The rapid advancement of AI has revolutionized industries worldwide, transforming the way businesses operate. While some organizations are still catching up, AI is undeniably a game-changer, reshaping industries and redefining enterprise operations.

Estimates from Goldman Sachs suggest that AI has the potential to increase global GDP by approximately 7% (almost $7 trillion) over the next decade by enhancing labor productivity. Even with conservative predictions, AI is poised to drive significant progress in the global economy.

Perception Problems Around AI

The impact of AI on the workforce is both profound and complex. While there are many proven instances where AI integration has led to performance improvements and enhanced well-being for employees, concerns about job displacement still loom large. Reports citing AI-related job cuts have only bolstered that fear, however it’s imperative to remember the dual nature of technological innovation.  

While certain jobs may become redundant, new opportunities, particularly in AI and tech-related fields, are emerging. Gartner’s optimistic prediction suggests that AI could generate over half a billion jobs by 2033, emphasizing the need for a workforce skilled in AI technologies and applications.

It’s also crucial to consider how current roles might evolve to integrate AI tools alongside human workers. For instance, doctors could leverage advanced data analysis software to improve diagnostic accuracy, while IT professionals might utilize generative AI to swiftly and precisely obtain the scripts they need. In these scenarios, human involvement remains indispensable, but tasks can be completed more quickly and accurately.

Shifting Our Mindsets

For IT departments, traditionally at the forefront of technological innovation, the rise of AI signals a paradigm shift. AI is revolutionizing the IT industry by automating and optimizing workflows, increasing team output, and boosting cross-organizational efficiency. Rather than replacing the IT technician, AI has the potential to serve as the ultimate assistant by automating the manual, tedious tasks and enabling the technician to spend their time on high-value projects they wouldn’t otherwise have the time to tend to. This transition, however, necessitates not just adaptation to new tools, but also a fundamental shift in mindset towards embracing intelligent systems.

Central to this shift is the concept of closed-loop AI systems—an aspect of responsible AI—which ensures that any inputs to the system (such as data, sensitive information, etc.) are never used for outputs outside of the organization. In other words, any information given to the AI stays within the system, ensuring no information is compromised outside the organization, and the data is not used to train the AI or algorithm.

The Importance of Training and Development

Training and development also play a critical role in this AI-driven evolution. Recent data showed that 66% of American IT professionals agreed it’s harder for them to take days off than their colleagues who are not in the IT department, which has serious implications for burnout, employee retention, and overall satisfaction. This makes AI integration more important than ever before. But first, proper training is essential.

As IT professionals are beginning to leverage AI’s power, emphasis must be placed on cultivating skills in data analysis, algorithm development, and system optimization. Especially as organizations embrace closed-loop AI systems, considerations around data security, ethics, and workforce upskilling become imperative.

AI companions are becoming increasingly essential to ensure efficient IT operations. Luckily, innovative solutions are emerging with capabilities like ticket summaries, response generation, and even AI solutions based on device diagnostics and ticket history to help streamline daily tasks and empower IT professionals to focus on higher-value issues.

Integrating Closed-Loop Systems to Supercharge Your AI Integration

The evolution of AI technology and closed-loop systems is set to revolutionize enterprise operations. As businesses navigate this future, embracing these advancements responsibly will be crucial for staying competitive and efficient. AI’s ability to enhance decision-making, streamline processes, and drive innovation opens new avenues for growth and success.

By integrating closed-loop systems and prioritizing responsible AI, enterprises can create more responsive and adaptive environments, ensuring continuous improvement and agility. The future of enterprise technology is here, and those who adapt and leverage these powerful tools responsibly will undoubtedly lead the way in their industries.

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Greetings George, Could you please share with us your professional journey and how you came to your current role as Chief Technology Officer at Upwave?

My professional journey has been a bit unconventional. I studied philosophy at Yale, not expecting to go into tech. I ended up working as an investment analyst, doing a lot of data analysis and model building on topics like how government stimulus impacts the economy.

Realizing finance wasn’t my passion, I started a music startup that ultimately didn’t pan out. Nearly 8 years ago, I joined Upwave as a software engineer focused on data. Over time, I became manager of our data team, director of engineering, VP of engineering, and now CTO overseeing all product and technology.

Given the urgency of embracing AI-driven strategies, how has Upwave integrated artificial intelligence into its operations to drive innovation and maintain a competitive edge?

AI is deeply integrated into everything we do at Upwave. We’ve long used what I call “predictive AI” – machine learning techniques that have existed for decades. From the beginning, we built ML and statistical analysis algorithms to optimize ad campaigns.

In the last year and a half, we’ve also embraced the newer “generative AI” exemplified by tools like ChatGPT. Over the past 9 months, we’ve leveraged generative AI to help customers get more value from the brand measurement we provide. Just yesterday, we launched the open beta of our AI Campaign Insights Reports, which use generative AI to synthesize and summarize campaign results into easy-to-understand language, charts, and actionable recommendations. We’re very excited about it.

Revolutionizing brand ROI through marketing measurement is a critical aspect of modern business. Could you provide insights into Upwave’s approach to this and the impact AI-driven marketing analytics have had on enhancing brand performance?

Modern brand campaigns are extremely complex, with vast amounts of money spent across dozens or even hundreds of channels. It’s simply too much for humans to track and optimize manually. That’s where AI shines – at digesting huge datasets to find mathematical optimizations and make concrete recommendations to improve cost-effectiveness and ROI.

Upwave takes a two-pronged AI approach: First, using predictive AI for high-quality measurement and clear analysis of ROI opportunities. Second, leveraging generative AI to communicate those opportunities to customers as clearly and actionably as possible.

We’ve found that customers who lean into this data-driven, AI-powered approach are seeing dramatic performance improvements and ROI increases. The combination of unique data, insightful analysis, and powerful communication enabled by AI is a game-changer.

As the CTO at Upwave, what are your insights into the future of brand analytics, and how do you foresee AI shaping the landscape in the coming years?

