How can big data analytics help businesses overcome cybersecurity challenges?

Cybercrime has plagued the internet since its inception and over the years, cybercriminals have been becoming craftier, creating more complex cybersecurity threats.

Businesses are one of the main targets of cybercrime. In Australia, cyber attacks cost businesses approximately $29 billion every year and many are left crippled and unable to recover.

Fortunately, modern businesses have bolstered their defences against cyber attacks through cybersecurity infrastructures. Despite this, however, cyber threats are always evolving, becoming more aggressive and complicated, forcing businesses to keep updating their cybersecurity measures frequently.

While big data analytics have helped businesses improve many of their functions like sales, customer service and workload management, it can also help them improve their cybersecurity infrastructure.

Here, we’ll take a look at how big data analytics can help businesses overcome the challenges of cybersecurity.

Analysing historical data 

Historical data is a treasure trove of information for businesses. Just like finding patterns in sales and consumer buying patterns, businesses can use big data analytics to discover meaningful connections between past and present cyber attacks and find ways to make their cybersecurity stronger against these cybersecurity threats.

Big data analytics on historical data can also allow businesses to predict future threats. By analysing statistical information, businesses can identify patterns that deviate from the norm, helping them predict an imminent cyber attack.

Businesses can also pair risk assessment with quantitative data analysis to predict its susceptibility to potential cyber attacks, and using the insights they gain from this analysis, they can develop countermeasures against future attacks.

A great way for businesses to leverage their big data analytics is by incorporating machine learning; with machine learning, businesses can develop new responses to cyber attacks using the information collected and analysed from past attacks.

Monitoring work activities

Research has shown that many data breaches in businesses may have actually been carried out by an employee, though in most cases, cybercriminals may gain access into a company’s network by hacking an employee’s account and posing as their victims. 

Most of the time, an employee might not even be aware that their account was hacked.

With big data analytics, businesses can monitor workflows to detect any suspicious activity in their company network. For example, the business can analyse data based on employee movements during work hours and the data they accessed or changed during their work tasks to check for any usual behaviour, like opening confidential files without authorisation.

When suspicious logins and unusual activities on the company’s network are detected, the business can take action by identifying the threat and stopping it before it happens.

Improving intrusion detection 

Identifying possible weaknesses in a business cybersecurity infrastructure in real-time is very difficult; using big data analytics, businesses can automate this process and analyse logs, workflows and events to identify any usual activities and irregularities in data.

Cybersecurity breaches are becoming more complex and due to this, intrusion detection systems such as NIDS (Network Intrusion Detection Systems) have become more powerful and sophisticated in detecting cyber threats. 

Real-time big data analytics can be used to enhance these systems and give them a more comprehensive way to detect possible threats and put a stop to them before they gain access to a business network.

Identifying important incidences

Big data analytics can be used to analyse historical data and use the insights gained to improve a business cybersecurity infrastructure. The problem, however, is finding data on the relevant cybersecurity-related incidences.

Historical data, even that of a small business, can be vast, so finding the most important cybersecurity data from this sea of raw information can be tricky. 

With big data analytics, businesses can filter and categorise data so that it’s easier to identify important cybersecurity-related data from the past and their relationships with other relevant historical data.

Keep your business safe from cyber threats with big data analytics

Big data analytics has opened new avenues for businesses all over the world. From marketing to cybersecurity, the number of things businesses can improve with big data analytics is endless.

Keep your business ahead of the competition and safe from cyber threats with help from big data analytics.

How is big data analytics changing sports?

Everybody loves to watch sports with detailed and insightful commentary. Have we ever stopped to think, though, about how broadcasters and athletes make sports so compelling for us insatiable viewers?

Behind the scenes, today, big data is playing a major role in making these events more competitive and engaging for audiences.

This is not necessarily a new concept. The early 1990s, in fact, saw the introduction of data analytics to the sports industry. Since then, everyone, from amateur athletes to the big leagues, has used it to enhance athletic performance, audience engagement, and marketing and branding strategies. 

It has become so popular that the sports data analytics industry is estimated to grow to a whopping value of $4 billion by the end of 2022.

