing The Right Platform</h3><p>Selecting the right enhanced BI platform is a complex decision, and many issues come into play. Start by prioritizing those factors that will create the most value for your business (e.g., time savings, cost reductions, opportunities for innovation or risk avoidance or better productivity). You can then evaluate providers based on their ability to satisfy your priorities.</p><p>Ensure you’re matching the AI capability of your BI product to your user types. A natural language capability, for instance, is well suited to business users who can more easily understand what’s happening in their data. A statistical output of a data science model, on the other hand, is better suited to users with a higher degree of data literacy and experience in stats.</p><p>When implementing, start with the place where your organization needs automation most. You might begin where high veracity is needed despite large volumes of data or where analytics teams are having a hard time keeping up with demand. Implement a pilot team that looks at a subset of data to prove your AI solution is producing the appropriate results.</p><p>From the first day, focus on ROI. Get a clear idea of the actions you can take based on the insights you’re uncovering. That will allow you to gain a firm understanding of the value the solution is creating.</p><h3>Reshaping BI</h3><p>BI, in and of itself, shouldn’t be seen as a discrete, one-time project. It should continually evolve as part of the life of an enterprise built on data. In order to gain maximum value, you should make it part of the organizational DNA. I believe a long-term commitment to BI, especially in the age of AI and machine learning, is much more than a “nice to have.” As large, well-funded organizations have known for years — and as enterprises of all sizes are learning today — a robust and seamless BI ecosystem enhanced with predictive AI capabilities is one of the most powerful and consequential tools any business can have.</p>								</div>
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		<title>How Emerging Demands Of AI-Powered Solutions Help Gain Momentum Of Businesses</title>
		<link>https://watsac.com/how-emerging-demands-of-ai-powered-solutions-help-gain-momentum-of-businesses/</link>
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		<dc:creator><![CDATA[sabtain]]></dc:creator>
		<pubDate>Mon, 09 Nov 2020 07:18:34 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://watsac.com/?p=2410</guid>

					<description><![CDATA[The businesses of today are going through a tremendous digital transformation lead by artificial intelligence (AI) more than any other technology. AI arguably has been the most exciting and ambitious proposition out there for businesses and researchers alike. With IDC forecasting the AI market&#8217;s worldwide revenue crossing $300 billion by 2024 with a five-year CAGR &#8230;<p class="read-more"> <a class="" href="https://watsac.com/how-emerging-demands-of-ai-powered-solutions-help-gain-momentum-of-businesses/"> <span class="screen-reader-text">How Emerging Demands Of AI-Powered Solutions Help Gain Momentum Of Businesses</span> Read More »</a></p>]]></description>
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									<p>The businesses of today are going through a tremendous digital transformation lead by artificial intelligence (AI) more than any other technology. AI arguably has been the most exciting and ambitious proposition out there for businesses and researchers alike. With IDC forecasting the AI market&#8217;s worldwide revenue crossing $300 billion by 2024 with a five-year CAGR at 17.1 per cent, the future certainly looks bright. Though COVID-19 has slowed market pace; the work from home scenario will further catalyze AI’s adoption.</p><p>As per the 2019 Mckinsey Global Survey, there has been an approximate 25 per cent year-on-year increase in the use of AI in business operations. Also, a significant number of respondents have agreed on witnessing increased revenue in the AI applied areas of business and 44 per cent believe that it helped reduce costs. The AI market is witnessing this spur of growth owing to the adoption of big data and cloud-based applications, growing investment by tech leaders, and wide applicability and benefits of these AI-led solutions. Similar factors have worked positively in the increased usage of BI tools.</p><p>In 2019, the software category contributed 39 per cent to the AI global revenue. Companies are now discovering the historical data mines they hold, collecting new data from various sources including IoT devices, and looking to utilize this via advanced data analytics. The growing need to derive predictive insights from this data has induced the demand for an AI-based analytics platform.</p><p>AI helps take the BI game leaps and bounds ahead with machine learning and deep learning. It empowers BI with the ability to analyze data coming from multiple sources, learn from this data in real-time, and provide accurate granular predictive insights for faster business growth. AI always stays one step ahead of humans in terms of analyzing large data sets at scale with speed and accuracy. The influence of AI is simply not limited to analytics but also to data engineering. Data coming from multiple structured, unstructured, and semi-structured sources, needs to be transformed from silos to unified data. AI can accelerate and automate this process creating a single view and saving data analyst&#8217;s time and providing much-needed independence for business users.</p><p>AI-powered NLP bots take BI altogether to the next level by enabling users to extract insights via voice or chat using any language. For example, these BI bots can easily answer questions like &#8216;What is the sales forecast for the next two quarters?