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How data transformed from technical resource to strategic business asset



This article was contributed by Hassan Lâasri, consultant in data strategy, data governance, and data activation.

Since McKinsey’s report on big data in May 2011, we have entered an era where virtually everything we do on this planet is designed and digitized to generate data, consume it, or both. Recent projects, including the Metaverse, want to translate the earth into a virtual data planet. Since that report, data has been considered to be a strategic asset in any company whose business depends on data — not just Google, Amazon, Meta, Apple, and Microsoft, all of which have paved the way. But before we got to this point, data itself has gone a transformation from a purely technical resource to a valued asset.

Today, data is collected, organized, and enriched, not only to assess the performance of the company, but to predict its future. Very soon, those predictions will be widely turned into decisions and actions. The credit sector, the engine of the U.S. economy, is already showing us the way.

Data: A resource that has become a business asset

Barely ten years ago, data was an IT resource that supported business lines and functions. As such, it was generally managed by the IT department, whose mission was to build the data architecture, choose a database supplier, and design applications linking databases to the needs of internal users. These applications were mostly descriptive analytics dashboards, which allowed companies to get a status of how they were performing against their goals. This is what is meant by business intelligence (BI) in the financial departments of large organizations.

Then came the first transformation. Dashboards were improved by predictive analytics, whose scope of analysis is no longer just on what had happened in the past months and years, but on what might happen if there are significant changes in industry regulations, market dynamics, and company strategy. This transformation propelled the uses of data science and machine learning in business today and includes use cases in advertising, marketing, sales, customer relationship management, and supply chain management.

Since that change, a new transformation has been underway, beginning in the banking, insurance, and health sectors in the USA and China and crossing the oceans to land in Europe with promises, realities, and new regulations. It consists of transforming predictive analytics into operational decisions. The promise of this new transformation is to create a virtuous circle where not only is data analyzed, but that analysis is transformed into decisions and actions that generate new data. This prescriptive analytics complements descriptive analytics and predictive analytics in the same way that a robot learns to walk and walks by learning. You can compare this to reinforcement learning, but simpler technologies can do the work. For example, the technologies used by large financial institutions can automate the process of lending to individuals based on data the individual supplies and the risk scores learning models compute.

Contrary to what we might think, transforming data from a technical resource to a strategic asset is not easy. Indeed, all the big historical companies dream of being like Google, Amazon, Meta, Uber, and Airbnb, but they were not created with data or machine learning in their DNA. As a result, their existing data cannot be directly activated to gain a strategic competitive advantage. Historical companies need a new kind of data practice.

Data governance: A necessary step

It is not enough to bring together all the company’s data in a data platform for the data to be transformed into knowledge, forecasts, and decisions. Indeed, all data does not have the same age, the same structure, the same format, the same quantity, the same quality, and most importantly, the same utility. If an attribute is important for one business line, it is not automatically important for another business line, even within the same company. Each business line has its own vision of a product, of the customer, and of any entity managed by the various actors of the company. In the luxury sector, for example, a dress, a bag, or a piece of jewelry, although considered to be unique objects, are seen through different attributes according to the databases where the same objects are stored. Looking to exploit all the data available in a company to extract predictions and decisions requires a new project, known as data governance.

Like any general title, there is no consensus on the definition of data governance, and it should not be confused with data management or quality management. For my part, I define data governance as the organization, the processes, and the tools put in place so that the data is ready to be activated by the models and algorithms of data scientists so that data science is able to deliver on its promises. Successful initiatives have always had data owners who are businesspeople familiar with the needs of their profession, in addition to technical teams often made up of data architects, data modelers, data engineers, and sometimes data scientists.

In an architectural model represented as levels, where the highest level stands for business needs and the lowest level stands for technical resources, data governance would be found below data science and above data management. From a practical point of view, data governance integrates, unifies, and harmonizes data for data scientists to use according to the data stored in the source systems. It is at the level of data governance that the company policies are defined and the sectoral regulations are implemented.

The multitude of regulations that flourish all over the world, sometimes with regulations by states in the same country, makes data governance even more complex and an ability even more sought after, in the way that data science was at the start of the data era. Data architects, data scientists, and data stewards must now integrate discrepancies, such as the impossibility of exploiting the data outside the territory where it was collected, or even of using recommendation algorithms without explanatory capabilities. Dreaming of a “one size fits all” global data platform is no longer relevant. From now on, pragmatism prevails: one platform per continent, or even per country; otherwise, the budget would be worthy of those of the major transformation programs at the national level.

