#chief data officer

[jcmarchi]

Molham Aref, CEO & Founder of RelationalAI

New Post has been published on https://thedigitalinsider.com/molham-aref-ceo-founder-of-relationalai/

Molham Aref, CEO & Founder of RelationalAI

Molham is the Chief Executive Officer of RelationalAI. He has more than 30 years of experience in leading organizations that develop and implement high-value machine learning and artificial intelligence solutions across various industries. Prior to RelationalAI he was CEO of LogicBlox and Predictix (now Infor), CEO of Optimi (now Ericsson), and co-founder of Brickstream (now FLIR). Molham also held senior leadership positions at HNC Software (now FICO) and Retek (now Oracle).

RelationalAI brings together decades of experience in industry, technology, and product development to advance the first and only real cloud-native knowledge graph data management system to power the next generation of intelligent data applications.

As the founder and CEO of RelationalAI, what was the initial vision that drove you to create the company, and how has that vision evolved over the past seven years?

The initial vision was centered around understanding the impact of knowledge and semantics on the successful deployment of AI. Before we got to where we are today with AI, much of the focus was on machine learning (ML), which involved analyzing vast amounts of data to create succinct models that described behaviors, such as fraud detection or consumer shopping patterns. Over time, it became clear that to deploy AI effectively, there was a need to represent knowledge in a way that was both accessible to AI and capable of simplifying complex systems.

This vision has since evolved with deep learning innovations and more recently, language models and generative AI emerging. These advancements have not changed what our company is doing, but have increased the relevance and importance of their approach, particularly in making AI more accessible and practical for enterprise use.

A recent PwC report estimates that AI could contribute up to $15.7 trillion to the global economy by 2030. In your experience, what are the primary factors that will drive this substantial economic impact, and how should businesses prepare to capitalize on these opportunities?

The impact of AI has already been significant and will undoubtedly continue to skyrocket. One of the key factors driving this economic impact is the automation of intellectual labor.

Tasks like reading, summarizing, and analyzing documents – tasks often performed by highly paid professionals – can now be (mostly) automated, making these services much more affordable and accessible.

To capitalize on these opportunities, businesses need to invest in platforms that can support the data and compute requirements of running AI workloads. It’s important that they can scale up and down cost-effectively on a given platform, while also investing in AI literacy among employees so they can understand how to use these models effectively and efficiently.

As AI continues to integrate into various industries, what do you see as the biggest challenges enterprises face in adopting AI effectively? How does data play a role in overcoming these challenges?

One of the biggest challenges I see is ensuring that industry-specific knowledge is accessible to AI. What we are seeing today is that many enterprises have knowledge dispersed across databases, documents, spreadsheets, and code. This knowledge is often opaque to AI models and does not allow organizations to maximize the value that they could be getting.

A significant challenge the industry needs to overcome is managing and unifying this knowledge, sometimes referred to as semantics, to make it accessible to AI systems. By doing this, AI can be more effective in specific industries and within the enterprise as they can then leverage their unique knowledge base.

You’ve mentioned that the future of generative AI adoption will require a combination of techniques such as Retrieval-Augmented Generation (RAG) and agentic architectures. Can you elaborate on why these combined approaches are necessary and what benefits they bring?

It’s going to take different techniques like GraphRAG and agentic architectures to create AI-driven systems that are not only more accurate but also capable of handling complex information retrieval and processing tasks.

Many are finally starting to realize that we are going to need more than one technique as we continue to evolve with AI but rather leveraging a combination of models and tools. One of those is agentic architectures, where you have agents with different capabilities that are helping tackle a complex problem. This technique breaks it up into pieces that you farm out to different agents to achieve the results you want.

There’s also retrieval augmented generation (RAG) that helps us extract information when using language models. When we first started working with RAG, we were able to answer questions whose answers could be found in one part of a document. However, we quickly found out that the language models have difficulty answering harder questions, especially when you have information spread out in various locations in long documents and across documents. So this is where GraphRAG comes into play. By leveraging language models to create knowledge graph representations of information, it can then access the information we need to achieve the results we need and reduce the chances of errors or hallucinations.

Data unification is a critical topic in driving AI value within organizations. Can you explain why unified data is so important for AI, and how it can transform decision-making processes?