I’m a huge believer in the potential of generative AI. It’s easy to assume today’s capabilities are all these systems will ever have, but having worked in AI for over 15 years, I’ve seen firsthand how these tools continuously improve in compounding, accelerating ways. I’m very bullish.

Even if AI never advances further, it will still become widely deployed across brand advertising – analytics, media planning, creative, and so on. But these tools will only get more powerful over time, perhaps dramatically so.

I anticipate AI will substantially augment more and more day-to-day activities in advertising. Organizations that embrace AI, understand how to harness it, and adapt their workflows will become far more productive, efficient, and effective. Those resistant to change will be left behind.

The transformative potential of GenAI is fascinating. How do you anticipate AI advancements surpassing human capabilities and reshaping marketing content creation strategies at Upwave?

As I said, I expect these AI tools to improve dramatically over time, taking on more functions humans currently handle. But I see this more as AI augmenting rather than replacing humans.

Consider how a bulldozer is far more efficient than a human at clearing a construction site, yet you still have a human operating the bulldozer. One human with a bulldozer can now do the work of dozens or hundreds in far less time.

I expect a similar dynamic with AI – it will replace certain aspects of jobs humans do, but humans will still orchestrate the AI to achieve their goals, just far more efficiently. People will be able to produce more and better advertising, then use tools like Upwave to measure performance and continuously optimize in a virtuous cycle.

So while advertising will become much more automated, there will still be an important role for humans in guiding the process.

In your role as Chief Technology Officer, what personal strategies do you employ to stay informed about the latest advancements in AI and technology, ensuring Upwave remains at the forefront of innovation?

I’m rather obsessive about keeping up with AI given how transformative I believe it will be for both tech and advertising.

For me, this means firsthand experience – regularly using tools like ChatGPT, Claude, Anthropic, paying for premium versions, participating in hackathons to build hands-on AI projects. I even recently won the TED AI hackathon with a team.

Secondly, I stay closely involved with Upwave’s AI product development, frequently discussing capabilities and limitations with our engineers and PMs.

To track the cutting edge, for all its issues, I find Twitter an excellent source of direct info from top researchers. I also subscribe to several great newsletters that summarize key AI news.

It takes a multi-pronged approach as the AI field is moving extremely rapidly, but falling behind would be a huge risk.

What advice would you offer to our audience, particularly businesses seeking to integrate AI-driven strategies into their marketing and brand analytics efforts?

Take AI very seriously. It’s going to significantly impact nearly every business. Yes, there are dodgy “AI” products and snake oil salesmen out there, but that doesn’t negate the genuine value being created by real AI capabilities. And these tools are only going to get better over time.

Even if AI can’t perfectly solve your needs today, that may change by next month. Effectively applying AI still takes skill, and initial attempts may not pan out, but that doesn’t mean AI can’t provide huge value with the right approach.

I strongly recommend building real AI expertise, either in yourself or your organization. Understand both the potential of today’s tools and how that will evolve going forward. Your competitors certainly are. Allowing them to gain an AI advantage now risks them leaving you in the dust as that edge compounds.

So be discerning, but don’t dismiss AI. The risk of ignoring it is too great.

With your extensive experience in technology and marketing, what key considerations should companies keep in mind when implementing AI solutions for marketing purposes?

First, be thoughtful about applying AI to problems. Naively throwing AI at an issue without carefully considering the problem space and the model’s constraints can lead to reputational or even legal issues, as these systems can be erratic.

However, you also can’t overcorrect and entirely avoid AI just because it involves some risk. Throughout history, many valuable technologies have been dangerous when misused but extremely beneficial when wielded properly.

Within 5 years, I believe it will be nearly impossible to remain competitive in marketing without heavy AI usage. Building those capabilities now will be key to keeping pace.

Even with today’s AI tools, if applied effectively, there is substantial potential to unlock. Tools like Upwave can help you increase your ROI by 2-3 times, for instance. So there’s already a lot of value to capture from existing tools, and that will only grow.

Can you share a success story or milestone where Upwave has effectively utilized AI-driven strategies to enhance brand ROI or marketing performance?

Absolutely. While I can’t name names, Upwave offers Persuadability Scores which directly measure brand advertising’s impact, similar to tracking clicks or conversions for direct response.

We worked with a major financial services advertiser and DSP to feed in these AI-generated metrics, allowing the DSP to steer ads towards the highest-impact, best-ROI opportunities. The result was material performance improvements for campaigns using these Persuadability Scores. That’s a great example of predictive AI boosting marketing effectiveness.

On the generative AI side, our new AI Campaign Insights Reports provide easily digestible summaries and recommendations that enable customers to align internal stakeholders and optimize in-flight campaigns.

The substantial leverage of generative AI makes it far more time and cost efficient to perform the necessary analysis and communication to understand campaign performance and disseminate those insights to key decision-makers. We’re seeing strong customer uptake and satisfaction so far.

Finally, considering your expertise, what are your reflections on the future of AI in marketing, and any additional insights you’d like to share with our audience?

As I’ve touched on, AI capabilities are going to advance substantially, likely in ways that eventually unsettle people as AIs become able to handle much of the work humans currently do. In certain domains, AIs will simply outperform even the most skilled humans.

This means very significant changes are coming to marketing and advertising, whether we want them or not. These are global technological forces beyond any individual company’s control.

In this sense, an AI tsunami is approaching. We can either learn to surf that wave or get crushed by it, but we can’t stop it.

So it’s crucial for businesses of all kinds to very seriously consider how they’ll navigate the AI-transformed future and hopefully leverage AI as a competitive advantage. Because organizations that fail to appreciate the gravity of this shift are in for an extremely challenging decade ahead.

George London

Chief Technology Officer at Upwave

George is a seasoned technology leader who has spent his whole career helping companies use data to make better decisions. George started his career doing macroeconomic modeling and investment research at Bridgewater Associates (the world’s largest hedge fund), and then founded a startup that used data to help consumers explore and discover music.

As one of Upwave’s first engineering hires, George originally joined Upwave with the mission of building Upwave’s statistical capabilities from scratch. Since then he’s grown with the company to become Head of Data, then Vice President of Engineering, and now CTO. In his years at Upwave, George has both contributed to nearly every aspect of Upwave’s systems and product and has also hired, managed, and coached Upwave’s entire technical team.