Future data transformations

In less than a decade, data has evolved from a resource for evaluating business performance to an asset used to predict the future of the business. It will soon become an asset used to automate and improve decisions. These two rapid transformations were made possible thanks to an awareness of the strategic side of data governance, without which there would be no data intelligence. This helps transform businesses into lifelong learning organizations where data helps find opportunities, machine learning turns that data into knowledge, and AI turns that knowledge into action, closing the virtuous circle that data promises. Think of a marketing campaign where an AI uses data from previous campaigns to build a prospect profile, then chooses a communication channel to reach prospects, then selects the appropriate messages for different groups of prospects, and finally collects new data for the next campaign. This AI will relieve marketers of routine work, giving them more time for design and creativity rather than execution of campaigns.

As for its future, no expert and no algorithm will be able to predict it exactly. The one thing that’s certain is that data is interfering in all economic activities, to the point that it has become omnipresent as an asset that financiers value in the same way as customer bases, patents, trademarks, and other intangible assets. It’s data, not algorithms, which make Google, Meta, and Amazon the big three in digital advertising. It is also data that made Netflix and Amazon two powerful production companies. This explains why large organizations invest in internal data marketplaces, where the goal is not only to store large volumes of data, but to ensure that this data is consumed as competitive knowledge. This also explains why new entrants prefer to capture data first, even if it means losing money, for a much greater return on investment.

Hassan Lâasri is a consultant in data strategy, data governance, and data activation with more than 15 years of experience in business transformation, fostering revenue growth, and cost reduction.

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Forrester analysts share 5 shocking cybersecurity predictions for 2023 



The cybersecurity and risk privacy landscape is changing fast. Many analysts’ cybersecurity predictions for 2023 suggest that organizations aren’t just having to optimize existing processes to combat threat actors, they’re also having to reevaluate how they approach cybersecurity as a whole. 

Recently, Forrester analysts shared some of their top cybersecurity predictions for 2023 with VentureBeat. These highlight that there is a cultural shift taking place in how organizations manage risk and privacy concerns.  

Some of the most shocking predictions made by Forrester analysts include: cybersecurity employees turning into whistleblowers in response to burnout; C-level execs coming under fire for using employee monitoring; and more cyber insurance providers making the jump into the MDR market. 

Below is an edited transcript of their responses. 


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More than 50% of chief risk officers (CROs) will report directly to the CEO

“As firms embrace innovation and digital strategies, they now also face unprecedented change from systematic risk forces, evolving regulatory landscape, supply chains still in chaos, and a shift in customer expectations.

As firms expand their risk management strategies to include new sources of risk, and shift their center of gravity to include non-financial risks, the role of chief risk officer (CRO) is emerging as critical, even among non-financial firms.

But it’s not enough for today’s CROs to protect against the downside of risk (that is, compliance, insurance). As risk management gets more attention and gains prominence internally, CROs are being tasked with finding opportunities for growth.

In this capacity, risk management is not a ‘cost of doing business’ but an opportunity to ‘do more business.’ This creates a shift in reporting structure, with more CROs reporting directly to the CEO.” 

Forrester senior analyst Alla Valente

A C-level executive will be fired for their firm’s use of employee monitoring 

“With the rise of remote and anywhere work options, some employers are turning to technologies for electronic monitoring of employees. Companies must prioritize privacy rights and employee experience if implementing any monitoring technology, whether it’s for tracking employee productivity, enabling a return-to-office strategy, or addressing concerns of insider risk. 

“It’s a business initiative that companies must be very careful with in planning and implementation, because there are many opportunities for disaster from a regulatory and workforce perspective.

“Monitoring efforts can violate data protection laws like [the] GDPR, as well as newly enacted laws in New York and Ontario, Canada that are specifically related to employee monitoring. In 2023, we can expect more lawmaker attention on issues of workplace surveillance, like the accountability bill proposed in California.

“We are also likely to see more employee protests, as well as labor union strikes and organizing in response to monitoring efforts seen as intrusive and an overreach from employers.”

