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Best Ways to Drive Corporate Growth in Uncertain Times

Are you feeling the pressure to drive corporate growth in these uncertain times? You’re not alone. The current business climate is rife with challenges and uncertainties, making it more important than ever for companies to find innovative ways to thrive. Let’s explore some of the best strategies for driving corporate growth during uncertain times. Whether it’s through diversification, innovation, and adaptation, or strategic partnerships and collaborations, there are plenty of avenues to explore!

The importance of driving corporate growth in uncertain times

In today’s ever-changing business landscape, driving corporate growth is crucial, especially during uncertain times. The ability to adapt and thrive in the face of uncertainty can determine the long-term success or failure of a company.  Driving corporate growth allows businesses to stay ahead of their competitors. By continuously seeking opportunities for expansion and improvement, companies can gain a competitive edge that sets them apart from others in their industry.

 

Corporate growth helps create stability and resilience within an organization. In uncertain times, when market conditions are volatile and unpredictable, companies that have focused on growth strategies are better equipped to weather economic downturns and navigate through challenging circumstances.

Additionally, driving corporate growth fosters innovation. When companies actively seek new markets or develop new products/services, they stimulate creativity within their teams. This not only drives profitability but also enables organizations to remain relevant in changing customer demands. Furthermore, sustained growth leads to increased shareholder value. As a company expands its operations and generates higher profits over time, shareholders benefit from increased returns on their investments.

 

Strategies for driving growth during uncertain times:

  • Diversification: One of the most effective strategies for driving corporate growth in uncertain times is diversification. By expanding into new markets or offering new products or services, companies can reduce their reliance on a single source of revenue and mitigate risks associated with economic uncertainty. Diversification allows businesses to tap into untouched customer segments, explore untapped opportunities, and capitalize on emerging trends.
  • Innovation and Adaptation: Another key strategy for driving growth during uncertain times is innovation and adaptation. Companies that are able to quickly identify changing customer needs and market dynamics can adjust their business models, products, or services accordingly. This may involve leveraging technology to streamline processes, developing new solutions tailored to current demands, or even completely pivoting the business model.
  • Strategic partnerships and collaborations: Collaborating with strategic partners can be a powerful way to drive corporate growth in uncertain times. By joining forces with complementary businesses or industry leaders, companies can access new resources, expertise, distribution channels, and customer bases. Strategic partnerships also provide opportunities for shared knowledge transfer and mutual support during challenging times.

 

Case studies of companies that have successfully driven growth during uncertain times

Case studies of companies that have successfully driven growth during uncertain times serve as valuable sources of inspiration and guidance for businesses seeking to navigate through challenging economic landscapes. One such example is Amazon, which experienced significant growth during the 2008 global financial crisis. Instead of retreating, the company recognized the opportunity to expand its product offerings and capitalize on consumers’ increasing preference for online shopping. This strategic move not only helped Amazon maintain its position in the market but also propelled it towards becoming a dominant force in e-commerce.

 

Another notable case study is Netflix, which faced stiff competition from DVD rental stores when it first entered the market. However, instead of succumbing to industry norms, Netflix disrupted the traditional model by introducing a subscription-based streaming service. By focusing on innovation and adapting to changing consumer preferences, Netflix was able to drive substantial growth even during uncertain times.

 

In both these cases, diversification played a crucial role in driving corporate growth amidst uncertainty. These companies identified new opportunities within their respective industries and capitalized on them effectively. Additionally, they prioritized customer-centric strategies by constantly innovating and adapting their business models according to evolving consumer needs.

 

Strategic partnerships and collaborations are another key driver of growth during uncertain times. Take Uber’s partnership with Spotify as an example – this collaboration allowed Uber riders to personalize their trip experience with music while simultaneously providing Spotify access to millions of potential subscribers. By leveraging each other’s strengths and reaching new audiences together, both companies were able to achieve sustained growth even in turbulent times.

These case studies demonstrate that successful corporate growth during uncertain times requires visionary leadership that embraces change rather than shying away from it. It demands an agile mindset that can identify opportunities amidst challenges while remaining focused on delivering value to customers.

 

By studying these success stories closely, businesses can gain insights into effective strategies for driving corporate growth amid uncertainty – whether it be through diversification efforts or innovative partnerships – ultimately helping them thrive despite unpredictable circumstances

Growth doesn’t happen overnight; it requires ongoing effort. Remember that every company’s path to success will differ based on its unique circumstances. Therefore it’s important to have a well-defined strategy that requires continuous evaluation.

Best strategies for Cloud Cost Optimization

Cloud services have revolutionized how organizations store, manage, and access their data, offering unparalleled flexibility and scalability. However, as with any resource, it’s essential to optimize costs and maximize savings in this virtual realm. A cloud cost-saving strategy involves optimizing the usage of cloud computing resources to reduce overall cloud expenses while maintaining or even improving operational efficiency and performance.

 

When it comes to cloud costs, there are several components that need to be understood in order to effectively manage and save money. One key component is the cost of computing resources, which includes virtual machines, storage, and networking. These costs can vary depending on factors such as usage patterns, data transfer rates, and storage capacity.

