Companies are diving into artificial intelligence. Unlocking enterprise AI success depends on four main factors. Tectonic is here to help you address each.
Thank you for reading this post, don't forget to subscribe!- Trust is Important: Clients need solutions they can rely on. Salesforce’s Einstein Trust Layer is a game-changer.
- Data is the lifeblood of business: Unlocking insights from siloed data is crucial. Data Cloud is the secret ingredient.
- Speed matters: Agility is essential. Turnkey solutions like Einstein Copilot are the way forward.
- Flexibility is key: One size doesn’t fit all. Offering model choice is a must.
Trust is Important-Trust is Everything
Data is everything—it’s reshaping business models and steering the world through health and economic challenges. But data alone isn’t enough; in fact, it can be worse than useless—it’s a risk unless it’s trustworthy. The solution lies in a data trust strategy: one that maximizes data’s potential to create value while minimizing the risks associated with it.
Data Trust is Declining, Not Improving
Do you believe your company is making its data and data practices more trustworthy? If so, you’re in line with most business leaders. However, there’s a disconnect: consumers don’t share this belief. While 55% of business leaders think consumers trust them with data more than they did two years ago, only 21% of consumers report increased trust in how companies use their data. In fact, 28% say their trust has decreased, and a staggering 76% of global consumers view sharing their data with companies as a “necessary evil.”
For companies that manage to build trust in their data, the benefits are substantial. Yet, only 37% of companies with a formal data valuation process involve privacy teams. Integrating privacy is just one aspect of building data trust, but companies that do so are already more than twice as likely as their peers to report returns on investment from key data-driven initiatives, such as developing new products and services, enhancing workforce effectiveness, and optimizing business operations.
To truly excel, companies need to create an ongoing system that continually transforms raw information into trusted, business-critical data.
Data is the Backbone-Data is the Key
Data leaks, as shown below, are a major factor on data trust and quality. As bad as leaked data is to security, data availability is to being a data-driven organization.
Extortionist Attack on Costa Rican Government Agencies
In an unprecedented event in April 2022, the extortionist group Conti launched a cyberattack on Costa Rican government agencies, demanding a $20 million ransom. The attack crippled much of the country’s IT infrastructure, leading to a declared state of emergency.
Lapsus$ Attacks on Okta, Nvidia, Microsoft, Samsung, and Other Companies
The Lapsus$ group targeted several major IT companies in 2022, including Okta, Nvidia, Microsoft, and Samsung. Earlier in the year, Okta, known for its account and access management solutions—including multi-factor authentication—was breached.
Attack on Swissport International
Swissport International, a Swiss provider of air cargo and ground handling services operating at 310 airports across 50 countries, was hit by ransomware. The attack caused numerous flight delays and resulted in the theft of 1.6 TB of data, highlighting the severe consequences of such breaches on global logistics.
Attack on Vodafone Portugal
Vodafone Portugal, a major telecommunications operator, suffered a cyberattack that disrupted services nationwide, affecting 4G and 5G networks, SMS messaging, and TV services. With over 4 million cellular subscribers and 3.4 million internet users, the impact was widespread across Portugal.
Data Leak of Indonesian Citizens
In a massive breach, an archive containing data on 105 million Indonesian citizens—about 40% of the country’s population—was put up for sale on a dark web forum. The data, believed to have been stolen from the “General Election Commission,” included full names, birth dates, and other personal information.
The Critical Importance of Accurate Data
There’s no shortage of maxims emphasizing how data has become one of the most vital resources for businesses and organizations. At Tectonic, we agree that the best decisions are driven by accurate and relevant data. However, we also caution that simply having more data doesn’t necessarily lead to better decision-making.
In fact, we argue that data accuracy is far more important than data abundance. Making decisions based on incorrect or irrelevant data is often worse than having too little of the right data. This is why accurate data is crucial, and we’ll explore this concept further in the following sections.
Accurate data is information that truly reflects reality or another source of truth. It can be tested against facts or evidence to verify that it represents something as it actually is, such as a person’s contact details or a location’s coordinates.
Accuracy is often confused with precision, but they are distinct concepts. Precision refers to how consistent or varied values are relative to one another, typically measured against some other variable. Thus, data can be accurate, precise, both, or neither.
Another key factor in data accuracy is the time elapsed between when data is produced and when it is collected and used. The shorter this time frame, the more likely the data is to be accurate.