Unified data ensures that all the knowledge an enterprise has – whether it’s in documents, spreadsheets, code, or databases – is accessible to AI systems. This unification means that AI can effectively leverage the specific knowledge unique to an industry, sub-industry, or even a single enterprise, making the AI more relevant and accurate in its outputs.

Without data unification, AI systems can only operate on fragmented pieces of knowledge, leading to incomplete or inaccurate insights. By unifying data, we make sure that AI has a complete and coherent picture, which is pivotal for transforming decision-making processes and driving real value within organizations.

How does RelationalAI’s approach to data, particularly with its relational knowledge graph system, help enterprises achieve better decision-making outcomes?

RelationalAI’s data-centric architecture, particularly our relational knowledge graph system, directly integrates knowledge with data, making it both declarative and relational. This approach contrasts with traditional architectures where knowledge is embedded in code, complicating access and understanding for non-technical users.

In today’s competitive business environment, fast and informed decision-making is imperative. However, many organizations struggle because their data lacks the necessary context. Our relational knowledge graph system unifies data and knowledge, providing a comprehensive view that allows humans and AI to make more accurate decisions.

For example, consider a financial services firm managing investment portfolios. The firm needs to analyze market trends, client risk profiles, regulatory changes, and economic indicators. Our knowledge graph system can rapidly synthesize these complex, interrelated factors, enabling the firm to make timely and well-informed investment decisions that maximize returns while managing risk.

This approach also reduces complexity, enhances portability, and minimizes dependence on specific technology vendors, providing long-term strategic flexibility in decision-making.

The role of the Chief Data Officer (CDO) is growing in importance. How do you see the responsibilities of CDOs evolving with the rise of AI, and what key skills will be essential for them moving forward?

The role of the CDO is rapidly evolving, especially with the rise of AI. Traditionally, the responsibilities that now fall under the CDO were managed by the CIO or CTO, focusing primarily on technology operations or the technology produced by the company. However, as data has become one of the most valuable assets for modern enterprises, the CDO’s role has become distinct and crucial.

The CDO is responsible for ensuring the privacy, accessibility, and monetization of data across the organization. As AI continues to integrate into business operations, the CDO will play a pivotal role in managing the data that fuels AI models, ensuring that this data is clean, accessible, and used ethically.

Key skills for CDOs moving forward will include a deep understanding of data governance, AI technologies, and business strategy. They will need to work closely with other departments, empowering teams that traditionally may not have had direct access to data, such as finance, marketing, and HR, to leverage data-driven insights. This ability to democratize data across the organization will be critical for driving innovation and maintaining a competitive edge.

What role does RelationalAI play in supporting CDOs and their teams in managing the increasing complexity of data and AI integration within organizations?

RelationalAI plays a fundamental role in supporting CDOs by providing the tools and frameworks necessary to manage the complexity of data and AI integration effectively. With the rise of AI, CDOs are tasked with ensuring that data is not only accessible and secure but also that it is leveraged to its fullest potential across the organization.

We help CDOs by offering a data-centric approach that brings knowledge directly to the data, making it accessible and understandable to non-technical stakeholders. This is particularly important as CDOs work to put data into the hands of those in the organization who might not traditionally have had access, such as marketing, finance, and even administrative teams. By unifying data and simplifying its management, RelationalAI enables CDOs to empower their teams, drive innovation, and ensure that their organizations can fully capitalize on the opportunities presented by AI.

RelationalAI emphasizes a data-centric foundation for building intelligent applications. Can you provide examples of how this approach has led to significant efficiencies and savings for your clients?

Our data-centric approach contrasts with the traditional application-centric model, where business logic is often embedded in code, making it difficult to manage and scale. By centralizing knowledge within the data itself and making it declarative and relational, we’ve helped clients significantly reduce the complexity of their systems, leading to greater efficiencies, fewer errors, and ultimately, substantial cost savings.

For instance, Blue Yonder leveraged our technology as a Knowledge Graph Coprocessor inside of Snowflake, which provided the semantic understanding and reasoning capabilities needed to predict disruptions and proactively drive mitigation actions. This approach allowed them to reduce their legacy code by over 80% while offering a scalable and extensible solution.

