Tag Archives for " Streaming Analytics "

How streaming analytics is helping companies decide which shows to produce

streaming analytics

Streaming platforms are all the rage these days with every major media company now offering their streaming services for their audience.

Netflix used to be the only major player in the streaming industry a few years ago, but newcomers like Hulu, Apple TV+ and Disney Plus have now started to break the monopoly of Netflix.

To understand how much the new streaming services have grown, one need only look at Disney Plus. The service has seen a meteoric rise in viewership and subscriber base, which is very impressive considering Disney only started its streaming service in 2019.

Its impact has been such that it now looks like every single show Disney is offering on the streaming platform will be successful.

The influx and the success of streaming services on the market, though, is the result of changing audience preferences and the increased accessibility to the internet, which is likewise fueled by the widespread use of smartphones.

The streaming industry is even eating away at the market share of theatres. All this boils down to the quality of content provided on these streaming platforms. 

One tool that is helping creators create quality content is data analytics, more specifically, streaming analytics.

In this blog post, we will explore how streaming analytics is helping these platforms produce successful TV shows, movies, and documentaries.

Streaming platforms are using viewing statistics to improve their existing offerings

Streaming platforms have multiple flagship shows which they bank on to attract new viewers. Stranger Things, Sherlock Holmes and Peaky Blinders are such shows, and each has millions of fans. There is a good reason for this phenomenon.

These shows ran for multiple seasons and got progressively better with every season, which was the only reason fans kept tuning in.

Sherlock Holmes, for example, was picked up by Netflix from BBC because decision-makers at the streaming giant figured out that there is a demand for clever shows like it. Netflix not only bought the show but also increased the production quality. A factor that contributed to creating higher customer engagement.

Data analytics played a huge role in Netflix picking up the show. If streaming analytics hadn’t revealed the potential of the show, the streaming platform would not have picked it up and the show would have stayed a small-time production on the BBC. The same is true for Peaky Blinders.

The point here is that streaming analytics is helping these companies validate their decisions and improve the production quality of their existing shows.

Data analytics is helping produce new shows

Annually, streaming services spend billions of dollars on content creation. The financial stakes are high, which is why decision-makers at these organisations use streaming analytics to ensure they make the correct production decisions and avoid investing financial resources on potentially unsuccessful productions.

Netflix, for example, has realised there is demand for shows that are grounded in reality or shows that are based on real events. In response to these findings, they have allocated millions to making movies and TV shows in this genre.

An example of this kind of decision making is Netflix’s decision to make a docuseries about the Gamestop and Wall Street saga.

Disney Plus too is making more Marvel TV shows, because analytics have revealed that there is demand for long-form superhero entertainment. Initially, Marvel only planned on making TV shows on Disney Plus. This has now been increased due to positive reception by fans for shows like Wandavision, and The Falcon and The Winter Soldier.

The bottom line here is that streaming companies use streaming analytics to make creative decisions on how they produce entertainment.

Streaming analytics is helping streaming platforms produce higher quality content

It seems like ever since Netflix became a phenomenon, more and more companies are trying their hand at streaming.

Most of these streaming services are using data analytics to make decisions on what content to produce, axe or improve.

As for us, the audience, streaming analytics is giving us quality entertainment to binge on.

How streaming data is stopping businesses from closing their doors

Streaming data can help businesses stay afloat during the pandemic.

A reported one in four small organisations twenty-four per cent have already temporarily shut down. Among those who haven’t shut down yet, forty per cent are reported to most likely close their doors. Forty-three per cent believe they have less than six months until a permanent shutdown is unavoidable. Furthermore, permanent closures have reached 97,966 in the UK representing 60% of closed businesses that won’t be reopening. Can streaming data help these companies during a crisis?

Organisations everywhere are suddenly thrust into rapid-fire decision-making mode when the pandemic struck. They needed to ensure workers’ safety and maintain business continuity—to mobilise teams for remote working, sustain operations (to the extent possible), manage customers channel partners, and steady disrupted supply chains—all while adjusting to seismic shifts in customer demand.

But to make the right decisions, companies need the right data, and they need it in a timely fashion. How many actually had it?

Data analytics secret weapon – streaming data

The world generates an unfathomable amount of data every minute of every day, and it continues to multiply at a staggering rate. Over 2.5 quintillion bytes of data are created every single day, and it is only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth. Organisations, now more than ever, are quickly shifting from batch processing to real-time data streams to keep up with modern business requirements.

Streaming data — also called real-time data — is information that arrives continuously from various sources. By using stream processing technology, data streams can be processed, stored, analysed, and acted upon as it is generated in real-time.

Evolving approaches in response to unsettling times

Data collection is only one piece of the puzzle. Today’s enterprise businesses simply cannot wait for data to be processed in batch form. Instead, everything from fraud detection and stock market platforms to rideshare apps and e-commerce websites rely on real-time data streams.

Paired with streaming data, applications evolve to not only integrate data, but to process, filter, analyse data in real-time, as it is received. This opens a plethora of use cases such as real-time fraud detection.

In these uncharted waters, where the tides continue to shift, it’s not surprising that analytics, widely recognised for it’s problem-solving and predictive prowess, has become an essential navigational tool. Analytics can perform several functions organisations need: forecasting demand, identifying potential supply-chain disruptions, targeting support services for at-risk workers, and determining the effectiveness of crisis intervention strategies.

