As organisations generate more and more data, it becomes important to be more aware of the shortcomings of using big data analysis tools for IoT data. We are seeing a trend in the industry where organisations are dealing with larger bodies of data, but need to generate insights at a faster rate than before. Yet, managing big data from IoT is not easy, and this blog will explore some of the reasons why that is.
Data visualisation is challenging
Developing a cohesive process for collecting and analysing data is one of the biggest challenges for big data analysis tools. This is because data visualisation is difficult to do though it is a huge part of the data analysis process. Put simply, data visualisation is the process of taking raw, complex data and converting it into an easily readable format, like graphs.
The objective of data visualisation is to make complex data sources easier to understand. However, it is difficult to replicate the process with data generated from IoT devices. This is because data generated from different sources, like IoT devices, are heterogeneous in nature. The data often presents itself in structured, semi-structured and unstructured formats, making it difficult to execute proper data visualisation using big data analysis tools.
Iot devices are constantly streaming data in real-time.
This places a strain on data storage capacity and management processes. Since IoT devices, like sensors, are constantly streaming data, questions arise on the best method for storing and managing it.
While the obvious solution would be to move past physical servers and into cloud-based infrastructure, there is still the challenge of managing the data so that organisations can generate useful insights in quick time. Usually, such measures would involve using edge analytics to start the data analysis process as soon as possible.
While there is no denying that IoT sensors can sense and communicate a ton of data when applied to different applications, there is a question mark over its integrity. How can we ensure that data is not being leaked? How can we guarantee that privacy concerns are addressed? Is there any guarantee that data collected meets the organisation’s objectives?
This is a challenge when it comes to using big data analysis tools on IoT data. While the tools can break down and analyse data, making sure that the findings are ethically obtained can be challenging.
IoT devices can work without stopping. While the ability to constantly generate data is a huge advantage, there are some concerns to be had. For example, how can we ensure that big data analysis tools have the necessary power to run round the clock? This is a problem organisations have to consider when implementing their analytics framework.
Device security is a huge concern for most organisations relying on IoT sensors to get work done. This is especially the case for organisations using edge analytics as part of the data collection and analysis process. Some of the challenges include, but are not limited to, networking, data storage, and computing power. To work around this problem, cybersecurity becomes a major factor.
Every IoT device generates an enormous amount of data, which may or may not lead to confidentiality concerns. It is important to ensure that data is collected and stored in a way that meets these requirements.
While there is no denying that IoT devices are a huge asset for organisations, there are some downsides to using them. To work around some of the shortcomings, it is important to work with an experienced data analyst or a team of skilled analysts.
The right team can help organisations optimise their big data analysis tools to ensure you are getting the most out of your IoT devices. The right data analytics team can help organisations optimise their data analytics infrastructure around the use of IoT devices to maximise the quality of findings while minmising the downsides.
Visit the Selerity website to learn more about optimising big data analysis tools.
Data lakes are the key to streamlining data collection and analysis. However, there is no denying the obvious benefits of these lakes but, like most technologies, there are some disadvantages to using a data lake. It’s important for organisations to be aware of its shortcomings before investing in it. This blog post attempts to address some of the problems that come with data lakes. If not implemented properly, the lake could end up hurting the organisation more than benefiting it.
There are several technical and business challenges of using data lakes.
Data lakes are an open-source of knowledge designed to streamline the analytics pipelines. However, the open nature of the lake makes it difficult to implement security standards. The open nature of the lake and the rate data is inputted, makes it difficult to regulate the data coming in. To eliminate this problem, data lake designers should work with data security teams to set access control measures and secure data without compromising loading processes or governance efforts.
However, it’s not just security that’s causing problems with data lakes. It’s also an issue of quality. Data lakes collect data from different sources and pool it in a single location, but the process makes it difficult to check data quality. It is problematic because it leads to inaccurate results when the data is used for business operations. When the data is inaccurate, the findings will be inaccurate, causing a loss of confidence in the data lake and even in the organisation. To resolve this problem, there needs to be more collaboration between data governance teams and data stewards so that data can be profiled, quality policies implemented and have action taken to improve quality.
Metadata management is one of the most important parts of data management. Without metadata, data stewards (those who are responsible for working with the data) would have little choice but to use non-automated tools like Word and Excel. Moreover, data stewards spend most of the time working with metadata, as opposed to actual data. However, metadata is not implemented on data lakes, which is a problem, in terms of data management. The absence of metadata makes it difficult to perform vital big data management functions like validating it or implementing organisational standards. Since there is no metadata management, it becomes less reliable, hurting its value to the organisation.
