With each passing year, cyber-security reaches new levels becoming increasingly sophisticated. But as cyber-security becomes more formidable, cyber-threats and the rate of malicious attacks increase proportionately. This and the many controversial security threats in 2018 subsequently led to wide concerns regarding the volatility in cloud security. Apparently, many organizations still believe cloud security has a lot of exploitable vulnerabilities.

Despite this, cloud spending is at an all-time high according to Gartner. The report from the popular research firm found that public cloud spending in 2018 exceeded $170 billion. A little over $10 billion was spent on cloud security and management. Gartner also predicts that the number will go up by at least 20% this year.

While cloud security professionals are already coming up with great new ways to combat breaches similar to the ones that turned heads in 2018, cloud security still needs more advancements considering that the increasing complexity of cloud environments are bringing forth newer, tougher security challenges.

That said, let’s check out a few such challenges that enterprises may have to beat this year.

Cloud complexity is on a whole another level

The cloud is already a complex technology. With businesses embracing multi-cloud and hybrid cloud environments more than before, cloud complexity has reached even greater levels making data storage and transfers much harder.

The benefits of a multi-cloud architecture are enticing more businesses to come forward and adopt it, though only a few of the adopters are thoughtful enough to invest in a multi-cloud management strategy. Even with a strategy in place, a multi-cloud ecosystem presents a lot of data security and data management challenges.

So essentially, businesses will be operating on a highly volatile multi-cloud environment spanning on and off-premises systems, and more than one platform. The challenge is to develop advanced solutions and devise efficient implementation strategies to not only retain some amount of control but also to stabilize the environment.

Data breaches will cost a lot

As more businesses adopt the cloud, it’s safe to assume that more critical and sensitive data will be shifted to the cloud. Hence, any potential breach would be ridiculously expensive.

According to a Cost of Data Breach study by IMB and Ponemon in 2018, the global average cost of data breach went up as high as $3.86 million. This was a 6.4% increase compared to the data breach cost in 2017.

In 2019, the stakes are higher which means the data breach cost figure is likely to go even higher. Adding to the challenge is the rising complexity of how data breach attacks are carried out. It’s not at all easy even to detect a breach.

Threats are smarter now

Modern businesses are smarter thanks to powerful technologies like AI and big data. But they seem to have neglected the fact that technology is a double-edged sword. Thanks to technology, cyber-criminals are now capable of executing smarter attacks as well; even capable of using virtual assistants and chatbots for attacks.

Cyber-criminals can now develop chatbots that can superimpose themselves on websites and bait visitors into clicking malicious links, reveal sensitive information, or download malware-attached files.

Evolving mobility & BYOD

Back in the day, enterprises stored their sensitive data in the company premises on a tightly-guarded internal server. Data management was not that hard. But such a system may sound like a thing of the past now. This is the age of BYOD.

BYOD or Bring Your Own Devices is the norm now, as employees of an organization are no longer confined like enterprise data on on-premise servers. Authorized employees can now access official company data on their personal mobile devices, and from anywhere in the world. This means the likelihood of employees accessing sensitive data over public Wi-Fi or shared networks cannot be neglected.

This in turn means that the cyber-security department of the enterprise must be capable of protecting the data that’s probably getting transferred from one continent to another.

Employees will also be utilizing convenient data transfer techniques like for instance, over Slack or Google Drive or by sharing via DropBox. An enterprise won’t be having control over such platforms, making it difficult for them to remain GDPR and HIPPA-compliant. Making the matter more complex is the looming threat of data breaches.

One approach that’s widely considered to counter such scenarios is to invest in active directories or single sign-on (SSO) identity so that all devices can be brought under one network, enabling companies to instantly shut down all devices if a threat is suspected or identified.

Things to take into account

Smarter tech apparently invites smarter hackers. And while 2019 is widely considered as the year when smarter technologies become more accessible, technology professionals have their work cut out for them. This is of paramount importance to companies that have invested in cloud computing technologies as they will soon be forced to face and overcome a number of difficult security challenges this year.

Well thought out cloud security strategies and practices, data safety planning, and diligence are probably the most important things that can save enterprises from losing millions in a data breach. Professionals using the cloud will need to exercise caution during exchanges of sensitive data. They will need to figure out stricter password policies and learn about potential phishing techniques.