 

Another important factor in cloud costs is data transfer fees. Transferring data between different regions or zones within a cloud provider’s infrastructure can incur additional charges. It’s essential to have a clear understanding of how these fees are calculated and consider strategies such as optimizing data placement to minimize these costs.

 

Additionally, many cloud providers charge for outbound bandwidth usage. This means that any traffic leaving your cloud environment will be subject to additional fees. By monitoring and analyzing your outbound traffic patterns, you can identify opportunities for optimization and potential cost savings.

 

One often overlooked aspect of cloud costs is idle resources. It’s not uncommon for organizations to provision more resources than they actually need or forget about those no longer in use. By regularly reviewing your resource utilization and implementing automation tools like auto-scaling or scheduling shutdowns during off-peak hours, you can reduce waste and optimize spending.

 

Licensing plays a crucial role in determining overall cloud costs. Some software licenses may require additional fees when deployed in a virtualized environment or across multiple instances within the same region. Understanding these licensing implications upfront can help avoid unexpected expenses down the line.

Develop a Cloud Cost-Saving Strategy

 

When it comes to managing cloud costs, having a well-defined strategy in place is essential. A cloud cost-saving strategy should not only focus on reducing expenses but also ensure optimal resource utilization and performance.

 

The first step in developing your strategy is to understand your current cloud spend and identify areas of potential optimization. This can be done by analyzing usage patterns, identifying idle resources, and evaluating the performance of different service tiers or instance types. Once you have identified areas for improvement, it’s important to set clear goals for cost reduction. These goals should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, you might aim to reduce overall cloud costs by 20% within six months.

 

Next, consider leveraging automation tools to streamline cost optimization processes. These tools can help automate tasks such as scheduling instances based on workload demands or rightsizing resources based on actual usage data. By automating these processes, you can free up valuable time and resources while ensuring cost savings are consistently achieved.

 

Implementing best practices for cloud cost management is another key aspect of your strategy. This may include regularly monitoring and optimizing storage costs by deleting unused data or implementing lifecycle policies. It could also involve leveraging spot instances or reserved capacity options when appropriate to take advantage of discounted pricing models.

 

To further enhance your approach to cloud cost-saving strategies implementation:

  • Track and analyze spending trends over time
  • Implement tagging mechanisms for better visibility into resource allocation
  • Set up cloud monitoring and alerting to track resource utilization and costs in real time.
  • Assign meaningful tags to resources and use cost allocation tools to track spending by team, project, or department. This helps identify areas where cost optimization is needed.
  • Choose the most cost-effective region and availability zone for your workload. Leverage multi-region redundancy only when necessary for high availability.
  • Regularly review your cloud bills from various providers, analyze usage patterns, and forecast future costs to make informed decisions
  • Evaluate the use of specialized third-party tools that offer more granular insights into spending patterns
  • Ensure your team is knowledgeable about cloud cost management best practices. Training and awareness can go a long way in reducing wasteful spending.
  • Cloud cost optimization is an ongoing process. Continuously monitor and refine your strategies based on changing business needs and technology advancements.

 

Implementing a successful cost-saving strategy in the cloud requires a combination of monitoring, automation, and a commitment to optimizing resources. By developing a comprehensive cloud cost-saving strategy that encompasses all these elements – understanding current costs; setting SMART goals; utilizing automation tools; and implementing best practices – businesses can achieve significant savings while maintaining operational efficiency in their cloud environments.

Best Practices for Managing and Analyzing Big Data

 

From social media posts and customer transactions to sensor readings and online searches, the sheer volume of data generated on a daily basis is staggering. It’s understood that with this flood of information comes great opportunity – if one knows how to manage and analyze it effectively. Data analytics plays a crucial role in today’s business landscape. It enables organizations to uncover valuable insights from the vast amount of data they collect and make informed decisions based on these findings.

Managing and analyzing big data effectively requires adopting certain best practices. Here are some key considerations:

 

Define clear objectives: Managing and storing big data can be a daunting task, but with the right approach, it becomes much more manageable. The first step is to prioritize your business needs. Start by identifying the key objectives and goals you want to achieve through data analysis. This will help you determine what type of data you need to collect and store and ensure your analysis aligns with your business needs.

 

Data quality and preprocessing: Ensure data quality by addressing issues such as missing values, outliers, and inconsistencies. Preprocess the data by cleaning, transforming, and integrating it to make it suitable for analysis. Embrace all data collection and storage practices that align with your business needs.

 

Data storage and infrastructure: There are numerous analytics tools available today that can help you make sense of your big data. Choose appropriate storage and infrastructure solutions that can handle the volume, variety, and velocity of big data. Consider investing in scalable storage solutions that can grow as your data grows. A robust infrastructure that can handle large volumes of data efficiently is mandatory! Consider options like distributed file systems, cloud storage, and scalable databases. Cloud platforms offer flexible storage options, allowing you to scale up or down based on demand. They also provide automated backup and disaster recovery capabilities, ensuring the safety and availability of your data.

 

Scalable and parallel processing: Utilize distributed processing frameworks like Apache Hadoop or Apache Spark to handle the processing of large-scale data sets across clusters of machines. This enables parallel processing and improves efficiency.