Similarly, EY Financial Services experienced a dramatic improvement by slashing their legacy code by 90% and reducing processing times from over a month to just several hours. These outcomes highlight how our approach enables businesses to be more agile and responsive to changing market conditions, all while avoiding the pitfalls of being locked into specific technologies or vendors.

Given your experience leading AI-driven companies, what do you believe are the most critical factors for successfully implementing AI at scale in an organization?

From my experience, the most significant factors for successfully implementing AI at scale are ensuring you have a strong foundation of data and knowledge and that your employees, particularly those who are more experienced, take the time to learn and become comfortable with AI tools.

It’s also important not to fall into the trap of extreme emotional reactions – either excessive hype or deep cynicism – around new AI technologies. Instead, I recommend a steady, consistent approach to adopting and integrating AI, focusing on incremental improvements rather than expecting a silver bullet solution.

Thank you for the great interview, readers who wish to learn more should visit RelationalAI.

[alliance00]

C-Level Recruitment is the process of hiring top executives, such as CEOs, CFOs, and CTOs, for an organization. In this blog, let's discuss c-level recruitment trends in 2023.

[garymdm]

The Data-Driven Enterprise of 2025 – How Close Are We?

In January 2022, McKinsey and Company released a forward-looking report that boldly predicted the landscape of data-driven enterprises in 2025. The report, aptly titled “The Data-Driven Enterprise of 2025,” laid out a vision that could revolutionize the way businesses operate. By 2025, technology advances, the recognized value of data, and increasing data literacy will transform what it means to…

[strider-the-pony-rider]

Unlocking the Secrets of Reliable Management: Inspire, Empower, and Do well

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Management is an important skill that can make or damage the success of people, groups, and also companies. It exceeds plain management and includes inspiring as well as leading others in the direction of a typical goal. Efficient leadership has the power to transform cultures, drive technology, as well as achieve phenomenal outcomes. Nonetheless, it is not a characteristic that is integral in every person. It requires continuous knowing, self-reflection, and also the determination to adjust and develop. In this post, we will dig right into the secrets of reliable management, exploring essential concepts and methods that can aid individuals unlock their full potential as leaders.One of the fundamental facets of reliable management is the capacity to motivate others. Leaders should have the ability to communicate a compelling vision, inspire their staff member, and also spark a sense of passion and also purpose. By setting clear objectives, leading by instance, and also promoting a positive as well as comprehensive workplace, leaders can develop a feeling of common possession and also commitment among their group. Empowerment is an additional critical element of efficient management. By counting on their staff member'capacities, entrusting obligations, and providing possibilities for development as well as advancement, leaders can equip individuals to take ownership of their job, choose, as well as add to the general success of the organization. Ultimately, reliable management has to do with achieving results, whether it be driving technology, exceeding targets, or producing a favorable influence. By personifying the concepts of inspiration and also empowerment, leaders can assist their groups towards success and develop a heritage that lasts beyond their period.

Read more here Legal Affairs

[ensign-smith]

So I was Tasha Yar at a Halloween party this year, and had this conversation

rando: yeah she's Yar -- if she were Data, her uniform would be a different color

me: -pause- No. Yar and Data wear the same color uniform

rando: Are you sure? I think he wears blue

me: I am very sure

rando: I SWEAR he wears blue, you know, the sciences uniform! Let's google this

me: We don't need to google this because I literally have pictures of him saved on my phone

this is the first picture i pull up

[walkman-cat]

could I request something Davey?

ive had my star trek au on the brain wbwbwb so here’s davey in the au (he’s taking spot the cat off the bridge) (im so sorry it’s not regular davey wbwbw)

[transhologram]

theyre giving the robots trauma

[lipstickontheglass1985]

star trek tng s5e5 disaster would be such a beautiful name for a babygirl btw

[rightnewshindi]

हिमाचल में मुख्यमंत्री, मंत्रियों और विधायकों की कितनी है सैलरी, हर महीने टेलीफोन,डाटा ऑपरेटर, कार्यालय समेत मिलते हैं यह भत्ते

Himachal News: हिमाचल प्रदेश में इन दिनों कर्मचारियों ने अपने लंबित डीए और एरियर की मांग को लेकर राज्य सरकार के खिलाफ मोर्चा खोल रखा है। कर्मचारियों के साथ-साथ विपक्ष के भी सरकार निशाने पर है। दरअसल विपक्ष समय-समय पर फिजूलखर्ची को लेकर सरकार पर सवाल उठाता रहता है। खासकर आर्थिक तंगी से जूझ रही हिमाचल सरकार में मंत्रियों को मिलने वाले वेतन और सुविधाओं पर एक बार फिर चर्चा होने लगी है। कर्मचारियों…

[dhallblogs]

OT Security: The Achilles’Heel for Manufacturing.