George holds a BA in Philosophy from Yale University and lives in Oakland with his wife and labradoodle.

The post AITech Interview with George London, Chief Technology Officer at Upwave first appeared on AI-Tech Park.

Are you overloaded with chores that are trivial and take a huge amount of time in the functioning of your business? Well, this is where hyperautomation comes into play and allows handling such extended and complicated business rules. This only translates to the next level of automation, or, in other words, a set of technologies undergoing revolution to revolutionize aspects of efficient working.

Picture intelligent robots working together with data analysis and machine learning to be able to orchestrate complex processes. The ability is to make all of this a reality through platforms of hyperautomation, which enable businesses to realize breakthrough results.

But is it worthwhile? It’s all about the ROI. Business managers will be in a position to show how hyperautomation impacts business operations so that they can make data-driven decisions and realize the actual potential of this transformational technology.

I. Cost Savings

Information technology (IT) isn’t all about fancy gadgets and troubleshooting; rather, it’s about wanting to streamline your business. Here’s how a solid IT strategy—one like how most managed service providers would do or go about this—does this:

• Streamlined Operations: Automation eliminates what may be considered conventional activities, hence freeing more time for your staff to burrow into literally cream jobs, representing less labor cost and higher productivity.
• Fewer Errors, Lower Costs: Proactive maintenance of systems will help detect and nip problems in the bud before snowballing into more costly errors. This sets you up to have smooth operations and reduces the risk of experiencing frustrating downtimes.
• Resource Efficiency: A planned strategy for your IT enables your business to optimize its resources. You will efficiently use those at your disposal while cutting out unnecessary costs and ensuring a good return on investment.

In simple words, the focus on optimization in IT can really streamline your company’s financial position.

II. Better Efficiency 

Efficiency would be the key to reaping maximum results. Three important areas to consider are: lean processes, speed and productivity, and scaling. Lean processes make the workflow smooth with the help of automation. This could eradicate possible losses of effort and give a flow to the work. Better handling of tasks is bound to bring an increase in productivity, ensuring that you accomplish much within a short span of time. Finally, scalability ensures that your operation has the ability to scale with growth without running into inefficiencies or a spike in costs. This focus will help drive your business at full throttle.

III. Enhanced Decision-Making Capabilities

Imagine that capacity for analyzing information in the blink of an eye, predicting the future, and gaining crystal clarity about your data—that is AI-powered decision-making.

• Real-time Analytics with AI/ML: Get insights as you need them, making it possible to make quick yet very effective decisions.
• Predictive Analytics: Be able to foresee risks and opportunities and act before they even materialize.
• Business intelligence: making data into knowledge so as to act upon in strategic decisions. 

These powerful tools are designed to take your decision-making capabilities to the next level, hence leading you toward a future of informed success.

IV. Best Practices: 

Building a Sustainable Automation Journey

A well-defined base is one of the core principles of a robust automation system. Such a framework can enable a three-step autonomous decision-making process for long-term maximization of the effectiveness of any system.

• Assessment and Initial Planning: We start with a thorough review of your operations. It responds immediately and highlights the opportunities that are perfect for automation, and thus, the solutions that have been chosen would meet your needs to the best.
• Technology Selection and Integration: It is the conductor of the automation symphony that is going to be created with the help of the right orchestration platform. Architect and implement this platform in such a way that it does not cause interruptions.
• Monitor and Continuously Improve: Automation is a process that encompasses a number of steps and, thus, can be referred to as an automation process. Our reviews will be periodical, and we will analyze the results on a regular basis to make firm decisions for a better return on your automation investments.

By following this framework, you will create sustainable automation improvements that can be extended to make long-term positive enhancements and derive value from them.

V. Unleash the Power of Hyperautomation

Hyperautomation will be a way that will afford one the likelihood of bolstering operations, embracing your staff, and availing of enormous savings.

The time to start is now!

• You can consider hiring an MSP to partner for expertise in hyperautomation solutions.
• Analyze your current processes and functions and determine where there are potential ‘automation opportunities’.
• For orchestration platforms that are still new and continuously developing, analyze and assess their capabilities; choose a solution that would suit the needs of the company best.

The concept is not just hype, and hyperautomation is one of the most potent forces out there for businesses. Major benefits of intelligent automation, data analysis, and machine learning include doing more with less, driving efficiency, and putting data at the center of decision-making.

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Table of contents:

1. Serverless Data Warehousing: A Revolution for the Modern Data Master
2. Serverless vs. Traditional Data Warehousing: A Comparative Analysis
3. Building a B2B Serverless Data Warehouse on AWS: Recommended Design Patterns
  3.1 Data Ingestion Pipeline
  3.2 Data Transformation and Orchestration
  3.3 Data Storage and Catalog
  3.4 Querying and Analytics
  3.5 Visualization and Reporting
4. Real-world Use Cases for Serverless Data Warehousing
5. The Evolving Landscape of Serverless Data Warehousing: Future Considerations
  5.1 Advanced Redshift Serverless Features
  5.2 Hybrid and Multi-Cloud Integration
  5.3 Security and Governance

Data warehouses have an older design, which becomes stifling in a world where information and data escalate at an exponential pace. Just try to picture hundreds of hours dedicated to managing infrastructure, fine-tuning the clusters to address the workload variance, and dealing with significant upfront costs before you get a chance to analyze the data.

Unfortunately, this is the best that one can expect out of traditional data warehousing methodologies. For data architects, engineers, and scientists, these burdens become a thorn in their side in terms of innovation and the process of gaining insights from increasingly large data sets.

1. Serverless Data Warehousing: A Revolution for the Modern Data Master

But what if there was a better way? Serverless data warehousing is a new concept, and it provides a revolutionary solution away from the chaining constraints that come with managing complex infrastructure. Think about the future, where servers are self-provisioning and can scale up or down based on the load. A world where one pays only for the resources consumed or needed, excluding hefty charges and data investments.