Data security and privacy: Implement robust security measures to protect sensitive data. Access controls, encryption, monitoring, and regular audits are essential for safeguarding against unauthorized access or breaches. Protecting privacy should always be a top priority when working with large datasets.

 

Data governance and compliance: Establish data governance policies and procedures to ensure compliance with relevant regulations, such as data retention, privacy laws, and industry standards. Document data lineage, establish data ownership, and maintain proper documentation.

 

Data visualization: Use effective data visualization techniques to present complex data in a clear and meaningful way. Presenting findings in a visual format helps stakeholders easily understand complex insights derived from big data analyses. Use charts, graphs, infographics or interactive dashboards to convey key messages effectively.

 

Machine learning and statistical techniques: Employ appropriate machine learning algorithms and statistical techniques to analyze big data. These techniques can uncover patterns, identify correlations, make predictions, and derive actionable insights.

 

Iterative and exploratory analysis: Big data analysis is often an iterative process. Explore different algorithms, models, and parameters to refine your analysis iteratively. Document and communicate your findings throughout the process.

 

Collaboration and interdisciplinary approach: Encourage collaboration among data scientists, domain experts, and business stakeholders. This interdisciplinary approach fosters a better understanding of the data, improves analysis, and promotes data-driven decision-making.

 

Continuous learning and improvement: Stay up to date with the latest tools, techniques, and advancements in big data management and analysis. Continuously learn from previous projects, experiment with new methods, and strive for improvement.

 

By following these best practices for managing and analyzing big data, your organization will gain valuable insights that can fuel innovation, drive informed decision-making, and ultimately lead to success in today’s highly competitive business landscape. But remember, the specific best practices may vary depending on the nature of your data, industry, and objectives. Regularly assess your processes and adjust them as needed to ensure you’re effectively managing and analyzing big data.

Major Consequences Of Having Poor Data Quality For Your Business

how data powers business opportunities

 

Organizations are collecting and generating more information than ever before but simply having a lot of data does not make a business data driven. Issues related to data quality maintenance is infecting numerous businesses. IT department which aren’t taking any steps in order to improve accuracy of their data, can lead companies to pay a big price. Generating trusted information isn’t always easy, though.  Nearly half of organizations are already in error due to poor data quality.

 

Poor data quality can impact organizations in a very negative way by have serious financial consequences. Regulatory fines, monetary losses from bad business decisions, and legal fees resulting from errors can add up to millions of dollars. IBM estimates the total cost, to U.S. organizations only, to be $3.1 trillion dollars annually. Moreover, when it comes to patient or consumer safety, bad data can cost lives.

 

A qualitative database with complete market information is very useful for the effective generation of new leads and the restructuring of existing one. Results of a campaign must be reflected in the database and information must always be accurate, complete, correct and unique. Yet this is not always the case. During customer contact, organizations too often receive answers such as: “I do not have permission to leak confidential information”, “Cloud applications? No, we do not use ‘and’ I’m not the right person for this conversation ‘. 46% of the organizations sometimes go wrong due to poor data quality, according to research by the New York Times. What price do organizations actually pay for this? I have listed the three most important consequences.

 

  1. Target-less costs are incurred

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A definite price tag is not linked to bad data quality, but that organizations make costs and miss out on profit is beyond doubt. U.S. organizations think approximately 32% of their data is inaccurate and believe this negatively affects their revenue in terms of weak resources, lost productivity, or wasted marketing and communications spend. Moving companies, changing e-mail addresses and reorganizing organizations. This means that mail is sent to incorrect addresses, e-mails do not arrive, and departments can no longer be reached. The mail is packed, the e-mail is typed, and the phone picked up, but these actions do not yield any results. Wasted time. And time is money.

 

  1. Sales and marketing without result

If companies work with outdated data, chances are that they do not have insight into who they should approach at which company. People change jobs, retire or come to the streets after a merger or takeover. If the database is not continuously updated and cleaned up with this information, effective customer approach becomes difficult. The right DMU is not available and companies do not get the right person. They do not go any further and even go two steps backwards. The target group is not reached and at the same time they strike a flatter with the potential customer. All this because companies do not have their data up to date.

 

  1. Reputation damage

As an organization, you want to avoid blunders and with a large arc for possible errors. You do not want to write to companies that are just bankrupt or are seeking contact with people who have already left the company. These missteps make sure that people talks negatively about your organization and that is the last thing you want. In short, get your facts straight. Make sure you do not run towards a wrong direction and avoid the above missteps. Provide a database containing all customer data and refresh them regularly. Only then can companies effectively carry out marketing and sales activities.

How Companies Can Leverage Their Existing Data Assets To Unlock New Business Opportunities?

Have you already heard about the latest update about Facebook who wants to play Cupid? At their F8 developer conference, the social network announced its entry into the online dating service. Why not? As Facebook users were able to reveal their relationship status since February 2004, with the existing user data form the ideal source it’s possible to find the perfect partner with the help of a suitable algorithm. However, this operation requires valid and high-quality data. At the same time, this announcement is a very good example of how companies can leverage their existing data assets to unlock new business opportunities.