In an era where digital transformation is reshaping industries, the manufacturing sector faces a unique set of cybersecurity challenges. As manufacturers increasingly integrate advanced technologies into their operations, the convergence of Operational Technology (OT) and Information Technology (IT) introduces both opportunities and vulnerabilities. This blend of legacy systems with modern innovations has made cybersecurity a critical concern, as the sector grapples with complex threats ranging from ransomware attacks to supply chain vulnerabilities.

ALSO READ MORE- https://apacnewsnetwork.com/2024/07/ot-security-the-achillesheel-for-manufacturing/

[jcmarchi]

Mathias Golombek, Chief Technology Officer of Exasol – Interview Series

New Post has been published on https://thedigitalinsider.com/mathias-golombek-chief-technology-officer-of-exasol-interview-series/

Mathias Golombek, Chief Technology Officer of Exasol – Interview Series

Mathias Golombek is the Chief Technology Officer (CTO) of Exasol. He joined the company as a software developer in 2004 after studying computer science with a heavy focus on databases, distributed systems, software development processes, and genetic algorithms. By 2005, he was responsible for the Database Optimizer team and in 2007 he became Head of Research & Development. In 2014, Mathias was appointed CTO. In this role, he is responsible for product development, product management, operations, support, and technical consulting.

What initially attracted you to computer science?

When I was in fourth grade, my older brother had some lessons where they learned to program BASIC, and he showed me what you can do with that. Together, we developed an Easter riddle on our Commodore 64 for our youngest brother, and ever since then, I have been fascinated by computers. Computer science in general is all about solving problems and being creative and I think that aspect attracted me the most to the field.

Can you share your journey from joining Exasol as a software developer in 2004 to becoming the CTO? How have your roles evolved over the years, especially in the rapidly changing tech landscape?

I studied Computer Science at The University of Würzburg in Germany and started at Exasol as a software developer in 2004 after graduating. After my first year with Exasol, I was promoted to Head of the Database Optimizer Team and then Head of Research and Development. After that, I served as Head of R&D for seven years before stepping into my current role as CTO in 2014.

From the beginning, I was amazed at what Exasol was doing — this German technology company fighting against big names like Microsoft, IBM, and Oracle. I was blown away by the opportunity in front of me — as a developer, creating this massively parallel processing (MPP), in-memory database management system was  heaven on earth.

I’ve enjoyed every moment of working with this talented engineering team. As CTO, I oversee Exasol’s product innovation, development and technical support. It’s been exciting to see how much the Exasol team has grown globally as we work to support our customers and their evolving needs. The fundamentals are the same ��� we’re still an in-memory database system, but now we’re empowering our customers to harness the power of their data for AI implementations.

Exasol has been at the forefront of high-performance analytics databases. From your perspective, what sets Exasol apart in this competitive space?

Business leaders are constantly tasked with navigating how to do more with less. In recent years, this has become even more challenging as the economy continues to be tumultuous and the proliferation of AI technology has taken up budget and time.

As a high-performance analytics database provider, Exasol has remained ahead of the curve when it comes to helping businesses do more with less. We help companies transform business intelligence (BI) into better insights with Exasol Espresso, our versatile query engine that plugs into existing data stacks. Global brands including T-Mobile, Piedmont Healthcare, and Allianz use Exasol Espresso to turn higher volumes of data into faster, deeper and cheaper insights. And I think we’ve done a great job of mastering the delicate balance between performance, price and flexibility so customers don’t have to compromise.

To support companies on their AI journeys, we also recently unveiled Espresso AI, equipping our versatile query engine with a new suite of AI tools that enable organizations to harness the power of their data for advanced AI-driven insights and decision-making. Espresso AI’s capabilities make AI more affordable and accessible, enabling customers to bypass expensive, time-consuming experimentation and achieve immediate ROI. This is a game-changer for enterprises who are focused on driving innovation and delivering value in the age of AI.