Real-time response to real-time pandemic

With the right analytics capabilities in place, organisations are well-positioned to facilitate operations and quickly harvest actionable insights from the wealth of enterprise data — data that is processed and updated regularly and in real-time. Carefully designed streaming data analytics algorithms, applied to data in analysing a specific business issue can dramatically reduce subjectivity and bias in supporting clearer-eyed decision-making.

Organisations that have already invested in streaming data and analytics platforms are now approaching the situation much more steadily than more. The “State of BI & Analytics Report 2020: Special COVID-19 Edition” revealed that analytics usage and business optimism are up as companies navigate the choppy waters of the new normal. According to the reports, businesses are leaning on analytics for business insights and efficiencies more than they did pre-COVID-19. It also found that 49% of companies are using data analytics “more or much more” than before the COVID-19 crisis. And for good reason.

Prevent closure with streaming data to power through the pandemic

Nobody likes uncertainty, but the novel coronavirus situation is a black swan event, the impact unexpected and still largely unpredictable, nearly 10 months into it. That being the case, perhaps it’s no surprise to see that data streaming analytics is driving value amid this cloudy landscape, as businesses search for order amidst the chaos.

According to a recent survey of 300 analytics professionals conducted by International Institute for Analytics (IIA), 43 % of respondents stated that analytics is at the front of their activities helping their organisations make major decisions in response to the COVID-19 crisis. These decisions aid organisations by not forcing their doors closed but finding alternate ways to optimise their operations.

For more information on how streaming data could help businesses from closing their doors, visit our website.

Survey of 300 analytics professionals conducted by Burtch Works and the International Institute for Analytics
State of BI & Analytics Report 2020: Special COVID-19 Edition

Is streaming analytics the asset you need right now?

Want real-time insights into your business? Then streaming analytics is your best bet. Learn what streaming analytics is all about, here.

The idea of streaming analytics or real-time analytics isn’t new, but the idea holds more weight than ever before with what’s going on in the world right now. Organisations need technology that empowers them to operate with greater speed and efficiency, and the ability to analyse data in real-time can do that. In this blog post, I explain what streaming analytics is and how it can help organisations during these trying times.

What is streaming analytics?

As the name implies, streaming analytics refers to analytics capabilities that can capture and analyse data as it is being streamed into the system. Unlike conventional analytics systems, real-time analytics does not require data to be stored and cleaned for analysis because the system is analysing data through continuous querying.

There are two types of real-time analytics: On-demand real-time analytics and continuous real-time analytics. The former focuses on a reactive analysis approach, while the latter takes a more proactive approach to analysis.

With real-time analytics, the system processes a query from a user to give results immediately. Meanwhile, continuous real-time analytics regularly updates the system with alerts in real-time without user input. Depending on their needs, some systems are better suited for some organisations over others.

Why is streaming or real-time analytics helpful to organisations?

Streaming analytics is a vital asset for any business because it can capture and process data as it is coming through the system. This allows the organisation to create contextually relevant reports based on the latest data. With this system, businesses can create and finalise their findings within minutes, allowing them to act faster and be more proactive.

Real-time analytics can be especially beneficial for organisations during these trying times. For example, if you want to create business plans for the future, you would want to know the latest information on the COVID-19 virus.

Streaming analytics allows you to integrate the data from a relevant database, as it is being updated, to create reports based on the latest data. Alternatively, the analytics system can also update an external database, if necessary.

Analytics platforms with real-time capability allow organisations to create their strategies around timely relevant data, which improves the quality of decision-making and gives them more accurate options. Furthermore, it improves the overall analysis process because it removes a redundant step, allowing businesses to generate insights at a faster, more efficient rate.

With streaming analytics, organisations are better equipped to handle the future. Organisations will now find it much easier to anticipate in terms of what might happen in the long run and plan for it. This is because real-time analytics reflects the latest developments in a situation, something which is invaluable when we need to know how the pandemic will hurt our fortunes and lives.

Data visualisation is another huge benefit of streaming analytics. Visualisation is vital for the processing of information – let’s face it, it’s much easier to analyse data when it’s presented as graphs and charts instead of lines of numbers. With real-time analytics, organisations are in a better position to analyse their KPIs with in-depth detail.

Organisations can breakdown and examine every granular view of their performance. This includes costs, sales and even information. Given current circumstances, the ability to analyse every aspect of operations could pave the way for innovative solutions to the massive problem that is the COVID-19 pandemic.

Under normal circumstances, streaming analytics allows organisations to better understand their customers and buying preferences. Given the pandemic, customer behaviour would have changed significantly, but how are they spending their extra time? How are they obtaining their necessities? Are they still in the market for goods and services? Are they out of a job?

Streaming analytics makes it easier for any organisation to answer these questions. Organisations directly involved in combating the virus would want to know the latest on people’s movements, the direction the virus is spreading, along with hundreds of other variables. Thanks to analytics, organisations now have that capability.

Investing in the right platform

Public and private organisations across different industries are having a hard time navigating the pandemic. During this time, every organisation is going to need every tool that can help them stay afloat.

Streaming analytics allows organisations to be more responsive, agile and smarter in their decision-making process.