Conclusion

Data breach is an avoidable scenario of course. But avoiding data breach and overcoming security challenges today will require on-cloud enterprises to invest more money and time into securing their data just in case. They will have to make sure that their employees are aware of what’s at stake with regular security awareness workshops.

Ultimately, they have to realize that ensuring cloud security is not the responsibility of one particular department in an organization. It’s a shared responsibility that demands knowledge, diligence, and dedication. If your organization has already moved to the cloud and is willing to invest in augmenting cloud security, AOT’s cloud expertise could be what you are looking for. We can ensure that your organization gets the best out of its cloud in terms of benefits and security. Get in touch with us.

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As more technologies are introduced for businesses to leverage for growth and improved performance, the importance of using data wisely comes into focus. Modern businesses generate tremendous amounts of data that hide many insights that could be of great benefit to the business’ progress. Therefore, businesses today have to figure out not only how to protect and secure this data but also to use them for benefits.

Before big data gained a foothold, businesses simply focused on customer experience & satisfaction as the best approach to grow faster. Now they realized the secret to accelerated growth lies in the data they generate. There has also been a shift in focus from customer-focused processes to operational performance improvements. The data from operational processes can be analyzed to help businesses tweak their approach to faster growth from the inside out, instead of investing lots of resources in how they appear fine to their customers.

The principle is rather simple really.

If you are good inside, you will be great outside.

In this blog, we will explore why operational analytics would be all the buzz in the coming years and how operational analytics can benefit modern businesses.

The Many Merits of Operational Analytics

More profit

Identifying and reducing unnecessary expenses is a good approach to increasing the profit margin. This is one of the primary goals of any business today. They can do this better if they look into their data. Operational data analytics helps businesses identify areas that need improvements and processes that need to be streamlined. This contributes to reducing expenses while improving efficiency, subsequently resulting in higher profits.

In 2017, Capgemini Consulting published a Whitepaper with their research on operational improvements and their benefits to organizations. The research had found that over 70% of the surveyed companies were more focused on operations than consumer experience. The research also found that operational improvements can lead to a raise in profits to exceed $115 billion globally every year. Customer analytics can drive only about $38 billion yearly in profits according to the report.

Better decision-making

Many companies often rely on reputed consultancy firms to make big decisions. If the business is willing to pool the resources they invest in consultancy companies to operational analytics, they can make decisions themselves; a more cost-effective approach indeed. Proper data analytics helps businesses make decisions quickly and even proactively.

Competitive edge

The advancements in Artificial Intelligence and Machine Learning opened up new possibilities for analytics and improved data analytics accuracy significantly. Big data analytics is known to grant competitive advantages for businesses provided it’s fed operational data instead of customer data. The results would help them save money and make better, more profitable investment decisions.

More satisfied customers

This may sound counterintuitive when this blog is all about shifting focus from consumer-centric processes to operational analytics. Investing in operational analytics does come with benefits for the business’ consumers as well. Without operational analytics, it may take a while for businesses to identify customer experience pain points or the factors that are causing the drop in customer satisfaction.

Data can speak faster with operational analytics enabling businesses to identify performance issues and fix them right away leading to improved customer satisfaction and more satisfied customers.

Holistic view of data for easier data management

Operational analytics grants a business’ stakeholders a holistic view of the business data facilitating more efficient data management in the process. It’d be easier to identify system issues in real-time and to devise an appropriate data security strategy as well. One other great advantage is that the stakeholders will be able to discover the interrelationships between certain networks which means root-cause analyses (when necessary) would be more efficient and faster.

More employee engagement

With all the data stored in a central repository, it can be shared with the employees as well to gain insights from them. This way the employees will also be empowered to contribute more to the company’s growth and success. The approach essentially encourages collaboration wherein all the teams in the entire organization will work synchronously for the company’s success.

Conclusion

Operational analytics is still seen as a new concept by many organizations that are still focused on customer-centric improvements. There evidently is a shift in trend but organizations need to keep in mind that operational analytics isn’t something that delivers great results overnight. It takes time as there are many factors involved including the issues associated with data silos, accessibility issues when it comes to third-party data, and the lack of mandate from stakeholders. Nevertheless, this is the right time to see what your company’s operational data is hiding and how you can leverage those insights to soar higher with changing times.

AOT can help you analyze your operational data with robust, bespoke analytics solutions with cognitive capabilities. Let us know if you are ready to give operational analytics a go this year.