 

Business can generally succeed in improving their own data quality by improving their data governance processes and the developing suitable strategies for complete data management. First of all, it is important to define the criteria for good data, which may vary depending on the company’s activity. These include aspects such as relevance, accuracy and consistency – in this case, data from different sources should not contradict each other. It’s also helpful to investigate where errors in master data are particularly likely to creep. Because here too the well-known programming wisdom applies: garbage in, garbage out. Poor data sources lead to poor results.

 

In practice, sources of error can be found throughout the value chain of data management. These can be human input errors during data acquisition, defective sensor data or incomplete data imports in automated processes. But also, different formats of data can lead to errors, in the simplest case when one has to input the data in US, in case of uncertainty about whether the metric or imperial (Anglo-American measurement system) is used. In addition, organizational deficiencies lead to data errors, for example, if it is not clearly defined who is responsible for which data sets.

 

In order to achieve more quality data, five points can be identified that help to increase the value of your own data.

 

Clarify goals:

 

Everyone involved in the project should agree on the business goals to be achieved with an initiative for better data quality. From sales to marketing to management, each organizational unit has different expectations. While decision-makers need more in-depth analysis with relevant and up-to-date information, it may be critical for a sales representative that address data is accurate and complete.

 

Find and catalog data:

 

In many organizations, data is available in a variety of formats, from paper files and spreadsheets to address databases to enterprise-class business applications. An important task is to localize these databases and to catalog the information available there. Only when the company knows what data can be found in which database and in what format, a process for improving the data quality can be planned.

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Harmonization of data:

 

Based on the initial inventory, a comparison is now made with the target to be achieved. This can result in a variety of tasks, such as a standardization of spellings, data formats and data structures. It uses tools for data preparation and deduplication to provide a harmonized set of data, while data profiling solutions help analyze and evaluate data quality.

 

Analysis, evaluation and processing:

 

If you consolidate your data and process it in a cloud, data lake or data warehouse, you can flexibly perform a wide variety of data preparation tasks there by using data integration and data management software solutions. Anyone who has to process streaming data, originated from sensors or Internet of Things, has the option of using cloud resources to check the incoming data in a very flexible way and to sort out fake data packets.

 

Establish continuous processes:

 

Ensuring data quality is a continuous process. After all, new data is always collected and integrated into our own systems. Even if external data sources already provide high-quality data for further processing, it is still necessary to constantly check and validate your own data stocks via a data monitoring system. There are very different solutions, such as, self-service data cleansing solutions, rule-based data transformation applications, self-learning software solutions that independently monitor data formats and detect and correct statistical anomalies. Already today, algorithms for deep learning or artificial intelligence are able to handle many tasks around data management in big data scenarios. However, it is important that responsibilities for data management are named and that quality assurance processes are confidently secured in the company’s processes.

 

Conclusion:

 

Managing quality data is a team work that spans in all functional areas of a company. Therefore, it makes sense to provide the employees in the departments with tools to secure the data quality in self-service. In particular, cloud-based tools that can be rolled out quickly and easily in the departments are ideal for this. Thus, equipped companies can succeed gradually in improving their data quality and increasing the value of their data. This leads to satisfied employees and happy customers.

BMC 12th Annual Mainframe Research Results- 5 Myths Busted by the 2017 Mainframe Survey

The mainframe is the oldest yet effective way to process data on a large scale, that’s what the 12th annual mainframe researched from BCM have enlightened. Large companies in industry, trade, and services, as well as public administration, will continue to rely on mainframe technologies in the future showed the survey results.

 

A mainframe is especially good at processing transactions. Banks and insurers often opt for such a system because they have to process large amounts of transactions. The big advantage of a mainframe is that it has hardware developed specifically for this task. This makes transaction processing much faster than if it were done in the cloud on traditional servers. Because a mainframe is good at processing transactions, it is also very suitable for blockchain technology, for example.

 

In the 12th mainframe study, BMC has enlightened the prejudices and myths about the mainframes. More than 1000 executives’ managers and technical professionals from large companies participated to shear their experience.

 

The first key finding of the survey is that 91% of respondents believe that the use of mainframes will continue towards an expansion and support of digital business demands. In addition, they predict this technology as a long-term important platform. This has an increase of 89% compared to 2016 results. The reason for this growth is, first of all, digital business, and second is the need of innovation of operations and technologies used within organizations.

BMC Mainframe Survey

 

The results also show that mainframes and data centers retain their role as a relevant and growing data center in many companies. Participants reported that the mainframe continues to be their platform of choice due to its excellent availability, security, centralized data serving and performance capabilities. Even with the modernization of their work processes and technologies, many organizations continue to rely on mainframes.

 

But they also cited some challenges associated with mainframe growth, including struggles to reduce IT costs, data privacy and security, speed recovery and simplify the increasingly complex mainframe and hybrid data center environment. But they remain positive about technology. They believe that mainframes remain an IT main platform despite cloud technologies and continue to be the backbone of digital transformation.

 

The mainframe will attract new workloads

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The study results underscore the strategic importance of this secure technology to large enterprises and government agencies and contradict many prejudices against mainframes. 51% respondents affirm that over half of their data resides on the mainframe, with an increase of transaction volume of 52%.

 

Mainframe ACTIVITY

 

66% of respondents say their current mainframe requirements are causing them to reduce their maintenance windows. After all, the main applications are transactional systems, large volumes of data, and analytics. The organizations want to further increase their availability in the future. The survey shows that the mainframes are not already fully optimized, as is widely believed.