Forrester principal analyst Heidi Shey 

Expect three cyber insurers to acquire MDR providers 

“Cyber insurers will move aggressively into the MDR segment, calculating that it’s better to provide detection and response services for the clients they insure, rather than relying on the clients to do it themselves. This will continue the trend kicked off by Acrisure in 2022. 

“MDR acquisitions give insurers: 1) high-value data about attacker activity to refine underwriting guidelines; 2) unparalleled visibility into policyholder environments; and 3) the ability to verify attestations.

“Security leaders buying MDR from an insurer should factor in how the insurer will make use of telemetry in underwriting — which will likely not go in the buyer’s favor; whether they think the insurer will invest in delivering cybersecurity services like MDR; and if they think their insurer can help them stop active attacks in process.” 

Forrester VP principal analyst Jeff Pollard 

“Security professionals and attackers alike use post-exploitation kits like Cobalt Strike, Metasploit, Mimikatz and many others. Some providers share disclosures or include a due-diligence process for sales to ensure customers are not using the technology for harm. 

“As more of these tools crop up, enterprises and governments will pressure providers to ensure tools don’t get into the wrong hands, which will affect how these tools are created and shared. 

“In 2023, this will lead to litigation against a provider, which may establish precedent for other software products to be caught in the crossfire, specially as tensions build over third-party breaches. Mitigate your exposure by securing what you sell as part of your cybersecurity program.”

Forrester senior analyst Allie Mellen 

A Global 500 firm will be exposed for burning out its cybersecurity employees 

“Weaknesses in cyber defenses have the opportunity to impact society at mass levels. The teams at the heart of these defenses are understaffed and burning out. A 2022 study finds that 66% of security team members experience significant stress at work, and 64% have had work stress impact their mental health. 

“Similar findings were reported for incident responders, who work more than 12-hour days in the first week of an incident. Burnout extends well beyond mental health, resulting in attrition health risks and even death. 

“In a critical national infrastructure study, 57% of security directors cited burnout as a top reason for leaving [the] profession. Additionally, a WHO study shows that those who work 55 hours a week have a 35% higher risk for strokes. And in 2022, there have been burnout-related deaths of tech employees in Australia and China

“In 2023 a security employee will come forward about unsafe working conditions following a line of tech whistleblowers. Evaluate and address the inputs to burnout, provide physically and psychologically safe environments, and support security teams with the tools, processes and budgets they need to do their jobs.”

Forrester VP and principal analyst Jinan Budge

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How to capitalize on AI and data to personalize live events



The use of technology has helped make live events a better overall experience for attendees in a wide variety of ways. Guests can now book tickets online, look up valuable information on their smartphones, and even attend events virtually.

Event organizers have benefitted, too; technology has made it easier to organize, manage, and keep track of everything and everyone before, during, and after events. During the pandemic, it was technology that made it possible to continue events in new forms — often smaller and more personal than their large, in-person counterparts but not necessarily any less successful.

Now, technology is offering a new way to make live events even more powerful for guests and organizers through AI and data collection. With smartphones in nearly everyone’s pockets and bandwidth continually improving, businesses have access to an unprecedented wealth of personal and behavioral data through interactions online and through smartphone apps. This data makes it possible for event organizers to create experiences that are personalized to each attendee’s exact tastes, similar to how streaming services (like Netflix) and social media platforms (like TikTok) tailor content for each of their users.

In the event space, however, data and AI can do more than just provide standard personalized recommendations. They can be the tools that deliver an ultra-personal experience every step of the way. Organizers can use their platforms to do things like connect like-minded people, recommend bookings based on preferences and schedules, provide live translation services, and even offer video and audio highlight reels tailored to each individual.


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As an entrepreneur who has worked in the event booking space for years now, I’ve seen how events have evolved, especially over the last few years, and I’m excited to see the ways in which AI and data will push the space even further. However, I also know that there are plenty of pitfalls when it comes to collecting and storing that data. The Identity Theft Resource Center reports that the number of breaches was higher than ever last year, and according to a report from KPMG, consumers are becoming increasingly concerned about how their data is handled by companies. In order to succeed, it will be crucial for event organizers to properly balance the potential of AI with the importance of data security.