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Many factors influence business transformation and evolution today. But among all those factors, data stands out unrivaled. The data that a business generates hide the key to the business’ evolution with respect to its operations. However, it’s not easy to mine those valuable information from such tremendous amounts of data unless big data analytics is involved. Modern day technologies make it possible for businesses to uncover valuable insights from the data and figure out the key to dominate in their respective industries.

So basically, a business needs advanced data management and analytics capabilities, data scientists, and an efficient infrastructure to leverage their own data to trigger an evolution that keeps up under dynamic market conditions and enables them to stay ahead of the competition.

Realizing this, many businesses today try to utilize the power of big data analytics to improve themselves. But not all of them succeed. Successful big data integration is a challenge in itself, and that’s what we are going to discuss about in this blog.

Bringing big data into the picture

Data integration impacts the processing, collection, and transfer of data within an organization regardless of whether the data is old or new. Big data integration is for large, complex data sets. The integration itself is complex, and should be done carefully while keeping in mind the importance of the resulting accuracy for better decision-making.

Big data analytics is a whole another process with which businesses can reap great benefits. We’ve discussed that bit before. But integrating big data is the first challenge to be addressed.

Here are a few things to consider for successful big data integration.

The right talent

Big data combined with disruptive technologies like AI and Machine Learning can deliver astonishing results. In addition, many new tools evolved in the big data sector recently – from modern relational database tools to data layouts designed to reduce storage footprints and improve accessibility, which can make things much simpler for enterprises.

The new Hadoop ecosystem, the advancements in analytics, efficient data management frameworks etc. present enterprises with different types of approaches for integrating big data. However, it’s easier said than done because to do all this, the enterprise will need the right talents. It’s best to rely on people or organizations with great experience when it comes to leveraging big data technologies.

Data quality is a priority

Many businesses tend to give data quality a thought after implementing big data analytics. This is certainly not a wise approach. Data quality should be prioritized before big data integration. There will be semi-structured and unstructured data involved which can complicate data set integration.

The analysis itself requires relevant, good quality data to deliver good results. The business needs to assess their data quality just to check whether they can have relevant data sets to feed to the algorithm once the integration is done. Just a lot of data is not the only condition that should be satisfied for implementing big data.

Data is your asset

The seemingly trivial data that a business generates, if mined the right way, hides a lot of real and assessable values. Not all data are valuable however. Data science helps differentiate relevant data. The point is that a business’ data is an important resource that helps make better decisions in the future leading to better productivity and enhanced growth rate. While integrating big data, make sure all the relevant data are safely secured, managed, and accessible. Data is an asset and should be treated that way rather than as a means to better times.

Be aware of the risks involved

Big data and analytics are sophisticated. The complexities obviously emphasizes the presence of risk factors. With big data analytics, the business gets to visualize and predict several outcomes. But the accuracy of these predictions depend on how well the integration was done. Doing it the wrong way may lead to unfair results, erroneous analyses, and misleading numbers.

This is why it’s crucial, during integration, to understand how things can go wrong, where things can go wrong, and how wrong things can get. Risk assessment helps in integrating big data the way it should be integrated.

Possible synchronization issues

Once the big data platform is online, data will have to be imported from multiple sources for analysis at different rates and on different schedules. This means there is a good chance for the data to go out of sync with the originating system. For instance, the data from one source might be outdated while data from a different source isn’t.

In addition traditional data management, data transformation sequences etc. also raises the risk for the data to go out of synchronization. This should be taken into account before big data integration, so as to devise a data migration management strategy that minimizes risks.

Various challenges

It’s important to be ready for any challenges that arise while integrating big data, including big data solution cost and compatibility, data volume, data transformation rates, data validation mechanisms, associated expenses etc. Another challenge is to implement a system where the data are processed at high speeds so the results are delivered and made accessible on time. Achieving that level of performance will require bespoke solutions and qualified technical support.

As of now, considering where technologies stand, an enterprise is better off seeking assistance from an experienced big data specialist and data scientists rather than investing in an in-house team. Laying a solid foundation to big data is the first step to using big data the right way. That’s why the initial steps should be taken under the supervision of big data experts.

AoT’s expertise in big data comes from our experience and commitment when it comes to mastering disruptive technologies so we can leverage them the right way. We can help integrate big data into your business while reducing associated risks. If you like to learn more about big data integration and how we can help with it, get in touch with us.