The 2024 AI and Analytics Report by Exasol highlights underinvestment in AI as a pathway to business failure. Could you expand on the key findings of this report and why investing in AI is critical for businesses today?

As you stated, the main takeaway from Exasol’s 2024 AI and Analytics Report is that underinvestment in AI leads to business failure. Based on our survey of senior decision-makers as well as data scientists and analysts across the U.S., U.K., and Germany, nearly all (91%) respondents agree that AI is one of the most important topics for organizations in the next two years, with 72% admitting that not investing in AI today will put future business viability at risk. Put simply, in today’s environment, businesses that are not thinking about AI are already behind.

Businesses are facing pressure from stakeholders to invest in AI – and there are many reasons why. Investment in AI has already helped organizations across industries – from healthcare to financial services and retail – unlock new revenue streams, enhance customer experiences, optimize operations, increase productivity, accelerate competitiveness and more. The list only grows from there as businesses are starting to find specific ways to leverage AI to fit unique business needs.

The same report mentions major barriers to AI adoption, including data science gaps and latency in implementation. How does Exasol address these challenges for its clients?

Despite the critical need for AI investment, businesses still face significant barriers to broader implementation. Exasol’s AI and Analytics Report indicates that up to 78% of decision-makers experience gaps in at least one area of their data science and machine learning (ML) models, with 47% citing speed to implement new data requirements as a challenge. An additional 79% claim new business analysis requirements take too long to be implemented by their data teams. Other factors hindering widespread AI adoption include the lack of an implementation strategy, poor data quality, insufficient data volumes and integration with existing systems. On top of that, evolving bureaucratic requirements and regulations for AI are causing issues for many companies with 88% of respondents stating they need more clarity.

As AI deployment grows, it will become even more important for businesses to ensure strong data foundations. Exasol offers flexibility, resilience and scalability to businesses adopting an AI strategy. As roles such as the Chief Data Officer (CDO) continue to evolve and become more complex –– with growing ethical and compliance challenges at the forefront –– Exasol supports data leaders and helps them transform BI into faster, better insights that will inform business decisions and positively impact the bottom line.

While AI has become critical to business success, it’s only as effective as the tools, technology and people powering it on the backend. The survey results emphasize the significant gap between current BI tools and their output – more tools does not necessarily mean faster performance or better insights. As CDOs prepare for more complexity and are tasked to do more with less, they must evaluate the data analytics stack to ensure productivity, speed, and flexibility – all at a reasonable cost.

Espresso AI helps to close this gap for the enterprise by optimizing data extraction, loading, and transformation processes to give users the flexibility to immediately experiment with new technologies at scale, regardless of infrastructure restriction – whether on-premises, cloud, or hybrid. Users can reduce data movement costs and effort while bringing in emerging technologies like LLMs into their database. These capabilities help organizations accelerate their journey toward implementing AI and ML solutions while ensuring the quality and reliability of their data.

Data literacy is becoming increasingly important in the age of AI. How does Exasol contribute to enhancing data literacy among its clients and the wider community?

In today’s data-rich working environments, data literacy skills are more important than ever – and quickly becoming a “need to have” rather than a “nice to have” in the age of AI. Across industries, proficiency in working with, understanding and communicating data effectively has become vital. But there remains a data literacy gap.

Data literacy is about having the skills to interpret complex information and the ability to act on those findings. But often data access is siloed within an organization or only a small subset of individuals have the necessary data literacy skills to understand and access the vast amounts of data flowing through the business. This approach is flawed because it limits the amount of time and resources dedicated to utilizing data and, ultimately, the data literacy gap creates a barrier to business innovation.

When people are data literate, they can understand data, analyze it and apply their own ideas, skills and expertise to it. The more people with the knowledge, confidence and tools to unravel and take meaning from data, the more successful an organization can be. At Exasol, we support data leaders and businesses in driving data literacy and education.

In addition to the education component, businesses should optimize their tech stacks and BI tools to enable data democratization. Data accessibility and data literacy go hand in hand. Investment in both is needed to further data strategies. For example, with Exasol, our tuning-free system enables businesses to focus on the data usage, rather than the technology. The high speed allows teams to work interactively with data and avoid being restricted by performance limitations. This ultimately leads to data democratization.