Serverless data warehousing opens up this very possibility. By leveraging the power of the cloud, data engineers or scientists can focus on what truly matters: turning collected information into insights from which organizations can make relevant decisions and gain benefits.

2. Serverless vs. Traditional Data Warehousing: A Comparative Analysis

3. Building a B2B Serverless Data Warehouse on AWS: Recommended Design Patterns

As data architects and engineers, we need to see the importance of proper data pipelines for solid B2B analytics and insights. In this case, serverless data warehousing on AWS remains a suitable solution due to its flexibility and affordability. Now, let us explore the proposed design patterns for creating your B2B serverless data warehousing architecture.

3.1 Data Ingestion Pipeline

The building block is to create a proper data ingestion process that feeds into the ‘real-time’ layer. Here, the AWS Kinesis Firehose stands out. It is a fully managed service that can integrate streaming data in real-time from B2B sources like your CRM or ERP system. Firehose consumes the data and directs it to storage layer S3, which is a low-cost storage layer for storing raw and processed data.

3.2 Data Transformation and Orchestration

In most cases, transformations are made when extracting value from raw data. Enter AWS Glue as the serverless ETL (extract, transform, load) solution. Glue allows you to fulfill data transformations with Python scripts and, at the same time, manage all the stages of data ingestion. This helps in the proper flow of data from B2B sources to the data warehouse without any hitches.

3.3 Data Storage and Catalog

Amazon S3 can be considered the foundation of your data store or data lake. This fast-scaled-out object storage service is an economical solution to store all the B2B data, both in its raw and transformed forms. Also, manage and use the AWS Glue Data Catalog effectively. This centralized metadata repository reduces the problem of finding your data by making data search easy by presenting a list of the data stored in S3 in a catalog.

3.4 Querying and Analytics: 

When it comes to querying large volumes of B2B data securely kept in S3, the hero is Amazon Redshift Serverless. Without requiring manual provisioning of resources, this serverless data warehouse scales workload resources from time to time. Redshift Serverless enables the carrying out of complex analytics on B2B data conveniently.

On the other hand, for ad-hoc B2B data analysis or when dealing with small amounts of data, use Amazon Athena, which is a serverless interactive querying service. Athena enables a user to plug into S3 and query data using standard SQL, so it was developed for flexibility to support impromptu data analysis.

3.5 Visualization and Reporting

Now that the B2B data is wrangled and analyzed, it is time to generate visuals from the derived insights. Amazon QuickSight is the best bet in this regard. Without the need for virtualization of servers, this business intelligence service helps in creating dashboards and reports, allowing better communication of data regarding business-to-business commerce to various stakeholders.

This set of AWS services allows for a scalable, economical, and highly available serverless data warehouse only for B2B analytics.

4. Real-world Use Cases for Serverless Data Warehousing

AWS serverless data warehousing helps B2B businesses analyze large amounts of data and derive maximum value from it. Here’s how it shines in several key areas:

• Customer 360 View: Picture a 360-degree perspective of the B2B clients. Serverless data warehousing can take data from various sources, for instance, the CRM (for example, Salesforce), marketing automation tool (for instance, Market), and website analytics tool (e.g., Google Analytics) through AWS Kinesis Firehose. This data can then be transformed and stored in Amazon S3 for future analysis. By using Amazon Redshift Serverless, the data can be queried to drive insights about customers’ behavior through the different stages of the interaction. This positions the business to dictate the terms of marketing strategies, initiate an effective customer service response, and, in sum, reduce churn rates.
• Sales Performance Analysis: No more speculation when it comes to sales. Redshift Serverless enables the processing and examination of historical sales data by using serverless data warehousing. Product distribution allows for determining when certain products are popular with the customer, what geographical areas of customer interest are the highest, and more. It can be employed in the planning of specific sales that may be expected and, thus, the determination of quotas, since information gathered by this intelligence can be used to devise product offerings that meet a given customer’s needs and the variation of pricing techniques for optimum profitability.
• Supply Chain Optimization: Serverless data warehousing can help turn the B2B supply chain from a reactive to a proactive one. With such data from the Warehouse Management System (WMS) and supplier portals fed into the system instantly, a real-time picture of the available stock situation can be gained, allowing one to avoid stock-outs and find the best delivery route. This enables one to competitively bargain with suppliers on prices, reduce holding costs, and effectively deliver to B2B customers, resulting in their satisfaction and thereby creating loyalty.

These are just a few examples; there are many other possibilities when it comes to applying the value of attentiveness. AWS serverless data warehousing is a critical tool that any B2B organization can use to unlock organizational data in B2B environments to optimize business decision-making across functions, resulting in better efficiency, profitability, and customer relations. 

5. The Evolving Landscape of Serverless Data Warehousing: Future Considerations

The advancement of serverless data warehousing continues gradually, opening up enchanting opportunities for data architects and engineers. Here, we delve into some key areas shaping the future landscape:

5.1 Advanced Redshift Serverless Features: 

What if Redshift Serverless had AI to automatically scale tiered resources in the future? This could mean even more efficient scaling, which happens by automatically responding to workload variations and optimizing expenses. 

5.2 Hybrid and Multi-Cloud Integration: 

With the increasing complexity of data ecosystems, integrating serverless data warehouses with other cloud platforms or on-premises data sources will be important. This will enable you to consolidate the big data platforms and assert full control over their integration throughout the organization. 

5.3 Security and Governance: 

Security and data control are still critical issues to address while implementing data warehousing solutions. With serverless data warehousing, trends and best practices for access controls, encryption techniques, and integration into well-established governance frameworks are awaiting to be implemented. That way, it will be possible to protect the B2B data that is usually considered private while at the same time facilitating efficient data use. 

The future of serverless data warehouses is rather promising and is already proving to present a mighty and versatile platform for B2B data processing. That is why both data architects and engineers are in the right place to be at the core of this great journey. 

Let’s harness the opportunity of serverless solutions such as Redshift Serverless with AWS Rich Ecosystem in order to develop fail-safe, efficient, and cost-optimized data-warehousing solutions that support B2B data-driven decisions.