In this post, we take a closer look at why big data analytics became so popular and how it is changing the sports industry—forever.

It helps coaches make more insight-driven recruitment decisions

Player recruitment is an important process for any professional sporting team. At the end of the day, while talented players are important when winning championships and sponsorships, talent, alone, is not the only important factor. 

Other considerations play a role when it comes to athletic performance.

Modern sports franchises have understood this and are using data analytics tools to recruit the right players for their team culture.

The most famous example of this is played out in the movie, Moneyball. It is a movie based on real events involving a baseball coach from a cash-strapped club who uses data to hire talented, but undervalued players, in the hopes of winning the championship.

Since then, sports like soccer, cricket, football and basketball have adopted these techniques with great success. It has now become the de-facto method of recruiting players.

Big data analytics helps broadcasters create a better viewing experience

Anyone who has followed any sport for a considerable period will know how sports broadcasting has evolved over the years. 

Sports commentary, for example, has evolved from just calling the play to educating viewers with stats and facts to make the viewing experience more compelling.

Broadcasters now even employ stats gurus to provide more information for viewers to understand the context of any performance. 

Even simple graphical elements now carry important contextual information to help the viewer understand the importance of each event; all of which is made possible with big data analytics.

Big data is helping us create better sporting strategies

Strategising is an integral part of any sport, be it an individual or team sport. Professional athletes and teams are dependent on these strategies to compete and win against their rivals.

Modern coaching makes use of big data sets to create winning strategies to help individual athletes and the team as a whole. Data science allows coaches of professional teams, in particular, to create hyper-personalised athlete matchups and other strategies for every match the team plays. This way the team’s tactics are left unpredictable yet effective.

One good example is Liverpool FC’s use of data science to dominate opponents in the recent Premier League and Champions League. Liverpool’s coach used data science to change the outcome of the games as they were being played to great effect—they’re, after all, the winners of last year’s Premier League. 

It has become easier to boost fan engagement with fantasy sports

Broadcasters and sports officials have always looked to increase fan engagement in a bid to increase revenue. Big data has given them a new tool to do both.

Fantasy sports have become a quick and profitable way to increase fan engagement across any sport. They essentially allow any participant to build their virtual professional franchise based on real athletes. Fantasy football, alone, accounts for 25 million active participants, mostly from the US.

Many fantasy sports leagues are designed around big data analytics to help participants build an all-round fantasy team. According to a study done by Intel, 75% of fans wanted detailed real-time data to create better fantasy teams.

Join the big data analytics revolution to make sports more competitive and compelling

Big data has improved many industries over the last few years, and is no stranger to the sporting field. It has helped athletes enhance their performance, broadcasters their fan engagement, and coaches their gameplay tactics. Marketing data analytics plays a huge role in this process too.

Dive into data analytics to find out how you can transform sporting events and athletic performance with a handful of invaluable insights.

Big data analytics play a major role in not just the world of sports, but the business world as well. With our Selerity analytics desktop, you can have a SAS pro-analytics environment at your fingertips for all your data analysis needs. Speak to us today for more details.

How big data analytics platforms in manufacturing optimises production

The manufacturing industry has always thrived, thanks to the right technology and tools. 

During the 1800s, the industry thrived due to the development of machinery. Now, as the fourth industrial age takes over, we are seeing the advent of new technology that can boost productivity and remove traditional obstacles preventing the industry from reaching its full potential. 

The main challenge, today, is the absence of a platform that can analyse data and break it down into useful insights that optimise the manufacturing process. Fortunately, big data analytics platforms in manufacturing can optimise your production and cut down on costs.

While I have explored how data analytics can improve manufacturing in the past, in this post, I focus on how we can boost productivity and operating costs.

Optimising manufacturing processes with data analytics

Here are some of the ways big data analytics in manufacturing can add value to your operations.

Manufacturers can ensure quality across batches

While custom manufacturing is a consumer-friendly practice, there are certain industries where consistency in quality is key; for example, pharmaceuticals. In this industry, the product is often made in a series of batch processes, which can lead to inconsistencies in product quality.

By using data analytics, organisations can conduct multivariate data analysis. The intent, here, is to better define quality control parameters and use continuous process verification to ensure consistency in product quality. 

