With the constant growth in the popularity of audio content, the concept of reading has changed.Reading is a deliberate effort, where listening is more of an experience.It helps that audio is among the most immersive media formats that triggers memorability, trust, and connection. The rise of audiobooks in sales and the number of listeners cannot be ignored anymore.
Turning eBooks To AudioBooks could change the game completely for you. And, this isn’t just Me saying this :
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While recording and publishing your own podcast may sound simple enough, there are actually a lot of things that happen behind the scenes. Planning and creating content, editing your podcast audio files, marketing, and promoting your show will take their toll on you if you don’t know what you’re doing.
AudioBooks Profit Secrets is a step-by-step blueprint for launching a profitable Podcast. If you are a blogger, online business owner or niche marketer, tapping to other niche market would be a good idea to make more money online. Many people publish their podcasts for free, and that is good for marketing purposes so you can get your name and brand in front of a wider audience and sell more products and services. But there are also a number of ways to make money directly from your podcasts.
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Organizations that rely on data analysis to make decisions have a significant competitive advantage in overcoming challenges and planning for the future. And yet data access and the skills required to understand the data are, in many organizations, restricted to business intelligence teams and IT specialists.
As enterprises tap into the full potential of their data, leaders must work toward empowering employees to use data in their jobs and to increase performance—individually and as part of a team. This puts data at the heart of decision making across departments and roles and doesn’t restrict innovation to just one function. This strategic choice can foster a data culture—transcending individuals and teams while fundamentally changing an organization’s operations, mindset and identity around data.
Organizations can also instill a data culture by promoting data literacy—because in order for employees to participate in a data culture, they first need to speak the language of data. More than technical proficiency with software, data literacy encompasses the critical thinking skills required to interpret data and communicate its significance to others.
Many employees either don’t feel comfortable using data or aren’t completely prepared to use it. To best close this skills gap and encourage everyone to contribute to a data culture, organizations need executives who use and champion data, training and community programs that accommodate many learning needs and styles, benchmarks for measuring progress and support systems that encourage continuous personal development and growth.
Here’s how organizations can improve their data literacy:
1. LEAD
Employees take direction from leaders who signal their commitment to data literacy, from sharing data insights at meetings to participating in training alongside staff. “It becomes very inspiring when you can show your organization the data and insights that you found and what you did with that information,” said Jennifer Day, vice president of customer strategy and programs at Tableau.
“It takes that leadership at the top to make a commitment to data-driven decision making in order to really instill that across the entire organization.” To develop critical thinking around data, executives might ask questions about how data supported decisions, or they may demonstrate how they used data in their strategic actions. And publicizing success stories and use cases through internal communications draws focus to how different departments use data.
2.Train
Self-Service Learning
This approach is “for the people who just need to solve a problem—get in and get out,” said Ravi Mistry, one of about three dozen Tableau Zen Masters, professionals selected by Tableau who are masters of the Tableau end-to-end analytics platform and now teach others how to use it.
Reference guides for digital processes and tutorials for specific tasks enable people to bridge minor gaps in knowledge, minimizing frustration and the need to interrupt someone else’s work to ask for help. In addition, forums moderated by data specialists can become indispensable roundups of solutions. Keeping it all on a single learning platform, or perhaps your company’s intranet, makes it easy for employees to look up what they need.
3.Measure
Success Indicators
Performance metrics are critical indicators of how well a data literacy initiative is working. Identify which metrics need to improve as data use increases and assess progress at regular intervals to know where to tweak your training program. Having the right learning targets will improve data literacy in areas that boost business performance.
And quantifying the business value generated by data literacy programs can encourage buy-in from executives. Ultimately, collecting metrics, use cases and testimonials can help the organization show a strong correlation between higher data literacy and better business outcomes.
4.Support
Knowledge Curators
Enlisting data specialists like analysts to showcase the benefits of using data helps make data more accessible to novices. Mistry, the Tableau Zen Master, referred to analysts who function in this capacity as “knowledge curators” guiding their peers on how to successfully use data in their roles. “The objective is to make sure everyone has a base level of analysis that they can do,” he said.
This is a shift from traditional business intelligence models in which analysts and IT professionals collect and analyze data for the entire company. Internal data experts can also offer office hours to help employees complete specific projects, troubleshoot problems and brainstorm different ways to look at data.