Effectively and ethically leveraging data collection and AI to create a better event experience

I firmly believe the future of event planning will include AI-driven personalization. However, this will only be accomplished by companies who not only know the most effective way to deploy the data they collect but how to make sure that data is safe and that audiences are comfortable with how that data is being used. Here are a few places to start:

Prioritize the experience over publicity

AI is a very buzzy technology, which makes it a tempting playground for marketers looking for a quick publicity boost. It’s true that you’d probably be able to generate some marketing copy by adding in a couple of basic AI functions onto your platform, like a chatbot or a transcription service, but you’d also be failing to tap into the true transformative power of this technology.

By prioritizing experience over publicity, on the other hand, you can have your cake and eat it, too. A truly data-driven experience will make events more memorable for your guests while also effectively communicating your brand’s chosen story and vision. Focus on the elements that help deepen relationships and provide real insight into the personal preferences of your attendees. These are features like attendee matchmaking, live translations, personalized itineraries based on schedules and preferences, and recaps tailored to what each attendee would consider relevant.

The esports industry is one that has been depending on AI heavily since its advent. From publishers like EA and Ubisoft producing games built with the help of AI to players battling against AI combatants to hone their skills, AI has now been designed to help esports viewers better understand the game as it’s happening, quickly replay, and view predictions of how the matches might end.

Data and AI: Don’t take away the human connection

Imagine, for a moment, going to an event that didn’t have any people present whose jobs were to make sure everything ran smoothly and everyone was being taken care of. It doesn’t sound like an event most people would want to attend. That scenario is not what AI is meant to bring about.

However, for example, AI could take away a language barrier to facilitate communication and human connection. Translators can be very hard to come by, but AI-powered voice recognition can help bridge the gap by translating your conversation into your preferred language. Wordly is one such software that can translate via AI to 15 different languages. AI translation is also ideal for hearing-impaired persons, as the AI can show the conversation as text on a screen accompanied by an audio translation.

AI should be viewed as a tool for people to use, not as their replacement. It can also be leveraged to automate certain event planning tasks and to enhance the experience of attendees. It can’t, however, take over for the people involved in the planning and execution of events. To deliver a personalized event people actually want to attend, you need to keep the focus on the human element.

Stay security conscious

This is perhaps the most important part of any effort that involves the collection of personal data. When consumers provide you with their information, they’re trusting you to keep it safe. Even one breach or misuse of that data is enough to squander any goodwill you may have.

Some demographic groups will be more accepting of data collection and AI-driven features than others. A younger audience, for instance, is not only probably better equipped to take advantage of AI-enhanced features, but they’re also more willing to share their data to do so. An older audience, on the other hand, might not be particularly interested in some of these features or might not have the technical knowledge to use them.

Certain groups may also be much leerier about giving up their personal data, as well. Even if every guest signs a form saying they consent to data collection, that doesn’t actually mean they’re comfortable with it. It’s up to you to know where each of your audiences stands on these issues.

One of the best protections in cybersecurity is limiting how much data is stored in the first place. If you aren’t storing it, it can’t be hacked or breached. The only data present in your primary systems should be the information you’re planning to use in the short term. Everything else you’ve collected should be siloed in offline systems. These disks should only be accessible with credentials that are separate from those used in your primary system. All data should also be encrypted both during transit and when stored.

Live events have always had the potential to create truly profound experiences that deepen people’s relationships and create lasting memories. With the help of data, that potential can be more readily tapped, delivering the experiences audiences want on a person-by-person basis. As long as organizations handle that data responsibly and take into account the particular needs and preferences of each audience, data-driven AI technology can offer guests truly edifying event experiences that will stay with them long after they’ve gone back home.

Gideon Kimbrell is cofounder/CEO of, and cofounder/owner of software development company Syragon.

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Ask Yourself These 5 Questions to Make Better Decisions



Opinions expressed by Entrepreneur contributors are their own.

According to the 2019 Nobel Prize winner in economics, Daniel Kahneman, all decisions are made with partial information due to the systemic cognitive biases people bring to the decision-making process. However, decisions don’t require perfection to be effective. For corporate leaders bearing the pressure to make tough calls, asking the right questions is a systematic approach to gathering information that avoids the pitfalls in pursuit of perfection.

In a decade-long longitudinal study of over 2,700 leaders, Harvard Business Review (HBR) found that too many leaders were shrinking from making difficult decisions, and delays often did more damage than they sought to avoid. The bigger an organization gets, the less decisive it becomes.