&#8217; With this, business users can skip any complex query and leave it up to the bots to process the analysis.</p><p>It helps drive innovation and overall efficiency in businesses by optimizing resource utilization and automating various processes. Business leaders are now able to make data-driven futuristic strategies with intelligent trend forecasts and actionable insights. From identifying market gaps and business opportunities to streamlining tasks to improve employee productivity, AI-driven analytics can be truly valuable for any business.</p><p>Let&#8217;s have a closer look at how machine learning is driving success in multiple industries.</p><h3>Retail</h3><p>Retail perhaps has the widest utility for machine learning powered BI. With customers interacting and generating data on multiple channels, BI can present a unified view with predictive modeling correlating various data points. It enables retailers to analyze consumer behavior in depth, foresee purchase trends, predict needs, identify at-risk customers, and personalize offerings. Retailers can enhance customer segmentation and targeting, test campaigns, improve conversion rates, and induce customer loyalty. With machine learning, retailers can provide dynamic pricing in real-time by analyzing data like the weather forecast, purchase history, inventory levels, competitor pricing, and more. It does not just forecast sales, but also enhance inventory management and streamline the supply chain by predicting demand and identifying which product will sell faster and which may result in deadstock.</p><h3>Manufacturing</h3><p>Manufacturers are utilizing AI-based analytics tools on a day to day basis to optimize every aspect of their business. They are analyzing historic consumption data and other external factors to accurately predict demand and enhance inventory management by producing only the products in demand, based on the season or any other trend. With predictive maintenance, production units can be optimized by forecasting any equipment fault and triggering alerts based on the analysis of available data and save breakdown costs and prevent machine downtime. They can achieve maximum production quality by tracking device efficiency and save losses due to product quality deterioration. Also, with unified analytics, producers can have a single view of the whole operation.</p><h3>Healthcare</h3><p>Within healthcare, predictive analytics positively affects everyone right from patients, physicians to administrators. Using ML, high-risk patients can be identified, risk scored for potential health issues, and preventive care can be provided, and readmissions can be avoided. The appointments can also be streamlined by predicting patient utilization patterns and resources can be optimally deployed for a better experience. Also, clinical trials are being accelerated with accurate outcomes predictions and precision medicine development has further eased.</p><p>The benefits of AI-powered analytics are not just limited to these sectors but are extremely widespread for all industries. For example, AI enables predictive analysis of public data like social media and provides brands with the opportunity to do sentiment analysis of the audience, whereas financial institutions can predict frauds and prevent them.</p><p>It can be easily said that AI with its various underlying technologies is and will keep accelerating businesses. AI is transforming the way businesses are done, customers are shopping and how products are being manufactured and marketed. It may now be a factor of competitive advantage, however, as the technology, businesses, and data grow, AI will become imperative for continuous success. </p>								</div>
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		<title>How CEOs Can Lead a Data-Driven Culture by Thomas H. Davenport and Nitin Mittal, Harvard Business Review</title>
		<link>https://watsac.com/how-ceos-can-lead-a-data-driven-culture-by-thomas-h-davenport-and-nitin-mittal-harvard-business-review/</link>
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		<dc:creator><![CDATA[sabtain]]></dc:creator>
		<pubDate>Mon, 09 Nov 2020 07:12:51 +0000</pubDate>
				<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://watsac.com/?p=2399</guid>

					<description><![CDATA[While businesses across the world are trying to make more effective use of data, analytics, and AI, a key impediment is holding many of them back: The lack of a culture that truly values data/analytics capability and the superior decision making that can flow from it. Yet as we’ll describe, it’s possible to create a &#8230;<p class="read-more"> <a class="" href="https://watsac.com/how-ceos-can-lead-a-data-driven-culture-by-thomas-h-davenport-and-nitin-mittal-harvard-business-review/"> <span class="screen-reader-text">How CEOs Can Lead a Data-Driven Culture by Thomas H. Davenport and Nitin Mittal, Harvard Business Review</span> Read More »</a></p>]]></description>