What’s most effective depends on the company and its workforce: The right data literacy program will implement training, software tools and digital processes that motivate employees to continuously learn and refine their skills, while encouraging data-driven thinking as a core practice.
For more information on how you can improve data literacy throughout your organization, read these resources from Tableau:
As data collection and data sharing become routine and data analysis and big data become common ideas in the news, business, government and society, it becomes more and more important for students, citizens, and readers to have some data literacy. The concept is associated with data science, which is concerned with data analysis, usually through automated means, and the interpretation and application of the results.
Data literacy is distinguished from statistical literacy since it involves understanding what data mean, including the ability to read graphs and charts as well as draw conclusions from data. Statistical literacy, on the other hand, refers to the “ability to read and interpret summary statistics in everyday media” such as graphs, tables, statements, surveys, and studies.
As guides for finding and using information, librarians lead workshops on data literacy for students and researchers, and also work on developing their own data literacy skills. A set of core competencies and contents that can be used as an adaptable common framework of reference in library instructional programs across institutions and disciplines has been proposed.
Resources created by librarians include MIT‘s Data Management and Publishing tutorial, the EDINA Research Data Management Training (MANTRA), the University of Edinburgh’s Data Library and the University of Minnesota libraries’ Data Management Course for Structural Engineers.
The Covid-19 pandemic has carried a significant impact on the rate in which businesses are embracing digital transformation. The health crisis has created an almost overnight need for traditional brick and mortar shopping experiences to regenerate into something altogether more adaptive and remote. While some businesses are finding this transition toward emerging technology a little tricky, it’s proving to be a significant opportunity for entrepreneurs in the age of the “new normal.”
Astoundingly, data suggests that digital transformation has been accelerated by as much as seven years due to the pandemic, with Asia/Pacific businesses driving forward up to a decade in the future when it comes to digital offerings.
With entrepreneurs and new startup founders finding themselves in a strong position to embrace modern digital practices ahead of more traditional companies, we’re likely to see a rise in innovation among post-pandemic businesses. With this in mind, let’s take a deeper look into the ways in which digital transformation are benefiting businesses in the age of the new normal:
Fast, data-driven decisions.
Any digital transformation strategy needs to be driven by data. The emergence of big data as a key analytical tool may make all the difference in ensuring that startups take the right steps at the right time to ensure that they thrive without losing valuable resources chasing the wrong target audience, or promoting an underperforming product.
Enterprises today have the ability to tap into far greater volumes of data than ever before, thanks largely to both big data and Internet of Things technology. With the right set of analytical tools, this data can be transformed into essential insights that can leverage faster, more efficient and accurate decisions. Essentially, the deeper analytical tools are embedded in business operations, the greater the levels of integration and effect that may have.
By incorporating more AI-based technology into business models, it’s possible to gain access to huge volumes of big data that can drive key decisions. The pandemic has helped innovations in terms of data and analytics become more visible in the world of business, and many entrepreneurs are turning to advanced AI capabilities in order to modernise their existing applications while sifting through data at a faster and more efficient rate.
Leveraging multi-channel experiences.
Digital transformation is empowering customers to get what they want, when they want, and however they want it. Today, more than half of all consumers expect to receive a customer service response within 60 minutes. They also want equally swift response times on weekends as they’ve come to expect on weekdays. This emphasis on perpetual engagement has meant that businesses that aren’t switched on 24/7/365 are putting themselves at a disadvantage to rivals that may have more efficient operations in place.
The pandemic has led to business happening in real-time – even more so than in brick and mortar stores. Although customers in high street stores know they’re getting a face to face experience, this doesn’t mean that business representatives can offer a similar personalised and immediately knowledgeable service than that of a chatbot or a live chat operative with a sea of information at their disposal.
Modern consumers are never tied to a single channel. They visit stores, websites, leave feedback through mobile apps and ask questions for support teams on social networking sites. By combining these interactions, it’s possible to create full digital profiles for customers whenever they interact with your business – helping entrepreneurs to provide significantly more immersive experiences.
Fundraising via blockchain technology.
Blockchain technology is one of the most exciting emerging technologies today. Its applications are far-reaching in terms of leveraging new payment methods and brokering agreements via smart contracts, and while the use cases for these blockchain applications will certainly grow over the coming years, today the technology is already being widely utilised by entrepreneurs as a form of raising capital through Initial Token Offerings (ITOs), also known as Initial Coin Offerings (ICOs).