While the rapid evolution of technology is proving to be of great benefit to businesses, it’s still not easy for them to cope with the not-so-subtle changes or overcome the plethora of challenges. Keeping pace with evolving technology in itself is not easy, even for bigger businesses. In addition, they need to figure out effective and optimal ways to deal with the large amounts of data involved in their IT operations, changing customer demands, process inefficiencies etc.

Out of all these aspects, it’s the data that hold the key for a business’ rapid growth on par with evolving technologies and dynamically changing market conditions. But when enterprises keep relying on traditional data monitoring and management systems, the increasing volumes of data would pose a problem. Moreover, they’d be unorganized and insensible for the most part, leaving the enterprise’s data management staff to do some heavy lifting.

To deal with these issues around data, data management, or rather ‘service management’ in an enterprise, modern-day technology grants intelligent computing mechanisms and practices.

…and this is where AIOps originated.

But what is it?

AIOps or ‘Artificial Intelligence for IT Operations’ is a term popularized by Gartner. It’s a combination of data analysis and machine learning technologies designed to make internal management systems of enterprises more sensible or ‘smart’.

Why businesses need AIOps

Many businesses already prepared for the spike in data in advance, and set up various systems in place to handle the data flow on a daily basis – from project management methodologies to sophisticated IT management systems. The existing monitoring systems are capable of alerting the management when issues arise. But the problem arises when there is just too much data, and the system starts generating thousands of alerts every minute. The personnel are in for major persistent headaches.

In this scenario, AIOps platforms would combine big data and AI/ML functionalities to either enhance or partially replace a number of IT Operations tasks and processes which include performance monitoring, event identification and correlation, analysis, service management, and automation.

So essentially, AIOps adds one more layer over the enterprise’s platforms – something much smarter, and capable of simplifying operational and managerial tasks.

Here are a few notable advantages.

  • No need for organizational silos – In enterprises that generate overwhelming volumes of data, procuring relevant real-time data for analysis can be tricky. But if the AIOps platform is directly linked to the project/data management platform, it can gather necessary data and derive insights without the need for any silos.
  • Prioritizes issues for easier management – AIOps, unlike traditional systems, analyzes data to understand and prioritize issues, and then highlight issues based on their priorities to the management. This way, the authorities can focus on the issues that require immediate attention rather than putting a lot of effort assessing threat levels and risks to categorize issues.
  • Solves problems automatically – Doesn’t apply to all problems, but it’s still a highlighted capability of AIOps. Every time an issue is solved, the AIOps-based system records the methods involved. It understands the type of problem and an optimal and effective solution, making things easier for the IT staff. The system can be automated to solve certain specific recurring problems.

Digital transformation with AIOps

The world sees digital transformation of enterprises as a shift from legacy systems to modern dynamic frameworks that facilitate improved business agility, faster growth, and better decision-making. But when you look at it closely, you will notice that major changes from digital transformation take place particularly around three key areas – the business model, customer experience, and operational process.

AIOps, in essence, can be linked to these areas to generate insights from the data collected from them. Leveraging both big data and AI helps enterprises eliminate repetitive tasks, and makes internal systems more adaptable and responsive to change. Because it predicts sources of potential risks or threats, the enterprise can proactively plug security gaps and minimize risks. This action can also improve customer satisfaction.

To conclude, AIOps facilitate Continuous Integration and Continuous Deployment (CI/CD) for IT functions, making it a great addition to an enterprise’s accelerated IT ecosystem.

Conclusion

Despite Gartner’s deep analysis on the platform, AIOps still hasn’t gone mainstream yet, probably due to the fact that IT Ops team are still hesitant to use new-gen technologies that replace the ones they are already familiar with. However, the hard truth is that IT ecosystems of enterprises will inevitably change as long as technology continues to evolve.

Resisting the change would only do the enterprise more harm than good in the long run. AI and big data have already proven their mettle individually. Coupling them both is what AIOps basically does, and it can guide the enterprise to a better future through better decision-making and operations.

You can get AIOps and other similar AI-driven solutions from AoT Technologies, as we’ve been helping enterprises leverage these technologies for a while now. Interested in knowing how a custom machine learning-based solution can secure your business or empower it? Contact us now.

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