Now is the time for data democratization to shift from a topic of discussion to action within organizations. As more people across various departments gain access to meaningful insights, it will alleviate the traditional bottlenecks caused by data analytics teams. When these traditional silos come crashing down, organizations will realize just how wide and deep the need is for their teams and individuals to use data. Even people who don’t currently think they are an end user of data will be pulled into feed off of data.

With this shift comes a major challenge to anticipate in the coming years – the workforce will need to be upgraded in order for every employee to gain the proper skill set to effectively use data and insights to make business decisions. Today’s workforce won’t know the right questions to ask of its data feed, or the automation powering it. The value of being able to articulate precise, probing and business-tethered questions is increasing in value, creating a dire need to train the workforce on this capability.

You have a strong background in databases, distributed systems, and genetic algorithms. How do these areas of expertise influence Exasol’s product development and innovation strategy?

My background is a foundation of working in our field and understanding the technology trends of the last two decades. It’s exciting and rewarding to work with innovative customers who turn database technology into interesting use cases. Our innovation strategy doesn’t just depend on one individual, but a large team of sophisticated architects and developers who understand the future of software, hardware and data applications.

With AI transforming industries at an unprecedented pace, what do you believe are the essential components of a future-proof data stack for businesses looking to leverage AI and analytics effectively?

The rapid adoption of AI has been a prime example of why it’s important for enterprises to stay ahead of the evolving tech landscape. The unfortunate truth, however, is that most data stacks are still behind the AI curve.

To future-proof data stacks, businesses should first evaluate data foundations to identify gaps, bugs or other challenges. This will help them ensure data quality and speed – elements that are critical for driving valuable insights and fueling AI and LLM models.

In addition, teams should invest in the tools and technologies that can easily integrate with other solutions in the stack. As AI is paired with other technologies, like open source, we’ll see new models emerge to solve traditional business problems. Generative AI, like ChatGPT, will also merge with more traditional AI technology, such as descriptive or predictive analytics, to open new opportunities for organizations and streamline traditionally cumbersome processes.

To future-proof data stacks, enterprises should also integrate AI and BI. Businesses have been using BI tools for decades to extract valuable insights and while many improvements have been made, there are still BI limitations or barriers that AI can help with. AI can enable faster outcomes, enhance personalization and transform the BI landscape into a more inclusive and user-friendly domain. Since BI typically focuses on analyzing historical data to provide insights, AI can extend BI capabilities by helping anticipate future events, generating predictions and recommending actions to influence desired outcomes.

Productivity, flexibility, and cost-savings are highlighted as three ways Exasol helps global brands innovate. Can you provide an example of how Exasol has enabled a client to achieve significant ROI through your analytics database?

According to a 2023 Forrester Total Economic Impact Study, Exasol customers achieve up to a 320% ROI on their initial investment over three years by improving operational efficiency, database performance, and offering a simple and flexible data infrastructure.

One customer for example, Helsana, a leader in Switzerland’s competitive healthcare industry, came to Exasol to fill a need for a modern data and analytics platform. Before Exasol, Helsana relied on various reporting tools with data warehouses built on different technologies and ETL tools which created a tangled, inefficient architecture. Compared to the company’s existing legacy solution, Exasol’s Data Warehouse demonstrated a five to tenfold performance improvement.

Now, Exasol is central to Helsana’s AI journey, serving as the repository for the structured data that Helsana uses across all of its AI models and providing the

foundation for its analytics. With Exasol, the Helsana team has boosted performance, reduced costs, increased agility and established a solid AI foundation, all of which contribute to significant ROI on top of an increased ability to better serve customers.

Looking ahead, what are the upcoming trends in data analytics and business intelligence that Exasol is preparing for, and how do you plan to continue driving innovation in this space?

 The year 2023 introduced AI on a wide scale, which caused knee-jerk reactions from organizations that ultimately spawned countless poorly designed and executed automation experiments. 2024 will be a transformation year for AI experimentation and foundational work. So far, the primary applications of GenAI have been for information access through chatbots, customer service automation, and software coding. However, there will be pioneers who are adopting these exciting technologies for a whole plethora of business decision-making and optimizations. Looking beyond 2024, we’ll start to see a bigger push towards productive implementations of AI.