Data lakes are incredibly useful, but they are not immune to clashes within the organisation. If the organisation’s structure is plagued with red tape and internal politics, then little value can be derived from the lake. For example, if data analysts cannot access the data without obtaining permission, then it holds up the process and hurts productivity. Different departments might also have rules for the same data set, leading to differences in rules, policies and standards. This situation can be somewhat mitigated by having a robust data governance policy in place to ensure consistent data standards across the whole organisation. While there is no denying the value of data lakes, there need to be better governance standards to improve management and transparency.
Identifying data sources in a data lake is not often done, which is a problem in big data management. Categorising and labelling data sources is crucial because it prevents several problems like duplication of data. Yet, this is not done regularly, which is problematic. At the very least, the source of metadata should be recorded and available to users.
Big data management is made much easier with the use of data lakes. However, there are some challenges when it comes to using the centralised repository. These challenges can hinder the use of the data lake because it becomes harder to discover actionable insights when the data is flawed. If there is a problem with the data, then insights are useless. The main challenge of fixing these problems is implementing multi-disciplinary solutions. Fixing problems with data lakes requires comprehensive technical solutions, adjusting business regulations and transforming work culture. However, organisations need to address these problems. Otherwise, they will fail to draw maximum value from their data lakes.
Big data applications can be used for many public and private operations. While some believe that big data can only be used for specific purposes, they are used in different industries ranging from predicting epidemics outbreak in healthcare to advancing grading systems in education. However, despite their immense value to different industries, there is still uncertainty over the use of big data. The uncertainty arises due to a lack of confidence in the value of big data from certain third-parties. These uncertainties often obstruct organisations from undertaking major data analytics applications and prevent them from reaping the benefits.
The term ‘uncertainty’ can be used in both technical and business terms. From a more technical stance, ‘uncertainties’ in big data refer to sampling issues, differences in data collection devices or variance in environmental conditions. However, for this article, we look at the business uncertainties people have about big data analytics applications and its ability to dispel problems. While people are becoming more aware of data analytics thanks to IoT and 5G, there are still many people who don’t know about it. For sure, business-minded people might have heard that analytics can predict the company’s position for the next few years, but they do not know how it works, which is the root of all uncertainty about big data applications.
So what is the best way to dissuade uncertainty about big data applications? The best way to reduce uncertainty on big data platforms is to increase understanding of the technology, drive success and achieve stakeholder objectives. While every business person can’t get a deeper understanding of the technical processes behind big data applications, corporate individuals must have some basic knowledge of how data analytics platforms work and generate value for the company in question. This is where data analytics consultants are beneficial to an organisation, for they are in the best position to outline the basic mechanisms of big data analytics in a way that laymen could understand, without overwhelming them with technical information.
Communicating technical processes is all well and good, but businesses will only have their apprehensions removed when they see data analytics platforms deliver actionable, deliverable insights. Organisations will only trust or use a solution they have invested in if they know they will deliver results. Of course, along with the solution, there must be a clear, concise explanation of how the result was achieved. In my experience, all uncertainty about a solution is removed when an organisation gives clear, concise explanations on how the result is obtained. Hence, it is best to perform operations within a solution scoping exercise to ensure that data analysts can give clear, but concise explanations on big data applications.
Uncertainty in big data applications can only be truly eliminated if the analytics solutions can solve the problems an organisation is looking to address. The scope of the problem should be a realistic one to ensure that the desired outcome is attained. What is the desired outcome? Ensuring that the solution addresses the problems stakeholders have.
If data analytics solutions can deliver the desired outcomes to specific problems stakeholders have, then it will go a long way in removing uncertainties about big data applications. Knowing how to navigate and remove this uncertainty is one of the key skills stakeholders and data analysts need if they wish to incorporate big data applications into their organisation.
Working with a data consultant goes a long way in helping organisations overcome uncertainty in data analytics. Data consultants are perfectly suited to this role due to several reasons, including technical expertise and their position within the organisation. Data consultants have a thorough understanding of the data analytics platform, having used it to complete different projects, making them the ideal focal points to explain the workings of the data analytics platform in achieving organisational objectives. Their position in the company also makes them perfectly positioned for this function.
Unlike full-time employees, consultants are not caught up in the politics of the organisation because their paycheck is not dependent on how their bosses feel about them. So they have little reason to do nothing else but perform their duties. Working with a data consultant is one of the best ways to overcome uncertainty in big data applications because of their experience and position within the organisation.