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									<p>While businesses across the world are trying to make more effective use of data, analytics, and AI, a key impediment is holding many of them back: The lack of a culture that truly values data/analytics capability and the superior decision making that can flow from it. Yet as we’ll describe, it’s possible to create a data-driven culture and accrue the competitive benefits that result.</p><p>In companies with strong data cultures, important decisions are informed by data and analytics and executives act on analytically derived insights rather than intuition or experience. While digital-native companies like Amazon and Alibaba have strong digital cultures, many traditional companies are struggling to make progress. That’s mostly because few undertake initiatives directly aimed at achieving the desired culture change.</p><p>Thus, it shouldn’t be surprising that a 2019 Deloitte survey of U.S. executives found that most – 63% – do not believe their companies are analytics-driven and 67% say they are not comfortable accessing or using data from their tools and resources. Data from surveys taken over time suggest that the problem may be getting worse. A New Vantage Partners survey of large U.S. firms, for example, found that only 31% of companies say they are data-driven, a figure that has declined from 37% in 2017. In 2019, more than three quarters reported that business adoption of big data and AI initiatives remains a major challenge. But 95% of them said that cultural, organizational, and process challenges presented the biggest roadblocks to adoption. Only 5% cited technology as the problem.</p><h3>The CEO’s role</h3><p>Clearly, culture depends in large part on the orientation of senior leaders, and especially the CEO. There is little doubt that a CEO’s own reliance on data – or lack thereof – in decision making and improving the business sends a powerful message to the rest of the organization.</p><p>But a CEO’s initial resistance or lack of awareness does not mean that an organization can’t make progress. Just as CEOs are often counselled on their communications and leadership skills, they can also be moved in the data domain through coaching, either by an internal champion, such as the chief data officer, or by outside experts. Linking data and analytics to issues the CEO already holds dear, such as customer focus or employee empowerment, can prove persuasive, as can pointing to outside factors that depend on having a data-based decision making such as regulatory requirements (for example in hospital readmissions) or the threat of more data-driven competitors.</p><p>While the CEO should become a visible champion of the new culture, he or she needs an operational partner. A logical candidate is the chief data officer, a role that is growing in prevalence, visibility and scope. The CDO is well positioned to become the data and insight change agent, leading the initiatives we describe below.</p><h3>Culture Change Programs</h3><p>In addition to trying to convert a passive or reluctant CEO, three types of change programs can move an organization in the right direction.</p><p>Carefully planned educational programs should be pushed into every level of the organization. Experiential programs such as design thinking exercises, group problem-solving, and hands-on hackathons tend to be more effective than talking heads. Position-appropriate exercises for staff at different levels can illustrate the benefits of analytics and data-based decisions; for example, executives can focus on framing the problem, and front-line employees can interpret the implications of analytics for customer relationships. To see an example of one such initiative, look at this simulation program developed by one of us (Davenport) to teach analytical decision-making in a consumer products company.</p><p>Education should focus not only on attitudes and knowledge about data, analytics, and AI, but also on skills for finding and manipulating data at every level, including senior management levels. A survey sponsored by the data analytics vendor Splunk of 1,300 senior executives found that while 81% of the executives agree that data skills are required to become a senior leader in their companies, 67% say they are not comfortable accessing or using data themselves. Seventy three percent felt that data skills are harder to learn than other business skills, and 53% believe they are too old to learn data skills. Effective education initiatives can prove them wrong.</p><p>TD Bank Group, for example, has developed a day-long educational program called “Data and Analytics Academy for the Non-Analytics Executive.” The Academy employs an immersive approach using a customized case study, simulations, and a series of exercises. In the program, participants work on framing a business problem such as identifying customers whose portfolio of banking products is not maximizing the performance of their assets, identifying internal and external data sources to help address it, and then operationalizing analytical solutions. So far, more than 300 executives have taken the course. The D&amp;A Academy is part of a broader set of programs designed to improve knowledge of and stimulate demand for data, analytics, and related technology. (See this article which details one such program.) These changes are working for TD; it recently made being “data-driven” one of its five strategic priorities.</p><p>Leading by example is also important. This requires showcasing leaders who visibly use analytics and AI in internal marketing programs to spread the value of the approach across an organization. Leaders’ exemplary behavior can also include modeling the desired attitude about data and analytics in meetings; leaders should frequently ask, “Do you have data to support that point?” and encourage others to do likewise.</p><p>While too few leaders recognize the importance of modeling and marketing their use of data and analytics, companies are increasingly designating champions of AI; 45% of U.S. executive respondents to the 2018 Deloitte “State of Enterprise AI” survey said their company was appointing such senior management champions. Forming communities of practice around analytics and AI are another way to publicize positive examples.