Furthermore, real-time manufacturing analytics can provide manufacturers with the insight they need to reduce batch variability. They can use the information to adjust process parameters during manufacturing to reduce deviations that might compromise product quality.

Manufacturers can predict trends, causes, and effects

Advanced data analytics uses high-level methods to conduct sophisticated multivariate data analysis (MVDA). 

This new level of analysis allows manufacturers to analyse data using several variables at the same time. What this means is that manufacturers can perform “what-if” calculations, allowing them to conjure multiple scenarios. The ability to project the future, based on different variables, can help your company future-proof business strategies and determine where your processes could go wrong. 

This makes it easier to make long-term structural adjustments that optimise production.

Analytics can remove bottlenecks in operational productivity

It’s somewhat common to encounter bottlenecks when it comes to production. These bottlenecks could be a result of poor equipment efficiency because when equipment breaks down, it can halt the entire production line, driving up operating costs. 

By using analytics in manufacturing, you can collect data from different sources, like ERP, the environment, and maintenance. This helps you determine when your equipment requires maintenance and prevent breakdowns during production.

Manufacturers can change input processes to discover new inputs

Advanced analytics in manufacturing can help organisations in the industry make unexpected discoveries through experimentation. For example, by using big data analytics in manufacturing, manufacturers can experiment with different inputs like carbon dioxide, temperature, and coolant pressure. 

This calculated experimentation allows you to make unexpected discoveries in your industry.

Analytics makes product customisation easier to pull off

Big data analytics platforms in manufacturing can make product customisation easier to execute. Customisation in manufacturing is becoming a pivotal part of the industry. But, while it sounds awesome in theory, it is very difficult to pull off in practice. This is because making the shift from mass production to custom production is not possible without the right equipment. 

Data analytics platforms make this a more practical feat. Big data analytics tools give manufacturers the insight they need to analyse the different factors that determine custom manufacturing, like customer preferences and production processes, making it much easier to customise manufacturing.

Removing complexities in the supply chain

In a global, connected environment, supply chains can be long and complex. 

Analytics in manufacturing can help streamline the supply chain, reducing operating costs and optimising production. With this level of insight, it allows manufacturers to examine the ins and outs of the supply chain and pinpoint existing weaknesses. 

Optimising production with big data analytics platforms in manufacturing

Big data analytics platforms in manufacturing help organisations maintain the right balance of production processes and variable inputs to optimise production and reduce operating expenses. 

Manufacturing analytics can help optimise business processes either by reducing the variable inputs needed or by discovering new methods to improve production.

Common challenges for big data analysis tools when using IoT

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.

Why is it difficult to work with IoT devices?

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 data tests storage capacity

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.

Data integrity and quality remain a problem

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.

Data analytics tools must be constantly working

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.

IoT device security is a cause for concern

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.

Data derived from IoT devices have confidentiality issues

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.

Addressing common challenges when using big data analysis tools

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.

Discovering the connection between Industry 4.0 and big data analysis

You may have heard the buzzword “Industry 4.0” in tech circles. How it promises to change our economy and restructure the way we live, work, and play. But what is the connection between Industry 4.0 and big data analysis? Big data analytics has a huge role to play in the development of smart technology. Let us explore the connection between Industry 4.0 and big data analysis in more detail.

Big data analytics and Industry 4.0

Analysing data streaming through these devices

When we think of Industry 4.0, we think of IoT, smart sensors, cloud computing, and more sophisticated technology. Big data analysis via analytics platforms play a huge role in the effective use of these technologies.

Technology from Industry 4.0 generates a lot of data, which needs to be analysed to gain value. Analytics platforms can conduct real-time big data analysis on data streaming from Industry 4.0 devices. Without big data analytics, it would be impossible to make sense of the data streaming through these devices.

Incorporate devices better into the production process

Self-service data analytics platforms are becoming more prevalent throughout different industries, and it is having an impact on the way we interact with Industry 4.0 technology. For example, the data generated can be consolidated into bulk with data analytics platforms. Self-service analytics systems can break down data in real-time to find patterns, faults, and visualise findings.