As modern businesses integrate data into more aspects of their operations, they stand to gain significant competitive advantages if done correctly. However, this also means there’s more at stake if the data is inaccurate. The following points will highlight why accurate data is critical to various facets of your company.
Ease and speed of access
Access speeds are measured in bytes per second (Bps). Slower devices operate in thousands of Bps (kBps), while faster devices can reach millions of Bps (MBps). For example, a hard drive can read and write data at speeds of 300MBps, which is 5,000 times faster than a floppy disk!
Fast data refers to data in motion, streaming into applications and computing environments from countless endpoints—ranging from mobile devices and sensor networks to financial transactions, stock tick feeds, logs, retail systems, and telco call routing and authorization systems.
Improving data access speeds can significantly enhance operational efficiency by providing timely and accurate data to stakeholders throughout an organization. This can streamline business processes, reduce costs, and boost productivity.
However, data access is not just about retrieving information. It plays a crucial role in ensuring data integrity, security, and regulatory compliance. Effective data access strategies help organizations safeguard sensitive information from unauthorized access while making it readily available to those who are authorized.
Additionally, the accuracy and availability of data are essential to prevent data silos from hindering data-driven decision-making across the organization.
Flexibility is the key to data success
We are living in the age of big data, with an astonishing 90 percent of all data created in just the past two years. This surge in data means that businesses must be prepared to handle increasingly vast amounts of information moving forward.
However, managing and structuring data is notoriously challenging. Research shows that only about 5 percent of businesses feel confident in their data management capabilities. This challenge is particularly pressing in industries like sports, education, and hospitality, where data volumes are continuously rising.
One of the growing concerns for businesses is data flexibility. As automated data services become more mainstream, the importance of data quality cannot be overstated. With big data worth over $274 billion in 2022, the need for effective management and processing of this data is more critical than ever.
The Importance of Flexible Data Management
Enhanced Scope for Analysis:
Data is central to various forms of analysis—whether it’s marketing, usage tracking, or budgeting. How a company manages its data directly impacts the quality of its reports. Two primary approaches to managing big data include the data warehouse, which organizes information in a structured manner, and the data lake, which offers more flexibility, allowing data to move freely. While data warehouses provide organization, they can also limit flexibility, which is why many businesses are turning to data lakes for a more dynamic approach.
Scalability:
As the volume of data generated globally continues to skyrocket, scalable data management becomes essential. With daily data production reaching 2.5 quintillion bytes, and storage requirements projected to exceed 44 zettabytes worldwide, companies must ensure their data management systems can scale effectively. This is crucial across various sectors, from education to healthcare, where data needs are constantly expanding.
Adaptability for the Future:
Flexible data management systems are designed to adapt to changing demands. This adaptability is crucial as consumer preferences and usage patterns evolve. Businesses that resist change risk obsolescence, whereas those that embrace flexibility, including the integration of artificial intelligence for automating data management, will be better positioned for future challenges.
Maintaining Data Quality:
The quality of data is just as important as its flexibility. Data duplication, outdated information, and poor-quality data can have serious consequences, particularly in industries like healthcare. Companies must focus on data cleaning and system upgrades to ensure that the data they retain is both accurate and relevant.
Moving Forward
To thrive in the era of big data, companies need to embrace flexible data management systems and a broader mindset. This includes not only keeping data accurate but also ensuring it remains adaptable to future needs. Leading organizations are already taking steps to implement flexible data management systems, recognizing that the ability to adapt is key to long-term success.
Organizational Flexibility:
Organizational flexibility refers to a company’s ability to successfully adapt to changing business environments, including technology, work structures, and human resource management. This adaptability is crucial for survival in competitive markets.
System and Team Flexibility:
System flexibility is the capacity of software to adapt to new situations without breaking down, while team flexibility refers to the ability of development teams to adjust to changes in project requirements or unexpected challenges.
Challenges and Solutions:
Implementing flexibility in the workplace comes with challenges such as communication, team bonding, employee engagement, and work-life balance. However, overcoming these challenges by fostering flexible organizational structures can enhance a business’s ability to adapt to changing market conditions.
In conclusion, as big data continues to grow, embracing flexibility in data management and organizational structures will be essential for businesses to remain competitive and responsive to future challenges.
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