 

47% expect increasing workloads through new applications. The survey shows that leaders’ attitudes toward mainframe are changing. In the long term, they see a strategic advantage in mainframes that help them better meet the demands of their digital business processes.

 

These results show the clear trend that executives continue to focus on mainframes and the rumor that they wanted to move everything to the cloud simply does not apply to every business and organization.

 

Even young IT professionals expect the future of mainframes. Contrary to the often-heard prejudice that young IT professionals are critical of mainframes, the BMC survey shows the opposite is the case. 53% of all participants were younger than 50 years. There is great enthusiasm for the future of mainframes among the under-30s too. In the 30 to 49 years, 69% predict mainframe growth. 54 In addition, the survey shows that not only older employees are working on mainframes.

 

Click here to download the study “Mainframe Survey 2017”.

Human Machine Partnership – Is 2018 the year of #MachineLearning?

Human Machine Partnerships2018 is all about the further rapprochement of man and machine. Dell Technologies predicts the key IT trends for 2018. Driven by technologies such as Artificial Intelligence, Virtual and Augmented Reality and the Internet of Things, the deepening of cooperation between man and machine will drive positively the digitization of companies. The following trends will and are shaping 2018:

 

Companies let AI to do data-driven thinking

 

In the next few years, companies will increasingly use the opportunity to let artificial intelligence (AI) think for themselves. In the AI systems, they set the parameters for classifying desired business outcomes, define the rules for their business activities, and set the framework for what constitutes an appropriate reward for their actions. Once these sets of rules are in place, the AI systems powered by data can show new business opportunities in near real time.

 

The “IQ” of objects is increasing exorbitantly

 

Computing and networking items over the Internet of Things are becoming increasingly cost effective. The embedding of intelligence into objects will therefore make gigantic progress in 2018. Networked device data, combined with the high levels of computing power and artificial intelligence, will enable organizations to orchestrate physical and human resources automatically. Employees are becoming “conductors” of their digital environments and smart objects act as their extension.

 

IQ of Things

 

AR headsets ultimate comeback in 2018

 

Its economic benefits have already been proven by augmented reality (AR). Many teams of designers, engineers or architects are already using AR headsets. Whether to visualize new buildings, to coordinate their activities on the basis of a uniform view of their developments or to instruct new employees “on the job” even if the responsible instructor cannot be physically present at the moment. In the future, AR will be the standard way to maximize employee efficiency and leverage the “swarm intelligence” of the workforce.

 

AR headsets

 

Strong bond of customer relationship

 

Next year, companies will be able to better understand their customers through predictive analytics, machine learning (ML), and artificial intelligence (AI) and use these technologies to improve their customer first strategies. Customer service will perfectly maintain the connection between man and machine. It will not be first-generation chatbots and pre-made messages that address customer concerns in the service, but teams of people and intelligent virtual agents.

 

Deeper Relationship with Customers

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The “Bias Check” will be the new spell checker

 

Over the next decade, technologies such as AI and Virtual Reality (VR) will enable those responsible to evaluate information without prejudgment and make decisions in an entirely balanced way. In the short term, AI will be used in application and promotion procedures to bring out conscious or unconscious prejudices. VR is increasingly being used as an interviewing tool to cover the identity of applicants with the help of avatars. “Bias checks” – “prejudice checks” – could become the standard procedure in decision-making processes in the future, just as spell-checking is today when it comes to writing texts.

 

Bias check

 

The mega-cloud is coming up

In 2018, an overwhelming majority of companies will adopt a multi-cloud approach and combine the different cloud models. To overcome the associated cloud silos, the next step will be the mega-cloud. It will interweave the different public and private clouds of companies in such a way that they behave as a single holistic system. With the help of AI and ML, this IT environment will be fully automated and consistently evaluated.

 

mega-cloud

 

IT security is becoming more important than ever

 

In today’s increasingly connected world, IT security companies need more than ever to rely on third parties. They are no longer individual instances, but parts of a bigger whole. Even the smallest errors in any of the connected subsystems can potentiate to fatal failures in the entire ecosystem. In particular, for multinational corporations, it’s a must in 2018 to prioritize the implementation of security technologies. This development is further fueled by new regulations, such as the GDPR regulation of the EU.

 

 

E-sports gaming industry ready for mainstream

 

Not least driven by virtual reality, the phenomenon of e-sports for companies in the media and entertainment industry 2018 finally become a fixture. Millions of other players and viewers are jumping on the bandwagon and making continuity e-sports mainstream for 2018. This phenomenon is representative of a bigger trend: even original physical activities such as sports are digitized. In the future, every business will be a technological business, and people’s free time will be shaped by networked experiences.

 

“People have been living and working with machines for centuries,” says Dinko Eror, Senior Vice President and Managing Director, Dell EMC Germany. “In 2018, however, this relationship is reaching a whole new level: man and machine will be more intertwined than ever, and that will change everything – from the way we do business to the design of leisure and entertainment.”