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Table of contents:
1. Real-time Analytics and Streaming Data in Depth
1.1 What is Real-time Analytics?
1.2 What is Streaming Data?
2. Key Components and Technologies
3. Powering Business Growth with Streaming Data
3.1 Financial Services
3.2 Healthcare
3.3 Retail
3.4 Manufacturing
4. The Future of Real-time Analytics with Streaming Data

As the business world revolves around globalization and faster results, top executives, data analysts, and even marketing managers look forward to real-time analytics. It enables them to harness the power of streaming data in their business and gain a vast amount of valuable information that can inspire the growth of the business.

A manufacturing giant takes global production to the next level by leveraging real-time analytics to predict equipment breakdowns before they happen, boosting productivity across all departments. This is the power of real-time analytics and this is where the real potential for any business is hidden: the potential to turn into the industry leader.

Real-time analytics enables you to possess the flexibility and vision to trump your rivals while building toward stable revenue decades ahead.

Q. What is Real-time analytics and streaming data?

Real-time analytics could be defined as data analysis that takes place with maximum efficiency, and within a short period, which will allow businesses to constantly adapt to events and make the correct decisions based on that data.

Real-time analytics uses streaming data as its primary source for feeding data into the analysis process. It is a stream of data that emanates from numerous sources, such as sensors, social sites, customers, and monetary transactions, for example. While the traditional batch method has a rigid approach that analyzes data at fixed intervals, streaming data analysis occurs on the spot from time to time.

This blog is your roadmap to making sense of real-time analytics, streaming data, and what’s next. Here, we will discuss and give evidence of the benefits that users will realize from this technology, review the enabling technologies required for real-time analytics, and explain, in detail, the different elements that are required to achieve reliable big data real-time analytics within organizations.

1. Real-time Analytics and Streaming Data in Depth

The ability to digest information as it is received and not wait longer is very useful in today’s information society. This is where real-time analytics comes in.

It elaborates on the results being acquired instantly, which allows for a flexible and immediate response to the needs of the business.

1.1. What is Real-time Analytics?

Real-time analytics is a way of getting insights from data as soon as it arrives. Real-time, in the context of big data, refers to analytics that are provided once the data has been processed, but without the delays of traditional batch processing. 

Real-time data visibility helps businesses respond to events in real-time, make timely decisions, and formulate strategies, especially when they notice deviations from the normal trend.

1. 2. What is Streaming Data?

In real-time analytics, the lifeblood is derived from streaming data, which means data is continually fed from various sources. Think of the feeder being on constantly and pumping data into your analytics centre. Some B2B examples include:

• Social media feeds – analyzing real-time sentiment about your brand and ads,
• IoT sensor data for factory machinery, supply chain, and building energy,
• Financial transactions to prevent and report fraud and embezzlement, more and less profits, 
• Customers’ website activity to monitor the behaviour and marketing strategy, and predict potential paying consumers.

2. Key Components and Technologies

Organizations need to be equipped with an analytics platform that delivers real-time data for efficient strategic decision-making all over the pyramid. By leveraging the use of data ingestion tools such as Kafka and Flume, you would be in a good position to transfer stream data without interfering with your current systems. Apache Spark or Flink and other appropriate iterative stream processing frameworks facilitate real-time analysis, which in turn helps to respond actively to the changes occurring in the market and customers’ behaviour.

For faster access to data, implement in-memory databases like Redis for a fast scan of the data, or the scalability aspects provided by Cassandra or MongoDB. Last of all, BI tools such as Grafana or Tableau facilitate concise and effective communication of insights to the parties concerned, as it helps correlate with the narrative.

In today’s faster and more complex B2B environment, real-time analytical capability is not a frill, but a necessity. If businesses incorporate these constituents and technologies into their solutions. They can fully harness the power of streaming data and make a tangible business impact.

3. Powering Business Growth with Streaming Data

The change to massive quantities of data is ongoing and real-time analytics has become the latest buzzword. By using streaming data, it becomes possible to garner a lot of information and help diverse business organizations make decisions faster and more accurately.

3. 1 Financial Services: 

Chief Risk Officers and Fraud Analysts: 

Real-time solutions allow fraud analysts or risk officers to respond in real-time to fraudulent activities protecting the financial health of an organization.

Investment Professionals and Traders: 

Unlock rapid business results with timely recommendations as the market moves. Breathtaking market insights and instant visualization of investments and trades make this technology uniquely efficient for professional investors and traders

3. 2 Healthcare: 

Physicians and Care Teams: 

Continual patient monitoring also eliminates the need to wait for the results in an emergency, allowing physicians or healthcare teams to adjust the course of treatment in the blink of an eye.

Healthcare Administrators and Public Health Officials: 

Using predictive capabilities, healthcare professionals can identify probable disease epidemics and, as a result, direct resources effectively, enabling preventive healthcare administration.

3.3 Retail: 

Marketing Directors and Customer Relationship Managers: 

CRMs and MDs can create effective and highly targeted customer interactions in real-time. Another aspect of customer-oriented strategies is to utilize available information to better address clients’ wants and needs to increase their interest and commitment.

Supply Chain Managers and Inventory Control Specialists: 

SCMs and Inventory control specialists can work with the suitable inventory with real-time analytics help. Eliminate the occurrence of stockouts, cut down on related expenses, and optimize all aspects of managing your stocks.

3.4 Manufacturing: 

Operations Managers and Maintenance Engineers: 

The adoption of condition-based monitoring and real-time analysis can be done by operation managers and maintenance engineers to plan out maintenance schedules. Detect potential faults in the equipment before they lead to stoppages, thus reducing downtimes while boosting productivity.

Supply Chain and Logistics Leaders: 

Logistics and supply chain leaders can do real-time supply chain monitoring. Manage delivery schedules to gain the most effective route plans, manage disruptions, and ensure that your product gets to your clients on time.

Real-time analytics and streaming data are not restricted to a certain field and are the master key that opens a business up for growth. With raw data feeding into systems in real-time as the fourth industrial revolution rapidly unfolds, organizations that adopt this disruptive innovation will stand to benefit from the evolving business environment.