With the ability to analyse data in real-time, organisations can overcome one of the biggest problems related to Industry 4.0 devices: Extracting value from data. Previously, it would have been difficult to utilise Industry 4.0 devices to their full potential because it would have been impossible to collect and analyse data in real-time. However, thanks to the discovery of analytics platforms, the raw data can provide valuable information.

Data derived from Industry 4.0 can generate tremendous value. For example, in the manufacturing industry, big data from machines can yield operating data, process quality, logistics information, and records of manual operations. The information can optimise production processes and make significant gains in operational efficiency.

Organisations can expand production processes

By incorporating big data analysis into the production process, organisations can make better use of Industry 4.0 devices. When that happens, it opens up organisations to new production techniques that were not there before. An excellent example is predictive maintenance. Big data analysis can analyse data in real-time to accelerate the rate of discovery.

Predictive maintenance is an effective cost-saving tool, with most organisations estimating that they can save over $100 million from pre-planned maintenance, compared to organisations that don’t. The use of advanced analytics, big data, and Industry 4.0 devices also pave the way for more advanced production processes, like automation.

Actuators and robots can play a huge role in optimising the production process. However, for them to work effectively, the machines need to be connected to software that can pick up data, interpret it, and in return, feed information back into the system.

Big data analysis allows for the exchange of data between robotics and software, which allows organisations to maximise returns on robotics and make it easier to generate an even higher ROI from the use of robotics and other software designed to automate certain production processes.

Overcoming device shortcomings

One of the biggest problems associated with IoT devices or Industry 4.0 is that they are operating within a diverse ecosystem that generates information in different formats. Given this problem, it is difficult to get devices to communicate with each other properly. However, by investing in platforms for big data analysis, these organisations would have a much easier time carrying out proper analysis, creating greater synergy within the ecosystem, and making it easier to generate sufficient ROI.

Entering a new era with big data analytics

The next decade is going to see significant changes in the way we live and work. As Industry 4.0 advances and becomes a significant part of business operations, we need to adopt the right technology that would allow us to maximise ROI on our analytics platforms. Technology that is conducive to big data analysis will allow organisations to maximise ROI on their analytics platforms.

Big data analytics in manufacturing powering the industry

With the high rate of adoption of sensors and connected devices, there has been a massive increase in the data points generated in the manufacturing industry. These data points can be of various types. Data types range from a metric detailing the time taken for a material to pass through one process cycle to a more complex one, like calculating the material stress capability in the automotive industry.

With this surge in data available, there is no wonder why big data analytics in manufacturing is a hot topic.

Manufacturing remains a critically important part of the world’s economic engine, but the role it plays in advanced and developing economies has shifted dramatically. The manufacturing industry market was valued at $904.65 million in 2019 and is expected to reach $4.55 billion in 2025.

Big data is essential in achieving productivity, improving efficiency gains and uncovering new insights to drive innovation. With big data analytics in manufacturing, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production.

Leaders in manufacturing enterprises understand the importance of process – KRC research study found that 67 per cent of manufacturing executives planned to invest in data analytics, even in the face of pressure, to reduce costs in this volatile climate.

To understand big data analytics in manufacturing and its impact, let us dive into how it’s intervention helps streamline operations.

Optimising asset performance and operations

Since manufacturing profits rely heavily on maximising the value of assets, asset performance gains can lead to big productivity improvements. By the same token, a reduction in asset breakdown can reduce inefficiencies and prevent losses. For these reasons, manufacturers focus on maintenance and continuously optimise asset performance.

Machine logs contain data on asset performance. This data is potentially of great value to manufacturers, but many are overwhelmed by the sheer volume of incoming information. Data analytics can help them capture, cleanse and analyse machine data to reveal insights that can help them improve performance.

In addition to enabling historical data analysis, data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdown and avoid unexpected downtime. A study found that big data analytics can reduce breakdowns by up to 26 per cent and cut unscheduled downtime by nearly a quarter.

Improving production processes and supply chains

In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. Efforts to streamline processes and optimise supply chains must be supported by the ability to examine every process component and supply chain link in granular detail. Data analytics gives manufacturers this ability.