With each passing year, the amount of data created on online platforms has increased astronomically. All of this information is crucial, as it allows businesses to understand the myriad of intricacies that exist in today’s markets. With more and more companies going international, the competition is fierce – and it’s only going to get tougher as the new decade rolls on. Similarly, with so many options available to them, consumers are pickier than ever, and gaining their trust and attention is paramount to making sales and conversions. It’s no surprise then that most organisations have begun to adopt big data analytics into their business process.
Big data systems are able to collect, store and process vast chunks of user data and make sense of them. Essentially, it translates all of this unstructured data into valuable insights that can be used in your everyday business processes. Now, it may seem that the utility of big data is mostly confined to business-consumer transactions; that the ultimate goal is driving conversion. In reality, the potential uses for big data analytics are far greater – it can play an integral role in the advancement of various industries and sectors. One of the best examples to illustrate this is the education sector.
Future insights can play a vital role in an academic setting and big data analytics can prove to be an amazing tool that can help improve the processes of teaching and learning. Here’s how.
Good curricula serve as the backbone of any education model, and designing one can often prove to be a challenging task, requiring extensive analysis and expert supervision. Still, no matter how inclusive and intricate a curriculum will try to be, it simply won’t be ideal for everyone that’s taking it up. Though it may seem impossible, it is possible to create customised curricula on an individual level through a combination of big data and online learning programs. So, how exactly can this be done?
Users these days are connected to a wide variety of platforms and devices, from smartphones and smartwatches to social media sites and online forums, and these are an excellent source of information for educators. Based on the big data analytics derived from all these platforms, you can create educational courses that better fit the needs and preferences of your student base.
Traditional classrooms stick to static courses with no real room for flexibility – everyone is expected to follow the same set of steps. The more streamlined and personalised curriculums that big data analytics allow for are a great start, but there’s no reason to stop there. You can take things a step further with big data systems by way of online learning classes. Here, you can allow students to do their own self-learning while you pinpoint target areas best suited for them.
Increasing student performance and providing an effective, stress-free learning experience should be the primary objective for contemporary educators. In order to achieve this, it’s paramount that they identify what the problem areas are for students, understand what their learning difficulties might be and correctly predict which students might be struggling and thinking of dropping out.
Implementing online learning methods, as we mentioned earlier, is one way to achieve this. But remember that you can keep track of this data as well. With big data analytics, you can easily visualise what subjects or areas your students prefer to self-learn. You can set up self-assessments that will store all the scores your students achieve, and through that, you will be able to identify which students might need additional help.
Accessing student performances, interests and strengths – especially over the course of their academic life – can provide a clear understanding of what their ideal career paths should be. This information can easily be relayed to students by educators; they could point out these patterns to undecided individuals and help them reach a decision.
What’s more, you can then begin collecting data on students who went through your education system and joined the workforce. Here, you can assess how effective their career choice was, and use it to optimise your curricula and make even better decisions in the future.
The rapid rate at which organisations have adopted big data in order to provide better experiences for their consumers has transformed the way markets look at data. The amount of raw data available to be processed is quite astronomical, and there is immense potential in the insights you can derive from big data analytics.
As far as the education sector is concerned, efficient utilisation of big data can lead to a great many improvements – for both students and educators. And even though it may take a while for education systems around the world to unlock the true potential of everything big data has to offer, the future certainly looks bright.
With the emergence and subsequent dominance of the internet, the amount of touchpoints businesses have with their customers has multiplied considerably. Social media, websites, blogs, forums, mobile devices – the list goes on and on. On these platforms, a gargantuan amount of data is created every day. If this data is properly stored and analysed, it could provide organisations with invaluable data regarding user behavioural patterns, preferences and even insights into their competitors. However, due to the sheer size and volume involved, big data management has been a challenge for most enterprises within the last decade – that fact has changed over the past few years.
With the introduction of new applications and techniques like cloud management, an increasing number of businesses have embraced big data. As a result, big data management and the insights it delivers have become the basis for many organisational processes, including decision making.
To start off with, it’s important to understand how big data analytics is utilised for decision making. While from the offset it may seem like a mystical process, the collection of big data analytics and their utilisation in decision making isn’t all that complicated.
Goals are identified by the business initially. These will be the benchmarks you use to test performance and identify whether the business is heading in the right direction. Once the goals and performance metrics are identified, it’s good practice to refine them. This ensures that only the best data is collected and that your analysis is ultimately better.
Following this, the most important step in big data management occurs – the data collection. The goal here is to use as many relevant sources as possible; as we said earlier, with the abundance of customer touchpoints, this shouldn’t be an issue. Data compiled can either be structured or unstructured and it will be up to the software you’re using to make sense of all this.
