Nowadays, with the rise of big data, greater machine-to-machine (M2M) communication and the Internet of Things (IoT), reliance on machines by humans became greater than ever before. In one sense, the human race is becoming more and more digitally and social media sophisticated but in contrast, we, humans and companies, are increasingly relying on analytics and machines to think and make critical decisions. Given this new paradigm, how can we manage and get everything under control again?
Data is the new natural resource
In 2012, Facebook handled over 500 terabytes of data, 300 million photos, 2.6 billion ‘likes’ and 2.5 billion content uploads, all of which, on a daily basis. We become More and more reliant on social media platforms to access and share things we care about and this situation will only intensify. In such context, Facebook, Twitter, Instagram and Pinterest logically continue to monetize their services and it is expected that the experience make of them will get more and more tailored to their needs, thanks to Big Data based technologies.
Did you know? Facebook already uses our data to predict our personalities with more accuracy than close friends and families. Every like, share, follow and comment, is data that tells social media companies what one likes or dislikes, what one’s actions will be, which cause or brand one will support and what one is likely to buy next. Not to mention, any action one takes using their browsers and search engines today will most likely link back to their social media profile, leaving behind a long trail of digital footprint that can be used to detect their next move.
Big Data… the chaos?
90% of the world’s data was created within the past two years. Off this data, only 20 percent is effectively structured, the remaining 80 percent of this newly created data is “unstructured” content stemming from sources such as Instagram photos, YouTube videos and social media posts. Given that this content is merely about people’sdoings, sayings andinterests, the ability to store, manage and analyze chaotic social data to gain new insights is a huge plus for marketers.
How can you structure this ocean of data?
The day your business will be capable of analysing and using 100% of this data, will be a GREAT day! In order to achieve that, companies can use content analytics to understand the content that is created, how it is used, the context it is in and the nature of that content. Content analytics are all about unstructured data and can be used to explain trends in structured data and provide valuable insights to organisations.
The objective is to deliver the relevant content to the right audience via the right channels at the right moment to support the right objective. Thanks to Content Analytics and the real time perspective it offers, your entreprise will be able to take more informed marketing decisions. The right data sets can open doors to Customer Intelligence based strategies, insights on new trends and market needs.
It is not about more content; it is about better content!
Unfortunately, most companies nurture a flawed notion of the use of social media for business. They set up pages on Facebook, Twitter and Google+ and begin mesuring their success counting likes, shares, tweets and +1s. Impressive as these numbers seem to be, these vanity metrics fail to provide details relating to marketing campaigns, including sales, new customers and revenue generation.
Knowing who reads what content, when, how often it isshared, the number of clicks, location of visitors, etc. is not sufficient anymore. You should also care about whether the content is actually useful to your audiences whileserving the business objective it was intended for. Content related trends or insights revealing information i.e. what content caused a drop in sales can help make better content and thus drive growth or revenue.
Understanding your business and the future of your social media sphere?
With the 3 “tives analytics”!
For many years we have been trying to understand how organisations around the world behave by analysing available data.
In the past, this used to be merely descriptive analytics. This answers the question “what happened in the past with the business?” With the availability of Big Data we entered the new area of predictive analytics, which focuses on answering the question: “what is probably going to happen in the future?” However, the real advantage of analytics comes with the final stage of analytics: prescriptive analytics. This type of analytics takes on this third question: “Now what?” or “so what?” It tries to give a recommendation for key decisions based on future outcomes.
What’s the difference between these three ‘…tives analytics’ and how do they affect your organisation? First of all, these three types of analytics should co-exist. One is not better than the other, they are just different, but all of them are necessary to obtain a complete overview of your organisation. In fact, they are more consecutive and all of them contribute to the objective of improving your decision-making.
Descriptive Analytics is About the Past
Descriptive analytics helps organisations understand what happened in the past: this can be from one minute ago to a few years back. It deals with the relationship between customers and products all while the objective is to gain an better understanding of what approach to take in the future : learn from past behaviours to influence future outcomes.
Netflix for example uses descriptive analytics to find correlations among different movies that subscribers rent and to improve their recommendation engine they used historic sales and customer data.
Descriptive analysis is an important source to determine what to do next and with predictive analytics such data can be turned into information regarding the likely future outcome of an event.
Predictive Analytics is About the Future
Predictive analytics provides organisations with actionable insights based on various data from a Big Data methodology: it can range from open, sales or social media data to historical and transactional data. Statistical models and algorithms are then used to capture relationships in these various data sets and provide an estimation regarding the likelihood of a future outcome. In order to do this, a variety of techniques are used, such as machine learning, data mining, modelling and game theory.
Predictive analytics can for example help identifying any future risks or opportunities. An example of predictive analytics is forecasting the demand for a certain region or customer segment and to adjust production based on the forecast. With predictive analytics it is important to have as much data as possible. More data means better predictions.
Prescriptive Analytics is About Predictions
Prescriptive analytics is the final stage in understanding your business. They not only deal with what will happen and when it will happen but also with the why and provide recommendations on about how to act upon it in order to take advantage of the predictions from stage 2.
Prescriptive Analytics is still in its infancy, it’s been around only since 2003, and it remains still so complex that there are very little best practices on the market. Only 3% of the companies use this technique, and still in a fashion that’s still plundered by a certain margin of error. In 2016’s Hype Cycle of Emerging Technologies by Gartner, prescriptive analytics was mentioned as an “Innovation Trigger” that will take another 5-10 years to reach the plateau of productivity.
Prescriptive Analytics uses a combination of many different techniques and tools, such as mathematical sciences, business rule algorithms, machine learning and computational modelling techniques as well as many different data sets ranging from historical and transactional data to public and social data sets.
One of the best examples is Google’s self-driving car that makes decisions based on various predictions and future outcomes. These cars need to anticipate what’s coming in the very near future and what could be the consequences of each possible decision it will make before it actually take one.
Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This will improve decision-making furthermore as future outcomes are taken into consideration, in the prediction.
WIIFM : What’s In It For Me?
Here are the 4 benefits from content analytics :
- It creates machine-readable content from your unstructured data that allows computers and machines to understand the unstructured content and link it to other data for additional insights.
- It improves retrievability thanks to semantic metadata that is added to the content.
- It helps organisations understand what content they have or need and more importantly why that is the case.
- It helps understanding the causes behind the trends and events that can’t be discovered with traditional business intelligence. It therefore helps explaining why things happen taking into account vast amounts of documents and content in real-time.
Content Analytics : beyond business intelligence
In the coming years, Content analytics will become more critical in order to discover new insights from big data, to criticise your organisation’s content, to deliver smarter social media experiences and to take better-informed decisions that take into account future outcomes. As such, Content analytics will undoubtedly impact any industry and any organisation and help them becoming more effective and efficient.
Thanks a bunch for reading. Tune in for more. That’s it from me, Valérie Maus de Rolley at World of Digits signing off.
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