With the right analytics platform, manufacturers can zero in on every segment of the production process and examine supply chains in minute detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.

Making product customisation feasible

Traditionally, manufacturing focused on production-at-scale and left product customisation to enterprises serving the niche market. In the past, it didn’t make sense to customise because of the time and effort involved to appeal to a smaller group of customers.

Data analytics is changing that by making it possible to accurately predict the demand for customised products. By detecting changes in customer behaviour, data analytics can give manufacturers more lead time, providing the opportunity to produce customised products almost as efficiently as goods produced at a greater scale. Innovative capabilities include tools that allow product engineers to gather, analyse and visualise customer feedback in near-real time.

According to a Deloitte review of the rise of mass personalisation, the ability to postpone production gives manufacturers new flexibility that allows them to take on made-to-order requests. The ability to postpone production can reduce inventory levels and improve plant efficiency. A streamlined manufacturing process is not only beneficial – it gives manufacturers a way to maintain efficiency while customising manufactured goods.

Big data analytics in manufacturing and the way forward

Big data analytics in manufacturing presents many promising and differentiating opportunities and challenges.

According to a McKinsey report, worldwide consumption will nearly double to $64 trillion. In such a scenario, data analytics provide manufacturers with a huge opportunity to predict, innovate and implement their approaches.

For more information on how big data analytics in manufacturing is powering the industry, visit our website!

Big data analytics has helped improve the productivity of many industries such as the manufacturing industry. Your organisation can also enjoy the benefits of leveraging your data analytics with the Selerity analytics desktop. 

Get in touch with us if you have any questions.

Big data analytics and its role in elections

In an increasingly politically aware world, big data analytics is the “trump” card most organisations and movements have when planning election strategies. With great power comes great responsibility and data analytics is a powerful tool that can be utilised to harness and reap a plethora of benefits.

We are seeing politicians use big data analytics to optimise their campaigns. For example, experts and journalists have coined Trump’s campaign a ‘data machine’ powered by AI capable of swinging voters, demonstrating the power of data and analytics systems.

This is not a new phenomenon, since the 50’s every party has used big data analytics to strategise their election campaigns.

Let us look into some of the big data systems and applications that have allowed countries and groups to manipulate the results in their favour.

The first “big” data break

The idea of using data in elections is not new. For example, the Kennedy administration used the “People Machine” to great success. It was, at the time, the largest such project ever conducted and it involved the use of massive data decades before “big data analytics” became a buzzword.

It was during this time that the use of computer simulation, pattern detection and prediction for election campaigns began. Opinion poll data from the archives of pollsters, George Gallup and Elmo Roper, created a model of the US electorate. The information gathered was pivotal in creating relevant strategies and ensuring those votes were coming in. This was seen even further back in our history when data was collected to better understand the masses.

A little bit of data goes a long way

A British “global election management agency” gained global traction because it utilised advanced data analysis along with strategic communication during electoral processes, which proved to be successful. They started in 2013 as an offshoot of the private intelligence company and self-described “global election management agency”. They were essentially in the big data analytics business.

This company used personal data to sway the outcome of the US 2016 presidential election and the UK Brexit referendum. But its reach extends well beyond the UK and US having supported more than 100 campaigns across five continents.

These data methods were used twice to help secure victory for Kenyan President Uhuru Kenyatta – first in 2013, then again in 2017. Officially, the company’s website boasts of doing in-depth research to uncover the issues driving voters in the country. They rebranded the entire party twice, wrote the manifesto, did research and finalised the messaging. They essentially created the platform, basing it on their findings derived from big data analytics to curate what the masses exactly wanted to see.

Jacinda Ardern – the Prime Minister in New Zealand – utilised big data analytics to bridge the gap between her policies and her voters, which swayed the constituents to her side and secured a second term, highlighting the importance of analytics in both elections and policy-making (although, it should be noted, that the Prime Minister’s aim was to refine her policies).

Big data analytics- A great and powerful tool

If we gauge anything, it is that these big data analytics platforms are mere tools and can be used for so much good, on the flipside of the coin, in a nefarious manner, if we are not cautious.