4. The Future of Real-time Analytics with Streaming Data

The integration of real-time analytics with AI and machine learning will provide a level of flexibility in the future of businesses that are unimagined.With these powerful combinations, businesses will be able to prevent, recover, and gain insights into processes, customers, and markets in real time.

In addition, the growth of the edge computing model suggests that data processing will occur in more localized settings, which will further reduce latency. This is especially true for industries such as manufacturing, where monitoring of production lines will be done in real time and can help avoid a range of expensive losses.

Real-time analytics is still a relatively young field, but as more and more organizations realize its potential, it can be expected that more diverse fields of business and industry will start utilizing it. Closely related, from third-party logistics providers seeking to improve the efficiency of delivery routes to banking institutions, hoping to identify suspect transactions, the possibilities are endless. The current trends of implementation and scaling point towards a future rich in new technologies and Business Intelligence (BI) mechanisms. This highlights the ongoing development driven by the increasing demand for real-time data analysis. Real-time analytics with streaming data is not something that businesses should just pursue as the latest trend; it is the proactive force that will radically alter the nature of business in years to come. Thanks to this technology and its updates, companies can achieve a competitive advantage and a sustainable development trajectory.

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Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. Lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. In the same way, many analytical positions will become redundant as smart chatbots assume more daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. The fast-growing demand for data analysis will remain immune to economic pressures, and those kinds of job positions will be less susceptible to budget cuts.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

AI Marketing Trends Impact Vertical Industry Groups

In addition to affecting job roles, the AI revolution is expected to supercharge marketing functions across nearly every type of industry. Two obvious examples include the retail and healthcare sectors. The retail industry has been quick to integrate AI to deliver efficiencies and increase sales. One emerging innovation is to combine neural networks with a shopper and a product to create new retail marketing experiences. For example, starting in 2024, you can expect an AI assistant to showcase an item of clothing on a model with similar dimensions to see exactly how it will look in various poses. Most industry watchers believe that such immersive, highly personalized virtual experiences will be the future of retail.

AI is also creating a radical new reality for the healthcare industry. For instance, digital twins are becoming increasingly ubiquitous for researchers, physicians, and therapists. A digital twin is a virtual model that accurately replicates a physical object or system. In this way, users can simulate physical processes through digital twins to test various outcomes without involving actual products or people, which greatly reduces operational costs and risks to public safety. For example, AI-powered digital twins could usher in new ways of marketing healthcare services for an aging population, by allowing people to live independently for longer. Or such twins might be used for future drug development projects.

AI will also play a pivotal role in the early diagnosis of potential health issues. For example, full-body MRIs will tap into the ability of AI to identify, analyze, and predict data patterns to help diagnose diseases long before any symptoms are visible to the human eye. In addition, AI will take a more prominent role in assisting medical staff to understand and interpret findings and provide treatments and care recommendations. All of these AI benefits will help sales and marketing teams to craft new messages that can communicate such considerable advantages to consumers.

Artificial intelligence engines have already upended marketing practices based on their extraordinary capacity for data analysis and efficiency, and this growth trend is only expected to continue in the coming years. To keep up with these technical developments, marketing professionals should become more comfortable using the AI tools which are rapidly remaking the entire marketing landscape.

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Table of Content

Introduction
1. The Role of Product Managers in the Digital World
1.1. Data Analysis and Interpretation
1.2. Product Vision and Strategy
1.3. User Experience and Design
2. Four Product Management Certifications
2.1. Product Management Program by CareerFoundry
2.2. New Product Development Professional (NPDP) Certification by PDMA
2.3. Certified Product Marketing Manager by AIPMM
2.4. SAFe Product Owner/Product Manager by Scaled Agile, Inc.
Conclusion

Introduction

Today, in the field of technology, product management is rapidly changing because of artificial intelligence (AI) and machine learning (ML). With these quick advancements in technology and the ever-growing reliance on data-driven decision-making, product managers find themselves at odds; they must forget old ways to learn new ones that fit into this digital age.

Rather than simply managing cutting-edge products or services developed by others, a product manager in today’s IT organization should be viewed as someone who can transform everything about them using any new technique or technology available while also engaging stakeholders like never before.

This article gives an overview of what the digital world means for you as a product manager and some popular certifications in this area.

1. The Role of Product Managers in the Digital World

Product managers should know the different technologies that are currently being used to process data, understand what each one does best, and how they can be applied.They need not only technical skills but also business acumen to identify many areas where innovation is possible within an organization through the use of data-driven strategies. These strategies will then guide them towards coming up with insights that will push for invention around those areas, leading to the successful launch of new products or services under their control.

1.1. Data Analysis and Interpretation

Product managers need to analyze large and complex datasets and identify trends, patterns, and insights to make informed decisions on product development optimization. They also need to collaborate with data scientists to develop product models, perform necessary statistical analysis, and conduct A/B testing. 

1.2. Product Vision and Strategy

The PM needs to work closely with different teams, which include business stakeholders, data scientists, and software engineers, to identify the product vision and roadmap. Along with that, PM needs to develop business cases to create a data-driven presentation and communicate the product vision and strategy to their stakeholders. 

1.3. User Experience and Design

Collaboration with UI and UX designers to create user-friendly and intuitive interfaces that enable customers to interact with data-driven services and products. The product managers need to conduct user research and usability testing to comprehend the customer’s needs and preferences and develop user personas and journey maps to inform product development and optimize UX. Let’s use an understanding of the top four trending product management certification courses that product managers can consider to build a strong portfolio in the competitive market.

2. Four Product Management Certifications

A product management certification is a great way for a product manager to refine their knowledge and gain insight into current AI and data-based product development ideas and product management strategies.

2.1. Product Management Program by CareerFoundry 

The Product School offers three product management certificates exclusively designed for different levels. The beginner level is for students who have yet to gain prior experience, as it covers basics such as assessing target opportunities, building software products, and how to launch a great product. The second level is exclusively for the Product Leader Certificate, which requires 2-4 years of knowledge as a PM and demands competence with DevOps, data analytics, AI, and ML technical skills. Post-certification, the product leader will learn about product strategy, user research, and the UX/UI design process. The final program is a product executive certificate that is customized for senior-level professionals with experience in a product leadership role to explore the demand for product leadership fundamentals and manage the growth of new technological products. 