At Exasol, we’re committed to driving innovation and delivering value to our customers, this includes helping them develop and implement AI at scale. With Exasol, customers can marry BI and AI to overcome data silos in an integrated analytics system. Our flexibility around deployment options also enable organizations to decide where they want to host their analytics stack, whether it’s in the public cloud, private cloud or on-premises. With Exasol’s Espresso AI, we are positioned to empower enterprises to harness the value of AI-driven analytics, regardless of where organizations fall in their AI journey.

Thank you for the great interview, readers who wish to learn more should visit Exasol.

[garymdm]

Will the Chief Data Officer become the Big Data Officer?

Will the #CDO become the #BigData Officer

Introduction: The Need for a Chief Data Officer According to a recent study conducted by research agency Loudhouse, a significant 61% of CIOs believe that it is crucial for their companies to appoint a Chief Data Officer (CDO) to the board within the next 12 months. The reason behind this pressing need is that the role of a CIO is no longer sufficient to handle the shifting priority from data…

[darkautomaton]

Best Practices in Corporate Risk Management in Hong Kong

With an increasingly complex legal, regulatory, economic, and technological environment, effectively managing organizational risks is critical for companies striving towards sustainable growth in Hong Kong. By taking a strategic approach to identifying key risk exposures and establishing governance policies to address vulnerabilities, both local and multinational corporations can enhance resilience.

Conduct Extensive Risk Assessments

The foundation for building robust risk oversight is to regularly conduct enterprise-wide assessments, tapping perspectives from leaders across functions on risks emerging within main business units, as well as at the corporate level. Special focus should be placed on emerging risks - from supply chain disruptions to fast-evolving cybersecurity threats. Risks posed by Hong Kong regulations and legal responsibilities around data, employment, IP, taxation and import/export controls should also be incorporated.

Appoint Centralized Risk Leadership

While business heads are accountable for risks within their domains, oversight at the core by a Chief Risk Officer and/or risk management committee provides critical independence and cross-functional coordination. Responsibilities span creating risk reporting procedures to keeping senior leadership and board directors appraised, to aligning mitigation plans with corporate strategy. Risk managers also liaise with insurance providers to secure proper coverage against financial hazards.

Implement Key Risk Policies

Findings from risk assessments should drive key policy changes, be it business continuity planning to address operational crises, instituting ethics training to reduce fraud and corruption, or enacting information handling protocols to avoid data leaks, hacking and illegal trading incidents that would undermine Hong Kong stock listings. Anti-money laundering and sanctions/export controls compliance also need special attention in Hong Kong as a gateway between China and global trade.

Monitor External Signals

In addition to internal risk monitoring, closely follow legislative or law enforcement policy shifts, as well as economic/political disruptions arising locally as well as in mainland China that stand to impact operations. Participate in trade groups and maintain contacts in agencies like InvestHK to receive critical market updates. Regular stress tests help evaluate Hong Kong megaprojects like the Greater Bay Area growth plan or One Belt One Road initiative - and gauge ensuing risk reprioritizations.

By approaching risk oversight as an integrated corporate capability monitoring both internal weaknesses and external threats, companies gain enhanced visibility into vulnerabilities which allows preemptively strengthening of operations against cascading Hong Kong/China hazards - thereby boostinglong-term performance and valuation for shareholders.

[solemntitty]

all my homies hate working administrative, time to go back to the real me (working medical tech shit again)

[kdrama-movies-more]

Ah God...years back I had hop-skipped a lot of Strong Girl Do Bong-soon from the halfway point and erased half the kidnapper plot from memory....

So the mystery part of Behind Your Touch is like, a jarring mashup of Bong-soon and Beyond Evil🙈 huh???

[md000001]

Navigating a Potential Recession: The Key Focus Areas for #CIOs

During a potential recession, the role of a Chief Information Officer (CIO) becomes even more critical in helping the organization navigate through economic challenges and uncertainties. These break into two types of action, those that help build the future, but improve efficiency, and those that reduce costs. Innovating for the future: Data-Driven Decision Making: Leveraging data analytics can…