2017 Digital Evolution Report – CyberCrime, Digitization, Blockchain and Artificial Intelligence

Cyber-crime, Smart-Cities, Digitization, Blockchain and Artificial Intelligence are those words which really got the hype on the platform of IT in 2017. Cybercriminals have smacked many companies many times. Digitization is progressing despite lame internet connections. Blockchain became Gold Chain and Artificial Intelligence is experiencing an incredible revival.

Key Technologies 2017

Ransomware: The ransom and the cyber blackmailer

 

Ransomware remains a leader in digital security threats. According to ITRC Data Breach report, in 2015 more than 177,866,236 personal records exposed via 780 data security breaches, and the previous mentioned number lift up to 30% in 2016 with security breaches arising on multiple fronts, companies, healthcare systems, governmental and educational entities, and individuals started to realize how real the threat of cybersecurity attacks was. 2017 so far, was a very highlighted year for cyber-crimes. 519 Cyber-attacks were placed from Jan 2017 until September 2017 affecting financial sectors, health-care sectors, gaming companies, containing information about credit cards, health data of billions of people around the world. With all these attacks phishing, spying on webcams or networked household appliances (IoT) remain risky.

 

Very popular in this year’s cyber attack list are the #wannacry and Equifax data breach attacks. These attacks unbaled 300000 computer systems for 4 days and affected financial data on more than 800 million customers and 88 million businesses worldwide and more than 45% of all detected ransomware.

Cyber policies are currently very much in vogue, but in which cases of damage do these insurances actually comes in? ABA, American Bankers Association, explains how companies should best go about finding a suitable policy and what makes good cyber insurance.

 

The General Data Protection Regulation (GDPR): What needs to be changed?

 

Companies only have a few months left to prepare for the new European #DataProtection Regulation. On 25 May 2018, all companies managing personal data of citizens of the European Union will be required to comply with the new regulations and requirements of the General Data Protection Regulation (GDPR).

This regulation will impose significant new obligations on companies that manage personal data, as well as severe penalties for those who’ll violate these rules, including fines of up to 4% of global turnover or € 20 million highest amount being withheld. But what is to change concretely? Here is a “Guide to compliance with the EU GDPR” and a framework to become step by step GDPR-fit.

 

Digital Transformation: Slow Internet connections as a brake pad

 

Digitization is progressing, but most users still complain about slow Internet connections. Despite the 7th place in the worldwide internet ranking, Belgium is still far behind the world’s fastest internet country. Notwithstanding all the shortcomings of the national IT infrastructure, companies are dealing with the technical and organizational challenges that result from the digital IT transformation.

 

The crazy rise of Bitcoin

 

In the period of a year the value of bitcoin has been multiplied by ten. A bitcoin was worth “only” 1000 dollars on January 1, 2017 … and 8000 dollars ten days ago. In April 2017 Japan officially recognised bitcoin and virtual currencies as legal methods of payment. You should know that Bitcoin represents less than 50% of the money supply of all cryptocurrencies in circulation. this is partly explained by the network situation and the rise of the Ethereum currency. Even if bitcoin is a legal in the vast majority of countries around the world, only a few governments have recognized the legal status of bitcoin in a particular regulatory manner.

 

IoT Projects: The 5 Biggest Mistakes and the Five Steps to Success

 

Closely linked to Digital Change is Internet of Things (IoT) and Industry 4.0 projects. Pioneers already pointed out the four biggest mistakes in IoT projects. If a company wants to exploit the potential of the IOT, it means a lot of work and often frustration – the technical, commercial and cultural challenges are manifold. Until an IoT solution is successfully established on the market, many decisions have to be carefully considered.

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But how does an IoT project succeed? Four steps are needed to make an IoT project a success.

 

Blockchain: The new gold chain

The blockchain is a much-debated technology with disruptive potential and three key characteristics: decentralization, immutability, and transparency. It could help to automate business processes, increase the security of transactions and replace intermediaries such as notaries or banks. Blockchain turns out to be the silent revolution that will change our lives. On top of that, it can turn into a gold chain for early adopters.

 

Cloud: Companies use public cloud despite security concerns

For years, companies have avoided the public cloud, as it is difficult to get a grip on in terms of security. However, this year, companies in the EMEA region increased their investment in the public cloud despite ongoing security concerns and lack of understanding of who is responsible for data security. However, caution is still needed to provide attacks such as wannacry.

 

Artificial intelligence

In 2016, Gartner put artificial intelligence and advanced machine learning in first place in its forecast for 2017, stating that this trend was really pronounced during 2017. Briefly 80 % of companies have already invest in Artificial Intelligence (AI). Nevertheless, one out of every 3 deciders believes that their organization needs to spend more on AI technology over the upcoming years if they want to keep pace with their competitors. Artificial intelligence penetrates into all areas of life. But how does it work?

One example is the automated and personalized customer approach to AI. With personalized campaigns and individual customer approach, the marketing of the future wants to win the battle for the buyer. As a rule, the necessary data are already available in companies, but the resources and software tools for their profitable use are not.
In 2018 Businesses will have an availability of AI-supported applications and should therefore focus on the commercial results achieved through these applications that exploit narrow AI technologies and leave the AI in the general sense to researchers and writers of science fiction;

 

The future of the human worker

AI systems can be used without a doubt. The world is becoming increasingly complex, which requires a thoughtful and wise use of our human resources. This can support high-quality computer systems. This also applies to applications that require intelligence. The flip side of AI is that many people are scared about the possibility of smart machines, arguing that intelligence is something unique, which is what characterizes Homo Sapiens. Not only that but many people still think that Artificial intelligence is the new threat to employment. It will replace the man and steal all the jobs. And they thinks that the future is dark.