However, not all campaigns set out to exploit big data analytics platforms, we see politicians utilising such data to know where their possible voters could originate from, cross-reference them with the topics supported by the candidate and use the feedback to refine their policies.

The future of big data analytics in elections

If current trends are any indication, future election campaigns will be further entrenched in data analysis methods so as to glean the best approaches, efforts and results. Data analytics will be used much after the campaigns as well, with it being an integral part of understanding and flagging problems plaguing different population sections.

Data analytics has evolved itself to become the brain of every election campaign since the early 2000s. Data analytics helps the election campaign committee understand the voters better and adapt their policies to their sentiments, demonstrating the versatility of analytics platforms.

To know more about the ways big data analytics and its value in different industries, please visit our website.

Big data analytics platforms and the entertainment industry

The global entertainment and media industry is set to grow at 4.4 per cent compound annual growth rate (CAGR) through 2020 to reach just under $2 trillion this year, according to PwC. This growth will be driven by the industry diversifying their offerings, the employment of big data analytics platforms as well as higher demand for new content.

The entertainment industry is evolving at an exceptional level. New technologies like smartphones and digital media allow consumers to connect to a new entertainment world enabled by these disruptive technologies. Technology is making entertainment a more data-oriented experience. Content creators can leverage the data to generate new practices and innovative opportunities to create better quality content and create new material for consumers.

As a result, there are some significant benefactors within the entertainment industry when it comes to leveraging big data analytics platforms, resulting in increased profitability and a better connection between producers and customers. It is no longer sufficient to merely publish a daily newspaper or broadcast a television programme. Contemporary operators must look to drive value from their assets at every stage of the data lifecycle.

To better understand the transformation of the entertainment industry as they adopt big data analytics platforms.

Predict customer preferences and needs

According to Deloitte, how people consume media has changed dramatically over the past decade, creating both challenges and opportunities for the industry. Millennials today spend more time streaming content over the internet than watching it on television, and more than 20 per cent of them habitually view videos on their mobile devices. Streaming services like Hulu and Netflix, continue to flourish with approximately 60 per cent of consumers subscribing to them. By 2021, 209 million people will be using video-on-demand services, up from 181 million viewers in 2015.

With millions of digital consumers across the globe, media and entertainment companies are eager to reshape media platforms. They want to provide more personalised content to their audiences. In that sense, they are in a unique position to leverage their big data analytics platforms to improve customer engagement and profitability.

Data insights could be utilised to predict the demand for specific genres of shows, music, movies and weekly shows for a given age group, and what each viewer could potentially be interested in based on prior interests.

Generate more revenue using big data analytics

Big data can also help media and entertainment companies generate more revenue. The data does so by suggesting new ways to incentivise consumer behaviour, revealing the true market value for content, feeding into the creation of new content and providing more data for targeted marketing. Targeted ads are a gold mine in the entertainment industry. Channels are utilising big data analytics to understand how and when to push ads to consumers along with other value-added information that appeals to the consumer (boosting their bottom line). Understanding what part of their demographic wants helps curate sales strategies to be more effective and generate more revenue as it only hits the customer with the most interest.

Optimise media streams and analysing factors leading to churn

Using insights gleaned from big data analytics, companies can understand when customers are most likely to view content and what device they’ll be using when they view it. With big data’s scalability, this information can be analysed at a granular ZIP code level for localised distribution.

This means that the ability of big data technology to ingest, store and process different data sources in real-time is a valuable asset to the companies who are prepared to invest in it.

By using big data to understand why consumers subscribe and unsubscribe, entertainment companies can develop the best promotional and content strategies to attract and retain customers. Unstructured big data sources such as call detail records, email and social media sentiment reveal often overlooked factors driving customer interest.

Big data analytics the new star in Tinseltown

The amount of data that media and entertainment companies can gather regularly is mind-blowing; however, it’s what they do with all that information that really makes all the difference. The entertainment industry is making a rapid transition towards a more personalised experience for consumers, thanks to big data analytics platforms. Marketing and distribution, in particular, are undergoing a rapid transformation to create an experience that is beneficial to both consumers and content creators.

Please visit our website for more information on how you can transform your industry with big data analytics platforms.