2.2. New Product Development Professional (NPDP) Certification by the PDMA

The Product Development and Management Association’s (PDMA) New Product Development Professional (NPDP) Certification is recognized as one of the best product management courses. It is designed for PM professionals with at least two years of experience in product development or innovation and seeks to cover key areas of product strategy portfolio management, new product processes, tools, and metrics, market research, and lifecycle management. By earning this certification, you will understand the best industrial practices and improve in the field of product development.

2.3. Certified Product Marketing Manager by the AIPMM

The Certified Product Marketing Manager (CPMM) course offered by the Association of International Product Marketing and Management (AIPMM) is quite famous among product marketing professionals. The core program focuses on strategic marketing, product lifecycle management, and go-to-market strategies. The certification process requires professional skills to manage and market products, followed by rigorous coursework and examinations.  

2.4. SAFe Product Owner/Product Manager by Scaled Agile, Inc.

Scaled Agile, Inc.’s SAFe Product Owner/Product Manager (POPM) Certification is exclusively curated for product managers, agile coaches, scrum masters, and project managers with Agile experience. This course equips professionals with skills to effectively operate within the Scaled Agile Framework and empowers them to enhance Lean-Agile practices and foster collaboration between teams and stakeholders. It involves training on backlog management, understanding customers’ needs, and delivering the right features for successful product development. 

Conclusion 

Product managers are the cornerstone of this ever-evolving digital age, as they play an essential role in harnessing the power of data and AI to drive business growth, sustainability, and customer satisfaction. As of 2024 and beyond, the role of the PM needs to have a remarkable combination of technical, business, and interpersonal skills that will reshape the future of business across industries. 

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Table of Content

Introduction
1. AI Applications for Addressing Environmental Issues
1.1 Predicting Climate Patterns
1.2 Energy Consumption Optimization
1.3 Air Quality Monitoring
2. Industry Leaders’ Perspectives on AI and Environment Sustainability
Winding Up

Introduction

We are well aware that in recent times, climate change has impacted the economic, social, and environmental systems across the planet, and unfortunately, its consequences are expected to continue in the future. 

It has been witnessed that cities in the United States, Philippines, China, and Madagascar are facing warmer, drier, and wetter climates, resulting in natural hazards; these extreme weather events have affected 145,000 human fatalities across cities, as they invite seasonal diseases, drought, famine, and even death. 

Therefore, with these adversities in mind, meteorological departments and governments across the country have started taking advantage of technologies such as artificial intelligence (AI) and machine learning (ML) that have the potential to protect the environment. 

In today’s special edition at AI Tech Park, we will discuss the use of artificial intelligence in monitoring environmental conditions and its potential to save the planet.

1. AI Applications for Addressing Environmental Issues

AI has always been the best possible solution, as it can perform any task that requires human intelligence. These machines are dependent on large amounts of data that can be easily analyzed, create patterns, and make appropriate decisions based on that data. 

Therefore, when AI and environmental sustainability are combined, it can deal with any environmental issues, such as cutting down forests, water crises, and climate change, as AI can accurately make data-driven decisions, letting us watch over the change in ecosystems and focus on planning and protecting nature.

Let’s look at the AI application in environmental solutions: 

1.1. Predicting Climate Patterns

AI can analyze historical and real-time data that empowers predictive modeling for each day’s climate patterns and any natural disaster. The advanced algorithm can forecast weather events, track slight to massive changes in climate conditions, and anticipate the intensity of natural disasters. The AI-driven predictive capabilities allow meteorologists to prepare people for disasters and develop evacuation plans and resource allocation. 

1.2. Energy Consumption Optimization

AI-driven technologies help energy engineers and scientists streamline energy consumption by analyzing patterns and demand fluctuations. For instance, smart grids are driven by intelligent algorithms that align with energy supply and demand. These systems are extremely useful as they efficiently integrate renewable energy sources, and their implementation will continue to increase in the long run. 

1.3. Air Quality Monitoring

The precise real-time air quality assessments are based on data analysis from smart sensors, enabling scientists and engineers to take prompt action in areas with high pollution levels. The ML models also come in handy for forecasting potential pollution levels based on various factors and, thus, taking proactive actions to mitigate air pollution. 

Read about The Convergence of Artificial Intelligence and Sustainability in the IT Industry 

2. Industry Leaders’ Perspectives on AI and Environment Sustainability 

When it comes to introducing AI-driven sustainability initiatives, leaders should ensure that all stakeholders are on board with the idea and must collaborate and think about this issue as a collective thing. 

Having a long-term vision is essential, as companies sometimes focus on immediate benefits that will help increase profit in the next quarter. But when companies start incorporating environmental, societal, and financial variables, it will help C-suites get a clear picture and give thought to the long-term implementation of sustainability and technology.

For any environmental and sustainability initiative, the C-suites must have a strategic vision with robust leadership and stakeholders’ commitment to developing a more resistant and structured plan that will help in creating sustainable business with improved outcomes for the customer and society. 

Read about The Role of CTOs in Integrating the Environmental, Social, and Governance Journey 

Winding Up

The role of AI in environmental sustainability will have a wide role in the future, as it will not only involve handling and analyzing more complex datasets but also enabling environmental prediction. 

Similarly, the integration of smart technology with the Internet of Things (IoT) will allow organizations to collect data and focus on enhancing environmental monitoring and resource management. To accelerate the development and adoption of AI-based solutions for environmental challenges, enterprises need to collaborate with every government, business, academia, and NGO at both local and global levels, as their expertise and knowledge will help in fostering innovation and investing smartly in tailored environmental applications.

Ultimately, the implementation of AI in addressing environmental challenges is just one part of the effort to transition to a more sustainable society. 