All collected data should subsequently be refined, and be categorised based on their importance for achieving the goals identified earlier. After unnecessary data is weeded out, it’s imperative to segregate everything based on what their purpose will be – is this going to help improve efficiency? Will this help improve consumer relations? And so on.
Once the data has been prepped it’s time to start analysing and applying. Here it’s imperative to choose the right tools and software for your big data management, as they can reap great benefits for your organisation. And now you’ll have your valuable insights, meaning you’ll be ready to execute strategies and make decisions based on them.
So, with everything set for you to start utilising big data in the decision-making process, what’s next?
For most organisations, the crux of their operations revolves around the relationship they maintain with their consumers. Strengthening and building upon it often serve as the key to a business’s successes. It’s a pretty simple equation – the more engaged your customers are with your product and brand, the better your conversion rate is going to be. This simple fact makes the goal of customer-related decisions relatively straightforward – ensure they are engaged and that you retain them.
Big data management provides the opportunity to do just that. Effectively utilising big data reveals previously unidentified trends and patterns about your consumers. This includes their buying patterns, product partialities and even the relationships they have with your competitors. With this information in hand, organisations can begin crafting tailored content – from product launches to full-blown marketing campaigns – for your consumer base.
All organisations strive to be more efficient. Decisions are always being made with the goal of improving performance in both the workforce and in everyday processes. The issue is, it’s not always inherently clear what the best choices are; it isn’t uncommon for organisations to resort to trial and error to identify the best practices. Big data is able to demystify all of this, however. With big data management, the outcome of efficiency-related business decisions can be calculated fairly precisely on a real-time basis.
Automation has also become a preferred option for many businesses looking to improve their efficiency. This even includes automating the decision-making process itself – and this is a data-driven affair. By melding big data with automation software, organisations can create a system that streamlines the decision making process and subsequently boosts work efficiency.
Companies always have a plan to grow; to expand their services, grow their consumer base and raise their brand image. The decision-making quandary with expansion is the investment that it requires. Once again, big data management alleviates this issue. Think of all the optimisation possibilities that are uncovered with effective utilisation of big data. Now add all the consumer engagement and retention opportunities it delivers. Simply put, decision-making brought about by the real-time analysis of data will create natural growth for your business, with no need for any additional investment.
As such, the role big data management plays in the organisation decision-making process is apparent – it’s a vital tool that eases the pressure and doubt that surround major business decisions. Effectively using big data when making decisions is near-guaranteed way to build better relationships, foster a better work environment and facilitate healthy growth for an organisation.
Smart grid big data analytics is promising to shake up an industry not known for its technological innovations: Utilities. There is a growing overlap between utilities and data, with data sensors and other equipment being integrated into the provision of utility resources. Energy companies are using smart grid analytics to measure various variables, like the amount of energy distributed from smart network triggers. Smart data analytics is, therefore, going to have a huge impact on how we live (if it hasn’t already). Hence, in this blog, we are taking a look at smart grid big data analytics.
Before explaining the benefits of smart grid analytics, it is necessary to explain what the smart grid is. It refers to the energy infrastructure of the future, fusing transmissions, transformers and substations that direct energy to households with modern technology like computers, automated technology along with other new equipment, which allows for digital communication or transmission of information, while also providing energy to households and organisations.
The ‘smart’ in smart grid refers to the additional infrastructure layer that allows for two-way communication between consumer devices and transmission lines. This two-way communication is possible thanks to the development of several innovations, like IoT and cloud computing.
The smart grid is a vital part of energy because it allows energy providers to draw full value from the smart grid. The smart grid refers to the new infrastructure where there is an emphasis on connected devices. It allows for a layer of communication between local actuators, central controllers and logistic units. This layer of communication is useful in many different areas because it allows for better response time during an emergency, more efficient use of resources and even improve the delivery of the network through automation. While the smart grid is all about keeping different devices like generators and consumer-end devices connected.
The smart grid is an exciting development because it represents a massive leap forward for the energy industry. It brings several benefits, like more efficient energy transmission, lower management costs, better security, operations costs and better integration of renewable energy. Naturally, the smart grid generates a lot of data, and smart grid analytics is needed to analyse the information produced. Otherwise, it would be impossible to extract any value from the data.