If you want to experience the benefits of big data analytics for your organisation, our innovative Selerity analytics desktop will give you access to a SAS pro analytics environment that you can use for many analytics applications. Call our support team for more details.

How AI and big data analytics will revolutionise the legal system

AI and big data analytics are changing the way lawyers think, the way they do business and the way they interact with clients. Artificial intelligence is more than just legal technology. It is the next great hope to solve or at least improve access-to-justice and completely transform our traditional legal system.

Let us dive into the fascinating world of AI, big data analytics and how it is transforming the very landscape of the legal system.

Legal analytics supercharged by AI and big data analytics

The latest legal software uses AI and big data analytics to make predictions from or detect trends in large datasets. Firms use legal analytics to predict trends and outcomes in intellectual property litigation and are now expanding to other types of complex litigation. With the advent of new technology, firms can also leverage a massive database of law firm billing records to provide baselines, a comparative analysis and suggestions for efficiency improvements to both in-house counsel and outside law firms.

Recently, we have also seen the use of legal analytics in judicial opinions to predict how specific judges may decide cases, including providing recommendations on specific precedents and language that may appeal to a given judge.

For example, the Wisconsin Supreme Court recently upheld the use of algorithms in criminal sentencing decisions. While such algorithms represent an early use of primitive AI, they open the door to use more sophisticated AI systems in the sentencing process. Several online dispute resolution tools are being developed to circumvent the judicial process.

Technology-Assisted Review increases efficiency by fifty-fold

Technology-assisted reviews (TAR) are the first major application of AI and big data analytics in legal practice. They use technological solutions to organise, analyse and search large, diverse datasets. Studies show that TAR improves efficiency in document review time by fifty-fold compared to human reviews.

For example, predictive coding is a TAR technique that can be used to train a computer to recognise relevant documents by starting with a “seed set” of papers coupled with human feedback. The trained machine can then review large numbers of documents very quickly and accurately, going beyond individual words to focus on the overall language and context of each document. Numerous global law firms now utilise TAR products due to their efficiency and accuracy.

Practice management assistants paving the way

Many technology companies and law firms are partnering to create programs that can assist with specific practice areas, including transactions, due diligence, real estate, bankruptcy, litigation research and preparation.

The first robot lawyer, ROSS is a tool that provides legal research and analysis for several law firms and can reportedly read and process over a million legal pages per minute. RAVN, a similar system developed in the United Kingdom, assists with due diligence in real-estate deals by verifying property details against official public records. According to the attorney in charge of implementing the program, RAVN can identify and work with specific variables to complete two weeks’ worth of work in two seconds, making it over 12 million times quicker than an associate doing the same task manually.

Legal bots

Legal bots are interactive online programs designed to interact with an audience to assist with a specific function or to provide customised answers to the recipient’s specific situation. Many law firms are developing bots to assist current or prospective clients in dealing with a legal issue based on circumstances and facts. Other groups are developing pro bono legal bots to assist people who may not otherwise have access to legal resources.

For example, a Stanford law graduate developed an online chatbot called DoNotPay that has helped over 160,000 people resolve parking tickets, and is now being expanded to help refugees with their legal problems.

Are AI and big data analytics the way forward?

Although widespread in industries from aerospace to waste management, artificial intelligence (AI), machine learning and big data are cutting-edge technologies not usually associated with the legal profession.

Long considered a conservative profession, the legal trade is undergoing a shift thanks to Fourth Industrial revolution technologies like AI, an increasingly important tool in a law firm’s kit.

Visit our website for more information on the intricate world of AI and big data analytics and how it intertwines with the legal field.

Just like it has been transforming the landscape of the legal system, AI has changed business forever. You can access the power of AI for data analytics using our Selerity analytics desktop and leverage your organisation’s data analytics capabilities. If you need any further information, contact our support team.

The challenges of using data lakes in big data management

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.

The challenges of data lakes in managing data

There are several technical and business challenges of using data lakes.

Issues with security and governance.

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.

Meta management becomes impossible

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.

Conflict in the organisation hinders full value

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 is difficult

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.

Addressing the challenges of big data management

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.