</p><p>Promotions and rewards can also encourage change. If those who make effective use of data and analytics get faster promotions and salary increases, others will notice. Of course, this approach requires leadership endorsement and sign-off and execution by Human Resources.</p><h3>Putting it all together</h3><p>Eli Lilly and Company is applying many of these strategies to shift its culture. Chief data and analytics officer Vipin Gopal, the first Lilly CDO, is building on the company’s deep research- and statistics-oriented culture to engage its employees with advanced analytics and AI. Among other objectives, Gopal is working to communicate the value that these methods can bring to their work, ultimately reducing the time and cost required to bring new medicines to patients.</p><p><strong>To that end, Gopal and his colleagues are pursuing a variety of strategies, including:</strong></p><p>Highlighting successes by early adopters and enlisting them help get others engaged;<br />Forming cross-functional teams that combine people with backgrounds in data analytics, business, and technology and combining computer science, applied math, engineering, and behavioral economics perspectives to bring diversity and innovate thinking to projects; and<br />Launching programs across the organization, including open houses, forums, communities of practice, educational initiatives, and a leadership council – in effect, building marketing capability for analytics and AI within the company that helps create advocates and ambassadors.</p><h3>Technology is everyone’s job</h3><p>Today, every job requires an orientation toward technology. Beth Galetti, Amazon’s senior vice president of worldwide HR, recently commented (when asked about the company’s $700 million reskilling investment for employees), “The most consistent thing we see that’s changing is the need for some level of technical skills in any job.” With basic tech-savvy, employees have not only the fundamental skill they need in a fast-evolving competitive environment, but the mindset required to support a flourishing data and analytics culture.</p><p>Cultural changes take a long time to mature, and culture is influenced over time by every leader who joins an organization. It’s important for someone to monitor changes in the data/analytics orientation of the leadership team (this might also be the CDO’s purview). Several years ago, the analytics group at one consumer products firm did an analysis of every senior manager to determine how oriented to data and analytics each was. Any manager deemed unsympathetic became the target of a customized persuasion initiative. If a manager left the company, all possible successors to the position were analyzed, and the most likely candidates were subject to persuasion interventions if they seemed to lack the desired data/analytics orientation. This program may seem manipulative, but it was in the service of the company’s success—and it’s the kind of thinking that advocates of data-driven cultures need to adopt.</p><p>In creating a data-driven culture, there’s no rest for the weary. We know of organizations that were hugely focused on data and analytics, but when the CEO champion left they drifted back to their old gut-based thinking and decision-making. From boards of directors to CEOs to analytics and AI leaders, everyone who believes in this focus should work to persuade others to adopt and maintain it. No one should assume that software and hardware alone will lead the organization to the cultural promised land. </p>								</div>
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		<title>Three Ways Data Science Can Help Solve Problems In Your Business, Forbes Magazine;</title>
		<link>https://watsac.com/three-ways-data-science-can-help-solve-problems-in-your-business-forbes-magazine/</link>
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		<dc:creator><![CDATA[sabtain]]></dc:creator>
		<pubDate>Mon, 09 Nov 2020 07:04:52 +0000</pubDate>
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		<guid isPermaLink="false">https://watsac.com/?p=2383</guid>

					<description><![CDATA[To the outsider, data science can appear to be modern-day alchemy. It may look like a combination of broad mathematical and statistical knowledge, the ability to hack and expertise in some specific field the data scientist has chosen to pursue. Finding that data scientist, however, who has a high proficiency across a broad spectrum of &#8230;<p class="read-more"> <a class="" href="https://watsac.com/three-ways-data-science-can-help-solve-problems-in-your-business-forbes-magazine/"> <span class="screen-reader-text">Three Ways Data Science Can Help Solve Problems In Your Business, Forbes Magazine;</span> Read More »</a></p>]]></description>
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									<p>To the outsider, data science can appear to be modern-day alchemy. It may look like a combination of broad mathematical and statistical knowledge, the ability to hack and expertise in some specific field the data scientist has chosen to pursue. Finding that data scientist, however, who has a high proficiency across a broad spectrum of industries and technologies is an ideal that might not be possible to attain. Thankfully, with the help of data science, the results are available without depending on one super-scientist.</p><p>The deeper and more relevant truth is data is not a magical realm operating outside of normal business practices and disciplines. Rather, data and the insights it provides are powerful tools used to identify, assess and resolve business problems in real-time. In this way, data science can be applied to business problems to improve practices while reducing inefficiencies and redundancies – strengthening customer satisfaction. </p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">The Dynamics Of Data Science And Business Problems</h3>				</div>
