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As personalized and user-centric offerings become a necessity for modern organizations, utilizing data is a critical component to understanding customer and stakeholder needs. From public sector bodies and healthcare providers to financial institutions and software suppliers, it is now imperative for organizations to collect, store and organize data effectively.

Yet, unfortunately, many organizations are struggling to maintain clean, actionable data. In fact, a recent survey found that two-fifths (39%) of organizations have little to no data governance frameworks1. Years of inconsistent data practices and working in silos have left many departments with ‘dirty’, inadequate data that cannot be actioned.

This ongoing lack of effective data governance has resulted in organizations missing the valuable insights that would otherwise help them become better service providers.

Organizations, across sectors, as well as public sector bodies, urgently need to take decisive action to mitigate against any further damage their current data collecting practices may be having. In addition, they must instill values that make data governance a priority. This would ensure the information they collect, and store, is not only clean but also actionable.

The manifestation of ‘dirty’, disorganized, data stems from a multitude of factors. From collecting duplicate and incomplete records to a lack of integration, too many organizations have unfortunately failed to manage data effectively. According to 2024 research, 44% of financial firms struggle to manage data stored across multiple locations2. This has hit their bottom line, with many incurring inflated costs. However, where, and how data is stored is not the only problem.

In organizations where data governance remains a concern, data is often fragmented and inconsistent across departments. Instead of having integrated systems that deliver a single, dependable, database, teams are working in data silos. For instance, separate sales and marketing teams at a digital bank may want to reach out to the same customers, or prospects, but have their own isolated data sets. In a borough council, the social housing and waste collection teams may need to contact the same residents, yet they do not share their citizens’ records.

This disjointed approach causes ‘dirty’ data that is not only difficult to use because the information is incorrect but also challenging to clean and then maintain. What’s more, ‘dirty’ data leads to conflicting insights, impacting decision-making, customer experience and overall business efficiency.

Commercial organizations risk falling behind competitors who can adjust their product lines in accordance with customer and market demands. Meanwhile, public sector bodies may not be delivering crucial services to the right citizens.

Poor data management comes in many forms, but perhaps the most prominent reason for ‘dirty’ data revolves around ownership. While many heads of departments perceive data governance as a responsibility of an organization's IT team, it is their department colleagues who actually use data on a day-to-day basis. An IT team can offer support by ensuring software and systems are working properly, but they are not the ones utilizing information to interact with customers and stakeholders.

After all, it is the departments, such as finance, sales and marketing, that need customer and stakeholder engagement to succeed and that benefit from clean, actionable data. The same can be said for local authorities. For example, the social care and education teams need clean data to ensure they can identify the residents that qualify for their services. With this in mind, it is then reasonable to suggest that the prime beneficiaries of clean data should be the ones managing it. Fostering a culture of data responsibility, driven by a desire to create a single view of customer or citizen information, while investing in staff training, is the first step to resolving the human aspect of effective data governance.

The technical aspect involves adopting appropriate solutions to help with the initial clean up and then maintaining data accuracy. While having the right intentions is fundamental to establishing effective data governance, introducing appropriate technology allows departments to put their drive for change into practice.

The sheer volume of data that organizations need to collect, store and process has led to legacy, or rules-based, software being no longer fit for purpose. Instead, artificial intelligence (AI) and machine learning, have been developed to notice patterns and inconsistencies in data. Newer tools can handle larger volumes, so they are deployed to irradicate data duplication and are even at the stage to offer predictive data modelling.

These technologies maintain clean data and support the generation of actionable insights so organizations can accommodate customers’ and/or citizens’ present and future needs. Successful adoption will happen gradually but once this is achieved, automated data cleansing will boost productivity. By automating the manual processes that eroded people’s time, organizations can empower humans to prioritize and fulfil the tasks they do best.

The responsibility for data governance cannot rest solely with IT teams. It must be a shared priority across departments, where those who rely most on data take an active role in ensuring its quality.

The benefits of clean data go beyond having the easily accessible information that is always in the right place, at the right time. Breaking down data silos allows better cohesion and collaboration, which then in turn helps deliver actionable insights. From personalized marketing campaigns and optimizing supply chains to issuing council tax bills and allocating social care budgets, clean data allows organizations to run more efficiently.

By investing in both technology, such as AI-powered automation tools, and a more responsible, and proactive, culture, companies can develop robust data management practices. Ultimately, the organizations that thrive will be those that treat data not as a by-product, but as a strategic asset.

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• Hackers seen targeting misconfigured JuypterLab instances
• They host malware in polyglot files on image sharing sites
• The Koske malware mines different crypto tokens

Security researchers recently discovered a new Linux malware hiding in pictures of cute animals.

Cybersecurity experts from AquaSec recently found a piece of malware called Koske circulating around the web. It relies on polyglot files - documents that can be read and processed differently, depending on the type of program running them.

The threat actors were apparently targeting JupyterLab instances exposed to the internet, and misconfigured in a way that allows remote command execution. After finding and accessing such endpoints, the attackers would pull .JPEG files from legitimate image hosting services such as OVH images, freeimage, or postimage. The pictures were of AI-generated panda bears, innocuous at first sight.

Through a script interpreter, the images are turned into a CPU and GPU-optimized cryptocurrency miners, using the server’s resources to generate more than 18 types of crypto tokens.

Cryptocurrency “mining” is essentially a process of supporting a blockchain network. In exchange for lending electricity, internet, and computing power to support the grid, users are given cryptocurrency tokens whose value depends on different things such as the number of users, the number of tokens in circulation, and the cost of mining.

Mining crypto this way generates relatively little profit for the attackers, some researchers said, while raking up huge costs for the victims - cloud compute power and electricity are often quite expensive.

AquaSec could not attribute the malware to a specific group definitively, but it did say that it found Serbia-based IP addresses used in the attacks, Serbian phrases in the scripts, and Slovak language in the GitHub repository hosting the miners.

In that context, the name of the malware would make some sense, since the word “Koske” in colloquial or dialectal form means “bones”.

The researchers believe that besides the image, the malware itself was written with the help of large language models (LLM) or automation frameworks.

Via BleepingComputer

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