Yet technological progress has never caused unemployment. On the contrary, since the industrial revolution, employment has multiplied. But, always, with each progress, fears resurge. Today, it is artificial intelligence that scares, or is used to scare. Economic history, and economic science therefore invites us to remain calm in the face of technological progress in general, and artificial intelligence in particular. By allowing the invention of new things to be exchanged, by stimulating entrepreneurship, it is not a danger but only an opportunity.

 

DATA based business models

Data Driven Business Model puts data at the center of value creation. This central place of data in the Business Model can be translated in different ways: analysis, observation of customer behaviour, understanding of customer experience, improvement of existing products and services, strategic decision-making, and marketing of data.

These data can be gathered from different sources, generated directly by the company, processed and enriched by various analyses and highlighted by data access and visualization platforms. Once data is collected, It’s essential to manage the multiple sources of data and identify which areas will bring the most benefit. Tracking the right data points within an organization can be profitable during the decision-making process. This allows an organization’s management to make data-driven decisions while amplifying synergy within the day-to-day operations.
As for revenue models, these can be based on a direct sale of data, a license, a lease, a subscription or a free provision financed by advertising.

 

Smart Cities – Privacy, Security, #CyberAttacks and #DataProtection


Smart city components

“Smart cities” is a buzzword of the moment. There is currently no single accepted definition of a “smart city” and much depends on who is supplying the characteristics: industry, politicians, civil society and citizens/users are four immediately and obviously disparate sets of stakeholders. It is easier perhaps not to define smart cities but to elaborate their key features in orser to better understand this concept. The connecting key infrastructure that is most often mentioned as making cities “smart” includes:

 

  • networks of sensors attached to real-world objects such as roads, cars, fridge, electricity meters, domestic appliances and human medical implants which connect these objects (=IOT) to digital networks. These IoT networks generate data in particularly huge amounts known as “big data”.
  • networks of digital communications enabling real-time data streams which can be combined with each other and then be mined and repurposed for useful results;
  • high capacity, often cloud-based, infrastructure which can support and provide storage for this interconnection of data, applications, things, and people.

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Scanning through numerous smart city projects and initiatives undertook, eight key activities can be identified that often define a smart city, ie: smart governance, smart infrastructure, smart building, smart connectivity, smart healthcare, smart energy, smart mobility and smart citizens.

 

A European survey shows that the benefits of smart cities are obvious, but IT security and technological challenges are a major barrier to their acceptance. Ruckus, a network connectivity provider, has published the results of its Smart Cities Survey with UK market research firm, Atomik Research. The survey surveyed 380 European IT decision-makers from the public sector.

 

The aim of the study is to understand the attitudes towards the implementation of smart city concepts and to learn what opportunities they offer to the industry. The majority of respondents (82%) believe that smart city technologies are helping to increase citizens’ security and reduce crime rates, for example via smart lighting or networked surveillance cameras. Although the benefits seem to be well known, fears of cyber attacks are a major barrier to the Smart City. For 58% of the IT decision makers surveyed, the biggest problem is followed by a lack of technology infrastructure and funding.

 

Benefits of citywide connectivity

 

The survey results show that the infrastructure and technology platforms created for Smart Cities could be used to add significant value to the public sector and to develop innovative applications that directly address citizens’ needs. Other areas that benefit from the smart city model include local health (81%) and transport (81%), which provide greater access to public services for citizens through extensive networking. According to IT decision-makers, smart city concepts also provide crucial benefits for the security of citizens (72%), public transport (62%) and the health service (60%).

Nick Watson, vice president of EMEA at Ruckus, said: “A basic understanding of the benefits to citizens shows that policymakers are aware of the benefits of this technology. As the return on investment becomes clearer and smart cities become more and more commonplace, targeted advocacy will allow organizations to work together to make the city of the future a reality. Of course, given the amount of sensitive data that could be divulged, it is not surprising that security concerns play a big role. Only a           secure, robust and reliable network will allow to address these concerns and create a secure foundation for smart cities. “

 

Benefits of smart cities

 

The survey shows that the public sector is well aware of the added value that smart cities has to offer. Almost two-thirds (65%) of respondents said smart cities bring benefits. 78% of respondents said that they recognize that there are strong economic reasons for investing in smart city concepts. These reasons include firstly the credibility of a smart city (20%) and future infrastructure (19%). On the other hand, there is the related attractiveness, which leads to the resettlement of companies (18%) and suggests that the true value of smart cities lies in generating revenue and boosting the local economy.

These findings are a positive step towards ideal framework conditions in which smart cities can successfully develop. To make smart cities a reality across Europe, it takes an overarching approach involving all departments of a city. However, the Ruckus survey also found that isolated projects (39%) still pose a major barrier to smart cities.

Although lack of funding is seen as the third most obstacles to rapid implementation, 78% of respondents across countries expect to have the budget for smart city solutions by 2019. This should also be facilitated by promotional announcements such as the Wifi4EU program. It gives cities the security that the infrastructure will be available to support smart technologies.