The smart grid produces large volumes of data, thanks to IoT devices like smart meters. IoT devices are placed in different areas of the smart grid, like the substations and consumer devices. These devices produce petabytes of data, and it’s impossible to make sense of the data without smart grid big data analytics. The analytics platforms can analyse data to generate invaluable findings that lead to several benefits, like cost reduction and operational efficiency. With smart grid analytics, energy companies can address issues, like finances and grid operations effectively and in a short time. This leads to several other improvements in grid optimisation and customer engagement.
A smart grid produces a lot of unstructured data and analysing this format of data can be very challenging. Moreover, in certain cases, unstructured data needs to be analysed in real-time. Unstructured data can be analysed by smart grid big data analytics. For example, SAS Asset Performance Analytics captures sensor and MDM data to improve performance, uptime and productivity.
Smart grid big data analytics comes in different formats to suit the needs of the energy company. Utility firms can choose between point solutions and a software platform containing a suite of software solutions. Point solutions are effective because they target a specific problem. However, a single multisolution platform offers its fair share of benefits because it allows for greater flexibility and can be seen as a long-term investment.
Furthermore, organisations can choose between an on-premise solution or a managed service. The on-premise solution provides the organisation with direct control over the analytics platform. However, it requires a significant investment to get the right talent and technology. Furthermore, building a team from the ground up takes a lot of time because a said team needs to get acclimatized to their work environment. Meanwhile, a managed service is much more affordable to set up because organisations do not have to deal with talent recruitment and capital expenditures. However, the tradeoff is that organisations do not have direct control over the platform. This level of flexibility between on-premise, managed and software as a service is one of the reasons why smart grid big data analytics is appealing to organisations.
Research indicates that the industry for smart grid big data analytics will grow to $4.8 billion by 2022 with a compound annual growth rate of 16%. Several trends in the utility industry are responsible for this growth.
The technology is relatively new, so most managers and executives are not as quick to embrace analytics. However, the growing popularity of IoT combined with the immense value to be gained in different areas, like customer management has made analytics an enticing proposition to many executives. However, it should be noted that in an industry as heavily regulated as utilities, change takes time to manifest.
While MDMs are still the norm and will continue to be so for quite some time. There is a growing trend where data sensors will overtake MDM as the device to measure utilities. Devices like cap banks, distributed PV solar panels, transformer sensors and voltage regulators represent the next wave of innovation in the utility industry.
Between the rise of unstructured data and the next wave of IoT devices, there is going to be a lot of data collected from different sources. To make sense of all the data collected, it needs to be integrated and represented in a format that generates useful insights. For these reasons, data integration is getting a lot of focus.
While smart grid big data analytics has the potential to transform the utility industry. It needs to be used properly to maximise its value. Not all data can be analysed in the same fashion. For example, some data should be assessed in the device itself, while other forms of data should be added to a data lake for analysis at a later time. To assess the right data at the right time, organisations need to look at analytics platforms that work at the right location. Hence, why smart grid analytics is vital, it can be divided into two categories: Back-office analytics and distributed analytics.
Backoffice analytics are perfect for certain functions, like overseeing grid connectivity, load forecasting and reliability reporting. For example, load forecasting collects data for analysis, so that utility companies know how much power is needed to meet short, medium and long-term demand. It reduces uncertainty, increases operational efficiency and provides better insight when making investment decisions. Meanwhile, distribution analytics can analyse data from meters, sensors and other devices. This type of analytics platform performs several real-time functions that include outage decision, voltage management and real-time load disaggregation. For example, real-time load disaggregation can identify how energy is used in distant loads and daily usage patterns. If utility organisations can learn about loads in real-time, they can devise measurements that improve energy management. It also identifies new ways to better serve customers.
When the right data is analysed in the right place, it brings several benefits to the organisation. Firstly, it allows for quick action. For real-time decision-making to be effective, granular, one-second data is needed to address the problem. This type of data can only be found on a local device due to lower latency and higher data volume. Having the right analytics platform analyse the data also ensures that useful information is generated at the right time and place. Secondly, organisations can be assured that they have the right data for the right purpose. For example, if there is a problem, having the right data allows organisations to determine if the problem is a device-based issue, a network-level issue or a system-wide issue. Furthermore, the right data analytics platform can make a huge difference, especially if real-time data is important for operations. Leveraging the right data in the right place leads to several improvements, like better customer engagement, smarter energy efficiency, superior asset management and stronger system integrity. It is also a better use of resources by the organisation.
Smart grid analytics is going to have a huge impact on the future. With the energy infrastructure of the developed world moving towards a smart grid, there needs to be an analytics platform that can capture and analyse data from different endpoints. The right analytics platform allows utility companies to distribute resources more efficiently, cut costs and discover better ways to serve customers. Furthermore, the right data analytics platform allows them to make the most out of the data produced. Every analytics company is looking to provide some variant of smart grid big data analytics, including SAS because utility companies will be looking for any way to improve energy management.