As an alternative to the use of traditional banks, venture capital firms, angel investors or crowdfunders, ITO tokens can be made available for exchanges where they can trade freely. These tokens are comparable to equity in a company, or a share of revenue for token holders.
Interested investors can buy into the offering and receive tokens that are created on a blockchain from the company. The tokens could have some practical use within the company where they can be spent on goods or services, or they could purely represent an equity share in a startup or project.
There are currently numerous companies that use blockchain technology to simply and secure its operations. From large corporations like HSBC’s Digital Vault, which is blockchain-based custody platform that allows clients to access details of their private assets to small education startups like ODEM, which aim to democratize education.
Another company that’s pioneering blockchain technology within the world of business is OpenExO, which has developed its own community-driven utility token EXOS, to help build a new transformation economy that helps companies to accelerate, democratise and internationalise their innovation.
Salim Ismail, OpenExO founder, is the former Yahoo technology innovator who developed the industry of Exponential Organizations. He has become a household name in the entrepreneur and innovation landscape, and now he launches the blockchain ecosystem that includes Fortune 500 companies, cities and even countries.
Reaping widespread rewards.
Although digital transformation could begin with a focus on just one facet of a startup, its benefits can be far reaching for employees, consumers and stakeholders alike. It could limit the mundane tasks required of workers, offer greater levels of personalisation for consumers and free up new skills to be developed in other areas of a business.
This, in turn, helps to build more engaged and invested teams that know the value of fresh ideas and perspectives. Although the natural adaptability of entrepreneurs makes the adoption of digital transformation an easier one to make than for established business owners, the benefits can be significant for both new and old endeavours.
The pandemic has accelerated the potential of emerging technologies by over seven years in some cases, the adoption of these new approaches and tools can be an imperative step in ensuring that your business navigates the age of the new normal with the greatest of efficiency.
By: Dmytro Spilka / Entrepreneur Leadership Network VIP – CEO and Founder of Solvid and Pridicto
Digital Transformation (DT or DX) or Digitalization is the adoption of digital technology to transform services or businesses, through replacing non-digital or manual processes with digital processes or replacing older digital technology with newer digital technology. Digital solutions may enable – in addition to efficiency via automation – new types of innovation and creativity, rather than simply enhancing and supporting traditional methods.
One aspect of digital transformation is the concept of ‘going paperless‘ or reaching a ‘digital business maturity’affecting both individual businesses and whole segments of society, such as government,mass communications,art,health care, and science.
Digital transformation is not proceeding at the same pace everywhere. According to the McKinsey Global Institute‘s 2016 Industry Digitization Index,Europe is currently operating at 12% of its digital potential, while the United States is operating at 18%. Within Europe, Germany operates at 10% of its digital potential, while the United Kingdom is almost on par with the United States at 17%.
One example of digital transformation is the use of cloud computing. This reduces reliance on user-owned hardware and increases reliance on subscription-based cloud services. Some of these digital solutions enhance capabilities of traditional software products (e.g. Microsoft Office compared to Office 365) while others are entirely cloud based (e.g. Google Docs).
As the companies providing the services are guaranteed of regular (usually monthly) recurring revenue from subscriptions, they are able to finance ongoing development with reduced risk (historically most software companies derived the majority of their revenue from users upgrading, and had to invest upfront in developing sufficient new features and benefits to encourage users to upgrade), and delivering more frequent updates often using forms of agile software development internally.This subscription model also reduces software piracy, which is a major benefit to the vendor.
With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:
How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
What prevents people from more deeply exploring and using data?
In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?
With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.
Current realities and barriers to scale smarter decision-making with AI
With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.
Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.
At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.
Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.
The opportunity to solve data science challenges
This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:
Reducing data exploration and prep work
Empowering analyst experts to deliver data science outputs at lower costs
Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
Extending, automating, and accelerating analysis for business groups and domain experts
Reducing time and costs spent on deploying and integrating models
Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias
Business scenarios that benefit from predictive analytics
There are several business scenarios where predictive capabilities can be immensely useful.
Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.
A solution to consider: Tableau Business Science
We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.
This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.
When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.
Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.
