Data and analytics: delivering clarity at a time of great uncertainty

“Economic challenges will still make it problematic for businesses to get a full sense of what lies ahead but in order to traverse the current and post-pandemic landscape, those organisations able to make insight-driven decisions will be far more likely to prosper in the coming months and years.”

In light of this Don-Carolis, outlines three key data and analytics trends that will characterise the most forward-thinking businesses in 2021.

Quantifying the true value of ROI

“The focal point of quantifying ROI is so businesses can prioritise investment, but in reality, it can be quite hard for data and analytics initiatives. This is because some of the impacts are direct, such as time saved by automating repetitive tasks, whereas others are less tangible, for example the impact of making quicker and/or better decisions. However, this should not be a deterrent to quantifying impact, because the thought process required for the quantification can itself give benefit.

“Our opinion is before embarking on any data and analytics initiative, the range of likely benefits and business impacts should be discussed, understood and, to the extent possible, measured and quantified. This will also help the business prioritise different data and analytics initiatives. Our experience tells us investment is often mistakenly focused on the ‘visible’ elements (such as front-end dashboards), rather than the ‘behind the scenes’ elements (such as robust data management). In 2021 and beyond, successful business leaders should have their desired outcomes front and centre of any initiatives, and then build data and analytics initiatives around these to ensure a stronger ROI.”

A smarter approach to data governance

“Whilst it is imperative data is handled in a safe and secure manner, an overcautious approach can result in data being only made available to a minority of users. Good data governance is about liberating information, so it can be shared in a secure and appropriate manner. Establishing a centralised, curated and governed source of non-sensitive data and a trust-based, risk-aware data-sharing model will become increasingly vital for unlocking the benefits of data analytics. Again, having a strong CDO or dedicated data team is key to preventing a company’s data becoming kept under lock and key.”

James concludes: “There are many benefits to improving data analytics maturity levels, including improved forecasting, generating better actionable insights and heightening your understanding of competitors. To progress in these uncertain times, it is critical business leaders continue to leverage the investments made into data initiatives, in order to facilitate smarter decision making and bring clarity at a time of great uncertainty.”

Learning with — and about — AI technology

Between remote learning, more time spent at home, and working parents trying to keep their kids occupied, children across the United States have clocked in record-breaking hours of screen time during the pandemic. Much of it is supervised and curated by teachers or parents — but increasingly, kids of all ages are watching videos, playing games, and interacting with devices powered by artificial intelligence. As head of the Personal Robots group and AI Education at MIT, Media Lab Professor Cynthia Breazeal is on a mission to help this generation of young people to grow up understanding the AI they use.

At “AI Education: Research and Practice,” an Open Learning Talks event in December, Breazeal shared her vision for educating students not only about how AI works, but how to design and use it themselves — an initiative she calls AI Literacy for All. The AI Education project Breazeal is leading at MIT is a collaboration between MIT Open Learning and the Abdul Latif Jameel World Education Lab, the Media Lab, and the MIT Schwarzman College of Computing. Through research projects, hands-on activities, and scalable learning modules, Breazeal and her AI Education affiliates across MIT are creating a robust resource hub for educators, parents, and learners of all ages to understand how AI functions in different day-to-day roles, and how to approach both using and creating artificial intelligence with a basis in ethics, inclusion, and empathy.

TrueCue reveals the top data analytics priorities for 2021 and beyond Looking back over the past year, it’s clear that for many organisations, regardless of size or industry, technology was invoked to survive the crisis. Much has been reported about the rapid migration to the cloud and the move to support remote working but according to James Don-Carolis, Managing Director of TrueCue, data, and the value which can be obtained from actionable, business intelligence, often acts as the differentiator between success and failure:

by TrueCue reveals the top data analytics priorities for 2021 and beyond

At Open Learning Talks, Cynthia Breazeal and Eric Klopfer discuss artificial intelligence education.

Source; (2021)

Enterprises risking data disaster by not fully exploring cloud backup timeframes

The issue of shared responsibility in cloud security is an issue which refuses to go away. Yet according to a new report from backup and disaster recovery managed services provider (MSP) 4sl, organisations are risking a data disaster by misunderstanding cloud providers’ backup processes.

The study, which polled 200 UK enterprises, found a majority of respondents believe the backup times for their various cloud products are longer than the advertised standards.

The hyperscale clouds are a primary example. The report notes Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform do not offer backup as standard on its own. Securing such data has long been a booming channel industry for independent MSPs and others – at least until AWS, for instance, launched AWS Backup at the start of this year to take a cut.

Yet the vast majority of those polled believed backup did exist as standard. More than four in five agreed this for AWS (81%) and Azure (84%), while an overwhelming 92% of respondents said so for Google.

Even for products with standard backup included, respondents believed they were getting more than they had – although a difference was evident in how much. For Office 365 SharePoint Online and Teams files, where the backup is 93 and 90 days respectively, around half (55% and 50%) knew where they stood. For products with only a short sprint, such as 14 days for Teams messages and Office 365 Exchange Online, this drops to 22% and 27% respectively.

“With cloud infrastructure services and applications firmly entrenched in 21st century IT strategy, enterprises need to be certain that their cloud and backup strategies are operating in concert – with any change to cloud strategy accompanied by changes in backup policy,” the report notes. “However, this is not consistently the case.”

The one product which came out of the rankings relatively unscathed was Salesforce. The CRM giant promises 90 days as standard backup retention, with more than half of respondents (55%) knowing this and almost four in five whose backups are therefore not at risk as a result.