Big data analytics can be challenging when you use conventional methods of data analysis. Try our Selerity analytics desktop and gain access to an innovative SAS pro analytics environment. For more information on this product, give us a call.
Big data analytics is used to make sense of the growing volume of data to generate profitable insights. However, did you know that it can protect against cybercrime as well? With over 4.5 billion data breaches in the first half of 2018, cybercrime has grown in both frequency and scope, breaching conventional defences and exposing the information of both institutions and individuals. However, a solution is difficult to come by because cyber crimes are constantly changing in nature. Is there a fool-proof solution that will block cybercrime? In this blog post, I am going to explain why data analytics is the future for combatting this serious threat to information.
Before big data analytics, there were two challenges to combatting cybercrime: The growing volume of data and the range of attacks. Cybercrime does not follow one distinguishable pattern or method (at least, on the surface) because there are several attacks that occur, ranging from hacking organisation records to credit card fraud. It becomes nearly impossible to combat these incidents, which are growing more and more frequent.
The second reason is the growing volume of data. With organisations like banks, hospitals and government organisations gathering petabytes of data, it becomes even more difficult to find suitable methods for protecting the data. Without data analytics, employees have to comb through large data volumes, forcing them to look for a needle in a haystack. For these reasons, cybercrime has been impossible to combat, at least with conventional methods.
Big data analytics provides a solution because it is built to handle growing volumes of big data. Data analytics is more than capable of handling the large volumes of data organisations store. The reason behind this capacity is because of the sophisticated data algorithms that make up a data analytics framework. By using big data analytics, it is possible to process, manage and secure large volumes of data.
The second reason is that big data analytics frameworks can breakdown and discover the differences between various cybercrime attacks, like hacking and online fraud. This is because analytics can breakdown the data surrounding the attack and discover the similarities by studying patterns with pattern recognition technology, despite the differences in the attack method.
One of the biggest advantages of big data analytics is its ability to detect anomalies. Whether it is in the network or on devices, analytics can detect odd behaviour, which can then be flagged for further investigation. Big data analytics can detect anomalies because it can analyse data on a massive scale to discover connections and patterns. Hence, if there is a deviation from the norm, analytics will sense it at once, and flag it for further investigation. It is an incredible asset to have because it can pinpoint and help network analysts in the right direction, allowing them to target the time and energy towards the most likely causes of an attack.
Big data analytics can do more than just analyse data – it can also predict future attacks before they even occur. Analytics can predict the future (or some variant of it) because of its ability to study data and draw conclusions from it. This is especially the case when AI and machine learning are incorporated into an analytics platform. The ability to anticipate attacks before they happen is one of the most effective ways to combat cybercrime because it allows organisations to protect their data more effectively and develop a network that guards data.
By investing in big data analytics, organisations can identify the scope and breadth of the cybercrime offense taking place. Organisations can categorise the type of cybercrime attacks and how frequently they occur, ensuring heightened levels of criminal justice. Analytics can also leverage historical data to study the type of attack, the frequency of attacks and the type of information that’s frequently targeted. With this information, organisations can plan for cybercrime attacks intelligently, pouring more resources into vulnerable areas.
Cybercrime does not happen in isolation, there is a growing consensus amongst professionals, that there is an ulterior motive for stealing information from organisations. In fact, some security professionals have stated that cybercrime is an important source of funding for terrorism. Therefore, if organisations can cut down the incidence of cybercrime by half, they would able to cut funding to terrorism by half. It is one of the reasons why SAS, one of the leading providers in commercial analytics, devotes a lot of time and resources to combatting cybercrime. If organisations are to combat cybercrime meaningfully, they will need to install or optimise a big data analytics platform to protect their data – all while improving the livelihoods of the people around them.
DevOps practices are becoming more and more prominent in software development and with good reason. This quiet revolution in software development has allowed them to develop software at a rate that meets consumer expectations. However, we have not discussed the impact of big data and predictive analytics on DevOps. In this blog post, I am going to explain how predictive analytics and big data augments DevOps practices and what this means for software development.
What do we refer to when we say DevOps?
Before diving into how big data and predictive analytics affects DevOps practices, we need to discuss what we mean by DevOps.
DevOps is the new method for software development, it espouses a shift from the traditional waterfall methodology and linear, sequence-based development to a method that is based on collaboration between different team members and automation to develop a continuous process of software development and deployment.