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									<p>It&#8217;s easy to lose sight of the forest amidst so many towering trees. Many times, the real problem isn&#8217;t the problem you&#8217;re looking at. It&#8217;s what you cannot see. This analogy can easily apply to any business, big or small, that has so many priorities it is unable to see the most pressing. That is where data science can step in.</p><p>The answers to your toughest problems are right in front of you. The best source for the data needed to solve your business problems is your business. The challenge is in the actual size of the data. Human eyes cannot see patterns in datasets this massive. It takes a computer, often more than one, and analytics to harvest meaningful insight. Data analytics and business intelligence can use KPIs to identify priorities based on the relevant data for that problem. </p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">The Future Of Work Now: AutoML At 84.51°And Kroger
10 Data Analytics Myths That Can Hamper Your Business
The Next Generation Of Artificial Intelligence</h3>				</div>
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									<p>Here are three examples of how your data, analytics and scientists can make beautiful music by employing data to identify problems and opportunities – while orchestrating a perfect, almost artistic, solution.</p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">1. Innovative Upgrades And Improvements</h3>				</div>
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									<p>Understanding what drives and motivates your purchasing public is a secret businesses have longed to know ever since commerce began. It is often driven by gut feelings or broad and rough analysis of glaringly obvious data. Now, it is possible to refine data analysis to the point where your data scientists can understand what will trigger action from prospective buyers sometimes better than those buyers know themselves.</p><p>Innovating your existing product or service through upgrades and improvements is one way to use data at your disposal to boost revenue-deepening customer relations. Customers love their familiar devices, but they may love them more when they are given a new look, feel, or function which makes them better and more relevant.</p><p>Data science solutions can show developers opportunities where increased interest and sales are simply hidden within the product or service itself. Discovering this is a direct result of a focused effort to use analytics to understand customer motivations. </p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">2. Developing New Products And Services</h3>				</div>
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									<p>There are times when the need arises for an entirely new product or service, often one which is interrelated with your existing business goals and operations. A prime example of this new development may be found in the company Netflix. It began its service as a convenient and affordable alternative to renting movies. As demands and technology evolved, their service gradually evolved too. First, by offering streaming services as a secondary viewing option. Quickly, customers recognized its convenience and streaming service grew to become the most popular viewing model.</p><p>Smartly, Netflix also anticipated the demand by gamers, adding games to their entertainment rolls. Finally, they wisely saw the opportunity of squeezing the last pennies of profit by selling their previously viewed games and movies to loyal customers. This example perfectly demonstrates how a company can follow data each step of the way to piggyback off initial successes for continued growth and success. </p>								</div>
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					<h3 class="elementor-heading-title elementor-size-default">3. Data-Value Identification</h3>				</div>
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									<p>It is one thing to have data, it’s another to see the potential it offers. Earlier, I mentioned that some view data science as magical and mystical. While this is not exactly accurate, it must be admitted that there are some magical aspects of data science. Data can unlock new value in familiar situations and opportunities by providing new potential and direction.</p><p>Freeform analysis is an arena where data science can flex its muscles. The ability to analyze and assess without having a specific goal, search or preformed conclusion in mind can lead to unexpected and sometimes illuminating places.</p><p>Patterns that are far too nuanced for the human mind to anticipate can easily be captured by algorithms. Think of it as a broad scanning of your massive data landscape which can often highlight or reveal previously unseen or unexplored terrain and offer something entirely new. This treasure trove of valuable data and information can potentially expand your customer base and increase individual sales volume because you are now serving a different set of clientele.</p><p>One of the best outcomes of using data science to solve business problems is that you often end up inspiring and motivating data scientists on your team. They can feel like more than simply analysts of information. Rather, they&#8217;re part of a team imagining elegant solutions that add value to customers, communities and business culture. By using the data your business already collects, data science has the potential to help solve various problems in new ways. It&#8217;s another tool in your toolbox to build up your business by tackling obstacles head-on. </p>								</div>
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