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Table of contents

Introduction
1. Data Science Council of America (DASCA) Senior Data Scientist (SDS)
2. IBM Data Science Professional Certificate
3. Open Certified Data Scientist (Open CDS)
4. SAS Certified Data Scientist
5. Microsoft Certified Azure Data Scientist Associate Certification
Winding Up

Introduction

As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.

With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills is equally important for budding and seasoned data scientists to earn a handsome salary and also stay on top of the competition.

In this article, we will explore the top 10 data science certifications that are essential for budding or seasoned data scientists to build a strong foundation in this field. 

1. Data Science Council of America (DASCA) Senior Data Scientist (SDS)

The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program is designed for data scientists with five or more years of professional experience in data research and analytics. The program focuses on qualified knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. This data science program has five trackers that will rank the candidates and track their requirements in terms of their educational and professional degree levels. 

2. IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is an ideal program for data scientists who started their careers in the data science field. This certification consists of a series of nine courses that will help you acquire skills such as data science, open source tools, data science methodology, Python, databases and SQL, data analysis, data visualization, and machine learning (ML). By the end of the program, the candidates will have numerous assignments and projects to showcase their skills and enhance their resumes.

3. Open Certified Data Scientist (Open CDS)

The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experienced certification program for candidates who are looking for an upgrade in their data science skills. The programs have three main levels: level one is to become a Certified Data Scientist; level two is to acquire a Master’s Certified Data Scientist; and the third level is to become a Distinguished Certified. This course will allow data scientists to earn their certificates and stay updated about new data trends. 

4. SAS Certified Data Scientist

SAS Global Certification Program is an advanced-level certification for data scientists who want to update their knowledge on the latest technological advancements in open-source tools and SAS Data Management. The course further provides an understanding of how to manage and improve data and offers an idea of unstructured and structured data transformations and the importance of data access. This certification program is also divided into three pathways aimed at different professionals in data science: the first course is for Data Curation Professionals, which has 4 courses, whereas the second course is for Advanced Analytics Professionals, which has 9 courses. The last course is for AI and machine learning professionals and has five courses. 

5. Microsoft Certified Azure Data Scientist Associate Certification

The Microsoft Azure Data Scientist Associate certification focuses on data scientists and professionals who have professional knowledge about data science and machine learning projects on the Azure platform. This certificate validates the candidate’s ability to utilize and implement ML into Azure and MLflow for data science tasks. The program focuses on upgrading the candidate’s skills in machine learning, AI solutions, NLP, computer vision, and predictive analytics and deploying an understanding of data governance and storage.

Winding Up

Earning a certification in data science courses and programs is an excellent way to kickstart your career in data science and stand out from the competition. However, before selecting the correct course, it is best to consider which certification type is appropriate according to your education and job goals.

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Table of Contents

Introduction
1. The Evolution of Data Literacy in the Technological Era
2. Establishing a Data Literacy Program
Wrapping Up

Introduction

As we have entered the digital era, data and analytics strategies (D&A) have become important, as these technologies can transform any business during a massive data spike. According to global research, it was observed that around 2.5 quintillion bytes of data are produced by IT companies every day; therefore, to understand the importance of data, every employee must be data literate.

For a better understanding of data, the Chief Data Officers (CDOs) play an important role in making every employee data literate, i.e., able to understand, share, and have meaningful insight into data.  

With this mindset, organizations can seamlessly adopt emerging and existing technologies and transform their business outcomes across all departments while fostering quality decision-making, innovation, and a better customer experience. The CDOs 

In this exclusive AI TechPark article, we will discuss the evolution of data literacy and how it can transform any organization into a data-literate one.

Read more about The Value of the Chief Data Officer in the Data Governance Framework 

1. The Evolution of Data Literacy in the Technological Era

In the past few decades, data literacy has experienced a significant transformation with the introduction of new technologies and the explosion of data. This shift has ignited from traditional data analysis to a modern era of big data that has redefined the way organizations can make data-driven decisions. 

To analyze data, data scientists and analysts were confined to basic statistics and simple datasets. Even to analyze the data, data professionals needed more tools, narrow, small-scale datasets, and internal data sources. However, in the late 20th century, there were a lot of technological advancements, such as the introduction of data storage, big data, and cloud computing. This helped data scientists collect and process massive amounts of data from complex, unstructured datasets that could be further analyzed for deeper insight.

Read more about Navigating the Future With the Integration of Deep Learning in Big Data Analytics 

As a result of these technological advancements, the power of data has become a center point for developing strategic planning and seamlessly operating business efficiency in the IT industry. Thus, data literacy becomes equally important to developing a data-literate workforce and ensuring that professionals harness the full potential of data for competitive advantage in the data-driven landscape.

2. Establishing a Data Literacy Program

To achieve the goal of robust D&A strategies, the CDOs need to address the existing skill gaps among each employee by rolling out data literacy training programs. These programs can help every employee who is handling data daily by developing D&A skills and acquiring knowledge on data literacy as a part of organizational culture.

Before initiating the programs, CDOs should look for speakers who are data analysts, data stewards, and architects, as they can be the right people to speak about data naturally and effortlessly. 

Secondly, CDOs must interact with the employees to understand communication barriers and conduct data literacy assessments to identify the gaps and encourage employees to work on them. 

Employees who are not aware of the subject matter often find it tedious to understand; therefore, it is essential to develop an open environment of fun and games to teach about data.

Finally, CDOs must arrange a data literacy proof-of-concept workshop where participants can describe real-life common use cases, which can be a lesson for every employee to raise awareness and understand the data literacy gaps. The data teams can also hold biweekly meetings and discussions with the participants and provide them with the KPIs they need to work on. 

With this approach, CDOs can eliminate the data literacy gap and generate better methods of data analysis without being biased. 

Read more about Modernizing Data Management with Data Fabric Architecture 

Wrapping Up

Data is necessary, empowering at both individual and organizational levels by creating a pathway to harness real-world data-driven decision-making and data-driven organizational strategy. 

In an era where artificial intelligence, data analysis, machine learning, and big data are driving critical business decisions and the ability to steer through complex datasets and extract business insights, data literacy is the epitome of enhancing employability, making informed business decisions, driving innovation, and gaining a competitive edge.

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