Rather than building a software package one step at a time and releasing a final product, DevOps practices focus on developing and deploying software one feature at a time, starting with the barebones and improving the feature with each iteration. This allows for streamlined software development, more efficient practices and better quality with fewer bugs (even if it is a little thin at first).
As you can imagine, DevOps practices are more than just a production pipeline for software, it is a cultural shift in the software development sector.
Augmenting DevOps practices with big data and predictive analytics
DevOps deploys features at an incredible rate – the average time for software deployment is four seconds. However, as effective as it is now, I have several reasons to believe that DevOps will be even better with predictive analytics and big data.
Improved testing practices
Big data and predictive analytics improve DevOps practices with more efficient and better testing practices. Testing, bug detection and bug fixing are crucial steps in building software under DevOps. Analytics improves this process thanks to machine learning algorithms, which can detect new errors, alert testers and even compile testing libraries to help fix these bugs, making testing more efficient and reducing the chances of bugs slipping through cracks. Data-handling is also a source of problems because the more complex an app is, the larger the data set is and the more likely you are going to find bugs. Tools to handle big data and predictive analytics can detect errors early in the production pipeline, which makes it easier to analyse big data for errors.
Better supervision in production environments
Big data and predictive analytics help developers overcome the challenges of deploying software in the production environment.
One of the core DevOps practices is mimicking the production environment in the development environment. However, this can be incredibly challenging because the production environment is influenced by different sources of data that is not easily found in the development environment, making it difficult for developers to develop the application accordingly. However, this problem is resolved with help from big data and data specialists because they can anticipate the types of data that will affect the software when it launches in the production environment.
When an app launches in the production environment, predictive analytics can monitor the performance of the applications to give a picture of their normal performance. With this monitoring system, analytics can create awareness of fluctuations in performance and change resource levels appropriately. For example, if there is an increase in performance, then the system provides additional resources, if the system is idle, then it will take resources away. This method significantly improves DevOps practices because it prevents several issues, like memory leaks and DDOS attacks.
Better app security
Security is a vital feature any developer needs to invest time in. Predictive analytics can help tremendously in this regard because machine learning algorithms in predictive analytics can study usage patterns of different developers, this data allows them to anticipate anomalies, predict potential data breaches and malicious use. The system can even prevent security breaches in real-time saving millions of dollars and improving security.
Paving the way for better software delivery
DevOps practices are beefier, faster and more efficient with predictive analytics and big data in the mix. This is only going to improve the rate of software delivery and the quality of software will continue to improve exponentially thanks to technologies like predictive analytics and DevOps.
Big data is defined by the three big Vs: Volume, velocity and variety. The volume stands for the sheer size of the data, velocity refers to how fast data is generated and, finally, variety includes structured and unstructured data. The volume of big data is tremendous, the amount of data generated by US companies alone can occupy ten thousand libraries the size of the Library of Congress. The expansion of the Internet of things (IoT) and self-driving cars will see the amount of data collected grow significantly. With the enormous potential of big data, it is important for organisations to know the key tenants of big data optimisation.
Here are five key methods for big data optimisation
Big data is large, complex and prone to errors, if not standardised correctly. There are many ways big data can turn out to be inaccurate, if not formatted properly. Take, for example, a naming format – Micheal Dixon can also be M. Dixon or Mike Dixon. An inconsistent format leads to several problems, like data duplication and skewered analytics results. Therefore, a vital part of big data optimisation is setting a standard format so that petabytes of data have a consistent format and the ability to generate more accurate results.
It is not just enough to implement algorithms that analyse and finetune your big data. There are several algorithms used to optimise big data, such as diagonal bundle method, convergent parallel algorithms and limited memory bundle algorithm. You need to make sure that the algorithms are fine-tuned to fit your organisation’s goals and objectives. Data analytics algorithms are responsible for sifting through big data to achieve objectives and provide value.
Remove latency in processing
Latency in processing refers to the delay (measured in milliseconds) when retrieving data from the databases. Latency hurts data processing because it hurts the rate you get results. In an age where data analytics offers real-time insights having delays in processing is simply unacceptable. To significantly reduce the delay in processing, organisations should move away from conventional databases and towards the latest technology, like in-memory processing.
Identify and fix errors
A key part of big data optimisation is fixing broken data. You can have fine-tuned algorithms and install the best analytics platforms, but it does not mean anything if the data is not accurate. If the data is incorrect, it leads to inaccurate findings, which hurts your ROI. In such cases, a data analyst will have to go in and fix data to make sure everything is accurate. Big data can have plenty of errors, like duplicated entries, inconsistent formats, incomplete information and even inaccurate data. In such cases, data analysts have to use various tools, like data deduplication tools to identify and fix errors.