Even the most brilliant leaders can demand too much data before deciding. While it’s well-known that executives have to make crucial decisions with limited information, it’s less evident that those who require all the possible information slow their team’s ability to execute.

The difference between the two groups shouldn’t be confused with comparing the merits of a fast decision and a more calculated one. There is a better formula to weigh the information that first helps discern which — a rapid or slow decision — is required: First, ask who, what, when, where, why and how.

Related: These Decision-Making Tactics Can Help You Formalize Your Process and Make Better Choices

Backing out of the rabbit hole

Nine times out of 10, when people start working with me, they present roughly 20% of the relevant information needed to make a sound decision. I always request they go back and uncover the answers in at least 60% of the available data — that carves a clearer path to a quality decision.

Typically, senior leaders don’t have the time to drill into the day-to-day details of presenting issues, so streamlining information helps avoid the analysis paralysis of multiple possibilities popularized by psychologist Barry Schwartz.

Schwartz found that, with voluminous options, consumers find it challenging to choose because they are left wondering if one of the options not taken would have been better. Schwartz theorizes a presumed alternative leads people to question their decisions. If even good choices are subject to 20/20 hindsight, it becomes more important to put a pin in the cycle of seeking more data.

Related: These Decision-Making Tactics Can Help You Formalize Your Process and Make Better Choices

When to act fast and when to think slow

In “Thinking, Fast and Slow, Kahneman divides our brains into two metaphorical systems: System 1 thinks fast and System 2 thinks slow. The first system operates automatically and intuitively. The second requires reasoning and focus. Intuition, he warns, is frequently wrong and needs to be backed by experience and analysis for it to lead to effective decision-making.

Because people are inherently judgmental, I’ve witnessed leaders make decisions only to “back into the facts” to support why they made that decision. It’s a categorically wrong approach to critical thinking.

The reasoned analysis of Stoicism offers another model. Sometimes mistaken for being coldly analytical, this ancient philosophy also engages curiosity. Many people want to solve a problem immediately rather than get curious about why it happened. But, they might be trying to solve the wrong problem or failing to consider the adjacent challenges that will come up after that problem.

As leaders, we must look at organizational impact through a broad lens. If the blast radius of a poor decision is going to be big, slower decision-making is required. If the effect is likely minor, a faster decision is ideal.

Related: How to Make Better Decisions

Five Ws and an H: Asking the right questions

When I have a decision to make, asking who, what, when, where, why and how offers the minimum information needed to make an informed decision while avoiding data overkill.

  • Who?
    • Identifies all the parties involved, impacted stakeholders and who will carry out any action. Asking this question reveals who needs support and who has further information or insight. This can also highlight the relevant managers for other delegations.
  • What?
    • This question offers a summation of the issues presented, not a long narrative. It describes the event or chain of events leading to the problem and shows what type of decision is necessary.
  • When?
    • This offers a timeline of events and a timeframe for a needed outcome, displaying whether a fast or slow decision is required.
  • Where?
    • Identifies the location of the issue or bottleneck within the organization and whether a decision crosses international borders or relates to just one set of laws. The “where” provides a snapshot of the blast radius of any decision.
  • Why?
    • This helps us understand the necessity of choice by briefly deconstructing the problem and the context of events. It also illustrates the chain of responsibility for the problem and the solution.
  • How?
    • Reveals what circumstances culminated in bringing the issue about and why it made its way to the executive level. This step may offer the cause and effect of the problem and the solution.

These questions also help remove the anxiety of how a decision might impact individuals personally. In the HBR study, leaders often delayed decision-making for fear of upsetting others or losing status. Fear clouds judgment. Like an excellent Stoic, if we can stay within the intellectual sphere, we can make a logical decision.

Related: 7 Tips for Making Quality Business Decisions

A decision-making template

I’ve encountered leaders who will ask for copious amounts of data before even risking a decision. It becomes an endless cycle. But determining the who, what, when, where, why and how is a very simple, practical and valuable tool that can save businesses time and resources. It avoids the cognitive laziness of fast thinking and the overwhelm brought to bear by an abundance of choices that characterize slow thinking. In the language of Stoicism, this framework helps leaders lean into the virtues of wisdom and temperance to make decisions that lead to more substantial, positive outcomes for both individuals and organizations.

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