Yet the findings – perhaps not entirely surprising given 4sl’s line of business – should come as a warning to organisations. “The desire to pass on responsibility for backup to service providers is understandable – backup environments are becoming extremely complex, and the peace of mind that a responsible partner is managing backup can be invaluable,” said Barnaby Mote, 4sl CEO and founder. “However, enterprises need to understand that in the main the standard level of backup provided for infrastructure or software as a service won’t meet their needs.”

Organisations back up data as a matter of course, not least for privacy and compliance but also to garner insights and analysis. Speaking to this publication in August, David Friend, CEO of cloud storage provider Wasabi Technologies, noted his view that storage would become a ‘commodity’, and that issues of cost around backing up what where would simply no longer exist.

“We shouldn’t think of data as sort of a scarcity… more a mindset of data abundance,” said Friend. “The idea that data storage gets to be so cheap that it’s not worth deleting anything. We have to think about data as something which has probably got future value in excess of what we think it might have today; we need to think of cloud storage the same way we think of electricity or bandwidth.”

Learning with — and about — AI technology

How Is Big Data Analytics Using Machine Learning?

It is no longer a secret that big data is a reason behind the successes of many major technology companies. However, as more and more companies embrace it to store, process and extract value from their huge volume of data, it is becoming a challenge for them to use the collected data in the most efficient way.

That’s where machine learning can help them. Data is a boon for machine learning systems. The more data a system receives, the more it learns to function better for businesses. Hence, using machine learning for big data analytics happens to be a logical step for companies to maximize the

Makes Sense Of Big Data

Big data refers to extremely large sets of structured and unstructured data that cannot be handled with traditional methods. Big data analytics can make sense of the data by uncovering trends and patterns. Machine learning can accelerate this process with the help of decision-making algorithms. It can categorize the incoming data, recognize patterns and translate the data into insights helpful for business operations.

Compatible With All Elements Of Big Data

Machine learning algorithms are useful for collecting, analyzing and integrating data for large organizations. They can be implemented in all elements of big data operation, including data labeling and segmentation, data analytics and scenario simulation.

Below are some instances to illustrate how machine learning can be put to use to analyze big data:

  • Carrying out market research and segmentation. The target audience is the cornerstone of any business. Every enterprise needs to understand the audience and market that it wants to target in order to be successful. That is the reason enterprises need to carry out market research that can delve deep into the minds of potential customers and provide insightful data. Machine learning can help in this regard by using supervised and unsupervised algorithms to interpret consumer patterns and behaviors accurately. Media and the entertainment industry use machine learning to understand the likes and dislikes of their audiences and target the right content to them.
  • Exploring customer behavior. Machine learning does not stop after drawing a picture of your target audience. It also helps businesses explore audience behavior and create a solid framework of their customers. This system of machine learning, known as user modeling, is a direct outcome of human-computer interaction. It mines data to capture the mind of the user and enable business enterprises to make intelligent decisions. Facebook, Twitter, Google and others rely on user modeling systems to know their users inside out and make relevant suggestions.
  • Personalizing recommendations. Businesses need to offer personalization to their customers. Be it a smartphone or a web series, companies need to establish a strong connection with their users to deliver what’s relevant to them. Big data machine learning is best put to use in a recommendation engine. It combines context with user behavior predictions to influence user experience based on their activities online. This way, it can empower businesses to make correct suggestions that customers find interesting. Netflix uses machine learning-based recommender systems to suggest the right content to its viewers.
  • Predicting trends. Machine learning algorithms use big data to learn future trends and forecast them to businesses. With the help of interconnected computers, a machine learning network can constantly learn new things on its own and improve its analytical skills every day. In this way, it not just calculates data but behaves like an intelligent system that uses past experiences to shape the future. An air conditioner brand can depend on machine learning to predict the demand for air conditioners in the next season and plan its production accordingly.
  • Aiding decision-making. Machine learning uses a technique called time series analysis that is capable of analyzing an array of data together. It is a great tool for aggregating and analyzing data and makes it easier for managers to make decisions for the future. Businesses, especially retailers, can use this ML-boosted method to predict the future with commendable accuracy.
  • Decoding patterns. Machine learning can be highly efficient to decipher data in industries where understanding consumer patterns can lead to major breakthroughs. For example, sectors like healthcare and pharmaceuticals have to deal with a lot of data. Machine learning can help them analyze the data to identify diseases in the initial stage among patients. Machine learning can also allow hospitals to manage patient services better by analyzing past health reports, pathological reports and disease histories. All of these can lead to better diagnoses at healthcare centers and boost medical research in the long run.

Right Steps For Effective Transition To Machine Learning

Switching to machine learning can be a big leap for businesses and cannot be simply integrated as a topmost layer. It entails redefining workflows, architecture, data collection and storage, analytics and other modules. The magnitude of system overhaul should be assessed and communicated clearly to the right stakeholders.

A step-by-step approach, as cliched as it may sound, is what works best for any such transition. First, enterprises need to build a robust AI- and ML-based strategy that is in sync with their business goal. Secondly, they should remember that quality data is key to realizing the full potential of machine learning tools. Companies need to create a corporate culture around data. The right people and the right data can make a huge difference. Finally, time is of the essence, and businesses need to act fast.

As the volume of data keeps increasing with time, collecting and managing data is becoming a herculean task for businesses. Besides, collecting data is only half the work. Managing and deducing meaning out of the data thus collected to improve marketing strategy and increase revenue is the bigger battle. Implementing machine learning for big data analytics is certainly a technology enhancement I would suggest for your business if you want to use your big data optimally.

Chithrai ManiForbes Councils Member

Forbes Technology Council

COUNCIL POST| Membership (fee-based)


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