Eliminate unnecessary data
Not all data collected is relevant to your organisation’s objectives as bloated data bogs down algorithms and slows down the rate of processing. Hence, a vital part of big data optimisation is to eliminate unnecessary data. Once unnecessary information is eliminated it increases the rate of data processing and optimises big data.
Leverage the latest technology
Data analytics is constantly evolving and it is important to keep up with the latest technology. Recent technological developments, like AI and machine learning, make big data optimisation easier and improves the quality of work. For example, AI paves the way for a host of new technologies, like natural language processing that helps machines process human language and sentiment. Investing in the latest technology improves big data optimisation because it accelerates the process while reducing the chances of errors.
Bringing it all together
Big data optimisation is the key to accurate data analytics. If data is not properly optimised, it leads to several problems, like inaccurate findings and delays in processing. However, there are at least six different ways to optimise big data. These methods including standardising format, tuning algorithms, leveraging the latest technology, fixing data errors and removing any latency in processing. If data is optimised, it improves the rate of data processing and the accuracy of results.
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Big data is becoming an integral part of business operations, with organisations using generated data for better insights and smarter decision-making. However, what many people do not know is that data falls into two categories: first-party and third-party data. The difference between the two data sets lies in their data source. Data from third parties is unrelated to an organisation and its stakeholders but still plays a huge role in business operations. In this blog, I’ll be diving into the hidden importance of data from third-party sources.
Why should organisations use third-party data?
It’s tempting for many stakeholders to stick with proprietary data in favour of external data. However, there are several reasons why stakeholders should use third-party data to their benefit.
Close the gaps
Third-party data closes any gaps in organisational data. Organisations produce a lot of data from their operations, however, there are still gaps organisations cannot close on their own. If the gaps in data are not closed, it affects the quality and reliability of their analysis. By using external data, organisations can close data gaps, which leads to more accurate and reliable findings. The results of analytics are heavily dependent on the quality of data, hence organisations need to close these gaps if they want to get the best findings possible.
Account for external events
Organisations, be it public or private, profit or non-profit are affected by external events. Demographic changes, geographical trends, macroeconomic changes and much more affect business operations. However, many organisations do not generate first-party data on external events. But by leveraging third-party data in their analytics process, organisations can accurately calculate how external events will affect operations. Hence, predictive analytics becomes more comprehensive and accurate.
Easier time working with partners
Most organisations don’t work on their own. They have a network of partners in the form of suppliers, investors, regulators, stakeholders – to name a few. Many of these partners are based in different parts around the world. Using third-party data can expand insights not found in first-party information, making it easier to work with their partners around the world. With external data, organisations get a better understanding of shifting larger trends of consumer behaviour, geopolitical events and competitor initiatives.
What is the value of third-party data to organisations?
Third-party data enhances the value of insights and findings in data analytics. For example in a marketing context, organisations can expand their functionality by personalising marketing offers because external data provides hundreds of data points that are not found anywhere else. Third party data also expands the functionality of the company in several ways.
For example, marketers can get access to location data by working with a third-party that they otherwise would not have access to. Thus, allowing them to devise clever marketing campaigns based on the information.
Organisations are in a better position to anticipate shifts in demand and supply – they can even launch products and services with better success than ever before, while HR departments can make smarter decisions on talent management. Organisations can even discover new revenue streams they would not have been able to discover with just their own data.
Not every organisation is in a position to collect high-quality data. Such organisations can benefit tremendously from third-party data. For example, start-ups might have a hard time managing the workforce because their data is not the most detailed or the most accurate. However, by acquiring third-party data they can make smarter decisions about managing their workforce. As a result of this, with external data, organisations can sidestep the pains that come with maturity, allowing them to grow steadily without suffering any major setbacks.
Third-party data holds tremendous value to organisations of all sizes. By using external data, companies can close the gaps in their data, account for external events, and have an easier time working with partners around the world. Furthermore, with external data, many startups can avoid the pains of maturity by accessing rich external data. However, despite its obvious benefits, it’s important to note that external data is not perfect.
There are drawbacks to using external data, especially given that the quality of data is a huge factor. Organisations will have to assess the quality of the data before putting it to good use. Another major obstacle when it comes to accessing high-quality data is that some companies simply cannot afford third-party data.
If you wish to know more about big data, analytics and AI, visit our blog for more information.