 

Overcome barriers

 

To provide these services, a stable public WiFi network is crucial. 76% of respondents agree that this is the most important factor in successfully implementing smart city concepts. 34% agree that Wi-Fi is more important than a wired network. Wi-Fi is probably the preferred infrastructure because people are familiar with it and it gives everyone access to information. If you want to be able to connect with your citizens and use the services you offer more effectively, you need a suitable infrastructure to connect with the public in a way that benefits them.

WLAN is the “glue” for intelligent cities’ network. It makes it easier to distribute the load and reduces connection problems. The access point at the edge of the network is the ideal interface that acts as a message broker by delivering traffic, performing and returning simple data processing, and placing the software through controllers.

However, not all WLAN technologies are the same. Power supply (53%), interference (52%) and backhauls (45%) are the biggest obstacles to setting up a public WLAN infrastructure. 51% of IT decision makers called the consolidation of existing networks as another crucial obstacle. This is particularly important because the number of connected devices is increasing at a time when existing networks are not prepared for the exponential growth of data consumption. IT decision makers have the clear task of choosing the right technology partner to meet the technological needs of their city.

For Ruckus, the findings of this study are an opportunity to engage in dialogue with various public-sector organizations on how smart city technologies and a public Wi-Fi network can add value. The survey shows that WLAN is considered necessary for the creation of smart cities because:

  • It gives access to everyone information (71%);
  • it delivers the necessary infrastructure to offer additional services (70%);
  • it overcomes the digital divide between citizens (67 percent);
  • it is cheaper for governments (61%);
  • it could lead to better service (37%);

The research shows that Wi-Fi is a key contributor to helping smart cities deliver reliably and sustainably, but along the way, European policymakers still have some obstacles to overcome. It is reassuring to see that there is a widespread belief that smart cities add value to society. But if the government and the public sector are not investing in the right technology, then they risk missing the numerous opportunities for cities, citizens and themselves.

#GDPR: Does your Business comply with the new #DataProtection requirements?

Our data is one of our most prized asset. As an organisation, our clients entrust us with this data. In our vision data and its security must be critical for each operations, innovation and competitive position. As an enterprise, you can be more successful in your respective line of business when you manage to get your data security right.

 

Therefore, the EU’s GDPR brings data protection legislation into line with new, previously unforeseen ways that data is now used. This wide Basic Data Protection Act (EU-GDPR) can be very complex and opaque. IBM Security has developed a five-phase framework to help organizations implement the mandatory regulation from 2018 onwards.

 

In addition to that, IBM Security has also worked in the past to create a service that will help companies prepare for the upcoming GDPR. Instead of accessing complicated, multi-dimensional matrices or diagrams, a simple framework was compiled.

 

Step by Step GDPR

 

Each journey begins with the first step, and so IBM Security has also extracted five separate steps for the journey to GDPR’s expertise. This allows companies to fallow a step by step guidelines through the five, to the point, phase framework. The framework also takes account of the fact that each company will have its own needs during the process. Therefore, it is designed as simply as possible.

 

Based on the main focus of the GDPR, the five steps within the framework are subdivided into the areas of data protection and security. Since both areas are closely interwoven, IBM Security has selected the following area definitions for us: In the field of data protection everything is about what data is collected and why they are managed, shared, processed and moved around. Security, on the other hand, is much more concerned with how data can be controlled and protected. This also means that within a company, security can be achieved without data protection, but no data protection can be guaranteed without adhering to security standards.

 

The five-phase framework for the GDPR

IBM’s GDPR Framework

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The approach for a basic GDPR expertise in five steps is the fallowing:

 

Phase 1: this first step is related to company assesses. It is necessary to examine which of the collected and stored data are affected by the GDPR guidelines. A plan is then drawn up to reveal this data.

 

Phase 2: is about the company’s own approach, a solid plan that governs the collection, use, and storage of data. This approach is based on the architecture and strategy on the basis of which risks and company objectives are exploited. Designing privacy, data management and security management are top priority.

 

Phase 3: the company’s way of doing are rethought. It is important to understand that the data gathered so far are as valuable to the people as they are to the company. At this point, sustainable data protection guidelines have to be developed. However, it is also about introducing safety controls and administrative controls (also: TOM – Technical and Organizational Measures) and appointing a Data Protection Officer so the GDPR training can be delivered to the right persons for the job.

 

Phase 4: in this phase, companies are ready to implement their data protection approach. Data streams are continuously checked from this phase, and access to data is monitored. In addition, security checks are performed and unimportant data is deleted.

 

Phase 5: the company is ready to comply with the GDPR guidelines. From then on, all requests for access, correction, deletion and transmission of data are met. In addition, by documenting all activities, the company is prepared for possible audits and can, in the case of a data lap, inform regulators and affected parties.

 

Above is the direct approach of IBM Security to make companies fit for GDPR. The way to get there is not always easy, but the framework should at least show it more clearly. Companies are themselves responsible for compliance with the applicable regulations and laws, which are included in the EU-GDPR. Note that IBM does not provide any legal advice and does not warrant that IBM’s services or products comply with applicable laws or regulations.

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